Prospects and obstacles of digital quality management in Saudi Arabia universities. A systematic literature review from the Last Decade

Figures & data

Figure 1. PRISMA statement 2020 selection and rejection of records.

Figure 2. Distribution of subject for records inclusion.

Figure 3. Distribution of records selected from 2012–2021.

Figure 4. Distributions of records affiliations from the Saudi Arabian universities.

Table 1. Keyword occurrences, classification, occurrence percentage, and relevance score

Classification	relevance score	Occurrences	Occurrences	Relevance score
Digital transformation	24%	adoption	7	0.8327
		artificial intelligence	3	0.6232
		artificial neural network	3	0.8318
		benefit	5	0.8105
		big data	4	0.3532
		big data analytic	3	0.7285
		blockchain	3	1.5521
		cloud	8	1.1994
		cloud service provider	4	1.7473
		communication	3	1.8907
		condition	3	0.8006
		genetic algorithm	3	1.16
		iot	7	0.6183
		reliability	4	0.3916
		term	3	0.4264
		Total	63
Education and learning	48%	context	3	0.4709
		contribution	4	0.7745
		covid	3	2.2643
		education	6	1.4907
		end	4	0.6761
		evaluation	4	1.0701
		factor	4	1.1554
		field	4	0.6929
		food	3	0.4402
		healthcare	3	1.2621
		implication	5	0.9658
		industry	5	0.5034
		learning	6	1.3993
		life	4	1.4382
		outcome	5	0.5171
		person	3	0.4144
		privacy	3	1.907
		processing	4	1.1633
		protocol	4	1.0031
		qos	4	1.0537
		risk	4	1.161
		self	3	0.3886
		sensor	5	1.1173
		skill	5	1.0904
		state	3	0.4961
		student	7	1.3015
		study	15	0.3666
		transparency	3	0.7847
		Total	126
Quality assurence	28%	access	4	1.4207
		accessibility	3	0.9989
		accountability	3	1.1141
		accuracy	5	0.6626
		assessment	4	1.9623
		capability	5	0.7149
		data management	4	1.7002
		data owner	3	1.9187
		data privacy	3	1.907
		digital transformation	4	0.8103
		lack	3	0.6974
		large amount	4	1.2183
		monitoring	4	0.5235
		need	7	0.4499
		node	4	1.2627
		number	4	0.6562
		quality assurance	3	1.0731
		saudi arabia	4	1.356
		smart city	3	1.3804
		Total	74

Table 2. Results of records, authors, year, setting, and classification

Authors	Year	Cited by	Settings	Segment
Alshammari S.T., Alsubhi K., Aljahdali H.M.A., Alghamdi A.M.	2021		cloud data	cloud computing environment
Zulfa M.I., Hartanto R., Permanasari A.E., Ali W.	2021		5 G technology, edge computing, and the Internet of Things (IoT)	Artificial Intelligence
Alkhowaiter W.A.	2020	41	digital payments and baking	digital banking
Altaf S., Ahmad S., Zaindin M., Soomro M.W.	2020	6	Wireless Sensor Networks (WSNs)	Artificial Neural Network (ANN)
Ren Y., Qi J., Cheng Y., Wang J., Alfarraj O.	2020	15	digital continuity	electronic record
Alaya B., Khan R.	2020	2	online video content	load management and replication algorithms
Shahid A., Almogren A., Javaid N., Al-Zahrani F.A., Zubair M., Alam M.	2020	47	Supply chains	complex networks
Ghaffar Z., Ahmed S., Mahmood K., Islam S.H., Hassan M.M., Fortino G.	2020	15	Cyber-physical-social systems	Cloud storage
Shorfuzzaman M., Masud M.	2019	3	data cloud	quality of service
Rani S., Chauhdary S.H.	2018	7	Internet of things (IoT)	Smart City
Sahi M.A., Abbas H., Saleem K., Yang X., Derhab A., Orgun M.A., Iqbal W., Rashid I., Yaseen A.	2017	61	e-Healthcare	privacy requirements
Hassan M.M., Albakr H., Al-Dossari H., Mohamed A.	2017	8	cloud-assisted body	healthcare applications
Malik S.U.R., Khan S.U., Ewen S.J., Tziritas N., Kolodziej J., Zomaya A.Y., Madani S.A., Min-Allah N., Wang L., Xu C.-Z., Malluhi Q.M., Pecero J.E., Balaji P., Vishnu A., Ranjan R., Zeadally S., Li H.	2016	37	‘Digital Age	cloud computing

Figure 5. The classification of literature using the VOS viewer.