Does social media marketing as moderating relationship between intellectual capital and organizational sustainability through university managerial intelligence? (empirical studies at private Universities in East Java)

Figures & data

Table 1. Development of the number of Universities In East Java for the academic years 2011/2012 to 2018/2019

Academic year	College Status
	State	Private
2011/2012	11	330
2012/2013	13	326
2013/2014	15	363
2014/2015	17	326
2015/2016	17	329
2016/2017	17	328
2017/2018	17	328
2018/2019	17	324

Source: Ministry of Research, Technology and Higher Education Annual Report (2019)

Table 2. Respondents’ responses regarding economic sustainability indicators

NO	STATEMENT		STS	TS	R	S	SS	Average
1.	Higher education institutions have many graduates who always increase from year to year	Σ	3	2	36	21	14	3,54
		%	3,9%	2,6%	47,4%	27,6%	18,4%
2.	Higher education institutions have affordable tuition rates (no complaint from students)	Σ	0	3	16	42	15	3,90
		%	0%	3,9%	21,1%	55,3%	19,8%
3.	Higher education institutions have other sources of income (other than tuition fees) which are managed professionally	Σ	-	5	11	40	20	3,99
		%	-	6,6%	14,5%	52,6%	26,3%
4.	Higher Education institutions always pay employee salaries on time	Σ	3	2	38	20	13	3,50
		%	3,9%	2,6%	50%	26,3%	17,1%

Source: Primary data processed in 2020

Information: SS = Strongly agree, S = Agree, R = Doubt, TS = Disagree, STS = Strongly disagree

Table 3. Respondents’ responses regarding social sustainability indicators

NO	STATEMENT		STS	TS	R	S	SS	Average
1.	Higher education institutions have a contemporary curriculum according to the needs of graduate users	Σ	0	5	12	37	22	4,00
		%	0%	6,8%	15,8%	48,7%	29%
2.	Higher education institutions can create service satisfaction to all academicians	Σ	0	6	8	50	12	3,89
		%	0%	7,9%	10,5%	65,8%	15,8%
3.	The community around the campus very well accepts the existence of Higher Education institutions	Σ	0	0	13	38	25	4,12
		%	0%	0%	17,1%	50%	32,9%
4.	Higher education institutions produce graduates who are ready to work	Σ	0	4	6	48	18	4,05
		%	0%	5,3%	7,9%	63,1%	23,7%

Source: Primary data processed in 2020

Information: SS = Strongly agree, S = Agree, R = Doubt, TS = Disagree, STS = Strongly disagree

Table 4. Respondents’ responses regarding environmental sustainability indicators

NO	STATEMENT		STS	TS	R	S	SS	Average
1.	Higher education institution always uses environmentally friendly products	Σ	0	0	17	41	18	4,01
		%	0%	0%	22,4%	53,9%	23,7%
2.	The college runs academic administration services online	Σ	0	0	14	46	16	4,03
		%	0%	0%	18,4%	60,5%	21,1%
3.	Higher education ray Institutions carry out organizational activities in accordance with the applicable statutory system	Σ	0	0	25	29	22	3,96
		%	0%	0%	32,9%	38,2%	28,9%
4.	Higher education uses electricity, water, and other energy needs according to their needs (saving)	Σ	0	6	6	48	16	3,97
		%	0%	7,9%	7,9%	63,2%	21,1%

Source: Primary data processed in 2020

Information: SS = Strongly agree, S = Agree, R = Doubt, TS = Disagree, STS = Strongly disagree

Table 5. Respondents’ responses regarding human capital indicators

NO	STATEMENT		STS	TS	R	S	SS	Average
1.	Higher education institutions have lecturers with doctoral degrees with expertise in accordance with the field of science (study program) of more than 40% of the total lecturers	Σ	0	3	13	49	11	3,89
		%	0%	3,9%	17,1%	64,5%	14,5%
2.	Higher education institutions have professional educator certified lecturers of more than 40% of the total lecturers	Σ	0	1	8	51	16	4,08
		%	0%	1,3%	10,5%	67,1%	21,1%
3.	Higher education institutions have employees (lecturers and education staff) who can work in teams	Σ	0	1	13	46	16	4,01
		%	0%	1,3%	17,1%	60,5%	21,1%
4.	Higher education institutions have lecturers who can teach with contemporary methods according to student expectations	Σ	0	1	26	37	12	3,79
		%	0%	1,3%	34,2%	48,7%	15,8%

Source: Primary data processed in 2020

Information: SS = Strongly agree, S = Agree, R = Doubt, TS = Disagree, STS = Strongly disagree

Table 6. Respondents’ responses regarding structural capital indicators

NO	STATEMENT		STS	TS	R	S	SS	Average
1.	Higher education institutions have written guidelines regarding the system for recruiting, placing, and firing employees	Σ	0	1	13	42	20	4,07
		%	0%	1,3%	17,1%	55,3%	26,3%
2.	Higher education institutions have written guidelines regarding the monitoring and evaluation system for lecturer performance in the Tri Dharma	Σ	0	0	23	45	8	3,80
		%	0%	0%	30,3%	59,2%	10,5%
3.	Higher education institutions have written guidelines on monitoring and evaluating the performance of academic administration for education personnel	Σ	0	0	21	48	7	3,82
		%	0%	0%	27,6%	63,2%	9,2%
4.	Higher education institutions have written guidelines on how to provide academic administration services	Σ	0	0	20	50	6	3,82
		%	0%	0%	26,3%	65,8%	7,9%

Source: Primary data processed in 2020

Information: SS = Strongly agree, S = Agree, R = Doubt, TS = Disagree, STS = Strongly disagree

Table 7. Respondents’ responses regarding relational capital indicators

NO	STATEMENT		STS	TS	R	S	SS	Average
1.	Higher education institutions have good relations with alumni	Σ	0	7	10	50	9	3,80
		%	0%	9,2%	13,2%	65,8%	11,9%
2.	Higher institutions have good relations with the surrounding community	Σ	0	7	11	51	7	3,76
		%	0%	9,2%	14,5%	67,1%	9,2%
3.	Higher education institutions have good relations with government agencies, especially the Higher Education Service Institutions	Σ	0	1	10	54	11	3,99
		%	0%	1,3%	13,2%	71,1%	14,5%
4.	Higher education institutions have good cooperative relationships with professional organizations	Σ	0	5	17	50	4	3,70
		%	0%	6,6%	22,4%	65,8%	5,3%

Source: Primary data processed in 2020

Information: SS = Strongly agree, S = Agree, R = Doubt, TS = Disagree, STS = Strongly disagree

Table 8. Respondents’ responses regarding personal knowledge management

NO	STATEMENT		STS	TS	R	S	SS	Average
1.	Employment database is used as a basis for exploring the complete competence of employees	Σ	0	11	22	28	15	3,62
		%	0%	14,5%	28,9%	36,8%	19,7%
2.	All employees have the same opportunity to work according to their competencies/fields of knowledge	Σ	0	3	16	46	11	3,86
		%	0%	3,9%	21,1%	60,5%	14,5%
3.	High performing employees receive positive appreciation to motivate the employees concerned to improve their performance	Σ	0	0	14	44	18	4,05
		%	0%	0%	18,4%	57,9%	23,7%
4.	Employees who make mistakes are given a warning/sanction politely so as not to repeat the same mistakes	Σ	0	5	6	48	17	4,01
		%	0%	6,6%	7,9%	63,2%	22,4%

Source: Primary data processed in 2020

Information: SS = Strongly agree, S = Agree, R = Doubt, TS = Disagree, STS = Strongly disagree

Table 9. Respondents’ responses regarding university governance management

NO	STATEMENT		STS	TS	R	S	SS	Average
1.	Higher education institutions’ governance is determined based on laws and other applicable regulations	Σ	0	3	14	44	15	3,94
		%	0%	3,9%	18,4%	57,9%	19,7%
2.	Higher education institutions’ governance provides sufficient space for the alignment of the interests of shareholders and stakeholders	Σ	0	0	17	45	14	3,96
		%	0%	0%	22,4%	59,2%	18,4%
3.	There is an Internal Quality Assurance System (SPMI) which is established based on the applicable regulations	Σ	0	0	19	36	21	4,03
		%	0%	0%	25%	47,4%	27,6%
4.	To get a conducive organizational climate, all organizational problems are resolved objectively	Σ	0	0	3	55	18	4,20
		%	0%	0%	3,9%	72,4%	23,7%

Source: Primary data processed in 2020

Information: SS = Strongly agree, S = Agree, R = Doubt, TS = Disagree, STS = Strongly disagree

Table 10. Respondents’ responses regarding information technology capabilities

NO	STATEMENT		STS	TS	R	S	SS	Average
1.	Maximized its use of information technology as a tool to evaluate employee performance	Σ	0	0	18	40	18	4,00
		%	0%	0%	23,7%	52,6%	23,7%
2.	The use of information technology is maximized to facilitate the implementation of the Student Creativity Program	Σ	0	0	14	45	17	4,04
		%	0%	0%	18,4%	59,2%	22,4%
3.	The use of information technology is maximized in the process of admitting new students	Σ	0	0	26	31	19	3,91
		%	0%	0%	34,2%	40,8%	25%
4.	The use of information technology is maximized as a tool to ensure policy validity	Σ	0	6	5	50	15	3,97
		%	0%	7,9%	6,6%	65,8%	19,7%

Source: Primary data processed in 2020

Information: SS = Strongly agree, S = Agree, R = Doubt, TS = Disagree, STS = Strongly disagree

Table 11. Respondents’ responses regarding content creation indicators

NO	STATEMENT		STS	TS	R	S	SS	Average
1.	Higher Education institutions ensure that the content provided represents the personality of an information	Σ	3	2	33	23	15	3,59
		%	3,9%	2,6%	43,4%	30,1%	19,7%
2.	Higher Education institutions adapt the appearance of content to the penchant for students as the millennial generation	Σ	0	1	16	42	17	4,00
		%	0%	1,3%	21,1%	55,3%	22,4%
3.	Higher education institutions determine the depth of content only on essential matters that are worthy of being known by the public	Σ	0	4	11	41	20	4,00
		%	0%	5,3%	14,5%	53,9%	26,3%

Source: Primary data processed in 2020

Information: SS = Strongly agree, S = Agree, R = Doubt, TS = Disagree, STS = Strongly disagree

Table 12. Respondents’ responses regarding content sharing indicators

NO	STATEMENT		STS	TS	R	S	SS	Average
1.	Higher Education considers the level of public trust in content uploaded on several social media	Σ	3	2	33	21`	17	3,62
		%	3,9%	2,6%	43,4%	27,6%	22,4%
2.	Higher Education guarantees the same meaning of information uploaded on various social media	Σ	0	3	12	40	21	4,04
		%	0%	3,9%	15,8%	52,6%	27,6%
3.	Higher Education determines the time limit for each news broadcast so as not to cause saturation for users	Σ	0	7	12	42	15	3,86
		%	0%	9,2%	15,8%	55,3%	19,7%

Source: Primary data processed in 2020

Information: SS = Strongly agree, S = Agree, R = Doubt, TS = Disagree, STS = Strongly disagree

Table 13. Respondents’ responses regarding connecting indicators

NO	STATEMENT		STS	TS	R	S	SS	Average
1.	To get user responses as expected, Higher Education institutions consider the type of social media to be selected	Σ	0	0	13	41	22	4,12
		%	0%	0%	17,1%	53,9%	28,9%
2.	Higher education institutions consider the extent of the network in uploading the information they want to convey	Σ	0	4	11	39	22	4,04
		%	0%	5,3%	14,5%	51,3%	28,9%
3.	Higher education institutions use social media to link organizational policies with public interests	Σ	0	0	18	41	17	3,99
		%	0%	0%	23,7%	53,9%	22,4%

Source: Primary data processed in 2020

Information: SS = Strongly agree, S = Agree, R = Doubt, TS = Disagree, STS = Strongly disagree

Table 14. Respondents’ responses regarding community building indicators

NO	STATEMENT		STS	TS	R	S	SS	Average
1.	Higher education uses social media to build good communication from the academic community	Σ	0	0	15	43	18	4,04
		%	0%	0%	19,7%	56,6%	23,7%
2.	Colleges make use of social media to maximize the impact of promotions	Σ	0	0	25	30	21	3,95
		%	0%	0%	32,9%	39,5%	27,6%
3.	Higher education uses social media to exchange information, ideas, suggestions, and ideas in virtual groups	Σ	0	6	6	47	17	3,99
		%	0%	7,9%	7,9%	61,8%	22,4%

Source: Primary data processed in 2020

Information: SS = Strongly agree, S = Agree, R = Doubt, TS = Disagree, STS = Strongly disagree

Figure 1. Initial processing path diagram

Source: Data Processing Results (2020)

Table 15. Convergent validity test results by seeing the initial AVE value

Variable	Average Variance Extracted (AVE)
IC	0,417
KO	0,563
SMM	0,476
UMI	0,545
MOD IC-SMM	1,000
MOD UMI-SMM	1,000

Source: Smart-PLS Test Results

Table 16. Convergent validity test results by seeing the final AVE value

Variable	Average Variance Extracted (AVE)
IC	0,512
KO	0,581
SMM	0,502
UMI	0,527
MOD IC-SMM	1,000
MOD UMI-SMM	1,000

Source: Smart-PLS Test Results

Figure 2. Result path diagram

Source: Data Processing Results (2020)

Table 17. Reliability testing results

Variable	Composite Reliability
IC	0,880
KO	0,916
SMM	0,916
UMI	0,916
MOD IC-SMM	1,000
MOD UMI-SMM	1,000

Source: Smart-PLS Test Results

Table 18. Significance value of structural model

	Original Sample (O)	Sample Mean (M)	Standard Deviation (STDEV)	T Statistics (|O/STDEV|)	P Values	Information
IC -> KO	0,157	0,155	0,042	3,743	0,000	Significant
IC -> UMI	0,648	0,666	0,049	13,297	0,000	Significant
SMM -> KO	0,214	0,214	0,075	2,875	0,005	Significant
UMI -> KO	0,642	0,642	0,082	7,862	0,000	Significant
IC -> UMI -> KO	0,416	0,428	0,065	6,440	0,000	Significant
MOD IC-SMM -> KO	−0,063	−0,066	0,040	1,565	0,121	Not Significant
MOD UMI-SMM -> KO	0,012	0,012	0,042	0,293	0,770	Not Significant

Source: Smart-PLS Test Results

Table 19. R² evaluation test

Variable	R Square
UMI	0,433
KO	0,947

Source: Smart-PLS Test Results