Knowledge waste at the center of discussions in higher education institutions: a quantitative analysis

KLEIN, LEANDER LUIZ; RIBEIRO, DESIRÉE PRATI; VIEIRA, KELMARA MENDES; MATHEIS, TAIANE KEILA

doi:10.1590/1679-395120240030x

person LEANDER LUIZ KLEIN

Conceptualization (Lead) · Methodology (Lead) · Project administration (Lead) · Supervision (Lead) · Writing - original draft (Equal) · Writing - review & editing (Equal) · Validation (Lead) Ph.D. in Business Administration and Professor from the Department of Administration at the Federal University of Santa Maria (UFSM)

school UNIVERSIDADE FEDERAL DE SANTA MARIA / DEPARTAMENTO DE ADMINISTRAÇÃO, SANTA MARIA - RS, BRAZIL

0000-0001-6075-6107

person DESIRÉE PRATI RIBEIRO

Investigation (Supporting) · Data curation (Equal) · Formal Analysis (Equal) · Writing - original draft (Equal) · Writing - review & editing (Supporting) · Visualization (Supporting) Master’s student from the Postgraduate Program in Public Administration at the Federal University of Santa Maria (UFSM)

school UNIVERSIDADE FEDERAL DE SANTA MARIA / PROGRAMA DE PÓS-GRADUAÇÃO EM ADMINISTRAÇÃO PÚBLICA, SANTA MARIA - RS, BRAZIL

0009-0007-7646-3238

person KELMARA MENDES VIEIRA

Supervision (Supporting) · Methodology (Supporting) · Writing - review & editing (Lead) · Validation (Supporting) · Resources (Lead) · Funding acquisition (Lead) Ph.D. in Business Administration and Professor from the Department of Administration at the Federal University of Santa Maria (UFSM)

school UNIVERSIDADE FEDERAL DE SANTA MARIA / DEPARTAMENTO DE ADMINISTRAÇÃO, SANTA MARIA - RS, BRAZIL

0000-0002-8847-0941

person TAIANE KEILA MATHEIS

Data curation (Equal) · Formal Analysis (Equal) · Visualization (Lead) · Writing - original draft (Equal) · Writing - review & editing (Supporting) · Investigation (Lead) Doctorate student in the Postgraduate Program in Administration at the Federal University of Santa Maria (UFSM)

school UNIVERSIDADE FEDERAL DE SANTA MARIA / PROGRAMA DE PÓS-GRADUAÇÃO EM ADMINISTRAÇÃO, SANTA MARIA - RS, BRAZIL

0000-0003-2427-878X

E-mail:
leander.klein@ufsm.br

E-mail:
desiree.ribeiro@ufsm.br

E-mail:
kelmara.vieira@ufsm.br

E-mail:
taianetrim@gmail.com

EDITOR-IN-CHIEF

Hélio Arthur Reis Irigaray (Fundação Getulio Vargas, Rio de Janeiro / RJ - Brazil). ORCID: https://orcid.org/0000-0001-9580-7859

ASSOCIATE EDITOR

Fabricio Stocker (Fundação Getulio Vargas, Rio de Janeiro / RJ - Brazil). ORCID: https://orcid.org/0000-0001-6340-9127

Variables	Alternative	Frequency	Percentage (%)
Gender	Male	411	49.1
Gender	Female	426	50.9
Age range	From 22 to 32 years old	73	8.8
	From 33 to 39 years old	222	26.6
	From 40 to 49 years old	242	29.0
	From 50 to 58 years old	202	24.2
	Over 58 years old	95	11.4
Marital Status	Single	178	21.3
	Married or stable relationship	580	69.3
	Separated	66	7.9
	Other	13	1.6
Education	High School	2	0.2
	1st Cycle - Undergraduate	21	2.5
	2nd Cycle Diploma	69	8.2
	2nd Cycle - MSc	166	19.8
	3rd Cycle - PhD	579	69.2
Job position	Professor	512	61.2
	Administrative technician	110	13.1
	High Manager (Director, pro-rector, dean, or vice-dean)	32	3.8
	Low Manager - Professor or Technician with a management position (course coordination or department head)	183	21.9
Service time	Up to 5 years	177	21.1
	Up to 10 years	257	30.7
	More than 10 years	403	48.1
Type of HEI	Comunitary	102	12.2
	Private	125	14.9
	Public	610	72.9

Items	Percentage of answers
Items	Mean	1	2	3	4	5
ITEM 1 - There are professors with doctoral degrees who do not work in graduate school, wasting part of their knowledge.	3.81	6.8	8.4	18.6	29.2	37.0
ITEM 2 - Most of the knowledge generated by research produced by professors is not incorporated into the HEI management.	3.45	5.5	13.0	32.3	29.4	19.8
ITEM 3 - The knowledge of employees is little explored for extension activities.	3.46	6.6	14.3	24.5	35.0	19.6
ITEM 4 - The knowledge generated by the institution is little used to generate benefits for the community and companies.	3.12	12.8	20.4	23.5	28.3	14.9
ITEM 5 - The knowledge generated by research, for the most part, is not incorporated into undergraduate teaching activities.	3.12	10.3	21.3	27.2	28.4	12.8

Items	Percentage of answers
Items	Mean	1	2	3	4	5
ITEM 6 - There is a lot of tacit knowledge that ends up being lost when there is a change of people in positions.	3.68	3.6	10.9	23.4	37.3	24.9
ITEM 7 - When a new employee occupies a certain position, the tacit knowledge of the previous “occupant” is hardly shared.	3.21	7.9	18.5	30.2	30.8	12.5
ITEM 8 - There is a lot of tacit knowledge that ends up being lost due to the lack of adequate knowledge management.	3.56	5.4	10.0	27.2	37.3	20.1
ITEM 9 - Tacit knowledge is poorly shared among employees of my sector.	3.31	6.1	16.8	31.7	30.1	15.3

Items	Percentage of answers
Items	Mean	1	2	3	4	5
ITEM 10 - There are administrative technicians in education working in positions different than their area of expertise.	3.38	9.0	12.2	32.0	25.0	21.9
ITEM 11 - There are employees working in positions that require skills much lower than their training or education levels.	3.49	9.0	14.0	23.2	26.6	27.2
ITEM 12 - The academic backgrounds of many employees are higher than the skill levels required by their activities, which leads to a waste of knowledge.	3.49	8.8	12.9	22.3	31.9	24.0

Items	Percentage of answers
Items	Mean	1	2	3	4	5
ITEM 13 - Employee experiences are not fully explored.	3.27	8.2	21.9	20.8	32.5	16.6
ITEM 14 - Employee capabilities are not fully utilized.	3.16	9.3	21.9	24.1	32.6	12.1
ITEM 15 - Employee skills are not fully utilized.	3.27	7.9	22.9	20.2	32.1	16.8

Constructs	Mean	Standard deviation	Very low	Low	Medium	High	Very high
Explicit Knowledge Waste	0.59	0.23	5.9	15.4	27.6	32.2	18.8
Retention of Tacit Knowledge	0.62	0.23	4.1	11.6	27.3	34.2	22.8
Overspecialization	0.62	0.26	8.3	12.8	25.8	24.0	29.1
Underutilized Talent	0.56	0.27	10.2	18.4	26.1	27.9	17.4
Scale of Knowledge Waste	0.60	0.19	3.2	11.9	32.2	38.0	14.7

Constructs	Public		Private/Community		t-Test
Constructs	Mean	Standard deviation	Mean	Standard deviation	Value	Sig.
Explicit Knowledge Waste	3.56	1.10	3.05	1.14	5.79	0.00
Retention of Tacit Knowledge	3.71	1.05	3.31	1.11	4.65	0.00
Overspecialization	3.75	1.17	2.93	1.30	8.61	0.00
Underutilized Talent	3.30	1.22	3.08	1.25	2.25	0.02

Constructs	2nd Cycle Diploma and MSc		3rd Cycle - PhD		t-Test
Constructs	Mean	Standard deviation	Mean	Standard deviation	Value	Sig.
Explicit Knowledge Waste	3.35	1.09	3.46	1.15	-1.19	0.23
Retention of Tacit Knowledge	3.47	1.08	3.66	1.09	-2.29	0.02
Overspecialization	3.53	1.24	3.53	1.27	0.04	0.96
Underutilized Talent	3.31	1.20	3.21	1.25	0.98	0.32

Constructs	Up to 40 years old		More than 41 years old		t-Test
Constructs	Mean	Standard deviation	Mean	Standard deviation	Value	Sig.
Explicit Knowledge Waste	3.50	1.18	3.37	1.11	1.50	0.13
Retention of Tacit Knowledge	3.62	1.10	3.58	1.06	0.58	0.55
Overspecialization	3.67	1.27	3.34	1.25	2.46	0.01
Underutilized Talent	3.33	1.24	3.16	1.22	2.05	0.04

Constructs	High and low managers		Professor		Administrative technician		ANOVA
Constructs	Mean	Standard deviation	Mean	Standard deviation	Mean	Standard deviation	Value	Sig.
Explicit Knowledge Waste	3.37	1.14	3.41	1.15	3.64	1.06	2.20	0.11
Retention of Tacit Knowledge	3.50	1.07	3.65	1.08	3.57	1.11	1.41	0.24
Overspecialization	3.49	1.23	3.41	1.28	4.17	1.02	16.83	0.00
Underutilized Talent	3.14	1.19	3.21	1.25	3.60	1.16	5.57	0.00

[TFN1] Note: HEI - higher education institution.

[TFN2] Source: Research data (2023).

Knowledge waste at the center of discussions in higher education institutions: a quantitative analysis

El desperdicio de conocimiento en el centro de las discusiones en las instituciones de educación superior: un análisis cuantitativa

Abstract