TY - JOUR
T1 - Model of knowledge management readiness and initiatives for improvement in government agencies
AU - Sensuse, Dana Indra
AU - Hidayat, Deden Sumirat
AU - Setyaningrum, Ima Zanu
N1 - Funding Information:
The authors would like to thank Direktorat Riset dan Pengabdian Masyarakat from the University of Indonesia for funding this research through the “Publikasi Terindeks Internasional (PUTI) Q2 Tahun Anggaran 2022 Nomor: NKB-569/UN2.RST/HKP.05.00/2022” program. The authors would also like to thank Setyawan Widyarto (University Selangor, Malaysia) for being a collaboration partner.
Publisher Copyright:
© 2023, Emerald Publishing Limited.
PY - 2023
Y1 - 2023
N2 - Purpose: The application of knowledge management (KM) in government agencies is one strategy to deal with government problems effectively and efficiently. This study aims to identify KM readiness critical success factors (CSFs), measure the level of readiness for KM implementation, identify improvement initiatives and develop KM readiness models for government agencies. This model plays a role in the implementation of KM successful. Design/methodology/approach: The level of readiness is obtained by calculating the factor weights of the opinions of experts using the entropy method. The readiness value is calculated from the results of the questionnaire with average descriptive statistics. The method for analysis of improvement initiatives adopts the Asian Productivity Organization framework. The model was developed based on a systems approach and expert validation. Findings: Reliability testing with a Cronbach’s alpha value for entropy is 0.861 and the questionnaire is 0.920. The result of measuring KM readiness in government agencies is 75.29% which is at level 3 (ready/needs improvement). The improvement in the level of readiness is divided into two parts: increasing the value of factors that are still less than ready (75%) and increasing the value of all factors to level 4 (84%). The model consists of three main sections: input (KMCSFs), process (KM readiness) and output (KM implementation). Research limitations/implications: The first suggestion is that the sample of employees used in this study is still in limited quantities, that is, 50% of the total population. The second limitation is determining KMCSFs. According to experts, combining this study with factor search and correlation computations would make it more complete. The expert’s advice aims to obtain factors that can be truly tested both subjectively and objectively. Finally, regarding literature selection for future research, it is recommended to use a systematic literature review such as the preferred reporting items for systematic reviews and meta-analyses and Kitchenham procedures. Practical implications: The management must also prioritize KMCSF according to its level and make KMCSF a key performance indicator. For example, at the priority level, active leadership in KM is the leading performance indicator of a leader. Then at the second priority level, management can make a culture of sharing an indicator of employee performance through a gamification program. The last point that management must pay attention to in implementing all of these recommendations is to collaborate with relevant stakeholders, for example, those authorized to draft regulations and develop human resources. Originality/value: This study proposes a novel comprehensive framework to measure and improve KM implementation readiness in government agencies. This study also proposes a KMCSF and novel KM readiness model with its improvement initiatives through this framework.
AB - Purpose: The application of knowledge management (KM) in government agencies is one strategy to deal with government problems effectively and efficiently. This study aims to identify KM readiness critical success factors (CSFs), measure the level of readiness for KM implementation, identify improvement initiatives and develop KM readiness models for government agencies. This model plays a role in the implementation of KM successful. Design/methodology/approach: The level of readiness is obtained by calculating the factor weights of the opinions of experts using the entropy method. The readiness value is calculated from the results of the questionnaire with average descriptive statistics. The method for analysis of improvement initiatives adopts the Asian Productivity Organization framework. The model was developed based on a systems approach and expert validation. Findings: Reliability testing with a Cronbach’s alpha value for entropy is 0.861 and the questionnaire is 0.920. The result of measuring KM readiness in government agencies is 75.29% which is at level 3 (ready/needs improvement). The improvement in the level of readiness is divided into two parts: increasing the value of factors that are still less than ready (75%) and increasing the value of all factors to level 4 (84%). The model consists of three main sections: input (KMCSFs), process (KM readiness) and output (KM implementation). Research limitations/implications: The first suggestion is that the sample of employees used in this study is still in limited quantities, that is, 50% of the total population. The second limitation is determining KMCSFs. According to experts, combining this study with factor search and correlation computations would make it more complete. The expert’s advice aims to obtain factors that can be truly tested both subjectively and objectively. Finally, regarding literature selection for future research, it is recommended to use a systematic literature review such as the preferred reporting items for systematic reviews and meta-analyses and Kitchenham procedures. Practical implications: The management must also prioritize KMCSF according to its level and make KMCSF a key performance indicator. For example, at the priority level, active leadership in KM is the leading performance indicator of a leader. Then at the second priority level, management can make a culture of sharing an indicator of employee performance through a gamification program. The last point that management must pay attention to in implementing all of these recommendations is to collaborate with relevant stakeholders, for example, those authorized to draft regulations and develop human resources. Originality/value: This study proposes a novel comprehensive framework to measure and improve KM implementation readiness in government agencies. This study also proposes a KMCSF and novel KM readiness model with its improvement initiatives through this framework.
KW - Critical success factor
KW - Government agencies
KW - Improvement initiatives
KW - Knowledge management readiness
KW - Readiness model
UR - http://www.scopus.com/inward/record.url?scp=85153202226&partnerID=8YFLogxK
U2 - 10.1108/VJIKMS-05-2022-0173
DO - 10.1108/VJIKMS-05-2022-0173
M3 - Article
AN - SCOPUS:85153202226
SN - 2059-5891
JO - VINE Journal of Information and Knowledge Management Systems
JF - VINE Journal of Information and Knowledge Management Systems
ER -