Pendekatan Teoritis dalam Intervensi Perubahan: Strategi Memilih Intervensi yang Tepat
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Perubahan organisasi merupakan suatu kebutuhan yang tidak terhindarkan bagi organisasi yang ingin tetap relevan dan berkembang di tengah dinamika lingkungan bisnis yang terus berubah. Saat ini, implementasi perubahan seringkali menghadapi tantangan yang kompleks dan sulit diatasi. Salah satu faktor kunci ynag dapat meningkatkan keberhasilan rencana implementasi perubahan yakni pemahaman yang mendalam mengenai leverage point. Pendekatan teoritis dalam intervensi perubahan menjadi krusial dalam konteks manajemen organisasi yang dinamis. Artikel ini mengeksplorasi strategi memilih intervensi yang tepat berdasarkan teori-teori terkini dalam bidang manajemen perubahan. Metode penelitian yang digunakan yakni kajian pustaka adalah literatur review dengan mengkaji berbagai kajian pustaka khususnya jurnal yang berkaitan dengan tema penelitian. Dengan meninjau berbagai pendekatan teoritis seperti teori kontingensi, teori pemilihan intervensi, dan teori transformasi, artikel ini bertujuan untuk memberikan panduan yang komprehensif bagi pemimpin dan manajer yang menghadapi tantangan implementasi perubahan. Analisis mendalam terhadap berbagai strategi memilih intervensi dijabarkan, termasuk pertimbangan konteks organisasi, tingkat perubahan yang diinginkan, serta kebutuhan khusus yang harus dipenuhi. Implikasi dari pemilihan intervensi yang tepat terhadap keberhasilan implementasi perubahan juga dibahas, memberikan wawasan berharga bagi praktisi manajemen dalam mengelola transformasi organisasi secara efektif.
Kata kunci: leverage point, pendekatan teoritis, manajemen perubahanTeks Lengkap:
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Brijs, K., Ross, V., De Vos, B., Filtness, A., Talbot, R., Hancox, G., Pilkington-Cheney, F., Katrakazas, C., Michelaraki, E., Yannis, G., Kaiser, S., Furian, G., Lourenço, A., Wets, G., & Brijs, T. (2023). Framework for behaviour change implemented in real-time and post-trip interventions of the H2020 i-DREAMS naturalistic driving project. Transportation Research Procedia, 72, 2070–2077. https://doi.org/10.1016/j.trpro.2023.11.690
Brown, S. E., Shah, A., Czuber-Dochan, W., Bench, S., & Stayt, L. (2024). Fatigue after CriTical illness (FACT): Co-production of a self-management intervention to support people with fatigue after critical illness. Intensive and Critical Care Nursing, 82(January), 103659. https://doi.org/10.1016/j.iccn.2024.103659
Cao, K., Zhou, C., Church, R., Li, X., & Li, W. (2024). International Journal of Applied Earth Observation and Geoinformation Revisiting spatial optimization in the era of geospatial big data and GeoAI. International Journal of Applied Earth Observation and Geoinformation, 129(December 2023), 103832. https://doi.org/10.1016/j.jag.2024.103832
Chengzheng, L., Peng, P., & Lei, C. (2023). Robust SSRL analysis framework for intervention strategy construction in CSCL environment. Heliyon, 9(3), e14300. https://doi.org/10.1016/j.heliyon.2023.e14300
Chidera Victoria Ibeh, Onyeka Franca Asuzu, Temidayo Olorunsogo, Oluwafunmi Adijat Elufioye, Ndubuisi Leonard Nduubuisi, & Andrew Ifesinachi Daraojimba. (2024). Business analytics and decision science: A review of techniques in strategic business decision making. World Journal of Advanced Research and Reviews, 21(2), 1761–1769. https://doi.org/10.30574/wjarr.2024.21.2.0247
Cooper, E., Dolnicar, S., & Grün, B. (2024). Understanding how a commitment-based pledge intervention encourages pro-environmental tourist behaviour. Tourism Management, 104(January), 104928. https://doi.org/10.1016/j.tourman.2024.104928
Denninger, N. E., Brefka, S., Skudlik, S., Leinert, C., Mross, T., Meyer, G., Sulmann, D., Dallmeier, D., Denkinger, M., & Müller, M. (2024). Development of a complex intervention to prevent delirium in older hospitalized patients by optimizing discharge and transfer processes and involving caregivers: A multi-method study. International Journal of Nursing Studies, 150, 104645. https://doi.org/10.1016/j.ijnurstu.2023.104645
Fang, X., Goette, L., Rockenbach, B., Sutter, M., Tiefenbeck, V., Schoeb, S., & Staake, T. (2023). Complementarities in behavioral interventions: Evidence from a field experiment on resource conservation. Journal of Public Economics, 228(June 2022), 105028. https://doi.org/10.1016/j.jpubeco.2023.105028
Farhadi, F., Wang, S., Palacin, R., & Blythe, P. (2023). Data-driven multi-objective optimization for electric vehicle charging infrastructure. IScience, 26(10), 107737. https://doi.org/10.1016/j.isci.2023.107737
Frey, M., Gashaj, V., Nuerk, H. C., & Moeller, K. (2024). You can count on your fingers: Finger-based intervention improves first-graders’ arithmetic learning. Journal of Experimental Child Psychology, 244, 105934. https://doi.org/10.1016/j.jecp.2024.105934
Hu, H., Gong, S., & Taheri, B. (2024). Energy demand forecasting using convolutional neural network and modified war strategy optimization algorithm. Heliyon, 10(6), e27353. https://doi.org/10.1016/j.heliyon.2024.e27353
Kudlek, L., Jones, R. A., Hughes, C., Duschinsky, R., Hill, A., Richards, R., Thompson, M., Vincent, A., Griffin, S. J., & Ahern, A. L. (2024). Experiences of emotional eating in an Acceptance and Commitment Therapy based weight management intervention (SWiM): A qualitative study. Appetite, 193(August 2023), 107138. https://doi.org/10.1016/j.appet.2023.107138
Lanham, M. S. M., & Henstock, J. L. (2024). Feasibility and acceptability of patient- and clinician-level antithrombotic stewardship interventions to reduce gastrointestinal bleeding risk in patients using warfarin (AEGIS): a factorial randomized controlled pilot trial. Research and Practice in Thrombosis and Haemostasis, 102421. https://doi.org/10.1016/j.rpth.2024.102421
Larsen, J. K., Hollands, G. J., Garland, E. L., Evers, A. W. M., & Wiers, R. W. (2023). Be more mindful: Targeting addictive responses by integrating mindfulness with cognitive bias modification or cue exposure interventions. Neuroscience and Biobehavioral Reviews, 153(September 2023), 105408. https://doi.org/10.1016/j.neubiorev.2023.105408
Marshall, S., Fleming, A., Sahm, L. J., & Moore, A. C. (2023). Identifying intervention strategies to improve HPV vaccine decision-making using behaviour change theory. Vaccine, 41(7), 1368–1377. https://doi.org/10.1016/j.vaccine.2023.01.025
Massazza, A., Roberts, B., Fuhr, D. C., Woodward, A., Park, A. La, Sondorp, E., & McDaid, D. (2023). A qualitative study on the impacts of COVID-19 on the delivery of randomised controlled trials evaluating lay-delivered psychological interventions in five countries. SSM - Mental Health, 4(November 2022), 100251. https://doi.org/10.1016/j.ssmmh.2023.100251
Matheson, L., Greaves, C., Duda, J. L., Wells, M., Secher, D., Rhodes, P., Lorenc, A., Jepson, M., Ozakinci, G., Watson, E., Fulton-Lieuw, T., Mittal, S., Main, B., Nankivell, P., Mehanna, H., & Brett, J. (2024). Development of the ‘ACT now & check-it-out’ intervention to support patient-initiated follow up for Head and Neck cancer patients. Patient Education and Counseling, 119(November 2023). https://doi.org/10.1016/j.pec.2023.108033
Saha, C., Jana, D. K., & Duary, A. (2023). Enhancing production inventory management for imperfect items using fuzzy optimization strategies and Differential Evolution (DE) algorithms. Franklin Open, 5(June), 100051. https://doi.org/10.1016/j.fraope.2023.100051
Santopietro, L., Solimene, S., Lucchese, M., Di Carlo, F., & Scorza, F. (2024). An economic appraisal of the SE(C)AP public interventions towards the EU 2050 target: The case study of Basilicata region. Cities, 149(January), 104957. https://doi.org/10.1016/j.cities.2024.104957
Setyabudi, T. G., & Iswara, U. S. (2019). Perencanaan Laba Menggunakan Pendekatan Analisis Cost Volume Profit. Prosiding Seminar Nasional Dan Call for Papers UNISBANK (Sendi_U) Ke-5 Tahun 2019, 2018, 978–979.
Tripathy, J., Balasubramani, M., Rajan, V. A., S, V., Aeron, A., & Arora, M. (2024). Reinforcement learning for optimizing real-time interventions and personalized feedback using wearable sensors. Measurement: Sensors, 33(April), 101151. https://doi.org/10.1016/j.measen.2024.101151
Tripney, R., Kombeiz, O., & Dollard, M. (2024). Manager-driven intervention for improved psychosocial safety climate and psychosocial work environment. Safety Science, 176(August 2023), 106552. https://doi.org/10.1016/j.ssci.2024.106552
Vandelanotte, C., Trost, S., Hodgetts, D., Imam, T., Rashid, M., To, Q. G., & Maher, C. (2023). Increasing physical activity using an just-in-time adaptive digital assistant supported by machine learning: A novel approach for hyper-personalised mHealth interventions. Journal of Biomedical Informatics, 144(July), 104435. https://doi.org/10.1016/j.jbi.2023.104435
Xu, Z., Zheng, N., Lv, Y., Fang, Y., & Vu, H. L. (2024). Analyzing scenario criticality and rider’s intervention behavior during high-level autonomous driving: A VR-enabled approach and empirical insights. Transportation Research Part C: Emerging Technologies, 158(February 2023), 104451. https://doi.org/10.1016/j.trc.2023.104451
Yaiprasert, C., & Hidayanto, A. N. (2024). AI-powered ensemble machine learning to optimize cost strategies in logistics business. International Journal of Information Management Data Insights, 4(1), 100209. https://doi.org/10.1016/j.jjimei.2023.100209
DOI: https://doi.org/10.37531/yum.v7i2.6647
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