Objectives Although family members of persons with disabilities face elevated suicide risk, predictive models remain underdeveloped in Korean contexts. This study aimed to develop machine learning–based predictive models for suicidal ideation among family members of persons with disabilities and examine differential risk patterns by disability onset type.
Methods This cross-sectional study analyzed 124,783 adult family members (59.9% spouses, 20.3% parents/ascendants, 14.6% adult children, 5.2% extended family) from the 2018 Korean Disability and Life Dynamics Panel using survey weights. Four predictive models, including machine learning approaches, were compared using 31 variables. The dataset was divided into training (70%) and test (30%) sets, with stratified analyses comparing congenital and acquired disability groups.
Results Among the 124,783 family members analyzed, least absolute shrinkage and selection operator (LASSO) with cross-validation achieved optimal performance (area under the receiver operating characteristic curve, 0.875 training; 0.853 test). LASSO selected 16 of 31 variables for the total sample, with family members’ depression as the strongest predictor (β=0.554), followed by disabled persons’ suicidal ideation (β=0.425). Stratified LASSO analyses revealed that national basic livelihood beneficiary status was the strongest predictor for families with congenital disability (β=0.541), while family members’ depression was the strongest predictor for families with acquired disability (β=0.562), demonstrating distinct predictive patterns by disability onset.
Conclusion These findings show that predictive factors differ substantially by disability onset type, indicating the need for tailored intervention approaches and offering an evidence-based foundation for targeted suicide prevention strategies.
Objectives This study developed deep neural network (DNN) models capable of accurately classifying major adverse cardiac events (MACE) in patients with acute myocardial infarction (AMI) after hospital discharge, across 3 follow-up intervals: 1, 6, and 12 months. Methods: DNN models were constructed to predict post-discharge MACE across 4 categories. Multiple traditional machine learning models were implemented as controls to benchmark the performance of our DNN approach. All models were evaluated based on their ability to predict MACE occurrence during the specified follow-up periods. Results: The DNN models demonstrated superior predictive performance over conventional machine learning methods, achieving high accuracies of 0.922, 0.884, and 0.913 for the 1-month, 6-month, and 12-month follow-up periods, respectively. Conclusion: The high accuracy of our DNN models highlights their practical advantages for AMI diagnosis and guidance of follow-up treatment. These models can serve as valuable decision support tools, enabling clinicians to optimize the overall management of AMI patients and potentially enhance their hospitalization experience.
Citations
Citations to this article as recorded by
Artificial intelligence and biohealth: the Republic of Korea’s emerging priorities in health care R&D Jong-Koo Lee Osong Public Health and Research Perspectives.2025; 16(4): 309. CrossRef
Objectives The coronavirus disease 2019 (COVID-19) pandemic continues to pose significant challenges to the public health sector, including that of the United Arab Emirates (UAE). The objective of this study was to assess the efficiency and accuracy of various deep-learning models in forecasting COVID-19 cases within the UAE, thereby aiding the nation’s public health authorities in informed decision-making. Methods: This study utilized a comprehensive dataset encompassing confirmed COVID-19 cases, demographic statistics, and socioeconomic indicators. Several advanced deep learning models, including long short-term memory (LSTM), bidirectional LSTM, convolutional neural network (CNN), CNN-LSTM, multilayer perceptron, and recurrent neural network (RNN) models, were trained and evaluated. Bayesian optimization was also implemented to fine-tune these models. Results: The evaluation framework revealed that each model exhibited different levels of predictive accuracy and precision. Specifically, the RNN model outperformed the other architectures even without optimization. Comprehensive predictive and perspective analytics were conducted to scrutinize the COVID-19 dataset. Conclusion: This study transcends academic boundaries by offering critical insights that enable public health authorities in the UAE to deploy targeted data-driven interventions. The RNN model, which was identified as the most reliable and accurate for this specific context, can significantly influence public health decisions. Moreover, the broader implications of this research validate the capability of deep learning techniques in handling complex datasets, thus offering the transformative potential for predictive accuracy in the public health and healthcare sectors.
Citations
Citations to this article as recorded by
Opportunities and challenges of artificial intelligence in public health: a systematic review on technological efficacy, ethical dilemmas, and governance pathways Qin Gao, Lin Chen, Zhenyu Huang Frontiers in Public Health.2026;[Epub] CrossRef
Enhancing Influenza-Like Illness forecasting: An ensemble approach combining mathematical and deep learning models amidst the COVID-19 pandemic Ganghyun Yoon, Amanda Bleichrodt, Gerardo Chowell, Sunmi Lee Epidemics.2026; 54: 100901. CrossRef
Artificial Intelligence during a Public Health Crisis: A Trend Analysis of COVID-19 Research Sunghye Choi, Chang Kil Lee, Dohyeong Kim Journal of Environmental Health Sciences.2025; 51(3): 109. CrossRef
Detecting outbreaks using a spatial latent field Cosmin Safta, Jaideep Ray, Wyatt Bridgman, Yoo Min Park PLOS One.2025; 20(7): e0328770. CrossRef
Wastewater as an early indicator for short-term forecasting COVID-19 hospitalization in Germany Jonas Radermacher, Steffen Thiel, Aimo Kannt, Holger Fröhlich BMC Public Health.2025;[Epub] CrossRef
Potential impacts of socio-economic changes on urban acoustic environment: evidence from 20 cities in Southern China Ping Lu, Kai Yuan Zhan, Wei Zhen Xu, Wei Ren, Jiang Liu, Xin-Chen Hong Environment, Development and Sustainability.2025;[Epub] CrossRef
Bridging statistics and deep learning: a causal data-fusion framework for epidemic prediction Vasileios E. Papageorgiou, Georgios Petmezas, Aristeidis Georgakis, Nicos Maglaveras, George Tsaklidis Communications in Statistics - Simulation and Comp.2025; : 1. CrossRef
<sec>
<title>Objectives</title>
<p>The aim of this study was to investigate the impact of mixed cognitive intervention training using spaced retrieval training, and errorless learning in participants with early onset dementia. This was based on reality orientation therapy for cognitive function, depression, and occupational performance of patients.</p></sec>
<sec>
<title>Methods</title>
<p>Two early onset vascular dementia patients (> 65 years) with mild or moderate impairment were enrolled in a pre-test - post-test single-subject research design study. Prior to the study, the caregivers were interviewed about meaningful times, people, places, and areas of interest for the participant. A list of individual training words were selected based upon this information, and the participant was instructed to recall them after a 45-second, 90-second, 6-minute, and 12-minute delay. Baseline (3 sessions), intervention (20 sessions), and a second baseline period (3 sessions) were conducted. Activities of daily living were measured, and cognition was measured using the Consortium to Establish a Registry of Alzheimer’s Disease Korean version, whilst depression was measured using the Korean Form Geriatric Depression Scale, and task performance and satisfaction measured by the Canadian Occupational Performance Measure.</p></sec>
<sec>
<title>Results</title>
<p>After intervention, both participants showed improvements in activities of daily living (ADL), word list memory/recognition, trail making A, occupational performance, and satisfaction improvement, which was clinically significant in 1 participant who also had a reduced score in the scale of depression classifying him as not depressed.</p></sec>
<sec>
<title>Conclusion</title>
<p>Spaced retrieval training and errorless learning based on reality orientation therapy is an effective intervention in patients with early onset dementia and mild or moderate impairment.</p></sec>
Citations
Citations to this article as recorded by
Medical professionals’ perspectives and experiences in disclosing a dementia diagnosis: a grounded theory study Miao-Chuan Chen, Cheng-I Chu, Hung-Ru Lin BMC Geriatrics.2026;[Epub] CrossRef
Linguistic-cognitive intervention in a case of early-onset Alzheimer’s disease Beatriz Andrade de Souza, Maria Isabel D’Ávila Freitas CoDAS.2026;[Epub] CrossRef
Intervenção linguístico-cognitiva em um caso de doença de Alzheimer de início precoce Beatriz Andrade de Souza, Maria Isabel D’Ávila Freitas CoDAS.2026;[Epub] CrossRef
Non‐pharmacological interventions for people living with young‐onset dementia and their carers: A scoping review focussing on the support of participants' needs Iktae Kim, Yoosun Yang, Hongjin Cheon, Jiyeon Kim, Jun‐Ah Song Journal of Psychiatric and Mental Health Nursing.2024; 31(1): 14. CrossRef
Interventions for Persons with Young-Onset Dementia and Their Families: A Scoping Review Xiaoyan Cui, Junqiao Wang, Bei Wu, Qianhua Zhao, Xueting Tang, Jing Wang Journal of Alzheimer’s Disease.2024; 97(4): 1519. CrossRef
<sec>
<title>Objectives</title>
<p>This study presents the development and validation of a risk assessment program of highly pathogenic avian influenza (HPAI). This program was developed by the Korean government (Animal and Plant Quarantine Agency) and a private corporation (Korea Telecom, KT), using a national database (Korean animal health integrated system, KAHIS).</p></sec>
<sec>
<title>Methods</title>
<p>Our risk assessment program was developed using the multilayer perceptron method using R Language. HPAI outbreaks on 544 poultry farms (307 with H5N6, and 237 with H5N8) that had available visit records of livestock-related vehicles amongst the 812 HPAI outbreaks that were confirmed between January 2014 and June 2017 were involved in this study.</p></sec>
<sec>
<title>Results</title>
<p>After 140,000 iterations without drop-out, a model with 3 hidden layers and 10 nodes per layer, were selected. The activation function of the model was hyperbolic tangent. Precision and recall of the test gave F1 measures of 0.41, 0.68 and 0.51, respectively, at validation. The predicted risk values were higher for the “outbreak” (average ± SD, 0.20 ± 0.31) than “non-outbreak” (0.18 ± 0.30) farms (<italic>p</italic> < 0.001).</p></sec>
<sec>
<title>Conclusion</title>
<p>The risk assessment model developed was employed during the epidemics of 2016/2017 (pilot version) and 2017/2018 (complementary version). This risk assessment model enhanced risk management activities by enabling preemptive control measures to prevent the spread of diseases.</p></sec>
Citations
Citations to this article as recorded by
Farm-level transmission dynamics and risk assessment of H5 Avian Influenza Juan Zhang, Jialin Wang, Xiaomeng Wei, Li Li, Ming-Tao Li, Xin Pei, Zhen Jin Chaos, Solitons & Fractals.2026; 204: 117767. CrossRef
Quantitative risk assessment of avian influenza: A scoping review Mina Khoshbazm, Kelsey Spence, Marzieh Soltani, Lauren Grant, Yan Yan, Shayan Sharif, Rozita Dara Infectious Disease Modelling.2026; 11(4): 1583. CrossRef
A risk prediction model for HPAI outbreaks in Kuwait Musab Alshatti, Ali Al-Hemoud, Ahmad Othman, Hanan Al-Khalaifah Frontiers in Veterinary Science.2026;[Epub] CrossRef
A systematic review of mathematical and machine learning models of Avian Influenza Shixun Huang, Nicola Luigi Bragazzi, Zahra Movahedi Nia, Murray Gillies, Emma Gardner, Doris Leung, Itlala Gizo, Jude D. Kong One Health.2025; 21: 101203. CrossRef
Avian Influenza: Lessons from Past Outbreaks and an Inventory of Data Sources, Mathematical and AI Models, and Early Warning Systems for Forecasting and Hotspot Detection to Tackle Ongoing Outbreaks Emmanuel Musa, Zahra Movahhedi Nia, Nicola Luigi Bragazzi, Doris Leung, Nelson Lee, Jude Dzevela Kong Healthcare.2024; 12(19): 1959. CrossRef
Big data-based risk assessment of poultry farms during the 2020/2021 highly pathogenic avian influenza epidemic in Korea Hachung Yoon, Ilseob Lee, Hyeonjeong Kang, Kyung-Sook Kim, Eunesub Lee, Mathilde Richard PLOS ONE.2022; 17(6): e0269311. CrossRef
Artificial Intelligence Models for Zoonotic Pathogens: A Survey Nisha Pillai, Mahalingam Ramkumar, Bindu Nanduri Microorganisms.2022; 10(10): 1911. CrossRef
<sec>
<title>Objectives</title>
<p>This study was conducted to evaluate whether a “smoking cessation intervention” education program based on blended learning, was effective in improving nursing students’ perceived competence and motivation to perform a smoking cessation intervention for smokers.</p></sec>
<sec>
<title>Methods</title>
<p>A quasi-experimental, pretest–posttest design was conducted. The smoking cessation intervention education program based on blended learning, was administered to the experimental group (<italic>n</italic> = 23) in 5 sessions, consisting of 2 courses of an e-learning program and 1 course of a face-to-face learning program per session. The control group (<italic>n</italic> = 21) received the opportunity to participate in an e-learning program as well as receiving material of a face-to-face learning program, after completion of the smoking cessation intervention education program.</p></sec>
<sec>
<title>Results</title>
<p>The experimental group showed significant differences in autonomous motivation (<italic>t</italic> = −6.982, <italic>p</italic> < 0.001), controlled motivation (<italic>t</italic> = −3.729, <italic>p</italic> = 0.001), and perceived competence compared to the control group (<italic>t</italic> = −3.801, <italic>p</italic> < 0.001).</p></sec>
<sec>
<title>Conclusion</title>
<p>This study showed that a smoking cessation intervention education program adopting blended learning, was significantly effective in enhancing nursing students’ autonomous motivation and perceived competence to conduct a smoking cessation intervention. Further studies are needed to confirm longitudinal effects of this program.</p></sec>
Citations
Citations to this article as recorded by
Systematic literature review on the effects of blended learning in nursing education Ruowei Wang, Arumugam Raman Nurse Education in Practice.2025; 82: 104238. CrossRef
Development and evaluation of virtual simulation for smoking cessation counseling education program for nursing students Eun-Hye Lee, Sung-Rae Shin BMC Nursing.2025;[Epub] CrossRef
Effectiveness of debriefing after a short training on brief tobacco intervention for nursing students: A randomized clinical trial Antonio Jesús Ramos-Morcillo, César Leal-Costa, María Suárez-Cortés, Alonso Molina-Rodríguez, María Ruzafa-Martínez Nurse Education in Practice.2025; 86: 104393. CrossRef
Educational interventions promoting modifiable lifestyle behaviours for cardiovascular disease in pre-registration nursing students: a scoping review Laura Creighton, Gemma Caughers, Gary Mitchell, James McMahon, Lis Neubeck, Donna Fitzsimons BMC Nursing.2025;[Epub] CrossRef
The learning outcomes of smoking cessation training in undergraduate nursing students: A systematic review Guowen Zhang, Jie Zhou, Tzu Tsun Luk, Veronica Suk Fun Lam, Zhuangyan Yao, Man Ping Wang, Yee Tak Derek Cheung Nurse Education in Practice.2024; 75: 103907. CrossRef
Effectiveness of digital tools for smoking cessation in Asian countries: a systematic review Khang Wen Goh, Long Chiau Ming, Yaser Mohammed Al-Worafi, Ching Siang Tan, Andi Hermansyah, Inayat Ur Rehman, Zahid Ali Annals of Medicine.2024;[Epub] CrossRef
E-Learning Experiences Among Nursing Students: A Scoping Review Adam Marawa'a Advances in Medical Education and Practice.2024; Volume 15: 369. CrossRef
A Self-Applied Psychological Treatment for Gambling-Related Problems via The Internet: A Pilot, Feasibility Study Laura Diaz-Sanahuja, Carlos Suso-Ribera, Ignacio Lucas, Susana Jiménez-Murcia, Cintia Tur, Patricia Gual-Montolio, Macarena Paredes-Mealla, Azucena García-Palacios, Juana María Bretón-López Journal of Gambling Studies.2024; 40(3): 1623. CrossRef
The effectiveness of a motivational text-messaging program for smoking cessation after coronary angioplasty: a quasi-experimental study Mohammad Sadegh Mobaraki, Zahra Khademian, Fatemeh Shirazi BMC Research Notes.2023;[Epub] CrossRef
Investigation of Postgraduate Theses on Using of Web-Based Education in Nursing Education Hüsne YÜCESOY, Nülüfer ERBİL Middle Black Sea Journal of Health Science.2023; 9(3): 403. CrossRef
Efficacy of a blended learning programme in enhancing the communication skill competence and self-efficacy of nursing students in conducting clinical handovers: a randomised controlled trial Jessie Yuk Seng Chung, William Ho Cheung Li, Ankie Tan Cheung, Laurie Long Kwan Ho, Joyce Oi Kwan Chung BMC Medical Education.2022;[Epub] CrossRef
Effect of flipped learning-based smoking cessation intervention education program for nursing students in South Korea Yein Lee, Yunhee Kim The Journal of Korean Academic Society of Nursing .2022; 28(2): 147. CrossRef
Effectiveness of Diabetes Nursing Course Designed With Hybrid Learning Pedagogy: A Pilot Study Gülten OKUROĞLU, Şule ALPAR ECEVİT Clinical and Experimental Health Sciences.2022; 12(2): 507. CrossRef
Covid-19 ile Hemşirelik Eğitiminde Yeni Bir Yaklaşım: Harmanlanmış Öğrenme Elif KOCAAĞALAR AKİNCE, Duygu Ceren GÜNGÖR, Nilay ÖZKÜTÜK, Fatma ORGUN Uluslararası Sosyal Bilimler Akademi Dergisi.2022; 4(10): 975. CrossRef
Impact of tobacco cessation education on behaviors of
nursing undergraduates in helping smoker to quit smoking Li Zhang, Xian Huang, Tao Luo, Li Jiang, Mei Jiang, Han Chen Tobacco Induced Diseases.2021; 19(July): 1. CrossRef
Blended learning in undergraduate nursing education – A scoping review Don M. Leidl, Lauren Ritchie, Neda Moslemi Nurse Education Today.2020; 86: 104318. CrossRef
Comparing student achievement in traditional learning with a combination of blended and flipped learning Suhaila Halasa, Nimer Abusalim, Mohammad Rayyan, Rose E. Constantino, Omayah Nassar, Huda Amre, Moayad Sharab, Insirah Qadri Nursing Open.2020; 7(4): 1129. CrossRef
Objectives
Organizational learning is defined as creating, absorbing, retaining, transferring, and application of knowledge within an organization. This article aims to examine the mediating role of organizational learning in the relationship of organizational intelligence and organizational agility. Methods
This analytical and cross-sectional study was conducted in 2015 at four teaching hospitals of Yazd city, Iran. A total of 370 administrative and medical staff contributed to the study. We used stratified-random method for sampling. Required data were gathered using three valid questionnaires including Alberkht (2003) organizational intelligence, Neefe (2001) organizational learning, and Sharifi and Zhang (1999) organizational agility questionnaires. Data analysis was done through R and SPSS 18 statistical software. Results
The results showed that organizational learning acts as a mediator in the relationship of organizational intelligence and organizational agility (path coefficient = 0.943). Also, organizational learning has a statistical relationship with organizational agility (path coefficient = 0.382). Conclusion
Our findings suggest that the improvement of organizational learning abilities can affect an organization's agility which is crucial for its survival.
Citations
Citations to this article as recorded by
Business model innovation as a channeler of agility and redundancy in the international expansion of food and beverage companies Luis A. Millán-Tudela, Bartolomé Marco-Lajara, Esther Poveda-Pareja, Javier Martínez-Falcó International Journal of Innovation Studies.2026; 10(2): 100164. CrossRef
Cognitive break up in operational practice of good manufacturing system: knowledge-driven performance appraisal outlook Pradeep K. Jha, Suvadip Ghorai, Rakhi K. Jha, Surya Prakash Singh Benchmarking: An International Journal.2026; : 1. CrossRef
Shaping green workplace behavior in tourism and hospitality through organizational learning, workforce agility and organizational intelligence Abdelrahman A. A. Abdelghani, Bassam Samir Al-Romeedy, Eman Hassanien Taha, Sameh Fayyad, Alaa H. A. Masheh, Hebatallah A. M. Ahmed Scientific Reports.2026;[Epub] CrossRef
Leadership agility and its manifestations during the crisis: Qualitative evidence from African SMEs Obinna Alo, Ahmad Arslan, Tuija Mainela, Anna Yumiao Tian, Derek Watson Africa Journal of Management.2026; 12(3): 331. CrossRef
Understanding the role of organizational agility in the context of digital transformation: an integrative literature review Cheng Gong, Vincent Ribiere VINE Journal of Information and Knowledge Manageme.2025; 55(2): 351. CrossRef
Does learning culture enhance organizational performance? A serial mediator with knowledge management and organizational intelligence Jamini Ranjan Meher, Rohita Kumar Mishra, Rashmi Ranjan Panigrahi, Gokulananda Patel, Lalatendu Kesari Jena Knowledge Management Research & Practice.2025; 23(2): 107. CrossRef
The relationship between organisational agility and informal learning Sebo Boerma, Maarten de Laat, Marjan Vermeulen Management Review Quarterly.2025; 75(4): 3327. CrossRef
The impact of school administrators’ influence tactics on teachers’ organizational commitment: the role of learning agility Cemaliye Mahmutoğlu, Cevat Celep, Ayça Kaya Frontiers in Psychology.2025;[Epub] CrossRef
Dynamic Knowledge Management Capabilities for Successful Product Eco‐Innovation José Rabal Conesa, Daniel Jiménez Jiménez, Micaela Martínez Costa Business Strategy and the Environment.2025; 34(6): 7349. CrossRef
AGILITY’S ROLE IN COLLABORATIVE INNOVATION: THE SYNERGY OF PARTICIPATIVE LEADERSHIP AND COLLECTIVE INTELLIGENCE SINDA BEN SEDRINE DOGHRI, SARRA ZINOUBI, MARYEM DAHMANI International Journal of Innovation Management.2025;[Epub] CrossRef
Transforming Hospitality Decision-Making: The Impact of Generative AI on Cognitive Alignment and Adaptive Intelligence Jia Xinlin, Li Wenting, Khairul Anuar Mohammad Shah, Meng Na, Syed Shah Alam Journal of Quality Assurance in Hospitality & Tour.2025; : 1. CrossRef
Exploring the role of knowledge management and organizational agility in an emerging market Shafique Ur Rehman, Stefano Bresciani, Daniele Giordino, Amir A. Abdulmuhsin Journal of Innovation & Knowledge.2025; 10(5): 100761. CrossRef
THE MEDIATING ROLE OF ORGANIZATIONAL AGILITY ON THE RELATIONSHIP BETWEEN PLANNING OF MARKETING POLICIES AND MARKETING PERFORMANCE: FIELD STUDY OF A SAMPLE OF FOOD INDUSTRY COMPANIES IN ALGERIA Hakim KHALFAOUI, Khaled KHALFI, Sid Ali BELHAMDI, Mohamed HEMIS Modern Management Review.2025; 30(4): 7. CrossRef
The Impact of Organizational Agility on Innovation Performance: The Key Role of Digital Transformation Yusuf Akkoca Eskişehir Osmangazi Üniversitesi Sosyal Bilimler D.2025; 26(3): 1280. CrossRef
Comparing Promotability Outlooks: Industry Professionals vs. MBA Student Perspectives Vicente E. Montaño, Myrna S. Viado European Journal of Contemporary Education and E-L.2024; 2(1): 112. CrossRef
Survey-Based Studies of the Agility Construct in the Healthcare Sector: A Systematic Literature Review Jorge Flavio Sarmiento Falla, Waldemar Karwowski Applied Sciences.2024; 14(3): 1097. CrossRef
Structural Equation Modeling: Effects of Master Adaptive Learner and Clinical Learning Environment Attributes on Career Satisfaction and Intention to Stay Raine Osborne, Chloe Bailey, Daniel Dinsmore, Emily Lyter, Judy Burnfield, Holly Medearis, Amanda Roura Physical Therapy.2024;[Epub] CrossRef
The Impact of Strategic Agility on Operational Responsiveness and Firm Performance of Import, Export, and Logistics Enterprises Orawee Sriboonlue, Umawasee Sriboonlue, Suraporn Onputtha SSRN Electronic Journal.2024;[Epub] CrossRef
Práticas de Inteligência Organizacional voltadas ao planejamento e à execução do policiamento ostensivo Diogo Fernando Sampaio Pytlowanciv, Helena de Fátima Nunes Silva Informação@Profissões.2024; 12(1/2): 62. CrossRef
Social Media Engagement, Organizational Agility and Digitalization Strategic Plan to Improve SMEs’ Performance Hasan Abdul Rozak, Ardian Adhiatma, Olivia Fachrunnisa, Tina Rahayu IEEE Transactions on Engineering Management.2023; 70(11): 3766. CrossRef
Differential impact of work-based learning on employee agility, ambidexterity and proactive goal generation Charles Hanu, Hayford Amegbe, Monica Dede Tekyi Ansah Yawson, Philip Mensah Journal of Workplace Learning.2023; 35(1): 92. CrossRef
Learning Organization and Innovation Coaching Model Organizational Agility Approach: A Case Study Samet GÜRSEV European Journal of Science and Technology.2023;[Epub] CrossRef
Sağlık Kurumlarında Örgütsel Çeviklik: Kavramsal Bir Çerçeve Pelinsu Buket DOĞANYİĞİT Türkiye Sağlık Bilimleri ve Araştırmaları Dergisi.2023; 6(2): 66. CrossRef
Teachers' intrinsic and extrinsic motivation as mediators of the effect of principals' perceived distributed leadership on organizational learning capability Izhak Berkovich, Tahani Hassan International Journal of Educational Management.2023; 37(6/7): 1552. CrossRef
SAĞLIK KURULUŞLARINDA BİLGİ SİSTEMLERİ KULLANIMININ ÖRGÜTSEL ÇEVİKLİK VE ÇALIŞAN MEMNUNİYETİ ÜZERİNE ETKİSİ Mustafa ALTINTAŞ, Musa ÖZATA Anadolu Üniversitesi İktisadi ve İdari Bilimler Fa.2023; 24(4): 333. CrossRef
Factors influencing the change-supportive behavior: learning agility, perception of reasons and benefits for change Kettyplyn Kamila Sanches Franco, Elaine Rabelo Neiva Revista Caribeña de Ciencias Sociales.2023; 12(4): 1652. CrossRef
Why do I contribute to organizational learning when I am ostracized? A moderated mediation analysis Muhammad Kashif Imran, Jawad Iqbal, Tehreem Fatima, Syed Muhammad Javed Iqbal, Warda Najeeb Jamal, Muhammad Shahid Nawaz Journal of Management & Organization.2022; 28(2): 261. CrossRef
Examining the role of knowledge sharing on employee performance with
a mediating effect of organizational learning Jamini Ranjan Meher, Rohita Kumar Mishra VINE Journal of Information and Knowledge Manageme.2022; 52(2): 205. CrossRef
Influence of workforce agility on crisis management: Role of job characteristics and higher administrative support in public administration Pradeep Kumar Raut, Jyoti Ranjan Das, Jyotiranjan Gochhayat, Kalyan Prasad Das Materials Today: Proceedings.2022; 61: 647. CrossRef
Organisational agility, environmental knowledge and green product success Jose Rabal-Conesa, Daniel Jiménez-Jiménez, Micaela Martínez-Costa Journal of Knowledge Management.2022; 26(9): 2440. CrossRef
Towards Examining the Link Between Workplace Spirituality and Workforce Agility: Exploring Higher Educational Institutions Imran Saeed, Jawad Khan, Muhammad Zada, Rezwan Ullah, Alejandro Vega-Muñoz, Nicolás Contreras-Barraza Psychology Research and Behavior Management.2022; Volume 15: 31. CrossRef
A qualitative analysis and a conceptual model for organizational stupidity Samaneh Karimi-Ghartemani, Naser Khani, Ali Nasr Isfahani Journal of Organizational Change Management.2022; 35(3): 441. CrossRef
ENDÜSTRİYEL ÇEVİK İŞLETME İÇİN DENGELENMİŞ BAŞARI GÖSTERGELERİ Alperen Ekrem ÇELİKDİN Çukurova Üniversitesi Sosyal Bilimler Enstitüsü De.2022; 31(1): 38. CrossRef
Organizational learning culture and business intelligence systems of health-care organizations in an emerging economy Md. Shamsul Arefin, Md. Rakibul Hoque, Tareq Rasul Journal of Knowledge Management.2021; 25(3): 573. CrossRef
Investigating the role of social networking technology on the organizational agility: a structural equation modeling approach Shadi Ahmadi, Mohammad Javad Ershadi Journal of Advances in Management Research.2021; 18(4): 568. CrossRef
[Retracted] Mediating Effect of Organizational Learning Capacity on the Relationship between Relational Embeddedness and Innovation Performance in Freight Logistics Service Pengxia Bai, Qunqi Wu, Qian Li, Chenlei Xue, Lei Zhang, Zhihan Lv Complexity.2021;[Epub] CrossRef
Creating a Work Environment Conducive to Organizational Learning Bret Lyman, Marisa E. Biddulph, V. Grace Hopper, Morgan K. Horton, Camille R. Mendon, Kylie C. Thorum, Emily L. Smith The Journal of Continuing Education in Nursing.2021; 52(6): 281. CrossRef
Organizational Intelligence and Market Expansion in Jordanian Pharmaceutical Companies Najm A. Najm, Amany A. H. Alfaqih Organizations and Markets in Emerging Economies.2021; 12(1): 222. CrossRef
Modeling organizational intelligence, learning, forgetting and agility using structural equation model approaches in Shiraz University of Medical Sciences Hospitals Zahra Kavosi, Sajad Delavari, Mohammad Mehdi Kiani, Peivand Bastani, Mohebat Vali, Marzieh Salehi BMC Research Notes.2021;[Epub] CrossRef
KESAN INTEGRASI KELAKUAN KUMPULAN PENGURUSAN ATASAN KE ATAS KETANGKASAN ORGANISASI DI MALAYSIA: KAPASITI PENYERAPAN SEBAGAI PEMBOLEH UBAH PENGANTARA (The Effects of Top Management Team Behavioural Integration on Organisational Agility in Malaysia: Absorp Khairul Naziya Kasim, Sofiah Md Auzair, Amizawati Mohd Amir, Nor Liza Abdullah International Journal of Management Studies.2021;[Epub] CrossRef
Estudio bibliométrico de la relación del Cambio Organizacional con el Aprendizaje Organizacional Luis Alberto Bellon Álvarez, Francisco Javier López Cerpa, Margarita Isabel Islas Villanueva Vinculatégica.2021;[Epub] CrossRef
Dynamic talent management capabilities and organizational agility—A qualitative exploration Katharina Harsch, Marion Festing Human Resource Management.2020; 59(1): 43. CrossRef
Study of the impact mechanism of inter-organizational learning on alliance performance—with relationship capital as the mediator Lu Shan, Li Dan, Yu-mou Qiu Neural Computing and Applications.2020; 32(1): 117. CrossRef
Workforce agility and its links to emotional intelligence and workforce performance: A study of small entrepreneurial firms in India Deepanjana Varshney, Nirbhay Krishna Varshney Global Business and Organizational Excellence.2020; 39(5): 35. CrossRef
Love your mistakes!—they help you adapt to change. How do knowledge, collaboration and learning cultures foster organizational intelligence? Wioleta Kucharska, Denise A.D. Bedford Journal of Organizational Change Management.2020; 33(7): 1329. CrossRef
ÖRGÜT KÜLTÜRÜ ALGISININ ÖRGÜTSEL ÇEVİKLİK ÜZERİNDEKİ ETKİSİNİN İNCELENMESİ Suzan BASRI, Kürşad ZORLU Sosyal Ekonomik Araştırmalar Dergisi.2020; 20(39): 147. CrossRef
Investigating the Impact of Using Knowledge Management on Organisational Agility through Competitive Intelligence and Strategic Thinking Hossein Sayyadi Tooranloo, Sepideh Saghafi Journal of Information & Knowledge Management.2019; 18(02): 1950016. CrossRef
The effect of knowledge management, e-learning systems and organizational learning on organizational intelligence Zeynab Soltani, Batool Zareie, Leila Rajabiun, Ali Agha Mohseni Fashami Kybernetes.2019; 49(10): 2455. CrossRef
Knowledge management infrastructures and organizational intelligence in Iranian research centers Hamid Keshavarz, Mohammad Reza Esmaili Givi, Mohammad Reza Shekari Data Technologies and Applications.2018; 52(1): 2. CrossRef
Örgütsel Çeviklik Ölçeğinin Türkçeye Uyarlanması: Geçerlik ve Güvenirlik Çalışması Bülent Akkaya, Akif Tabak İş ve İnsan Dergisi.2018; 5(2): 185. CrossRef
Evaluating indicators of the agility of the green supply chain Hossein Sayyadi Tooranloo, Masoume Alavi, Sepideh Saghafi Competitiveness Review.2018; 28(5): 541. CrossRef
First
Prev
