Analyzing how COVID-19 moderates the relationship between organizational learning capabilities, technological innovation, supply chain management and enterprise performance in the automobile industry

被引:1
作者
Nazir, Samera [1 ]
Mehmood, Saqib [1 ]
Li, Zhaolei [1 ]
Nazir, Zarish [2 ]
Nazir, Sana [3 ]
机构
[1] Changan Univ, Sch Econ & Management, Xian, Peoples R China
[2] Univ Kotli Azad Jammu & Kashmir, Dept Econ, Kotli, Pakistan
[3] Univ Kotli Azad Jammu & Kashmir, Dept Comp Sci, Kotli, Pakistan
关键词
Organizational learning capabilities; Technological innovation; Supply chain management; Enterprise performance; COVID-19; METHOD BIAS; PLS-SEM; INTEGRATION; LINKAGE;
D O I
10.1108/BPMJ-02-2024-0116
中图分类号
F [经济];
学科分类号
02 ;
摘要
PurposeThis study explored how COVID-19 moderated the relationship between organizational learning capabilities (OLCs), technological innovation (TI), supply chain management (SMC) processes and enterprise performance (EP). It aimed to give ideas on how organizations could change and do well during big disruptions.Design/methodology/approachDesign: A structured questionnaire served as the data collection tool, employing a stratified sampling technique. Partial least squares (PLS) was utilized for data processing. Information was gathered from the automobile industry in Xian, China, providing an in-depth understanding of how COVID-19 moderated the variables under examination.FindingsThe study discovered that COVID-19 changed how organizational learning, TI, SCM and EP interacted. Some organizations had trouble keeping up with learning and innovation, but others used them to make their SCM stronger, leading to better performance. Also, different effects of COVID-19 were seen in various industries and organizations.Practical implicationsThis study provided practical implications for managers, policymakers and practitioners. It emphasized fostering OLCs and TI as crucial for resilience during disruptions like COVID-19. Strategic investments in SCM were highlighted to mitigate disruptions and seize opportunities. Additionally, context-specific approaches were underscored for navigating pandemic-induced challenges.Originality/valueThis study enhanced existing literature by analyzing how COVID-19 moderated the link between organizational learning, TI, SCM and EP. Through diverse methodologies and organizational contexts, it offered fresh insights into dynamic organizational responses to disruptions, advancing both theoretical understanding and practical knowledge in the field.
引用
收藏
页码:2184 / 2209
页数:26
相关论文
共 81 条
[1]  
Afthanorhan Asyraf, 2021, Journal of Physics: Conference Series, V1874, DOI 10.1088/1742-6596/1874/1/012085
[2]   Identification and analysis of enablers of SCM by using MCDM approach [J].
Agrawal, Vivek ;
Mohanty, Rajendra P. ;
Agrawal, Anand Mohan .
BENCHMARKING-AN INTERNATIONAL JOURNAL, 2020, 27 (06) :1681-1710
[3]   The impact of COVID-19 pandemic on dental practice in Iran: a questionnaire-based report [J].
Ahmadi, Hanie ;
Ebrahimi, Alireza ;
Ghorbani, Farhad .
BMC ORAL HEALTH, 2020, 20 (01)
[4]   THE COVID-19 PANDEMIC AND THE ANTECEDANTS FOR THE IMPULSE BUYING BEHAVIOR OF US CITIZENS [J].
Ahmed, Rizwan Raheem ;
Streimikiene, Dalia ;
Rolle, Jo-Ann ;
Pham Anh Duc .
JOURNAL OF COMPETITIVENESS, 2020, 12 (03) :5-27
[5]   Using PLS-SEM technique to model construction organizations' willingness to participate in e-bidding [J].
Aibinu, Ajibade Ayodeji ;
Al-Lawati, Ahmed Murtadha .
AUTOMATION IN CONSTRUCTION, 2010, 19 (06) :714-724
[6]  
Ali M., 2021, DYNAMICS INTELLECTUA, P275, DOI DOI 10.1007/978-981-16-1692-114
[7]   Evaluating Emergy Analysis at the Nexus of Circular Economy and Sustainable Supply Chain Management [J].
Alkhuzaim, Lojain ;
Zhu, Qingyun ;
Sarkis, Joseph .
SUSTAINABLE PRODUCTION AND CONSUMPTION, 2021, 25 :413-424
[8]   Blockchain Technology Application Challenges in Renewable Energy Supply Chain Management [J].
Almutairi, Khalid ;
Dehshiri, Seyyed Jalaladdin Hosseini ;
Dehshiri, Seyyed Shahabaddin Hosseini ;
Hoa, Ao Xuan ;
Dhanraj, Joshuva Arockia ;
Mostafaeipour, Ali ;
Issakhov, Alibek ;
Techato, Kuaanan .
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2023, 30 (28) :72041-72058
[9]  
Alnuaimi M, 2021, INT J INNOV LEARN, V29, P207, DOI 10.1504/IJIL.2021.112996
[10]   Predicting the actual use of m-learning systems: a comparative approach using PLS-SEM and machine learning algorithms [J].
Alshurideh, Muhammad ;
Al Kurdi, Barween ;
Salloum, Said A. ;
Arpaci, Ibrahim ;
Al-Emran, Mostafa .
INTERACTIVE LEARNING ENVIRONMENTS, 2023, 31 (03) :1214-1228