ARTÍCULO
TITULO

Understanding Learning Intention Complexities in Lean Manufacturing Training for Innovation on the Production Floor

Nai Yeen Gavin Lai    
Wai Choong Foo    
Chon Siong Tan    
Myoung Sook Kang    
Hooi Siang Kang    
Kok Hoong Wong    
Lih Jiun Yu    
Xu Sun and Nadia Mei Lin Tan    

Resumen

The Theory of Planned Behavior (TPB) is a useful framework that helps explain people?s behavior across a wide range of settings. The present study adopted the TPB to investigate factors that affect the complexity of the learning intention of workers involved with a lean manufacturing training initiative. Even though workers? training has been consistently listed as a critical success factor for innovative improvement initiatives, very few studies explore direct workers? learning intentions. This is particularly true within the area of lean manufacturing training. Hence, direct workers in an automotive parts manufacturing organization were invited to participate in this study, to which 204 workers voluntarily responded. The survey data was compiled and analyzed through stepwise regression to establish the effects of the different factors on learning intention in lean manufacturing training. It was determined from the empirical results that the participants? attitude toward learning from lean manufacturing training and the perceived behavioral control factors positively affected the workers? learning intention. Organization management could look into different measures and policies to stimulate better learning effects from training programs among the participants. Actions that could foster positive attitudes and confidence of workers towards lean training initiatives will be most helpful in enabling effective and innovative lean practices on the organization?s shop floors. The key theoretical and managerial implications, as well as the limitations of the study, are also discussed.

 Artículos similares

       
 
Xiaomei Zhong, Yongsheng Wu, Jie Yu, Lei Liu and Haibo Niu    
The formation of oil?mineral aggregates (OMAs) is essential for understanding the behavior of oil spills in estuaries and coastal waters. We utilized statistical methods (screening design) to identify the most influential variables (seven factors in tota... ver más

 
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, Francisco J. Ribadas-Pena and Néstor Bolaños    
In the context of academic expert finding, this paper investigates and compares the performance of information retrieval (IR) and machine learning (ML) methods, including deep learning, to approach the problem of identifying academic figures who are expe... ver más
Revista: Algorithms

 
Majid Zamiri and Ali Esmaeili    
In an era marked by swift technological advancements and an escalating emphasis on collaborative learning, understanding effective methods and technologies for sharing knowledge is imperative to optimize educational outcomes. This study delves into the v... ver más

 
Valentina Vendittoli, Wilma Polini, Michael S. J. Walter and Stefan Geißelsöder    
Additive manufacturing has transformed the production process by enabling the construction of components in a layer-by-layer approach. This study integrates Artificial Neural Networks to explore the nuanced relationship between process parameters and mec... ver más
Revista: Applied Sciences

 
Lei Yang, Mengxue Xu and Yunan He    
Convolutional Neural Networks (CNNs) have become essential in deep learning applications, especially in computer vision, yet their complex internal mechanisms pose significant challenges to interpretability, crucial for ethical applications. Addressing t... ver más
Revista: Applied Sciences