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The purpose of this paper is to timely control of a construction collapse accident effectively during its development process by constructing a stage model and then aligning IT with each stage to help provide the information for decision making.

Through comprehensive literature review, this paper first identifies the various IT applications in on-site construction monitoring and analyzes the existed disaster/crisis stage models, also the stage models are compared with the causation models to illustrate the strengths. Then, a three-step methodology was conducted to develop and apply the conceptual framework, including the construction of the four-stage model; the establishment of the conceptual framework of information technology (IT) support for management of construction accidents (ITSMCA); and a building collapse accident used to illustrate the proposed framework.

The accident is divided into four stages, which are incubation stage, outbreak stage, spreading stage and final stage. The real-time staged information to support decision making, such as the contributing factors of on-site workers, materials, equipment and workplace, can be provided by emerging IT. Therefore, IT is aligned with the variations of contributing factors’ attributes in the four stages and ITSMCA is constructed to help accidents management.

The focus of the framework presented in this paper is that the stage model is effective for it catches the variations of the attributes whose values can be provided by IT rather than research on the practical application of the IT system. The construction and application of the IT system will be the research focus in the future.

This paper presents a stage model of a building collapse accident and gives a comprehensive conceptual framework of ITSMCA, which align the IT with different stages of the collapse accident. The ITSMCA proposes a feasible ideology and practical method for real-time management of the collapse accident during the process.

In this paper, the problem of a nonlinear model – specifically the hidden unit conditional random fields (HUCRFs) model, which has binary stochastic hidden units between the data and the labels – exhibiting unstable performance depending on the hyperparameter under consideration.

There are three main optimization search methods for hyperparameter tuning: manual search, grid search and random search. This study shows that HUCRFs’ unstable performance depends on the hyperparameter values used and its performance is based on tuning that draws on grid and random searches. All experiments conducted used the n-gram features – specifically, unigram, bigram, and trigram.

Naturally, selecting a list of hyperparameter values based on a researchers’ experience to find a set in which the best performance is exhibited is better than finding it from a probability distribution. Realistically, however, it is impossible to calculate using the parameters in all combinations. The present research indicates that the random search method has a better performance compared with the grid search method while requiring shorter computation time and a reduced cost.

In this paper, the issues affecting the performance of HUCRF, a nonlinear model with performance that varies depending on the hyperparameters, but performs better than CRF, has been examined.

The objective of this study is twofold: first, to investigate the determinants of customers' switching intention from traditional payments to proximity mobile payments (PMPs) during the COVID-19 pandemic for specific insight on how these factors shape customers' switching intentions; second, this study discusses the relationship between traditional payments and PMP services.

The study data were collected from individual customers who used both traditional payments and PMP in a physical store during the COVID-19 pandemic. Structural equation modeling (SEM) was applied to analyze the validity of the variables and the causal relationships among variables based on 305 valid data.

The results show that the factor of traditional payments, that is, dissatisfaction positively and significantly influenced customers' switching intention. Factors of PMP, namely perceived usefulness (PUF) and perceived ease of use (EOU), positively and significantly impacted switching intention. In addition, the relationship between traditional payments and PMP, that is, low perceived substitutability was found to negatively influence switching intention, PUF and EOU.

First, the study targets are customers with experience in using PMP after the COVID-19 pandemic. It is suggested to compare customers who had experience using PMP before and after the COVID-19 pandemic. Second, although cash and bank cards belong to the category of traditional payments, they have different degrees of contact when transactions occur. The contact rate of bank cards is lower than that of cash. This study did not differentiate between cash and bank cards, which is the main limitation.

First, this study provides a reference to examine mobile payment usage from the perspective of both incumbent and alternative services conjointly under emergency situations, especially during the COVID-19 pandemic. Second, the application of migration theory to the context of mobile payment extends existing research on mobile payment. Third, this study is among the first to investigate the relationship between traditional payments and PMP.

China is currently developing and promoting an industrial cluster policy at the government level. By enacting the ‘Opinion on promoting industrial cluster development’, China is supporting the development of industrial clusters. Building an industrial cluster is done by using a single factor but requires many additional factors like regional characteristics, competitiveness factors are also diversified. To evaluate the competitiveness of the Chinese automobile industry cluster, a competitiveness element index should be developed and a competitiveness evaluation method is needed to evaluate the importance of each element. To accomplish this objective, this research applied the analytic hierarchy process (AHP) and focused on the importance of the competitiveness elements.

This research investigated the character is tics regarding cases of clusters and also analyzed the competitiveness of the Changchun automobile cluster located in northeastern China. The purpose of this research is to help Korean enterprises who enter China in the hopes that Korea will emerge as a top automobile production country.

The study aims to contribute to the training‐related literature by investigating the relationship between training and training transfer factors and company‐level training outcomes, and the relationship between the latter and company performance.

The authors propose and test a model linking constructs related to training (the volume and quality of training, supervisor support, peer support, and organisational incentives), constructs related to company‐level training outcomes (the acquisition and interpretation of information, and cognitive and behavioural changes) and company performance. In the empirical analysis the authors use structural equation modelling based on a sample of 247 service companies.

The study confirms a strong relationship between supervisor support and the volume and quality of training as well as between supervisor support and organisational incentives for training transfer. Organisational incentives are directly related to both studied company‐level training outcomes, i.e. the acquisition and interpretation of information, and cognitive and behavioural changes. In addition, they are also indirectly related to company performance through encouraging cognitive and behavioural changes. The volume and quality of training are related only to the acquisition and interpretation of information, while no direct relationship with company performance was found.

The study sends an important message to CEOs and HR managers showing them that companies should focus not only on the volume and quality of training but also on important training‐transfer‐related factors.

The study complements previous studies dealing with the relationship between training and performance by clearly distinguishing between training and training transfer factors, and by studying their combined relationship with company‐level training outcomes and, through that, with company performance. The links in the model are addressed in a way that has not yet been used in existing literature.