Digital transformation is a socioeconomic change across organizations, individuals, societies, and ecosystems formed by the utilization and adoption of digital technologies. This study modeled the... Show More

Digital transformation is a socioeconomic change across organizations, individuals, societies, and ecosystems formed by the utilization and adoption of digital technologies. This study modeled the factors that drive the degree of digital transformation in the financial services sector in South Africa. Data from 350 participants was modeled in terms of seven factors: organizational IT application portfolios; organizational culture; organizational structure; organizational dynamic capabilities; leadership; employee roles and skills; and ethics. The analysis showed that organizational culture predicted 81.7% of the digital transformation.

The automobile consumption credit business promotes the development of the automobile industry. However, the current credit system in China requires further refinement. Thus, the credit loan... Show More

The automobile consumption credit business promotes the development of the automobile industry. However, the current credit system in China requires further refinement. Thus, the credit loan business is associated with certain risks, and company profits are often negatively impacted by clients who default on loans. Based on the data, this article leverages the economic and financial theories of consumer credit risk control to construct a logistic model to predict customers' default probability. Then, a quadratic regression model is established to determine the optimal commission structure to balance profitability with incentives from retail stores. Results show that the macro-level variables are negatively associated with the probability of good behavior. The personal level variables exhibit a positive association. In addition, a negative coefficient in the quadratic profit equation indicates the presence of an inverted “U” relationship between profit and commission. Corresponding suggestions are put forward.

Understanding why users continue or discontinue using specific technology is vital for its providers. Existing literature has explored the reasons for continuance and discontinuance by taking into... Show More

Understanding why users continue or discontinue using specific technology is vital for its providers. Existing literature has explored the reasons for continuance and discontinuance by taking into account both rational and emotional factors. However, one question remains unanswered: Why do some users depend more on rational factors for decision-making, while others rely more on emotional factors? This study addresses this question by integrating cognitive decision-making styles and the fear of making incorrect decisions into traditional continued usage models. Data were gathered from 285 TV users and analyzed using structural equation modeling. The results demonstrate that the introduced constructs moderated the direct effects of rational and emotional factors. The contribution of the study lies in incorporating the underlying cognitive processes of decision-making. It presents two actionable variables that managers can utilize to categorize their customers and enhance the effectiveness of their use continuance strategies.

The aim of this study is to enhance the quality of civil engineering project management and optimize project control in order to ensure adequate construction resources and facilitate seamless... Show More

The aim of this study is to enhance the quality of civil engineering project management and optimize project control in order to ensure adequate construction resources and facilitate seamless project progression. By integrating building information modeling (BIM) technology with deep learning techniques, optimal control was examined at various stages of civil engineering project management. A simulation test was performed on a selected gymnasium engineering project, focusing on cost and resource control aspects. The findings revealed that, as the project advanced, the planned cost exceeded the actual cost by nearly 100,000 yuan in the final stage. The combination of BIM technology and deep learning model prediction substantially reduced the cost and material budgets of the engineering project. Data analysis showed that the average positioning error of the convolutional neural network algorithm for the project model was below 2%.