A study on the influencing factors of corporate digital transformation: empirical evidence from Chinese listed companies.

In an era where digital technology is reshaping business landscapes, understanding the factors that drive corporate digital transformation is essential. In this paper we explore these influencing factors, focusing on Chinese A-share listed companies from 2007 to 2021. Our approach involved a comprehensive analysis of multiple variables through regression techniques to determine their impact on digital transformation. The findings reveal the drive for reform in the digital transformation endeavours of enterprises. Notably, companies with higher gearing, overhead, and accounts receivable ratios exhibit a stronger inclination towards digital transformation. Conversely, enterprises in monopolistic industries and those at the inception stage of their life cycle show less propensity for such transformation. The findings of this research not only shed light on the strategic decisions behind digital transformation in response to financial and competitive challenges but also provide actionable insights for policymakers and business strategists. This study underscores the importance of contextualizing digital transformation efforts within the unique framework of industry characteristics and company development phases.

Driving forces of digital transformation in chinese enterprises based on machine learning.

With advanced science and digital technology, digital transformation has become an important way to promote the sustainable development of enterprises. However, the existing research only focuses on the linear relationship between a single characteristic and digital transformation. In this study, we select the data of Chinese A-share listed companies from 2010 to 2020, innovatively use the machine learning method and explore the differences in the predictive effects of multi-dimensional features on the digital transformation of enterprises based on the Technology-Organization-Environment (TOE) theory, thus identifying the main drivers affecting digital transformation and the fitting models with stronger predictive effect. The study found that: first, by comparing machine learning and traditional linear regression models, it is found that the prediction ability of ensemble earning method is generally higher than that of tradition measurement method. For the sample data selected in this research, XGBoost and LightGBM have strong explanatory ability and high prediction accuracy. Second, compared with the technical driving force and environmental driving force, the organizational driving force has a greater impact. Third, among these characteristics, equity concentration and executives' knowledge level in organizational dimension have the greatest impact on digital transformation. Therefore, enterprise managers should always pay attention to the decision-making role of equity concentration and executives' knowledge level. This study further enriches the literature on digital transformation in enterprises, expands the application of machine learning in economics, and provides a theoretical basis for enterprises to enhance digital transformation.

Study on the impact of digital transformation on the innovation potential based on evidence from Chinese listed companies.

Digital transformation has emerged as a powerful force in reshaping the business landscape and enabling organizations to enhance their capabilities. One critical aspect of this change is how it impacts an enterprise's innovation ability. To explore this question, we select data regarding China's A-share listed enterprises from 2007 to 2021 as the research sample. We employ crawler technology to gather keywords related to "digital transformation" from annual reports, portraying detailed journeys of enterprises' digital transformation. Through descriptive statistics and multiple covariance tests, a linear relationship is established between digital transformation and innovation ability. Benchmark regression is conducted and a robustness test is utilized to determine the robustness of the benchmark regression. The mechanism, heterogeneity, and moderating effects of this study are also tested. The results reveal that digital transformation makes a significant positive contribution to the innovation capability of enterprises. Meanwhile, among different types of enterprises, the impact of digital transformation on enterprise innovation capability shows heterogeneity. In terms of the impact mechanism, digital transformation can enhance the innovation output of enterprises by reducing the agency cost and improving the risk-taking level of enterprises, so as to further improve the innovation capability of enterprises. The research results of this paper provide essential theoretical support for the digital transformation of enterprises and the government's formulation of enterprises' digitalization strategies. More profoundly, it provides significant reference for how to further promote the digital transformation of Chinese enterprises.

Outcomes following KRASG12C inhibitor treatment in patients with KRASG12C-mutated solid tumors: A systematic review and meta-analysis.

OBJECTIVE: To assess the efficacy and safety of FDA-approved KRASG12C inhibitors in patients with KRASG12C-mutated solid tumors. METHODS: We searched PubMed, EMBASE, Cochrane Library, and major international conferences for clinical trials published in English up to March 6, 2023. Clinical trials investigating sotorasib or adagrasib and reporting the clinical outcomes of the objective response rate (ORR), disease control rate (DCR), or incidence rate of grade ≥ 3 adverse events (AEs) were eligible. The primary endpoint was the ORR. Secondary endpoints included the DCR, incidence rate of grade ≥ 3 AEs, and odds ratio (OR) of the ORR between patients with or without co-mutation. The Random-effects model was applied for the outcomes of interest. RESULTS: 18 studies with 1224 patients were included in this meta-analysis. The pooled ORR, DCR, and incidence rate of grade ≥ 3 AEs were 31 % (95 % CI, 25-37 %), 86 % (95 % CI, 82-89 %), and 29 % (95 % CI, 23-36 %), respectively. KRASG12C-mutated NSCLC patients with a co-mutation of KEAP1 exhibited a worse ORR than those with wild-type KEAP1 (OR: 0.35, 95 % CI: 0.16-0.77). CONCLUSIONS: This study provided a comprehensive understanding of the efficacy and safety of KRASG12C inhibitors in treating solid tumors and identified KEAP1 mutation as a potential predictive biomarker of inferior response in patients treated with KRASG12C inhibitors. These findings may assist in the design of future clinical trials for identifying populations that may benefit from KRASG12C inhibitor treatment.

Large-scale sports events, sports gambling market and promotion risk management: Theoretical model and case analysis based on option hedging theory.

Large-scale sports events have become a good opportunity for major enterprises to promote due to their high social attention; however, they also force enterprises to confront the risks of uncertainty and extreme loss. During the 2018 Russia World Cup, Vatti Co., Ltd.'s promotion activity "If France Wins, Get a Full Refund" suffered double losses economically and reputationally due to France's victory and the company's failure to fulfill its promise. This paper uses option hedging theory, and the risk management tools to construct a risk management model. Case analysis and program improvement were carried out. The research results show that using the winning odds can effectively control the risks. Companies should determine their promotion plan based on sale returns and the maximum implicit income generated by promotional activities. The research paper opens a new field using derivative financial instruments to control corporate promotion risks.

Preparation and Properties of Fluorosilicone Fouling-Release Coatings.

To improve the antifouling performance of silicone fouling-release coatings, some fluorosilicone and silicone fouling-release coatings were prepared and cured at room temperature with hydroxyl-terminated fluoropolysiloxane (FPS) or hydroxy-terminated polydimethylsiloxane (PDMS) as a film-forming resin, tetraethyl orthosilicate (TEOS) as a crosslinking agent, and dibutyltin dilaurate (DBTDL) as a catalyst. The chemical structure, surface morphology and roughness, tensile properties, and antifouling properties of the coating were studied by infrared spectroscopy, a laser confocal scanning microscope, contact angle measurement, tensile tests, and marine bacteria and benthic diatom attachment tests. The results showed that the FPS coatings were not only hydrophobic but also oleophobic, and the contact angles of the FPS coatings were larger than those of the PDMS coatings. The surface free energies of the FPS coatings were much lower than those of the PDMS coatings. Generally, the fluorine groups can improve the antifouling performance of the coating. Introducing nonreactive silicone oil into PDMS or FPS coatings can improve the antifouling performance of the coating to a certain extent. The prepared fluorosilicone fouling-release coatings showed good application prospects.

Preserving Minority Structures in Graph Sampling.

Sampling is a widely used graph reduction technique to accelerate graph computations and simplify graph visualizations. By comprehensively analyzing the literature on graph sampling, we assume that existing algorithms cannot effectively preserve minority structures that are rare and small in a graph but are very important in graph analysis. In this work, we initially conduct a pilot user study to investigate representative minority structures that are most appealing to human viewers. We then perform an experimental study to evaluate the performance of existing graph sampling algorithms regarding minority structure preservation. Results confirm our assumption and suggest key points for designing a new graph sampling approach named mino-centric graph sampling (MCGS). In this approach, a triangle-based algorithm and a cut-point-based algorithm are proposed to efficiently identify minority structures. A set of importance assessment criteria are designed to guide the preservation of important minority structures. Three optimization objectives are introduced into a greedy strategy to balance the preservation between minority and majority structures and suppress the generation of new minority structures. A series of experiments and case studies are conducted to evaluate the effectiveness of the proposed MCGS.