Africa is engaging more with technology and artificial intelligence (AI) and is no longer playing catch-up to the rest of the world in terms of technology and innovation. This paper examines how companies in Africa are employing AI to generate new sources of value through local enterprises. The findings demonstrate that the time-to-market of financial products can be reduced by up to 50 percent with AI and other operational efficiencies can be accumulated. Moreover, this research analysed the most significant barriers for companies across all eight sectors identified in adopting AI sustainably (i.e., infrastructure shortcomings, talent shortage, regulation-related challenges). The study demonstrates that the value or economic effects of AI could generate in the range of $100 billion per year; nonetheless, it suggests that realizing this value is not simply a matter of doing 'more of the same' (e.g., developing 'one-size-fits-all' Western solutions), but necessitates 'localised' adaptations for specific regions and countries. In the final section, recommendations for policymakers regarding cross-border data governance, and for investors to develop computationally efficient infrastructure based on renewable energy sources that will bridge the digital divide and enable smart economies are put forth. The research results of this article provide a basis for understanding how AI promotes enterprise development.

In August 2023, the Ministry of Finance initially proposed guidelines for the identification and accounting treatment of data assets. From the perspective of stock market reactions, this study analyzes the theoretical significance of the data assets recognition in firm financial reports and evaluates its policy effects. Text analysis is used to determine whether a firm has data assets based on data from Chinese A-share listed firms. The event study method is then used to determine that, following the announcement of the Interim Provisions, firms with data assets will experience a more positive market reaction than those without data assets, and the conclusion passes the robustness test. It demonstrates investors are optimistic about the entry of firm data assets into the Financial Statements. Investors think it will benefit shareholders by raising the firm value and more accurately reflecting its current state. Further analysis reveals that firms with lower audit quality, lower institutional investor shareholdings, and fewer analysts to follow, will experience a more positive market reaction. The conclusions of this study provide a relevant basis and reference for further promoting the reform of data assets entry into the Financial Statements.

This paper probes deeply into the necessity, evolutionary process and implementation path of enterprise financial transformation. With the development of the new generation of information technologies such as big data, intelligent technologies, mobile internet, cloud computing, the Internet of Things and blockchain, the traditional accounting processes and organizational structures are undergoing profound changes. The study points out that financial transformation is not only an adaptation to information technologies, but also a strategic measure for enterprises to realize the organic unity of growth, profitability and risk management. The study divides financial transformation into four stages, namely informatization, intellectualization, collaboration and valorization. Furthermore, it proposes a financial transformation path from a strategic perspective, which includes three levels: financial strategic management, financial organizational management and financial operation management. Finally, the paper innovatively puts forward the RESEARCH framework to drive CFOs to carry out effective financial transformation, covering eight aspects: Strategic Integration, Exemplary Leadership, Agile Organizational Design, Responsive Risk Framework, Ensuring Talent Supply, Real-time Business Synchronization, Highly Accountable for Results and Continuous Improvement Drive.

In the context of the platform economy, the labor process of food delivery riders is deeply embedded in algorithmic management, and their psychological bonds simultaneously involve three dimensions: the platform, trade unions and the occupation itself. Based on data collected from 208 food delivery riders through a two-wave time-lagged design, this study adopted latent profile analysis and identified four types of multifocal commitment profiles, namely the Platform Commitment-dominant Type, the Platform-Trade Union Dual-dominant Type, the Trade Union-Occupation Dual-dominant Type and the Full Commitment Type. These profiles reveal the complex structure of coexistence and differentiation in the psychological bonds of food delivery riders, demonstrate the diversity of commitment in digital labor at the micro level, and provide empirical evidence for understanding labor-capital relations under algorithmic management.

This study is based on 2,276 real transaction data from the Xianyu platform and uses a three-stage regression model to systematically analyze the moderating effect of blind box brand value on scarcity premium. The research first reveals the overall premium level of the market through descriptive statistics, then builds a basic model to verify the scarcity effect, and then introduces the brand fixed effect to control the inherent value differences of brands. Finally, it tests the interaction between brand value and scarcity through the moderating effect model. The empirical results show that the average premium rate of the hidden model reaches 164.9%, significantly higher than 7.2% of the regular model. Brand value has a significant moderating effect on the scarcity effect. The scarcity effect coefficient of SKULLPANDA (2.230) is 2.6 times that of MOLLY (0.862). The research conclusion provides important empirical evidence for the pricing mechanism of the blind box market, consumers' investment decisions, and brand IP operations.