Volume 207

With the increasing sophistication of financial management techniques, the ability to predict stock prices has become a critical endeavor for investors and traders. The Autoregressive Integrated Moving Average (ARIMA) model stands out as a widely used method for forecasting stock price movements. This model, which belongs to the class of time series analysis, is particularly adept at estimating daily returns with associated confidence intervals. The present study aims to apply the ARIMA model to forecast the share prices of Pop Mart. The dataset encompasses the period from December 2024 to May 2025, focusing on the opening prices as key variables. The analysis reveals that the ARIMA model is capable of capturing the fluctuations in stock prices with a certain degree of accuracy. The Mean Absolute Percentage Error (MAPE) value of 6.86%, which is significantly below 10%, suggests that the predictive performance is commendable. Furthermore, the insights gleaned from this model offer investors and traders valuable guidance for interpreting market trends and assessing potential risks, thereby enhancing their decision-making capabilities.

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This study examines the incentive dilemmas faced by food-delivery riders in the digital economy, focusing on how algorithmic management shapes workers’ psychology and behavior. Drawing on Self-Determination Theory and the crowding-out effect, it explores, via a literature review of journals, industry reports, how platform algorithms as forms of extrinsic motivation interact with riders’ intrinsic motivation. Through a labor-quota mechanism and the dispatch logic, platforms systematically suppress riders’ autonomy, alienate their sense of competence, and erode their relatedness, leading to a crowding-out effect in which extrinsic motivation undermine intrinsic motivation and manifest as professional burnout and identity loss. The study proposes a three-fold improvement path: reconstructing algorithmic transparency mechanisms, establishing career-development systems, and building multi-stakeholder collaborative networks. Platform employment models must move beyond a purely extrinsic framework and, through institutional design, better coordinate intrinsic and extrinsic motivation to achieve both decent work and sustainable development for riders. These findings offer a theoretical foundation and practical insights for breaking the “incentive trap” of algorithmic management and address a key gap in understanding how intrinsic and extrinsic motivations interact in digital labor.

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As global technology companies compete more and more, the way companies design their pay systems has become very important. A good pay system can help a company do well and keep talented people from leaving. This paper looks at the pay system of Amazon. This study examines the structural logic of Amazon's pay system through empirical case analysis, analyzes its benefits and drawbacks, and offers specific recommendations for improvement. First, the paper explains how Amazon pays its board members and key employees. This includes stock rewards (called RSUs), yearly pay, and the fact that Amazon does not give extra money for attending meetings. Then, the paper looks at how this system works in terms of motivation, fairness, and how well it fits the competitive job market. It also compares Amazon’s pay system with those used by other large tech companies, like Google and Microsoft, to see how well Amazon’s system helps it keep top talent and stay competitive. In the end, the paper gives some ideas for how Amazon could improve its system. These include linking pay more closely to performance, and creating more kinds of pay, not just stock.

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This study provides an in-depth analysis of Alphabet's executive compensation policies for the fiscal years 2020 to 2024. Through the systematic content analysis of public documents such as shareholder proxy letters, it is revealed that its compensation structure is centered on basic salary, annual bonus and the dominant long-term equity incentive. Research has found that Alphabet's compensation strategy aims to attract, retain and motivate top talents through market-leading compensation levels, while striving to closely align the interests of executives with the company's long-term strategy, shareholder value creation and corporate governance requirements through a sophisticated performance-linked mechanism. And it shows an evolving trend of strengthening performance orientation, paying attention to ESG factors and responding to market dynamics. This study provides a key reference for understanding how global tech giants balance incentives, risks and long-term value, and deal with complex governance environments. The findings offer valuable implications for executive incentive design in other innovation-driven enterprises navigating dynamic market conditions and increasing stakeholder scrutiny.

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