This paper selects Three Squirrels as the case company and uses the Balanced Scorecard to integrate financial and non-financial indicators for performance evaluation. The findings reveal that, from a financial perspective, Three Squirrels has a good capacity for debt repayment and operational capabilities, but there is a need to focus on improving profitability and development potential. From the customer perspective, the company enjoys high brand recognition. Internally, the management aspect shows that expense ratios during the period need control, and the product structure is relatively concentrated. In terms of learning and growth, Three Squirrels focuses on talent development, yet the investment in research and development is insufficient. Based on these findings, the paper provides targeted optimization suggestions: first, to improve the logistics system; second, to optimize the product structure; third, to increase research and development investments; and fourth, to enhance sales channels.

In the current fierce market competition, if a company wishes to enhance its competitiveness, it must improve the work efficiency of its employees and the quality of its products. Simultaneously, the significance of internal controls lies in ensuring the reliability of financial reports, and the authenticity and accuracy of accounting information. Robust and effective internal controls are vital in preventing financial fraud within companies. This paper selects company A, a company that received an adverse opinion in its 2022 internal audit report, as a case study. By discussing its current state of internal controls and analyzing the issues in the five elements of internal control, which include (1) a weak internal environment, (2) poor risk assessment capabilities, (3) lack of control activities, (4) delayed information and communication, and (5) ineffective internal monitoring. Based on the issues identified in the case study, this paper proposes recommendations for improving internal controls in listed companies, aiming to provide references for business development.

The tourism economy and ecological environment constitute a structurally complex interactive coupled open system, and correctly understanding and handling the relationship between them is key to sustainable development. Based on data from six provinces in the Pearl River Basin from 2018 to 2020, this study explores the spatiotemporal coupling characteristics of the tourism economy and ecological environment systems in the Pearl River Basin and predicts the coupling coordination degree for the next three years using a coupled coordination model and a grey G.M(1,1) prediction model. The results indicate that the comprehensive level of the two systems is declining overall, with significant spatial differentiation. The evaluation index of the tourism economy can be divided into two stages, with an overall upward trend from 2018 to 2019 and a downward trend in 2020, showing an east-high and west-low spatial distribution pattern. The ecological environment exhibits various trend changes such as V-shaped, inverted V-shaped, and increasing trends, with the lagging system mainly in the form of tourism lag. Most provinces are in a high-level coupling stage, with the coupling coordination degree showing a downward trend and the coordination level not high, presenting a spatial distribution pattern of “high downstream, low upstream.” In the coming years, the coupling coordination degree of most provinces in the Pearl River Basin will slowly rise, but it will take a long time to achieve an upward shift in coordination level.

With the rapid development of Airbnb in recent years, it has taken the main position in the sharing economy and also brought a great number of challenges to hotel competitors and regulators for its volatile supply. The benefit of agglomeration relative to Airbnb listing position and blocking behavior of listings has been discussed by Xie et al. (2019) and Peng (2020), and how the locational factor affects the blocking behavior remained unanswered. This paper will select the New York Airbnb listing data from Oct. 2014 to Dec. 2016 to compile the distance binary variables in 3 groups to identify whether they are near the top 10 attraction and then perform the logistic regression and t-test, which delineates the relationship between blocking behaviors and distance to attractions. We find that almost all top attraction variables significantly affect the Airbnb listings’ blocking behaviors, but no clear pattern of direction is shown. Considering the negative elasticity of income for NYC cab working hours, this paper performs t-tests on attractions’ yearly revenue indicating that while the coefficient estimates are both positive, their yearly revenue consistently has no significant differences of income in the 200m radius distance variable and no consistent result can be obtained in comparing attractions’ yearly revenue with opposite signs coefficient.