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Open AccessArticles
by Fengqi Du
2026,8(2);
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Abstract
As a vital component of government public services, the quality of tourism public services
directly impacts tourist experiences and regional tourism image. This study focuses on District X,
collecting data through questionnaire surveys and interviews. Grounded in service-oriented government
theory and collaborative governance perspectives, it analyzes existing issues in government tourism public
services across five dimensions, thoroughly examines their manifestations and root causes, and proposes
optimization strategies to provide actionable references for local governments in enhancing tourism service
quality.
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Open AccessArticles
by Keran Wang
2026,8(2);
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Abstract
With the rapid iteration of digital technologies such as big data, artificial intelligence, and
BIM, smart auditing has become a core direction for the transformation and upgrading of the construction
engineering auditing field, effectively addressing the pain points of traditional construction engineering
audit processes, such as cumbersome processes, low efficiency, and weak risk control. Based on the
technical characteristics and core connotations of smart auditing, this paper systematically analyzes
the problems existing in current construction engineering audit processes, such as process disconnect,
data silos, insufficient technology application, and mismatched personnel qualifications. Combining the
audit needs of the entire life cycle of construction projects, it constructs an optimization architecture
for construction engineering audit processes from the perspective of smart auditing, proposes targeted
efficiency improvement paths, and uses case studies to demonstrate the feasibility of the optimization
solutions, providing theoretical support and practical reference for the digital transformation of the
construction engineering auditing industry.
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Open AccessArticles
by Biyao Ye, Xinyi Li
2026,8(2);
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Abstract
Internal control serves as a crucial institutional framework for enterprises to mitigate operational
risks, enhance management efficiency, and ensure strategic implementation. As manufacturing enterprises
increasingly embrace globalization, digital transformation, and diversified operations, the functions
of internal control have expanded beyond traditional financial compliance to encompass areas such as
strategic execution, risk early warning, data governance, and supply chain coordination. Using Company
M as a case study, this paper examines its current internal control system, identifies areas for improvement
in strategic alignment, group governance, digital risk management, supply chain control, and evaluation
mechanisms, and proposes corresponding enhancement strategies. The study concludes that Company M
should further evolve its internal control framework from a compliance-oriented model toward one that
is strategically embedded, data-driven, and value-creating, thereby strengthening its risk management
capabilities and sustainable development potential in complex environments.
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Open AccessArticles
by Ziqi Zhang
2026,8(2);
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Abstract
This study examines whether negative green innovation performance feedback affects the
resolution of announced mergers and acquisitions (M&A). Drawing on performance feedback theory,
it argues that underperformance in green innovation can heighten managerial urgency during the post-announcement stage of a deal. In friction-laden transactions, however, such urgency may not speed
completion; instead, it may intensify valuation, financing, due diligence, and regulatory problems. Using
36,316 announced M&A deals by Chinese A-share listed firms from 2010 to 2022, this paper applies Cox
and cause-specific hazard models to deal duration data. The results show that negative green innovation
performance feedback is not robustly associated with faster deal completion once controls are included,
but is positively associated with a higher hazard of deal termination or withdrawal. The findings suggest
that green innovation underperformance is more strongly associated with execution risk than with faster
completion among already announced deals.
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Open AccessArticles
by Chenglong Cao
2026,8(2);
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Abstract
Aiming at the nonlinear noise feature and poor interpretability of financial time series prediction,
this paper constructs an interpretable LSTM prediction framework combined with SHAP, taking CSI
300 daily data from 2015 to 2025 as samples. The dataset is divided at 70%:10%:20%, and comparative
tests verify that the 5-day time window has optimal prediction accuracy. The model can fit medium-long
term index trends but shows obvious lag under drastic market fluctuations. SHAP analysis indicates that
opening, high, low and closing prices are core predictive features, while trading and volatility indicators
play auxiliary roles. This research balances prediction precision and model transparency, providing
quantitative decision support for index investment and risk control.
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