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Research of artificial intelligence (AI), has aimed at making machines intelligent via the simulation of natural intelligence, particularly human intelligence. During the past decades, there have been three major approaches aimed at achieving this goal, namely structuralism, functionalism and behaviorism. Unfortunately, they work separately and contradictorily to a large extent. The purpose of this paper is to present a better and more unified approach.

The paper analyses each of the three major approaches to AI, describing their advantages and disadvantages. There then follows an attempt to explore a new and more reasonable approach to AI. The new approach should be able to solve all the problems that the existing approaches can solve on one hand and can solve the problems that the existing approaches cannot solve on the other hand.

It was found that the more reasonable and more powerful approach is the one that directly touches the common and core mechanism of intelligence formation. This is due to the fact that the mechanism of intelligence formation is much more essential than other windows of an intelligent system, such as structure, function, or behavior. It was also found that the common and core mechanism of intelligence formation can be implemented through the information‐knowledge‐intelligence transformation. The third finding is that the three existing approaches are special cases of the mechanism approach under different conditions and can thus be harmoniously unified within the frame of the mechanism approach.

The three findings in the paper: the mechanism approach, the implementation of the mechanism approach, and the unification of the existed three major approaches, are important laws never found before in the literature. The breakthrough of the mechanism approach to AI will be of great significance to both theoretical and practical research in AI in the years to come.

High-speed turnouts are more complex in structure and thus may cause abnormal vibration of high-speed train car body, affecting driving safety and passenger riding experience. Therefore, it is necessary to analyze the data characteristics of continuous hunting of high-speed trains passing through turnouts and propose a diagnostic method for engineering applications.

First, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is performed to determine the first characteristic component of the car body’s lateral acceleration. Then, the Short-Time Fourier Transform (STFT) is performed to calculate the marginal spectra. Finally, the presence of a continuous hunting problem is determined based on the results of the comparison calculations and diagnostic thresholds. To improve computational efficiency, permutation entropy (PE) is used as a fast indicator to identify turnouts with potential problems.

Under continuous hunting conditions, the PE is less than 0.90; the ratio of the maximum peak value of the signal component to the original signal peak value exceeded 0.7, and there is an energy band in the STFT time-frequency map, which corresponds to a frequency distribution range of 1–2 Hz.

The research results have revealed the lateral vibration characteristics of the high-speed train’s car body during continuous hunting when passing through turnouts. On this basis, an effective diagnostic method has been proposed. With a focus on practical engineering applications, a rapid screening index for identifying potential issues has been proposed, significantly enhancing the efficiency of diagnostic processes.

Building on expectancy violations theory, this study aims to investigate the role of negative performance feedback in firm’s mergers and acquisitions (M&A) intensity, a typical risky strategic option which might entail negative reactions from shareholders, and also examine the moderating effects of top management teams (TMTs) regulatory focus on this relationship.

The authors use a longitudinal panel sample of 2,042 Chinese A-share listed manufacturing firms and data for the years between 2007 and 2019 collected from multiple data sources. Furthermore, the authors also conducted supplementary analyses and various robustness checks of the key variables.

The findings show that both the intensity and duration of negative performance feedback negatively impact firms’ M&A intensity. Besides, the effect of negative performance feedback on M&A intensity will be magnified when the focal firm of TMTs with high prevention focus.

During the period of performance depression, TMTs are supposed to focus on stability, keep an eye on potential risks and be prudent in making decisions like walking on eggshells to avoid making further losses.

This study develops a core mechanism – managers of underperformance firms prioritize meeting shareholder expectations as their foremost task to ensure minimal negative repercussions – and also highlights the role of fit between TMT prevention focus and negative performance feedback on M&A intensity.

The purpose of this paper is to introduce the RISE model on service innovation in coopetitive business environment. The case study illustrates why and how Chinese providers utilize ecosystems for innovative mobile payment service development to achieve coopetitive advantage based on firms’ superior resources.

The companies in the case study include Alipay (third-party actor), Bestpay (mobile operator), and UnionPay (banking). Empirical data comes from semi-structured interviews complemented with observations and documents. The analysis of the data follows grounded theory guidelines: creation of a theoretical framework, data collection, and interpretation of the data using the coding strategies of open coding, axial coding, and selected coding.

Inter-organizational co-innovation appears as a successful strategy for mobile payment service innovation. In addition to strategic choice on this, understanding of superior and inferior resources and capabilities influence firms’ coopetitive advantages in a coopetitive service development environment. Ecosystems are formed along with the innovating activities, and difficulties are caused by coopetition challenges. The RISE model enables the analysis and selection of strategic patterns for service innovation in a coopetitive environment.

The paper contributes to resource-advantage theory and platform ecosystem theory. The theories are used to analyze and model the effects of strategy execution for achieving win-win relationships in inter-organizational co-innovation. This paper helps executives to match their service innovation strategies to platform ecosystem architectures, as well as to understand how resource-advantage challenges affect the execution strategy of setting up their platform ecosystems.