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Against the backdrop of the global reshaping of supply chains, supply chain ecosystems have emerged as a critical force in ensuring the high-quality development of enterprises and fostering stable economic growth. However, a systematic theoretical understanding of how to construct these supply chain ecosystems remains nascent. This study aims to explore the mechanism of the process of building supply chain ecosystems between digital innovation platform enterprises and digital trading platform enterprises from the perspective of dynamic capabilities.

An explanatory case study is conducted based on a theoretical framework grounded on dynamic capabilities view. Two preeminent digital platform enterprises in China (Haier and JD.com) are studied. The authors primarily conducted this research by collecting a large volume of these Chinese public materials.

First, the construction processes of supply chain ecosystems in both digital platform enterprises can be delineated into three stages: embryonic, development and maturity. Second, digital innovation platform enterprises’ construction process is primarily influenced by factors such as production and operational collaboration, consumer demand and research and development. This influence is exerted through interactions on digital platforms and within sub-ecosystems. Meanwhile, digital trading platform enterprises’ construction process is influenced by factors such as infrastructure development, consumer demand and financial support, driving dynamic capability formation through multi-party cooperation and ecological interactions based on conceptual identity.

In the establishment of supply chain ecosystems, digital platform enterprises should prioritize the cultivation of opportunity expansion, resource integration and symbiotic relationship capabilities. Furthermore, this study shows that digital platform enterprises need to actively adjust their interactive relationships with cooperating enterprises based on changes in the market, industry, policies and their own developmental stages.

This study addresses prior deficiencies in understanding the comprehensive construction of supply chain ecosystems and provides significant insights to enhance the theoretical foundation of supply chain ecosystem studies. Additionally, this paper uncovers the dynamic capability development behaviors and contextual features inherent in the construction process of supply chain ecosystems by digital platform enterprises.

A project manager’s (PM) emotional intelligence (EI) is essential for project performance (PP). However, the cause and effect and the potential moderators of the relationship between EI and PP remain disputed. Some scholars found a positive association between PMs’ EI and project outcomes, while some other studies showed non or negative relation. This paper aims to find the relationship between PMs’ EI and PP and the factors that influence this relationship based on diverse prior research.

This paper conducts a meta-analysis of 5,229 observations based on 24 independent studies from 1990 to 2021.

Results show that PMs’ EI has a significant positive influence on PP, and the project complexity and measurement of PP are two critical moderators explaining inconsistencies in existing research.

The current study proposes suggestions for construction companies on PMs’ selection and training. This study also offers suggestions for PMs in management practice.

To the best of the authors’ knowledge, this study is the first to explore the inconsistencies in prior research results on the relationship between PMs’ EI and PP at the meta-analytic level. This research extends the current literature by revealing the factors leading to existing consistencies that are not explored before. This study implies that the meta-analysis method could help reach a balanced conclusion based on inconsistent results.

The purpose of this paper is to solve the problem of low efficiency on knowledge resources allocation in the strategic emerging industry (SEI), an incentive model of technology innovation based on knowledge ecological coupling is designed.

First, a principal–agent model of knowledge inputs and a knowledge ecological coupling model based on an improved Lotka–Volterra model are constructed. In addition, a numerical example about Chongqing Yongchuan industrial park, the emulation analysis and the associated discussions are conducted to analyze the equilibriums of principal–agent in different knowledge inputs. Further, the paper analyzes the evolutionary equilibrium in knowledge ecological coupling and reveals the dual adjustments of the node organization on knowledge inputs.

Thus, this paper shows that by establishing the relationships of knowledge ecological coupling based on “mutualism and commensalism,” node organization raises the level of knowledge inputs; an incentive mode of “knowledge ecological coupling relationship + technology innovation chain” is conductive to substantially improving the efficiency of knowledge resource allocation, and to stimulate the vitality of node organization for technology innovation in the strategic emerging industry (SEI).

This paper contributes to the extant researches in two ways. First, this paper reveals the dual adjustments of the node organizations in inputting knowledge, which broadens the vision and borders of the researches on traditional knowledge management. The methods of the traditional principal–agent model and the knowledge input/output profit model are also expanded. Second, this paper verifies that applying the mode of “knowledge ecological coupling relationship + technology innovation chain” in practice is conducive to enhancing the efficiency of the cross-organizational knowledge allocation in the strategic emerging industry (SEI).

Internet marketing underground industry users refer to people who use technology means to simulate a large number of real consumer behaviors to obtain marketing activities rewards illegally, which leads to increased cost of enterprises and reduced effect of marketing. Therefore, this paper aims to construct a user risk assessment model to identify potential underground industry users to protect the interests of real consumers and reduce the marketing costs of enterprises.

Method feature extraction is based on two aspects. The first aspect is based on traditional statistical characteristics, using density-based spatial clustering of applications with noise clustering method to obtain user-dense regions. According to the total number of users in the region, the corresponding risk level of the receiving address is assigned. So that high-quality address information can be extracted. The second aspect is based on the time period during which users participate in activities, using frequent item set mining to find multiple users with similar operations within the same time period. Extract the behavior flow chart according to the user participation, so that the model can mine the deep relationship between the participating behavior and the underground industry users.

Based on the real underground industry user data set, the features of the data set are extracted by the proposed method. The features are experimentally verified by different models such as random forest, fully-connected layer network, SVM and XGBOST, and the proposed method is comprehensively evaluated. Experimental results show that in the best case, our method can improve the F1-score of traditional models by 55.37%.

This paper investigates the relative importance of static information and dynamic behavior characteristics of users in predicting underground industry users, and whether the absence of features of these categories affects the prediction results. This investigation can go a long way in aiding further research on this subject and found the features which improved the accuracy of predicting underground industry users.