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This study explores the role of polychronic temporal orientation and decision-making decentralization on founders' perceptions of entrepreneurial self-efficacy (ESE).

Longitudinal survey data were collected from 141 business founders in China.

Findings suggest that decision-making decentralization is positively associated with founders' ESE. In addition, a polychronic temporal orientation is positively related to ESE, and this relationship is mediated by decision-making decentralization.

This study adds to existing knowledge on ESE and temporal related issues by presenting empirical evidence that explains how and why the temporal orientation context and the practice of decision-making decentralization can shape ESE perceptions among venture founders.

With the advent of the web computing era, the transmission mode of the Internet of Everything has caused an explosion in data volume, which has brought severe challenges to traditional routing protocols. The limitations of the existing routing protocols under the condition of rapid data growth are elaborated, and the routing problem is remodeled as a Markov decision process. this paper aims to solve the problem of high blocking probability due to the increase in data volume by combining deep reinforcement learning. Finally, the correctness of the proposed algorithm in this paper is verified by simulation.

The limitations of the existing routing protocols under the condition of rapid data growth are elaborated and the routing problem is remodeled as a Markov decision process. Based on this, a deep reinforcement learning method is used to select the next-hop router for each data transmission task, thereby minimizing the length of the data transmission path while avoiding data congestion.

Simulation results show that the proposed method can significantly reduce the probability of data congestion and increase network throughput.

This paper proposes an intelligent routing algorithm for the network congestion caused by the explosive growth of data volume in the future of the big data era. With the help of deep reinforcement learning, it is possible to dynamically select the transmission jump router according to the current network state, thereby reducing the probability of congestion and improving network throughput.

This paper aims to explore how manufacturing firms pursue business model innovation (BMI) through their use of outside-in thinking.

Survey data were collected on 175 Chinese manufacturing firms. A regression model was used to verify the research results.

Manufacturing firms rely on outside-in thinking to develop BMI under different market and institutional environments. From a whole-value-chain perspective, interacting with customers and sharing information with suppliers are two key ways to develop BMI.

Firms focus on customer needs, sense the dynamics of external markets and technology and seize market opportunities to measure outside-in thinking. Empirical results suggest using other measures of outside-in thinking. BMI itself can be multidimensional, so scholars could consider BMI’s diverse dimensions and measurements, which may demand different kinds of outside-in thinking.

Manufacturing firms can use outside-in thinking to overcome inertia and rigidity and increase their knowledge, information and technology. Managers should develop outside-in thinking to respond quickly to emerging economies. Managers should use value chain collaboration and improve the firm’s capacity to interact with customers and suppliers to apply the benefits of outside-in thinking to their BMI.

The study explores how outside-in thinking is a key driver of BMI. Applying the whole-value-chain view, it finds that interacting with customers and suppliers connects outside-in thinking with BMI. It also highlights the effects of intense market competition and volatile government regulation on BMI.

The purpose of this study is to examine how formal contracts affect alliance innovation performance. To understand the mechanism underlying the impact, this study tests whether relationship learning mediates the impact of formal contracts on alliance innovation performance and how guanxi moderates the mediating effect.

This study is conducted with a sample of 225 manufacturers in China. This paper used hierarchical regression analysis to test the hypotheses and used the PROCESS method to test the mediating effect of relationship learning.

Formal contracts positively affect relationship learning, which facilitates alliance innovation performance. Guanxi positively moderates the effect of formal contracts on alliance innovation performance. Relationship learning mediates the relationship between formal contracts and alliance innovation performance. Moreover, guanxi positively moderates the mediating effect.

Future research could investigate factors moderating the effect of guanxi on alliance innovation performance and moderating the effect of relationship learning on alliance innovation performance. Future research can also use secondary data to measure alliance innovation performance. Future researchers can examine how guanxi as a relational mechanism governance affects relationship learning.

Managers should conduct relationship learning in the process of alliance innovation and realize that reducing opportunism does not mean improving innovation performance. Moreover, managers should know that guanxi could contribute to alliance innovation performance with the help of formal contracts.

Prior studies have mainly focused on the fundamental requirement of governing knowledge exchange in alliances. Little is known about the mediating effect of relationship learning on the relationship between formal contracts and outcomes of innovation alliances. This study contributes to the literature by filling the gap.

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).