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The purpose of the paper is twofold: first, to observe the relationship between sale to list ratio (SLR)/ for-sale inventory (FSI)/ sale count nowcast (SCN) and real/nominal housing value, and second, to produce a handbook of empirical evidence that can serve as a foundation for future research on this topic.

This paper broadly compiles empirical evidence, using three of the most common causality tests in the field of housing economics. The analysis methods include lagged Pearson correlation test, Granger causality test and cointegration test.

Causal relationships were observed between SLR/FSI/SCN and both nominal and real housing values. SLR and SCN showed positive long-term correlations with housing value, whereas FSI had a negative correlation. Adjusting the housing value with the Consumer Price Index (CPI) to derive real housing values could potentially alter the direction of the causal relationships. It is crucial to distinguish the long-term relationship from temporal dynamics, as FSI displayed a positive immediate impulse–response relationship with nominal housing price despite the negative long-term correlation.

SLR/FSI/SCN are housing market parameters that have only recently begun to be documented and have seen little use in research. So far, housing market research has revolved around traditional macroeconomic indicators such as unemployment rate. To the best of the authors’ knowledge, this study is one of the first studies that introduce these three parameters into housing market research.

Quantitative reliability analysis can effectively identify the time the driving system needs to be maintained. Then, the potential safety problems can be found, and some catastrophic failures can be effectively prevented. Therefore, this paper aims to evaluate the reliability of the switched reluctance generator (SRG) driving system.

In this paper, a method considering different thermal stresses and fault tolerance capacity is proposed to analyze the reliability of an SRG. A full-bridge power converter (FBPC) instead of the asymmetric half-bridge power converter (AHBPC) is adopted to drive the SRG system. First, the primary fault modes of the SRG system are introduced, and a fault criterion is proposed to determine whether the system fails. Second, the thermal circuit model of the converter is established to quickly and accurately obtain the junction temperature of the devices. At last, the Markov models of different levels are established to evaluate the reliability of the system.

The results show that the two-level Markov model is the most suitable when compared to the static model and the one-level Markov model.

The driving system of SRG will be more reliable after the reliability of the system is evaluated by the Markov model. At the same time, an FBPC is adopted to drive the SRG. The FBPCs have the advantages of fewer switching devices, higher integration and lower cost. The proposed driving strategy of the FBPC avoids the current reversal and the generation of dead zone time, which has the advantage of reliable operation. In addition, a precise thermal circuit model of the FBPC is proposed, and the junction temperature of each device can be obtained, respectively.

This paper aims to study the issues of adaptive fuzzy control for a category of switched under-actuated systems with input nonlinearities and external disturbances.

A control scheme based on sliding mode surface with a hierarchical structure is introduced to enhance the responsiveness and robustness of the studied systems. An equivalent control and switching control rules are co-designed in a hierarchical sliding mode control (HSMC) framework to ensure that the system state reaches a given sliding surface and remains sliding on the surface, finally stabilizing at the equilibrium point. Besides, the input nonlinearities consist of non-symmetric saturation and dead-zone, which are estimated by an unknown bounded function and a known affine function.

Based on fuzzy logic systems and the hierarchical sliding mode control method, an adaptive fuzzy control method for uncertain switched under-actuated systems is put forward.

The “cause and effect” problems often existing in conventional backstepping designs can be prevented. Furthermore, the presented adaptive laws can eliminate the influence of external disturbances and approximation errors. Besides, in contrast to arbitrary switching strategies, the authors consider a switching rule with average dwell time, which resolves control problems that cannot be resolved with arbitrary switching signals and reduces conservatism.

The purpose of this paper is to solve the problem that mobile robots are still based on reactive collision avoidance in unknown dynamic environments leading to a lack of interaction with obstacles and limiting the comprehensive performance of mobile robots. A dynamic window approach with multiple interaction strategies (DWA-MIS) is proposed to solve this problem.

The algorithm firstly classifies the moving obstacle movement intention, based on which a rule function is designed to incorporate positive incentives to motivate the robot to make correct avoidance actions. Then, the evaluation mechanism is improved by considering the time cost and future information of the environment to increase the motion states. Finally, the optimal objective function is designed based on genetic algorithm to adapt to different environments with time-varying multiparameter optimization.

Faced with obstacles in different states, the mobile robot can choose a suitable interaction strategy, which solves the limitations of the original DWA evaluation function and avoids the defects of reactive collision avoidance. Simulation results show that the algorithm can efficiently adapt to unknown dynamic environments, has less path length and iterations and has a high comprehensive performance.

A DWA-MIS is proposed, which increases the interaction capability between mobile robots and obstacles by improving the evaluation function mechanism and broadens the navigation strategy of DWA at a lower computational cost. After real machine verification, the algorithm has a high comprehensive performance based on real environment and provides a new idea for local path planning methods.

An alliance portfolio can help latecomer firms to acquire the necessary knowledge and resources to catch up with market leaders. However, how latecomer firms construct an alliance portfolio in terms of the nature of windows of opportunity has not been fully analyzed. This paper aims to explore how latecomer firms can build appropriate coalitions according to the nature of the window of opportunity to achieve technological catch-up in different catch-up phases.

Based on a longitudinal case study from 1984 to 2018 of Sunny Group, now a leading manufacturer of integrated optical components and products, this paper explores the process of technological catch-up of latecomer firms building different types of alliance portfolio in different windows of opportunity.

This paper finds that there is a sequence when latecomers build an alliance portfolio in the process of catch-up. When the uncertainty of opportunity increases, the governance mechanism of the alliance portfolio will change from contractual to equity-based. Also, latecomer firms build market-dominated and technology-dominated alliance portfolios to overcome their market and technology disadvantages, respectively.

These conclusions not only enrich the theory of latecomer catch-up from the perspective of windows of opportunity but also expand research on alliance portfolio processes from a temporal perspective.

Based on the density dependence theory, this paper attempts to explore how two types of interdependence among firms located in the same national high-tech zones (NHTZs) – mutualism and competition – affect entrepreneurship in the NHTZs. The authors suggest that increasing firm density can help enhance legitimacy and form mutual networks. However, as the competition becomes fierce, the above positive relationship will weaken when the firm density exceeds a certain level. In addition, the authors are interested in whether the age of NHTZs would affect their sensitivity to legitimacy and competition and whether firm density affects entrepreneurship.

This article formulates two hypotheses from the theoretical deduction. The hypotheses are examined using the ordinary least squares (OLS) regression with a unique, unbalanced panel dataset of Chinese NHTZs spanning from 2014 to 2021. Considering potential endogeneity risk among the variables, the authors attempt to lag variables and ultimately find the results are still robust.

Drawing upon the density dependence theory, the empirical results show firm density is conducive to promoting entrepreneurship, while the positive relationship between community density and NHTZs' entrepreneurship gradually weakens as the firm density surpasses a certain level. The dynamics between mutualism and competition have different impacts on NHTZs' entrepreneurship. In addition, the results demonstrate that the linkage between firm interdependence and entrepreneurship is stronger for younger NHTZs. Firm density has an impact on entrepreneurship through legitimacy and excessive competition effects.

On the one hand, the research period of this paper is 2014–2021, as the China Torch Statistical Yearbook only started to publish operating revenues in 2014, so the data period of this paper is relatively short. More research can be done in the future when more data is disclosed. On the other hand, the qualitative analysis cannot be conducted because of the limited data and materials. In future research, the qualitative analysis of entrepreneurial activities in NHTZs, such as questionnaires or case studies, needs to be supplemented, which will be an interesting direction.

Most existing research has not distinguished the differences between NHTZs (Wang et al., 2019), especially the differences in legitimacy and access to resources caused by the age of NHTZs. This article considers the heterogeneity between NHTZs, which helps to provide theoretical and practical evidence for a transition economy like China to make trade-off decisions on balancing absorbing new entrants with promoting the efficient allocation of resources based on the density and age of NHTZs.

Drawing upon density dependency theory, this paper enriches the literature on agglomeration and entrepreneurship with a new perspective and extends the study to NHTZs.

First, this paper provides new evidence on how agglomeration affects entrepreneurship from an ecological perspective with the help of mutualism and competition interdependence. Most studies have explored the role of agglomeration in entrepreneurship, focussing on social networks, knowledge spillovers or resource endowments (Acs et al., 2013; Capozza et al., 2018; Yu, 2020). Drawing upon density dependency theory, this paper enriches the literature on agglomeration and entrepreneurship with a new perspective and extends the study to NHTZs. Second, the emphasis of science parks has been primarily on qualitative or case studies (Salvador et al., 2013; Guo and Verdini, 2015; Xie et al., 2018). We have diversified the quantitative research between agglomeration and entrepreneurship by using panel data from Chinese NHTZs from 2014 to 2021. Third, most existing research has not distinguished the differences between NHTZs (Wang et al., 2019), especially the differences in legitimacy and access to resources caused by the age of NHTZs. This article considers the heterogeneity between NHTZs, which helps to provide theoretical and practical evidence for a transition economy like China to make trade-off decisions on balancing absorbing new entrants with promoting the efficient allocation of resources based on the density and age of NHTZs. Finally, this paper meticulously investigates the profound influence and underlying mechanisms of firm density within NHTZs on entrepreneurship. It discerns two distinct mechanisms at play: the legitimacy effect and the impact of excessive competition resulting from firm density. This comprehensive analysis significantly contributes to our comprehension of the intricate interplay between firm density and entrepreneurship, shedding light on the dynamics of competition and mutual benefits.

This study aims to examine whether customer trust is influenced by the congruence and incongruence between customers' perceptions of two types of omnichannel integration—perceived transactional integration (PTI) and perceived relational integration (PRI). The authors further considered the perceived effectiveness of e-commerce institutional mechanisms (PEEIM) as the boundary condition of omnichannel integration's effect.

Drawing upon the stereotype content model, this study hypothesizes the influences of PTI and PRI on customer trust wherein PEEIM moderates the relationships. The research model was empirically examined based on the responses surface analysis of survey data collected from 311 omnichannel customers.

Results showed that when PTI and PRI are congruent, customers are inclined to trust brands that have high levels of PTI and PRI rather than low levels of PTI and PRI. Moreover, the incongruence between PTI and PRI is positively related to customer trust. PEEIM was found to weaken the congruence effect while strengthening the incongruence effect. The authors also examined customer distrust as another relational outcome to provide a robust check.

This study uncovers customer cognition of omnichannel integration and examines the influences on customer trust, therefore contributing to our understanding of omnichannel integration's effect from the customer perspective. Findings from this research provide insights for brand managers on deploying channel integration strategies and institutional mechanisms to manage customer trust.

The Chinese government hopes to achieve the goal of benefiting citizens by building a National Integrated Online Government Service Platform (NIOGSP). However, citizens' low adoption of the platform makes it difficult for the government to achieve its goal. Research on the influencing factors of citizen adoption of NIOGSP can help the government fully understand the concerns and needs of its citizens and take targeted measures to increase citizen adoption.

First, this research builds a model of the citizen adoption process, including attention, retention and motivation, based on an observational learning model. Next, research variables are determined based on social cognitive theory, literature review and real-world needs. Finally, based on the questionnaire survey and structural equation model, the influencing factors of each stage of the citizen adoption process model are studied and the relationship between the three stages of the model is verified.

Results show that perceived usefulness (PU) and self-efficacy (SE) positively affect attention. SE positively affects retention, while perceived privacy (PP) negatively affects retention. PU, social influence, PP and anxiety positively affect motivation.

The conclusion of this study can provide reference for governments in various countries to establish and improve online one-stop government. In addition, this study verifies the citizen adoption process model and finds that there is no obvious causal relationship between attention and retention, but both have positive effects on motivation.

The cost-oriented robotic assembly line balancing problem (cRALBP) has practical importance in real-life manufacturing scenarios. However, only a few studies tackle the cRALBP using exact methods or metaheuristics. This paper aims to propose a hybrid particle swarm optimization (PSO) combined with dynamic programming (DPPSO) to solve cRALBP type-I.

Two different encoding schemes are presented for comparison. In the frequently used Scheme 1, a full encoding of task permutations and robot allocations is adopted, and a relatively large search space is generated. DPSO1 and DPSO2 with the full encoding scheme are developed. To reduce the search space and concern promising solution regions, in Scheme 2, only task permutations are encoded, and DP is used to obtain the optimal robot sequence for a given task permutation in a polynomial time. DPPSO is proposed.

A set of instances is generated, and the numerical experiments indicate that DPPSO achieves a tradeoff between solution quality and computation time and outperforms existing algorithms in solution quality.

The contributions of this paper are three aspects. First, two different schemes of encoding are presented, and three PSO algorithms are developed for the purpose of comparison. Second, a novel updating mechanism of discrete PSO is adjusted to generate feasible task permutations for cRALBP. Finally, a set of instances is generated based on two cost parameters, then the performances of algorithms are systematically compared.