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Common literature review methods such as systematic review and narrative review are poorly suited to the investigation of complex management phenomena. Systematic reviews are highly driven by protocol and procedure, and are oft-criticized as reductive and poorly equipped to examine the interaction between phenomena and context, nonlinear processes, and empirical outcomes that are less predictable. Narrative reviews, on the other hand, are pluralistic and iterative and thus better suited to descriptions of the complex and unpredictable; however, they tend to lack methodological transparency, trustworthiness, and pragmatism in application. The “realist synthesis” approach to literature review can be seen as the middle-ground between these two common methods, offering both methodological rigor alongside flexibility and nuance. Realist synthesis takes an explanatory frame, with a focus on unearthing the theorized causal mechanisms at play beneath a phenomenon of interest.

This study aims to realize efficient, fast and safe robot search task, the belief criteria decision-making approach is proposed to solve the object search task with an uncertain location.

The study formulates the robot search task as a partially observable Markov decision process, uses the semantic information to evaluate the belief state and designs the belief criteria decision-making approach. A cost function considering a trade-off among belief state, path length and movement effort is modelled to select the next best location in path planning.

The semantic information is successfully modelled and propagated, which can represent the belief of finding object. The belief criteria decision-making (BCDM) approach is evaluated in both Gazebo simulation platform and physical experiments. Compared to greedy, uniform and random methods, the performance index of path length and execution time is superior by BCDM approach.

The prior knowledge of robot working environment, especially semantic information, can be used for path planning to achieve efficient task execution in path length and execution time. The modelling and updating of environment information can lead a promising research topic to realize a more intelligent decision-making method for object search task.

This exploratory study aims to help contribute to the understanding of online information search behaviour of PhD students from different humanities fields, with a focus on subject searching.

The methodology is based on a semi-structured interview within which the participants are asked to conduct both a controlled search task and a free search task. The sample comprises eight PhD students in several humanities disciplines at Linnaeus University, a medium-sized Swedish university from 2020.

Most humanities PhD students in the study have received training in information searching, but it has been too basic. Most rely on web search engines like Google and Google Scholar for publications' search, and university's discovery system for known-item searching. As these systems do not rely on controlled vocabularies, the participants often struggle with too many retrieved documents that are not relevant. Most only rarely or never use disciplinary bibliographic databases. The controlled search task has shown some benefits of using controlled vocabularies in the disciplinary databases, but incomplete synonym or concept coverage as well as user unfriendly search interface present hindrances.

The paper illuminates an often-forgotten but pervasive challenge of subject searching, especially for humanities researchers. It demonstrates difficulties and shows how most PhD students have missed finding an important resource in their research. It calls for the need to reconsider training in information searching and the need to make use of controlled vocabularies implemented in various search systems with usable search and browse user interfaces.

In the digital economy, digital capability has become an important dynamic capability of enterprises and plays an essential role in enhancing firm resilience. This study aims to investigate the relationships among digital capability, knowledge search, coopetition behavior and firm resilience based on knowledge-based view and resource-based view.

This study uses the hierarchical regression and bootstrapping methods to test the theoretical framework and research hypotheses. The survey data were collected from 241 Chinese enterprises.

Digital capability has significantly positive effects on knowledge search and firm resilience. Knowledge search positively affects firm resilience and partially mediates the relationship between digital capability and firm resilience. Coopetition behavior weakens the relationship between digital capability and knowledge search, and the mediating effect of knowledge search in the relationship between digital capability and firm resilience. The moderating effect of coopetition behavior on the relationship between digital capability and firm resilience is insignificant.

This study clarifies the effect of digital capability on firm resilience and uncovers the “black box” from digital capability to firm resilience. In addition, this research enriches the literature on digital capability and firm resilience and expands the application of knowledge-based view and resource-based view in the digital context.

Employees facing challenges in their careers – i.e. female, migrant, elderly and lower-educated employees – might expect job searches to have a low likelihood of success and might therefore more often stay in unsatisfactory positions. The goal of this study is to discover inequalities in job mobility for these employees.

We rely on a large sample of Dutch public sector employees (N = 30,709) and study whether employees with challenges in their careers are hampered in translating job dissatisfaction into job searches. Additionally, we assess whether this is due to their perceptions of labor market alternatives.

Findings show that non-Western migrant, elderly and lower-educated employees are less likely to act on job dissatisfaction than their advantaged counterparts, whereas women are more likely than men to do so. Additionally, we find that although they perceive labor market opportunities as limited, this does not affect their propensity to search for different jobs.

This paper is novel in discovering inequalities in job mobility by analyzing whether employees facing challenges in their careers are less likely to act on job dissatisfaction and therefore more likely to remain in unsatisfactory positions.

In general, the existing compressor design methods require abundant knowledge and inspiration. The purpose of this study is to identify an intellectual design optimization method that enables a machine to learn how to design it.

The airfoil design process was solved using the reinforcement learning (RL) method. An intellectual method based on a modified deep deterministic policy gradient (DDPG) algorithm was implemented. The new method was applied to agents to learn the design policy under dynamic constraints. The agents explored the design space with the help of a surrogate model and airfoil parameterization.

The agents successfully learned to design the airfoils. The loss coefficients of a controlled diffusion airfoil improved by 1.25% and 3.23% in the two- and four-dimensional design spaces, respectively. The agents successfully learned to design under various constraints. Additionally, the modified DDPG method was compared with a genetic algorithm optimizer, verifying that the former was one to two orders of magnitude faster in policy searching. The NACA65 airfoil was redesigned to verify the generalization.

It is feasible to consider the compressor design as an RL problem. Trained agents can determine and record the design policy and adapt it to different initiations and dynamic constraints. More intelligence is demonstrated than when traditional optimization methods are used. This methodology represents a new, small step toward the intelligent design of compressors.

Robotic arms’ interactions with the external environment are growing more intricate, demanding higher control precision. This study aims to enhance control precision by establishing a dynamic model through the identification of the dynamic parameters of a self-designed robotic arm.

This study proposes an improved particle swarm optimization (IPSO) method for parameter identification, which comprehensively improves particle initialization diversity, dynamic adjustment of inertia weight, dynamic adjustment of local and global learning factors and global search capabilities. To reduce the number of particles and improve identification accuracy, a step-by-step dynamic parameter identification method was also proposed. Simultaneously, to fully unleash the dynamic characteristics of a robotic arm, and satisfy boundary conditions, a combination of high-order differentiable natural exponential functions and traditional Fourier series is used to develop an excitation trajectory. Finally, an arbitrary verification trajectory was planned using the IPSO to verify the accuracy of the dynamical parameter identification.

Experiments conducted on a self-designed robotic arm validate the proposed parameter identification method. By comparing it with IPSO1, IPSO2, IPSOd and least-square algorithms using the criteria of torque error and root mean square for each joint, the superiority of the IPSO algorithm in parameter identification becomes evident. In this case, the dynamic parameter results of each link are significantly improved.

A new parameter identification model was proposed and validated. Based on the experimental results, the stability of the identification results was improved, providing more accurate parameter identification for further applications.

Prior literature has long argued that knowledge networks contain great opportunities for innovation, and researchers can identify these opportunities using the properties of knowledge networks (PKNs). However, previous studies have examined only the relationship between structural PKNs (s-PKNs) and innovation, ignoring the effect of qualitative PKNs (q-PKNs), which refer to the quality of the relationship between two elements. This study aims to further investigate the effects of q-PKNs on innovation.

Using a panel data set of 2,255 patents from the Chinese wind energy industry, the authors construct knowledge networks to identify more PKNs and examine these hypotheses.

The results show that q-PKNs significantly influence recombinant innovation (RI), reflecting the importance of q-PKNs analysed in this study. Moreover, the results suggest that the combinational potential of an element with others may be huge at different levels of q-PKNs.

This study advances the understanding of PKNs and RI by exploring how q-PKNs impact RI. At different levels of PKNs, the potential of the elements to combine with others and form innovation are different. Researchers can more accurately identify the opportunities for RI using two kinds of PKNs. The findings also provide important implications on how government should provide support for R&D firms.

With the continuous improvement of social and economic development as well as the rising level of demand for spiritual life, the design of cultural and creative products has ushered in new opportunities and challenges. Therefore the research related to cultural and creative products design is an inevitable choice for industrial innovation and market competition. The article aims to analyze the frontier hotspots and trend evolution of theoretical research on cultural and creative design in China by presenting different research fields, personnel and institutions embodied by cultural and creative products, thus providing a forward-looking development reference for China's special cultural and creative product design practice.

In this paper, the visualized citation analysis tool — CiteSpace V is used to analyze and map the relevant literature of Chinese cultural and creative design in CNKI (Chinese National Knowledge Infrastructure) as the data sources, through bibliometric methods, in order to study the application development of Chinese cultural and creative design and provide reference for the application research of Chinese cultural and creative industries.

With the improvement of China's comprehensive strength, cultural self-confidence and strategy are being paid more and more attention by scholars; with the future research of China's cultural and creative design theory, cross-integration becomes the future trend; with the gradual maturity of China's cultural and creative design methodology, how to choose the right method for pioneering research is a difficulty for the future development of cultural and creative design; with the continuous development of China's economy, the method construction of innovative cultural and creative industry system becomes the focus of future research; with the continuous progress of science and technology, the integration of emotion and product industry is the mainstream of future development on cultural and creative design.

Through an objective empirical analysis of the development of Chinese cultural and creative products, it will broaden the research horizons of relevant scholars, understand the development direction of China's cultural and creative industries, enrich the design practice application of Chinese cultural and creative products, enhance the understanding of international counterparts on Chinese cultural and creative design research and promote the exchange among international counterparts.

This study aims to modify a nature-based numerical method named the invasive weed optimization (IWO) method for mobile robot path planning in various complex environments.

The existing IWO method is quick in converging to a feasible solution but in a complex environment; it takes more time as well as computational resources. So, in this paper, the computational part of this artificial intelligence technique is modified with the help of recently developed evolution algorithms like particle swarm optimization, genetic algorithm, etc. Some conditional logic statements were used while doing sensor-based mapping for exploring complex paths. Implementation of sensor-based exploration, mathematical IWO method and prioritizing them for better efficiency made this modified IWO method take complex dynamic decisions.

The proposed modified IWO is better for dynamic obstacle avoidance and navigating a long complex map. The deviation of results in simulation and experiments is less than 5.5%, which validates a good agreement between simulation and real-time testing platforms.

As per a deep literature review, it has found that the proposed approach has not been implemented on the Khepera-III robot for smooth motion planning. Here a dynamic obstacle mapping feature is implemented. A method to selectively distribute seeds instead of a random normal distribution is also implemented in this work. The modified version of IWO is coded in MATLAB and simulated through V-Rep simulation software. The integration of sensors was done through logical conditioning. The simulation results are validated using real-time experiments.