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In the post-pandemic era urban ecosystem planning has become critically important. Given the emphasis on relevant issues concerning the complex interactions between human civilizations and natural systems within urban environments in the new normal, this article aims to enrich the field of knowledge management developing a cross-cultural analysis for clarifying the role of knowledge in planning and urban ecosystems.

This paper is conceptual in nature. Based on a theoretical foundation built by a critical literature review and data from the China Statistical Yearbook and China’s National Bureau of Statistics, this paper introduces some emerging real-impact topics regarding the connections between humanistic knowledge and urban planning. A comparative analysis between the capital city of Chang’an in the Tang dynasty of China and the capital city of Athens in Ancient Greek was used for explaining the influence of knowledge on successful urban planning.

The understanding the role of cross-cultural differences in knowledge management and practices for urban ecosystems offer the opportunities for rethinking consolidated approach to the interaction among social, economic, and environmental dimensions in urban settings.

This paper implies a new inter-disciplinary research field of great interest for the real impact KM community by illuminating how knowledge management is central in urban planning and across cultures.

This paper aims to investigate how location-based advertising (LBA) elicits in-store purchase intention. To deepen the understanding of LBA’s effect on consumers’ purchase decision, the research examines the role of consumers’ time consciousness in click intention in pull or opt-out LBA approaches. The study also explores how consumers react to LBA with an asymmetric dominance decoy versus a compromise decoy message.

Two field experiments were conducted, and a total of 363 volunteers within 3 km of a shopping mall participated. The participants were asked to turn on their global positioning system and then informed that a convenience store was planning to launch a mobile coupon subscription service. Data collected were analysed using analysis of variance, regression analysis, bootstrapping and spotlight tests.

The results demonstrate that consumers had a higher intention to click pull LBA than to click opt-out push LBA. Consumers with high time-consciousness had greater click intentions for pull LBA than for opt-out push LBA. Consumers with low time-consciousness, however, showed no difference in click intention for either LBA approach. Further, click intention mediates the effect of LBA on in-store purchase intention, and the asymmetric dominance decoy message is a more powerful strategy for LBA to increase the likelihood of in-store purchase.

This research provides insight into location-based services marketing by revealing how time-consciousness and decoy promotional messages affect consumers’ reaction to LBA and in-store purchase intentions. The findings offer practical suggestions for retailers on how to reach and engage with consumers more effectively through the use of LBA.

As a smart service, location-based advertising (LBA) integrates advanced technologies to deliver personalized messages based on a user’s real-time geographic location and needs. However, research has shown that privacy concerns threaten the diffusion of LBA. This research investigates how privacy-related factors (i.e. LBA type, privacy self-efficacy (PSE) and consumer generation) impact consumers’ value-in-use and their intention to use LBA.

This study developed and examined an LBA value-in-use framework that integrates the role of LBA type, consumers’ PSE and consumer generation into the technology acceptance model (TAM). Data were collected through two experiments in the field with a total of 374 consumers. The proposed relationships were tested using PROCESS modeling.

The results reveal that pull (vs push) LBA causes higher value-in-use in terms of perceived usefulness and perceived ease of use, leading to greater usage intention. Further, the differences in the mediated relationship between pull- and push-LBA are larger among consumers of low PSE (vs high PSE) and Generation Z (vs other generations). The findings suggest that the consumer value-in-use brought about by LBA diminishes when using push-LBA for low PSE and Generation Z consumers.

This research is the first to integrate the privacy-related interactions of LBA type and consumer characteristics into TAM to develop a TAM-based LBA value-in-use framework. This study contributes to the literature on service value-in-use, smart services and LBA by clarifying the boundary conditions that determine the effectiveness of LBA in enhancing consumers’ value-in-use.

This research aims to investigate how backstage visibility affects intangibility and perceived risk at the pre-purchase stage and how service credence moderates the effect of backstage visibility on intangibility and perceived risk. It also focuses on the effect of backstage visibility on perceived service quality and value at the post-purchase stage and the moderating role of the service contact level.

This research tests the causal relationships between backstage visibility and customers’ service evaluations through two experimental studies.

Study 1 shows that customers who are exposed to backstage cues perceive less pre-purchase risk in the service than those who are not exposed. Pictures plus text information are more effective than text illustrations alone for risk reduction. This risk reduction effect is stronger for high-credence than for low-credence services and is partially mediated by the perceived intangibility of the service. Study 2 reveals that customers with access to backstage cues perceive higher service quality and higher overall value from service experiences. The value increase is more significant for high-contact than for low-contact services.

Future research could apply different methods to different data sources to provide further insight about backstage visibility.

The findings of this research suggest that allowing customers to view some backstage activities before purchase helps tangibilize the service, achieve more effective communication with customers and create more positive service experiences.

To solve several “inorganic” problems generated recently by community development in China (e.g., waste of social resources, environmental pollution, and decreased economic energy efficiency), focus should be on improving the community traffic organic micro-circulation system. As a historic and mixed functional urban community, the micro-circulation system of Xi'an Railway Bureau exhibits representativeness and complexity. Based on existing research results and years of follow-up investigations, which concentrate on circulation patterns and inherent organic development requirements of the community traffic micro-circulation system, this paper builds an evaluation index system. Value function method was used to implement the index factor quantitative analysis and comprehensive evaluation. Several related strategies were proposed to improve the organic micro-circulation system of the community, which is based on the analysis of the evaluation, in order to adopt the trend in both increasing urban development and stock updating. The analysis results demonstrate that it is necessary to present guiding renewal strategies on community land, road, people, and the environment for those mixed functional communities which use progressive renewed mode. It compensates the problems of overly strong export-oriented system, which is caused by the lack of organic traffic micro-circulation, so as to achieve the selective opening of community external. The study mainly highlights the significance of the systematic analysis of evaluation in influencing strategies on community renewal.

With the rapid development of railway-intelligent video technology, scene understanding is becoming more and more important. Semantic segmentation is a major part of scene understanding. There is an urgent need for an algorithm with high accuracy and real-time to meet the current railway requirements for railway identification. In response to this demand, this paper aims to explore a variety of models, accurately locate and segment important railway signs based on the improved SegNeXt algorithm, supplement the railway safety protection system and improve the intelligent level of railway safety protection.

This paper studies the performance of existing models on RailSem19 and explores the defects of each model through performance so as to further explore an algorithm model dedicated to railway semantic segmentation. In this paper, the authors explore the optimal solution of SegNeXt model for railway scenes and achieve the purpose of this paper by improving the encoder and decoder structure.

This paper proposes an improved SegNeXt algorithm: first, it explores the performance of various models on railways, studies the problems of semantic segmentation on railways and then analyzes the specific problems. On the basis of retaining the original excellent MSCAN encoder of SegNeXt, multiscale information fusion is used to further extract detailed features such as multihead attention and mask, solving the problem of inaccurate segmentation of current objects by the original SegNeXt algorithm. The improved algorithm is of great significance for the segmentation and recognition of railway signs.

The model constructed in this paper has advantages in the feature segmentation of distant small objects, but it still has the problem of segmentation fracture for the railway, which is not completely segmented. In addition, in the throat area, due to the complexity of the railway, the segmentation results are not accurate.

The identification and segmentation of railway signs based on the improved SegNeXt algorithm in this paper is of great significance for the understanding of existing railway scenes, which can greatly improve the classification and recognition ability of railway small object features and can greatly improve the degree of railway security.

This article introduces an enhanced version of the SegNeXt algorithm, which aims to improve the accuracy of semantic segmentation on railways. The study begins by investigating the performance of different models in railway scenarios and identifying the challenges associated with semantic segmentation on this particular domain. To address these challenges, the proposed approach builds upon the strong foundation of the original SegNeXt algorithm, leveraging techniques such as multi-scale information fusion, multi-head attention, and masking to extract finer details and enhance feature representation. By doing so, the improved algorithm effectively resolves the issue of inaccurate object segmentation encountered in the original SegNeXt algorithm. This advancement holds significant importance for the accurate recognition and segmentation of railway signage.

Service innovation is a key source of competence for service enterprises. Along with the emergence of crowdsourcing platforms, consumers are frequently involved in the process of service innovation. In this paper, the authors describe the crowdsourcing ideation website—MyStarbucksIdea.com—and find the motivations of customer-involved service innovation.

Using a rich data set obtained from the website MyStarbucksIdea.com, a dynamic structural model is proposed to illuminate the learning process of consumers.

The results indicate that initially individuals tend to underestimate the costs of the firm for implementing their ideas but overestimate the value of their ideas. By observing peer votes and feedbacks, individuals gradually learn about the true value of ideas, as well as the cost structure of the firm. Overall, the authors find that the cumulative feedback rate and the average potential of ideas will first increase and then decline.

First, the previous researches concerning the crowdsourcing show that the creative implementation rate is low and the number of creative ideas decreases, and few scholars have studied the causes behind the problems. Second, the data used in this paper are true and valid, and it is difficult to obtain now. These data can provide strong empirical support for the model proposed in this paper. Third, it is relatively novel to combine the customer learning mechanism and heterogeneity theory to explain the phenomenon of reduced creativity and low implementation rate in crowdsourcing platform, and the research results can provide a reasonable reference for the construction of this industry.

The purpose of this study is to solve the Reynolds equation for finite journal bearings by using the physics-informed neural networks (PINNs) method. As a meshless method, it is unnecessary to use big data to train the neural networks, but to satisfy the Reynolds equation and the corresponding boundary conditions by using the known physics information.

Here, the boundary conditions are enforced through the loss function firstly, i.e. the soft constrain method. After this, an equation was constructed to build a surrogate model for satisfying the corresponding boundary conditions naturally, i.e. the hard constrain method.

For the soft one, in brief, the pressure results agree well with existing results, apart from the ones on the boundaries. While for the hard one, it can be noted that the discrepancies on the boundaries are reduced significantly.

The PINNs method is used to solve the Reynolds equation for finite journal bearings, and the error values on the boundaries for the results of the soft constrain method are improved by using the hard constrain method. Therefore, the hard constraint maybe also a good option when the pressure results on the boundaries are emphasized.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-02-2023-0045/

The purpose of this paper is to analyze the bird impact damage of fuselage composite stiffened structures by numerical method and to evaluate the damage and the bird impact resistance of different structures.

The deformation and damage of composite stiffened plates during bird impact are numerically analyzed by the explicit finite element software LS-DYNA. A comparative study on the numerical calculation results was conducted by using SPH (Smoothed Particle Hydrodynamics)-FEM (Finite Element Method) modeling and simulation. First, the I-shaped, T-shaped, straight stiffened plates and unstiffened plate were designed. Second, the accuracy of the bird model was verified and further used to evaluate bird strikes on composite stiffened plate. Third, the results of damage modes as well as displacements of the stiffened plates were compared.

The stiffeners can increase the local stiffness of the composite panel, which can effectively inhibit the bird’s movement along the impact direction. Adding stiffeners can change the panel matrix tension damage from global distribution to local distribution mode; however, the impact damage distribution and the ability to inhibit damage propagation can differ for different stiffened panels. Especially, the I-stiffened panel exhibits a better anti-bird strike performance.

The analysis of geometric parameters of structural components by numerical methods can reduce the cost of the design phase and has been widely used in aircraft design. The present study evaluated the bird impact damage of composite stiffened plates with different structures, which provides a guideline for selecting the stiffened plate structure in the fuselage skin.

This paper aims to examine the relationship between environmental, social and governance (ESG) performance and systemic risk sensitivity of Chinese listed companies. From the consumer loyalty and investor structure perspectives, the relationship between ESG performance and systemic risk sensitivity is analyzed.

Since Morgan Stanley Capital International (MSCI) ESG officially began to analyze and track China A-shares from 2018, 275 listed companies in the SynTao Green ESG testing list for 2015–2021 are selected as the initial model. To measure the systematic risk sensitivity, this study uses the beta coefficient, from capital asset pricing model (CPAM), employing statistics and data (STATA) software.

The study reveals that high ESG rating companies have high corresponding consumer loyalty and healthy trading structure of institutional investors, thereby the systemic risk sensitivity is lower. This paper reveals that companies with high ESG rating are significantly less sensitive to systemic risk than those with low ESG rating. At the same time, ESG has a weaker impact on the systemic risk of high-cap companies than low-cap companies.

The study helps the companies understand the influence of market value on the relationship between ESG performance and systemic risk sensitivity. Moreover, this paper explains explicitly why ESG performance insulates a firm’s stock from market downturns with the lens of consumer loyalty theory and investor structure theory.

The paper provides new insights on the company’s ESG performance that significantly affects the company’s systemic risk sensitivity.