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This paper aims to address the challenges of the capacitor tower maintenance robot during bolt tightening in high-voltage substations, including difficulties in bolt positioning due to tilted angles and anti-bird cover occlusion and issues with fast and accurate docking of bolts while the base is moving.

This paper proposes a visual servoing method for the capacitor tower maintenance robot, including bolt pose estimation and visual servoing control. Bolt pose estimation includes four components: constructing a keypoint detection network to identify the approximate position, precise positioning, rapid prediction and calculation of bolt pose. In visual servoing, an improved position-based visual servoing (PBVS) is proposed, which eliminate steady-state error and enhance response speed during dynamic tracking by incorporating integral and differential components.

The bolt detection method exhibits high robustness against varying lighting conditions, partial occlusions, shooting distances and angles. The maximum positioning error at a distance of 250 mm is 2.8 mm. The convergence speed of the improved PBVS is 10% higher than that of the traditional PBVS when the base and target remain relatively stationary. When the base moves at a constant speed, the improved method eliminates steady-state error in dynamic tracking. When the base moves rapidly and intermittently, the maximum error of the improved method in the tracking process is 30% smaller than that of traditional PBVS.

This method enables real-time detection and positioning of bolts in an unstructured environment with tilt angles, variable lighting conditions and occlusion by anti-bird covers. An improved PBVS is proposed to enhance its capability in tracking dynamic targets.

The purpose of this paper is to investigate the impact of size, asset quality, asset management, financial risk, gross domestic product and inflation rate on the financial performance of companies listed on the Jakarta Islamic Index of 30 industrial firms.

Based on the selected criteria, this study analysed an unbalanced panel of data from 30 industrial companies on the Indonesian capital market that are members of the Jakarta Islamic index. Profitability is measured using the dependent variables return on assets (ROA), return on equity (ROE) and stock prices. The influence of explanatory variables of internal factors, namely, size, asset quality, asset management, financial risk, gross domestic product and inflation is investigated using pooled OLS, fixed and random effect estimation.

The empirical findings indicate that the scale of a company has a significant impact on its performance, asset quality, asset management and financial risk. GDP has a substantial impact on financial performance, particularly as measured by ROA and ROE. This study’s ramifications have substantial effects on a broad spectrum of stakeholders. The results of this study provide the general public and investors with a greater understanding of the factors that influence a company’s performance on the Jakarta Islamic Index 30.

The implication of this research is that a deeper comprehension of the factors that influence the financial performance of companies within industrial sectors that follow Islamic finance principles can help design more effective strategies and policies.

This research has significant practical implications in a number of crucial areas. First, it provides a comprehensive comprehension of the company’s financial performance in the industrial sector in accordance with Islamic finance principles. Second, the research findings provide more precise guidance on how company size, asset quality and macroeconomic variables influence the performance of Indonesia's financial market.

The study’s authenticity and value hold considerable importance. This study introduces novel perspectives on the assessment of corporate financial performance within industrial sectors through the lens of Islamic finance principles. It offers valuable insights that have not yet been extensively investigated by scholars in the field.

This study aims to investigate the role of information normalization in online healthcare consultation, a typical complex human-to-human communication requiring both effectiveness and efficiency. The globalization and digitization trend calls for high-quality information, and normalization is considered an effective method for improving information quality. Meanwhile, some researchers argued that excessive normalization (standardized answers) may be perceived as impersonal, repetitive, and cold. Thus, it is not appreciated for human-to-human communication, for instance, when patients are anxious about their health condition (e.g. with high-risk disease) in online healthcare consultation. Therefore, the role of information normalization in human communication is worthy to be explored.

Data were collected from one of the largest online healthcare consultation platforms (Dxy.com). This study expanded the existing information quality model by introducing information normalization as a new dimension. Information normalization was assessed using medical templates, extracted through natural language processing methods such as Bidirectional Encoder Representations from Transformers (BERT) and Latent Dirichlet Allocation (LDA). Patient decision-making behaviors, namely, consultant selection and satisfaction, were chosen to evaluate communication performance.

The results confirmed the positive impact of information normalization on communication performance. Additionally, a negative moderating effect of disease risk on the relationship between information normalization and patient decision-making was identified. Furthermore, the study demonstrated that information normalization can be enhanced through experiential learning.

These findings highlighted the significance of information normalization in online healthcare communication and extended the existing information quality model. It also facilitated patient decision-making on online healthcare platforms by providing a comprehensive information quality measurement. In addition, the moderating effects indicated the contradiction between informational support and emotional support, enriching the social support theory.

The purpose of this study is to explore the factors influencing viewers’ engagement intention in travel live streaming (TLS) from a perceived value perspective.

This study used a mixed-methods approach. In Study 1, 48 semistructured interviews were analyzed based on grounded theory and perceived value theory, and a research framework was established to investigate the impact of viewers’ engagement intentions in TLS. In Study 2, partial least squares structural equation modeling (PLS-SEM) was used to empirically validate survey data from 255 TLS viewers.

Through an analysis of the interview content, it was found that the expertise and interaction of the live streamer in TLS as well as the immersion, aesthetics and novelty of the live streaming scene are key influencing factors that affect the engagement of TLS viewers. This finding was confirmed through empirical research.

This research provides practical suggestions for live streamers, TLS platforms and local government to increase viewer engagement. Specifically, it provides methods and directions for the individual improvement of live streamers, further promotes the development and construction of the platform and underscores the importance of government initiatives in policy support and regulatory framework development.

This study focuses on the less-researched field of TLS. Using a mixed-methods approach combining interviews and PLS-SEM, this study explores the key factors that affect the engagement of TLS viewers based on the characteristics of live streamers and live streaming scenes.

This research aims to investigate the interplay between individuals’ health status and their level of trust in both smart health-care systems and health-care providers and how these factors influence the decision to use such systems.

Drawing upon institution-based trust and affordance theories, the authors developed and empirically examined a research model using a sample from a prominent US university.

The findings reveal that both types of trust, specifically trust in smart health-care systems and trust in health-care providers, positively influence the intention to use these systems. Additionally, the authors identified that health status plays a dual moderating role in this context. It positively moderates the relationship between trust in health-care system providers and the intention to use, suggesting that individuals with better health are more inclined to use smart health-care systems when trust in providers is high. Conversely, health status negatively moderates the relationship between trust in the system and the intention to use it. This implies that trust in the system exerts a more pronounced influence on the intention to use the system among individuals with lower health status. This heightened impact can be attributed to the increased necessity for the system’s benefits among this group.

While the power analyses suggest our sample size is sufficient, caution is warranted when interpreting the study’s conclusions. These results have substantial implications for researchers and providers of smart health-care systems. They underscore the intricate dynamics between trust, health status and technology use, offering valuable insights for future investigations in this domain. Furthermore, they guide the design and implementation of smart health-care systems, emphasizing the need to consider the nuanced influence of health status on trust and use intentions.

Past research has focused on individuals’ trust in understanding the adoption of smart health-care systems; however, it did not consider how individuals’ health status can moderate their trust and intention to adopt such systems. In this study, the authors close this gap by investigating the moderating role of health status in the relationships between two types of trust and intention to use smart health-care systems through the lens of institution-based trust theory and affordance theory.

The motivation of value co-creation among the multi-actor in sharing economy was an important topic in interactive marketing communication research. This study investigated how customer gratitude expression leads to value co-creation of PSPs in the sharing economy, and also investigates the moderating effect of platform benevolent climate.

A three-wave field survey (Study 1) and two experiments (Studies 2 and 3) were given to respondents with sharing economy practitioners.

First, customer gratitude expression positively influenced PSP's perceived meaningful work, which in turn enhanced their value co-creation intention. Second, PSP's perceived platform benevolent climate moderated the relationship between customer gratitude expression and PSP's perceived meaningful work.

Prior research discussed PSPs' value co-creation intention mainly from the perspective of platforms and PSPs, but few considered customer-PSP interaction perspective. This study revealed how customer gratitude expression influences PSP's value co-creation intention in highly interactive digital business context, examined the boundary condition of gratitude expression, and extended the application scenarios of social information processing theory.

The study aims to examine how different types of green brand ritual (GBR) influence customer engagement behavior and the mediation mechanisms and boundary conditions of the positive and negative pathways.

The study conducts two online experiments to collect data from a total of 940 consumers in China. Hypotheses are tested by independent samples t-test, two-way ANOVA and Hayes' PROCESS model.

Different kinds of GBR have different effects on customer engagement behavior. Internal GBR is more likely to play a positive role by inciting connectedness to nature. External GBR is more likely to play a negative role by inciting psychological resistance. This dual effect is especially pronounced for warm brands rather than competent brands.

The study pioneers the brand ritual into the field of interactive marketing and enriches its dual effect research. Additionally, the study figures out whether the category of brand ritual can trigger negative effect.

Inappropriate brand rituals are worse than no rituals at all. The results provide guidance for green companies to design effective brand rituals to strengthen the connection with consumers. Green brands should describe brand rituals in vivid detail and consciously lead consumers to immerse themselves in them.

Blockchain technology (BCT) is considered a promising tool to improve the productivity of construction project management. Existing research has studied its potential costs and benefits for the construction industry. However, the potential costs and benefits of BCT failed to be compared as actual costs and benefits of specific applications for stakeholders. To fill this gap, this study seeks to analyze the cost-effectiveness of BCT-based applications in construction project management.

This study is conducted with a customized systematic literature review based on transaction cost theory to enable qualitative comparison. With a deliberately designed structure confining extraneous variables, the costs and benefits of BCT-based applications are identified and compared. The inherent dependent relations of processes and the evolution relations of functions are identified. The cost-effectiveness of blockchain adoption is then analyzed.

Seven functions and six challenges are identified within five processes. The result suggests all identified functions are cost-effective except for manual instruction (coding smart contracts manually). The smart contracts require explicit definition and logic to be effective. However, the construction projects essentially require the institution to be flexible due to unpredictability. The adoption of smart contracts and corresponding additional requirements can increase the transaction cost of bounded rationality.

As manual instruction is fundamental to realize other functions, and its advanced substitute relies on its broad adoption, its cost-effectiveness must be improved for applications to be acceptable to stakeholders. The establishment of a universal smart contract model and a universal, legitimate and efficient database structure are recommended to minimize the cost and maximize the effect of applications.

This study contributes to the knowledge by providing a comprehensive analysis of BCT adoption’s cost-effectiveness in construction project management. The adopted review structure can be extended to analyze the qualitative benefits and challenges of management automation in the early stages.

The purpose of this paper is to evaluate the role of handicraft production units within creative cultural clusters (CCCs) and their impact on urban regeneration, particularly within the historical fabric of Isfahan. It aims to identify the key factors contributing to the development of CCCs and how these clusters, in turn, influence tourism development.

The research method of this study is based on a quantitative approach, using a researcher-devised questionnaire as the primary data collection instrument. The statistical population included 227 units of handicraft makers in the historical fabric of Isfahan. The data processing and analysis were performed using SPSS software.

Statistical analysis confirms the creative industries, spatial and socio-cultural indices significantly impact tourism, with means of 14.19, 19.07 and 15.11. Clustering, cultural resources and tourism are key to CCCs growth, while innovation and cultural identity have less effect. CCCs enhance Isfahan’s historical fabric, improving urban space attractiveness and fostering social ties. Handicraft clusters around Naqsh-e Jahan square notably influence the economy and cultural identity, with shop placement reflecting the city’s character.

The originality of this research lies in its analysis of how handicraft production within CCCs contributes to urban regeneration and tourism in Isfahan, with a novel focus on the socio-cultural and economic impacts of these clusters in a historical context.

For a better understanding of the preferences and differences of young consumers in emerging wine markets, this study aims to propose a clustering method to segment the super-new generation wine consumers based on their sensitivity to wine brand, origin and price and then conduct user profiles for segmented consumer groups from the perspectives of demographic attributes, eating habits and wine sensory attribute preferences.

We first proposed a consumer clustering perspective based on their sensitivity to wine brand, origin and price and then conducted an adaptive density peak and label propagation layer-by-layer (ADPLP) clustering algorithm to segment consumers, which improved the issues of wrong centers' selection and inaccurate classification of remaining sample points for traditional DPC (DPeak clustering algorithm). Then, we built a consumer profile system from the perspectives of demographic attributes, eating habits and wine sensory attribute preferences for segmented consumer groups.

In this study, 10 typical public datasets and 6 basic test algorithms are used to evaluate the proposed method, and the results showed that the ADPLP algorithm was optimal or suboptimal on 10 datasets with accuracy above 0.78. The average improvement in accuracy over the base DPC algorithm is 0.184. As an outcome of the wine consumer profiles, sensitive consumers prefer wines with medium prices of 100–400 CNY and more personalized brands and origins, while casual consumers are fond of popular brands, popular origins and low prices within 50 CNY. The wine sensory attributes preferred by super-new generation consumers are red, semi-dry, semi-sweet, still, fresh tasting, fruity, floral and low acid.

Young Chinese consumers are the main driver of wine consumption in the future. This paper provides a tool for decision-makers and marketers to identify the preferences of young consumers quickly which is meaningful and helpful for wine marketing.

In this study, the ADPLP algorithm was introduced for the first time. Subsequently, the user profile label system was constructed for segmented consumers to highlight their characteristics and demand partiality from three aspects: demographic characteristics, consumers' eating habits and consumers' preferences for wine attributes. Moreover, the ADPLP algorithm can be considered for user profiles on other alcoholic products.