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The purpose of this paper is to clarify the conceptual confusion in the extant literature about organizational agility and explore its role in different relationships in the context of digital transformation.

An integrative review of the relevant literature on agility was conducted. The literature on organizational agility and other variables in recent quantitative research was also examined to explore its role in different relationships.

Organizational agility is the ability to quickly respond and proactively embrace unanticipated changes in dynamic environments through effective resource reconfiguration and rapid decision-making. The role of organizational agility in achieving digital transformation has not been addressed from a holistic conceptual perspective. This paper addresses that gap and proposes that organizational agility is the underlying mechanism for an organization to fully use and engage its workforce, operation and network in the process of digital transformation.

This research is an integrative review of the existing literature on the concept of agility and its relationships. The next phase of research needed for theory building will be the operationalization of constructs.

Organizations should strive to strategically develop both the reactivity and proactivity sides of organizational agility in achieving digital transformation that involves fundamental changes at different levels of the organization.

This paper explores the role of organizational agility in digital transformation through an integrative review of the relevant literature.

This study aims to provide a reference basis for waterproofing for the long-term safe operation of shield tunnels. Shielding subways in the long-term operation of tunnel tube seams leads to opening, dislocation and other issues, which in turn cause the tube sealing gasket to break and ultimately cause water seepage, and the existing symmetrical sealing gasket arrangement cannot meet the waterproofing requirements of the tunnel structure.

First, we carry out an indoor “one-seam” hydrostatic test to quantitatively determine the waterproofing performance of symmetric and four asymmetric arrangements of gaskets. And the arrangement with the best stability and waterproofing performance is selected. Second, we establish a three-dimensional numerical seepage model for the waterproof failure of gaskets with different arrangements, which mechanistically explains the whole course of the gradual failure of the waterproof performance of gaskets with the wedging of water. Finally, we compare and analyze the experimental results with the numerical results to verify the reliability of the different analysis methods.

The results of the research show that the gasket will undergo four stages: the initial stage, deformation stage, wedging stage, and breakthrough stage during the continuous wedging process of the water body. Compared with the symmetric arrangement of the gasket, the asymmetric arrangement of the effective contact part of the gasket stress wave peaks and troughs is smaller, the deformation stage of the ability to resist the deformation of the water pressure is stronger, and the role of the water pressure between the two sealing gaskets of the stress path is less likely to be damaged.

The current test can't fully reproduce real engineering site conditions as it ignores factors like temperature, time and aging during waterproofing tests and lacks tests based on actual application. Only one – seam test is done, lacking research on other seams. The current seepage model has difficulty reflecting some details and needs refinement.

The study focuses on the tube sheet joint problem in underground tunnels and proposes four asymmetric gasket arrangements, which are tested and analysed using a variety of methods. The results show that the asymmetric arrangement has a slower decline in waterproofing capacity and better stability, providing a new method and basis for solving tunnel waterproofing problems.

The study focuses on the tube sheet joint problem in underground tunnels and proposes four asymmetric gasket arrangements, which are tested and analysed using a variety of methods. The results show that the asymmetric arrangement has a slower decline in waterproofing capacity and better stability, providing a new method and basis for solving tunnel waterproofing problems.

Nowadays, thermal comfort plays a prominent role in contemporary construction practices. Appropriate thermal insulation not only offers energy efficiency benefits in buildings but also enhances occupant well-being, comfort, and productivity. Therefore, a comprehensive understanding of the thermal properties of building materials is essential. This research aims to prepare and investigate a lightweight gypsum-based composite incorporating nano montmorillonite with advanced thermal insulation properties, considering both quality and cost-effectiveness while ensuring environmental compatibility.

This study adopts a laboratory experimental approach. A gypsum sample (without additives) and seven samples of gypsum combined with varying percentages of sodium and calcium montmorillonite nanoclays undergo extensive testing and analysis. Subsequently, the properties of these samples are compared.

The results indicate that adding montmorillonite nanoclays to gypsum composites reduces the density of the tested samples and increases their porosity. Moreover, the thermal conductivity coefficient decreases in these samples, significantly improving the thermal insulation properties of the lightweight gypsum plaster. This improvement is more pronounced in samples containing sodium montmorillonite nanoclay compared to calcium-based samples. Additionally, the investigations reveal that compressive strength decreases with the addition of montmorillonite to the samples.

In this research, laboratory experiments were conducted to investigate the physical and mechanical properties of gypsum plaster with varying percentages of sodium and calcium montmorillonite nanoclays. The studied properties include density, porosity, thermal conductivity coefficient, and compressive strength. Additionally, stress-strain diagrams, elastic modulus, and initial and secondary critical stresses were analyzed for each specimen.

This article attempts to contribute to medical dispute resolution by examining the adoption of medical judicial expertise opinions in determining medical malpractice responsibility and its coordination with the judge’s legal opinions.

This article examines the legal basis and empirical data to demonstrate the decisive effect of medical judicial experts’ opinions in allocating medical malpractice responsibility and corresponding dispute resolution effectiveness.

High reliance on medical judicial expertise in medical dispute litigation not only unifies the judicial standards but also limits judges’ discretion, which brings the risk of contradiction between factual and legal findings, which currently ends in judges’ compromise.

The current medical malpractice provisions neglect the divergence of medical judicial expertise and judges’ opinions in determining medical malpractice responsibility, which produces difficulties in harmonizing awarded compensations and parties’ expectations, leading to problematic medical dispute litigation in Mainland China.

This paper aims to propose a solution for dissolving the “privacy paradox” in social networks, and explore the feasibility of adopting a synergistic mechanism of “deep-learning algorithms” and “differential privacy algorithms” to dissolve this issue.

To validate our viewpoint, this study constructs a game model with two algorithms as the core strategies.

The “deep-learning algorithms” offer a “profit guarantee” to both network users and operators. On the other hand, the “differential privacy algorithms” provide a “security guarantee” to both network users and operators. By combining these two approaches, the synergistic mechanism achieves a balance between “privacy security” and “data value”.

The findings of this paper suggest that algorithm practitioners should accelerate the innovation of algorithmic mechanisms, network operators should take responsibility for users’ privacy protection, and users should develop a correct understanding of privacy. This will provide a feasible approach to achieve the balance between “privacy security” and “data value”.

These findings offer some insights into users’ privacy protection and personal data sharing.

The study aims to analyze the synergy of artificial intelligence (AI), with scientometrics, webometrics and bibliometrics to unlock and to emphasize the potential of the applications and benefits of AI algorithms in these fields.

By conducting a systematic literature review, our aim is to explore the potential of AI in revolutionizing the methods used to measure and analyze scholarly communication, identify emerging research trends and evaluate the impact of scientific publications. To achieve this, we implemented a comprehensive search strategy across reputable databases such as ProQuest, IEEE Explore, EBSCO, Web of Science and Scopus. Our search encompassed articles published from January 1, 2000, to September 2022, resulting in a thorough review of 61 relevant articles.

(1) Regarding scientometrics, the application of AI yields various distinct advantages, such as conducting analyses of publications, citations, research impact prediction, collaboration, research trend analysis and knowledge mapping, in a more objective and reliable framework. (2) In terms of webometrics, AI algorithms are able to enhance web crawling and data collection, web link analysis, web content analysis, social media analysis, web impact analysis and recommender systems. (3) Moreover, automation of data collection, analysis of citations, disambiguation of authors, analysis of co-authorship networks, assessment of research impact, text mining and recommender systems are considered as the potential of AI integration in the field of bibliometrics.

This study covers the particularly new benefits and potential of AI-enhanced scientometrics, webometrics and bibliometrics to highlight the significant prospects of the synergy of this integration through AI.

This study investigates how artificial intelligence (AI) chatbots persuade customers to accept their recommendations in the online shopping context.

Drawing on the elaboration likelihood model, this study establishes a research model to reveal the antecedents and internal mechanisms of customers' adoption of AI chatbot recommendations. The authors tested the model with survey data from 530 AI chatbot users.

The results show that in the AI chatbot recommendation adoption process, central and peripheral cues significantly affected a customer's intention to adopt an AI chatbot's recommendation, and a customer's cognitive and emotional trust in the AI chatbot mediated the relationships. Moreover, a customer's mind perception of the AI chatbot, including perceived agency and perceived experience, moderated the central and peripheral paths, respectively.

This study has theoretical and practical implications for AI chatbot designers and provides management insights for practitioners to enhance a customer's intention to adopt an AI chatbot's recommendation.

1. The study investigates customers' adoption of AI chatbots' recommendation.

2. The authors develop research model based on ELM theory to reveal central and peripheral cues and paths.

3. The central and peripheral cues are generalized according to cooperative principle theory.

4. Central cues include recommendation reliability and accuracy, and peripheral cues include human-like empathy and recommendation choice.

5. Central and peripheral cues affect customers' adoption to recommendation through trust in AI.

6. Customers' mind perception positively moderates the central and peripheral paths.

The study investigates customers' adoption of AI chatbots' recommendation.

The authors develop research model based on ELM theory to reveal central and peripheral cues and paths.

The central and peripheral cues are generalized according to cooperative principle theory.

Central cues include recommendation reliability and accuracy, and peripheral cues include human-like empathy and recommendation choice.

Central and peripheral cues affect customers' adoption to recommendation through trust in AI.

Customers' mind perception positively moderates the central and peripheral paths.

This paper aims to understand how virtual influencers (VIs) can promote interethnic tolerance, online, among youth as a social good.

This paper conducted qualitative research using interpretive analysis with RTA (Reflexive Thematic Analysis) on data collected through focus groups.

This paper revealed that the quality of the VI’s content is a leading factor influencing VI's ability to promote interethnic tolerance, but creating content that is constantly authentic, appealing and inclusive to diverse ethnic groups in a particular context is challenging.

The study has some limitations related to the specific context on which it is focused and therefore transferring the findings to different contextual conditions requires caution, and also can be extended with positivist validation.

Findings from our study can drive future projects where real VI artifacts will play the role of a mediator in building strong relational trust among youth belonging to different ethnic groups. The findings indicate that VIs can be effectively used to foster interethnic tolerance if involve diverse team members who understand the ethnic culture deeply and social media experts, to create high-quality content to overcome some limitations.

VIs can be used as social interventions to promote interethnic tolerance and to reduce prejudices and anxiety in online youth communication.

To the best of the authors’ knowledge, no known studies investigated the potential of VIs for promoting interethnic tolerance among ethnically diverse youth groups. Hence, this study paves the way for further refinement on using cutting-edge technology for social good, since it is highly challenging, often resulting in wasted money.

Regular monitoring of bacteria, especially Escherichia coli, in wastewater is crucial to ensure the maintenance of public health. Amperometric detection proves to be a fast, sensitive and economically viable solution for E. coli enumeration. This paper reported a prototype amperometric sensor based on PANI-ZnO-NiO nanocomposite thin films prepared by sol–gel method and irradiated with gamma ray. The purpose of this study is to investigate the sensor performance of PANI-ZnO-NiO nanocomposite thin films to detect E. coli in water.

The films were varied with different compositions of ZnO and NiO by using the formula PANI-(ZnO)_1-x-(NiO)_x, with x = 0.2, 0.4, 0.6 and 0.8. PANI-ZnO-NiO nanocomposite thin films were characterized by using X-ray diffraction (XRD) and atomic force microscopy (AFM) to study the crystallinity and surface morphology of the films. The sensor performance was conducted using the current–voltage (I-V) measurement by testing the films in clean water and E. coli solution.

XRD diffractograms show the peaks of ZnO (1 0 0) and NiO (1 0 2). AFM analysis shows the surface roughness, and the grain size of PANI-ZnO-NiO thin films decreases when the concentration ratios of NiO increased. I-V curves show the difference in current flow, where the current in E. coli solution is higher than the clean water.

PANI-(ZnO)_1-x-(NiO)_x nanocomposite thin film with the highest concentration of ZnO performed the highest sensitivity among the other concentrations, which can be used to indicate the presence of E. coli bacteria in water.

Artificial intelligence (AI) customer service has grown rapidly in recent years due to the emergence of COVID-19 and the growth of the e-commerce industry. Therefore, this study employs the integration of the stimuli–organism–response (SOR) and the task-technology fit (TTF) frameworks to understand the factors that affect individuals’ intentions towards AI customer service adoption in Malaysia.

The study utilised a survey-based research approach to investigate the factors that affect individuals’ intentions towards AI customer service adoption in Malaysia. The data were collected by conducting an online survey targeting individuals aged 18 or above who had prior customer service interaction experience with human service agents but had not yet adopted AI customer service. A sample of 339 respondents was used to evaluate the hypotheses, adopting partial least squares structural equation modelling as a symmetric analytic technique.

The PLS-SEM analysis revealed that social influence and anthropomorphism have a positive direct relationship with emotional trust. Furthermore, communicative competence, technology characteristics and perceived intelligence were positively correlated with TTF. Moreover, emotional trust significantly impacts AI customer service adoption. In addition, AI readiness positively moderates the association between task technology fit and AI customer service adoption.

The study provides insights to individuals, organisations, the government and educational institutions to improve the features of AI customer service and its development in Malaysia.

The originality of this study is found in its adoption of the SOR theory and TTF to understand the factors affecting AI customer service adoption. Additionally, it incorporates moderating variables during the analysis, adding depth to the findings. This approach introduces a new perspective on the factors that impact the adoption of AI customer service and offers valuable insights for practitioners seeking to formulate effective strategies to promote its adoption.