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The patient-side manipulator (PSM) achieves high torque capability by combining harmonic servo system with high reduction ratio and low torque motor. However, high reduction ratio can increase inertia and decrease compliance of the manipulator. To enhance the backdrivability of the minimally invasive surgical robot, this paper aims to propose a resistance torque compensation algorithm.

A resistance torque compensation algorithm based on dynamics and Luenberger observer is proposed. The dynamics are established, considering joint flexibility and an improved Stribeck friction model. The dynamic parameters are experimentally identified by using the least squares method. With the advantages of clear structure, simple implementation and fast solution speed, the Luenberger observer is selected to estimate the unmeasured dynamic information of PSM and realize the resistance torque compensation.

For low-speed surgical robots, the centrifugal force term in the dynamic model can be simplified to reduce computational complexity. Joint flexibility and an improved Stribeck friction model can be considered to improve the accuracy of the dynamic model. Experiment results show that parameter identification and estimated results of the Luenberger observer are accurate. The backdrivability of the PSM is enhanced in ease and smoothness.

This algorithm provides potential application prospects for surgical robots to maintain high torque while remaining compliant. Meanwhile, the enhanced backdrivability of the manipulator helps to improve the safety of the preoperative manual adjustment.

Findings in the literature indicate leading financial analysts attract high levels of market attention and provide more accurate earnings forecasts prior to becoming all-star analysts. Furthermore, these analysts significantly impact the investment decisions of other market participants and thus the market price of assets. Therefore, this study examines the information role of leading financial analysts and identifies two significant conclusions. First, the positive outcomes of these analyst leaders are more informative and attract more followers. Second, informational herding by followers of these analysts is not as naïve as suggested in previous studies, as followers who smartly use information from analyst leaders tend to perform better. We also find that analysts who practice smart learning by studying and selectively employing analyst-leader decisions achieve better career outcomes.

This study aims to investigate the noise-inducing characteristics during the start-up process of a mixed-flow pump and the impact of different start-up schemes on pump noise.

This study conducted numerical simulations on the mixed-flow pump under different start-up schemes and investigated the flow characteristics and noise distribution under these schemes.

The results reveal that the dipole noise is mainly caused by pressure fluctuations, while the quadrupole noise is mainly generated by the generation, development and breakdown of vortices. Additionally, the noise evolution characteristics during the start-up process of the mixed-flow pump can be divided into the initial stage, stable growth stage, impulse stage and stable operation stage.

The findings of this study can provide a theoretical basis for the selection of start-up schemes for mixed-flow pumps, reducing flow noise and improving the operational stability of mixed-flow pumps.

The purpose of this study is to examine the relationship between the corporate social responsibility (CSR) performance of job-hopping executives at their former and subsequent firms.

We conduct regression analyses using a sample of firms listed on the Shanghai and Shenzhen Stock Exchanges from 2010 to 2020 to examine whether CSR performance is similar from one firm to the next as executives switch jobs.

We find a positive relationship between the CSR performance of former and subsequent firms under job-hopping executives. This relationship is the strongest in the year of the job switch; it weakens in the second year and eventually disappears in the third year. In addition, we show that this relationship benefits different CSR stakeholder groups and is contingent on executive and subsequent firm attributes and job-hopping characteristics. Furthermore, we demonstrate that firms that hire a new chief executive officer from a firm with a strong track record in CSR, the new firm experiences a significant surge in CSR performance compared with firms that do not experience such a shock.

This study has implications for executive hiring decisions.

This study extends the understanding of CSR determinants through the lens of inter-organisational ties associated with job-hopping executives.

The concept of ubiquitous assimilation in e-commerce, denoting the seamless integration of technologies into customer shopping experiences, has played a pivotal role in aiding e-satisfaction and, consequently, fostering patronage intention. Among these, text-based chatbots are significant innovations. In light of this, the paper aims to develop a conceptual framework and comprehend the patronage behaviour of artificial intelligence-enabled chatbot users by using chatbot usability cues and to determine whether the social presence and flow theories impact e-satisfaction, which leads to users’ patronage intention. The current research provides insights into online travel agencies (OTAs), a crucial segment within the travel and tourism sector. Given the significance of building a loyal clientele and cultivating patronage in this industry, these insights are of paramount importance for achieving sustained profitability and growth.

The research framework primarily focused on the factors that precede e-satisfaction and patronage intention among chatbot users, which include social presence, flow, perceived anthropomorphism and need for human interaction. The researchers collected the data by surveying 397 OTA chatbot users by using an online questionnaire. The data of this cross-sectional study were analysed using covariance-based structural equation modelling.

Findings reveal that e-satisfaction is positively linked with patronage intention and the variables of social presence and flow impact e-satisfaction along with chatbot usability cues. There were direct and indirect relations between chatbot usability and e-satisfaction. Moreover, the personal attributes, “need for human interaction” and, “perceived anthropomorphism” were found to moderate relations between chatbot usability cues, social presence and flow.

The impact of chatbot’s usability cues/attributes on e-satisfaction, along with perceived attributes – social presence and flow in the realm of OTAs contributes to the human–chatbot interaction literature. Moreover, the interacting effects of perceived anthropomorphism and the need for human interaction are unique in the current contextual relations.

Online food delivery applications (OFDAs) provide an expedient platform, and consumers’ access to food has been drastically altered, especially during and after the COVID-19 pandemic. This study aimed to completely explore the attributes that influence consumers' purchase intention and how an app's aesthetics can evoke feelings that predict continuous usage intentions for OFDAs. The food industry, especially restaurants, heavily relies on mobile technology to facilitate critical online food delivery during the pandemic crisis.

The data for this study are gathered from 477 food consumers located in the federal capital territory (FCT) of Islamabad, Pakistan, through convenient sampling by developing a self-administrated online survey. SmartPLS is used for structural equation modeling to test the proposed research model and perform bootstrapping and algorithmic analysis.

Our findings revealed that perceived value positively predicted consumers’ purchase intentions. Moreover, perceived value mediates the association of information quality, familiarity, time-saving, usability and reputation with purchase intentions and fear of COVID-19 moderates the relationship between perceived value and purchase intention.

This research work has significant implications for researchers, web developers, app designers, delivery services, restaurants and other enterprises as it demonstrates the importance of aesthetically pleasing OFDAs in eliciting positive emotions and bolstering consumers’ intentions to continue using the app for efficient food delivery services.

This study expanded the application of the technology acceptance model (TAM) and attention, interest, desire and action (AIDA) by examining consumers’ purchase intentions in the context of OFDAs. Further, the successful utilization of TAM enhanced the understanding of consumer perceptions and behavioral intentions about the usage of OFDAs.

This paper aims to comprehensively review the preparation methods of superhydrophobic surfaces (SHPS) for corrosion protection of Mg alloy in recent years.

The preparation methods, wettability and corrosion resistance of SHPS on Mg alloy in the past three years are systematically described in this paper.

Two types of SHPS, including single-layer and multilayer coatings for corrosion protection of Mg alloy are summarized. Preparing multilayered coatings with multifunction is the current trend in developing SHPS on Mg alloy.

This paper reviewed the preparation methods and corrosion resistance of SHPS on Mg alloys. It provides a valuable reference for researchers to develop highly durable SHPS with excellent corrosion resistance for Mg alloys.

This study aims to objectively synthesize the volume of accounting literature on financial statement fraud (FSF) using a systematic literature review research method (SLRRM). This paper analyzes the vast FSF literature based on inclusion and exclusion criteria. These criteria filter articles that are present in the accounting fraud domain and are published in peer-reviewed quality journals based on Australian Business Deans Council (ABDC) journal ranking. Lastly, a reverse search, analyzing the articles' abstracts, further narrows the search to 88 peer-reviewed articles. After examining these 88 articles, the results imply that the current literature is shifting from traditional statistical approaches towards computational methods, specifically machine learning (ML), for predicting and detecting FSF. This evolution of the literature is influenced by the impact of micro and macro variables on FSF and the inadequacy of audit procedures to detect red flags of fraud. The findings also concluded that A* peer-reviewed journals accepted articles that showed a complete picture of performance measures of computational techniques in their results. Therefore, this paper contributes to the literature by providing insights to researchers about why ML articles on fraud do not make it to top accounting journals and which computational techniques are the best algorithms for predicting and detecting FSF.

This paper chronicles the cluster of narratives surrounding the inadequacy of current accounting and auditing practices in preventing and detecting Financial Statement Fraud. The primary objective of this study is to objectively synthesize the volume of accounting literature on financial statement fraud. More specifically, this study will conduct a systematic literature review (SLR) to examine the evolution of financial statement fraud research and the emergence of new computational techniques to detect fraud in the accounting and finance literature.

The storyline of this study illustrates how the literature has evolved from conventional fraud detection mechanisms to computational techniques such as artificial intelligence (AI) and machine learning (ML). The findings also concluded that A* peer-reviewed journals accepted articles that showed a complete picture of performance measures of computational techniques in their results. Therefore, this paper contributes to the literature by providing insights to researchers about why ML articles on fraud do not make it to top accounting journals and which computational techniques are the best algorithms for predicting and detecting FSF.

This paper contributes to the literature by providing insights to researchers about why the evolution of accounting fraud literature from traditional statistical methods to machine learning algorithms in fraud detection and prediction.

Gamification is a strategic approach employed by practitioners to foster meaningful engagement and enhance the acceptance of recommendations. Gamification affordances (e.g. achievement, self-expression, interaction, and cooperation) catalyze significant psychological processes in consumers, leading to behavioral changes. Despite its application, a gap remains in understanding how these gamification affordances in e-commerce contexts impact customers' perceived values and drive recommendation acceptances.

Employing affordance theory and perceived value theory as our foundation, we have crafted a comprehensive framework that addresses the multifaceted nature of e-commerce gamification, thereby unifying the fragmented knowledge in this area. We implemented a quantitative research design to empirically test the proposed model.

The research reveals that the four principal affordances of gamification – achievement, self-expression, interaction, and cooperation – significantly enrich consumer values across hedonic, utilitarian, and social dimensions. This enrichment facilitates an increased propensity for accepting recommendations.

This study provides a novel lens through which to view the influence of gamification affordances on recommendation acceptance within gamified e-commerce settings. It delineates the effects of each affordance on consumers' perceived value and highlights the pivotal affordances that shape gamified e-commerce experiences. These insights yield actionable strategies for practitioners aiming to refine e-commerce gamification designs and cultivate more engaging consumer interactions.

This paper presents a new conceptualization of digital service anchored in a coconstitutive ontology of digital “x” phenomena, illuminating the pivotal role of the digital qualifier in the service context. Our objective is to provide a theoretically grounded conceptualization of digital service and its impact on the nature of the value cocreation process that characterizes digital phenomena.

Drawing from scholarly works on digital phenomena and fundamental principles of service-dominant logic, this paper delineates the essence of digital service based on the interplay between digitization and digitalization as well as the operational dynamics of generativity and its constitutive dimensions (architecture, community, governance).

The paper defines digital service as a sociotechnical process of value cocreation, where participants dynamically architect, govern and leverage digital resources. This perspective highlights the organic development of digital service and the prevalence of decentralized control mechanisms. It also underscores how the intersection between generativity’s dimensions—architecture, community and governance—shapes the dynamic evolution and outcomes of digital services.

Our conceptual framework sheds light on our understanding of digital service, offering a foundation to further explore its nature and implications for research and practice, which we illustrate using the case of ChatGPT.