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The existing technology acceptance models have not yet investigated functional and motivational factors impacting trust in and use of conversational artificial intelligence (AI) by integrating the feedback and sequential updating mechanisms. This study challenged the existing models and constructed an integrated longitudinal model. Using a territory-wide two-wave survey of a representative sample, this new model examined the effects of hedonic motivation, social motivation, perceived ease of use, and perceived usefulness on continued trust, intended use, and actual use of conversational AI.

An autoregressive cross-lagged model was adopted to test the structural associations of the seven repeatedly measured constructs.

The results revealed that trust in conversational AI positively affected continued actual use, hedonic motivation increased continued intended use, and social motivation and perceived ease of use enhanced continued trust in conversational AI. While the original technology acceptance model was unable to explain the continued acceptance of conversational AI, the findings showed positive feedback effects of actual use on continued intended use. Except for trust, the sequential updating effects of all the measured factors were significant.

This study intended to contribute to the technology acceptance and human–AI interaction paradigms by developing a longitudinal model of continued acceptance of conversational AI. This new model adds to the literature by considering the feedback and sequential updating mechanisms in understanding continued conversational AI acceptance.

This paper explores the Adoption of Technological Innovation (ATI) in the healthcare industry. It investigates how the literature has evolved, and what are the emerging innovation dimensions in the healthcare industry adoption studies.

We followed a mixed-method approach combining bibliometric methods and topic modeling, with 57 papers being deeply analyzed.

Our results identify three latent topics. The first one is related to the digitalization in healthcare with a specific focus on the COVID-19 pandemic. The second one groups up the word combinations dealing with the research models and their constructs. The third one refers to the healthcare systems/professionals and their resistance to ATI.

The study’s sample selection focused on scientific journals included in the Academic Journal Guide and in the FT Research Rank. However, the paper identifies trends that offer managerial insights for stakeholders in the healthcare industry.

ATI has the potential to revolutionize the health service delivery system and to decentralize services traditionally provided in hospitals or medical centers. All this would contribute to a reduction in waiting lists and the provision of proximity services.

The originality of the paper lies in the combination of two methods: bibliometric analysis and topic modeling. This approach allowed us to understand the ATI evolutions in the healthcare industry.

This paper focuses on the application of a robotic technique for modeling a three-wheeled mobile robot (WMR), considering it as a multibody polyarticulated system. Then the dynamic behavior of the developed model is verified using a physical model obtained by Simscape Multibody.

Firstly, a geometric model is developed using the modified Denavit–Hartenberg method. Then the dynamic model is derived using the algorithm of Newton–Euler. The developed model is performed for a three-wheeled differentially driven robot, which incorporates the slippage of wheels by including the Kiencke tire model to take into account the interaction of wheels with the ground. For the physical model, the mobile robot is designed using Solidworks. Then it is exported to Matlab using Simscape Multibody. The control of the WMR for both models is realized using Matlab/Simulink and aims to ensure efficient tracking of the desired trajectory.

Simulation results show a good similarity between the two models and verify both longitudinal and lateral behaviors of the WMR. This demonstrates the effectiveness of the developed model using the robotic approach and proves that it is sufficiently precise for the design of control schemes.

The motivation to adopt this robotic approach compared to conventional methods is the fact that it makes it possible to obtain models with a reduced number of operations. Furthermore, it allows the facility of implementation by numerical or symbolical programming. This work serves as a reference link for extending this methodology to other types of mobile robots.

This paper aims to examine the antecedents of adoption intention of profit-sharing investment deposits (PSID) among Moroccan customers.

Applying an extended version of diffusion of innovation (DOI) theory and using a non-probability sampling technique with convenience approach, a quantitative survey was developed and administered to 171 Islamic banking users. Structural equation modeling was then used to evaluate the significance of relationships between the various variables under study using SPSS 21.0 and AMOS 26.0 statistical packages.

Empirical findings of the structural analysis indicated a significant direct relationship between adoption intention and six out of seven variables: perceived relative advantage, perceived compatibility, perceived complexity, perceived risk, religiosity and social influence, all of which had a significant effect on Moroccan customers’ intention to invest their funds in profit-sharing based deposit instruments, whereas customer awareness exerted an insignificant positive effect.

The absence of a longitudinal study tracking the actual adoption behavior is the main limitation of this study. Furthermore, data were collected solely from Islamic banking users. Finally, despite being insightful, the empirical findings should be generalized with caution since the sample was purposely selected by the banks’ management.

This study implied that participatory banks should pay substantial attention to risk perceptions, as PSID adoption intention is typically inhibited by high perceived risks associated with these products. Moreover, this study provides great indications to Moroccan regulators and policymakers on a number of issues related to this emerging business.

To the best of the authors’ knowledge, this paper represents the first attempt to confirm the effectiveness of the Rogers’ DOI in examining the intention to adopt a financial innovation in the Moroccan context. It is also the first of its kind to address customers’ apprehensions regarding profit-sharing investment products.

The purpose of this paper is to improve autonomous flight performance of a fixed-wing unmanned aerial vehicle (UAV) via simultaneous morphing wingtip and control system design conducted with optimization, computational fluid dynamics (CFD) and machine learning approaches.

The main wing of the UAV is redesigned with morphing wingtips capable of dihedral angle alteration by means of folding. Aircraft dynamic model is derived as equations depending only on wingtip dihedral angle via Nonlinear Least Squares regression machine learning algorithm. Data for the regression analyses are obtained by numerical (i.e. CFD) and analytical approaches. Simultaneous perturbation stochastic approximation (SPSA) is incorporated into the design process to determine the optimal wingtip dihedral angle and proportional-integral-derivative (PID) coefficients of the control system that maximizes autonomous flight performance. The performance is defined in terms of trajectory tracking quality parameters of rise time, settling time and overshoot. Obtained optimal design parameters are applied in flight simulations to test both longitudinal and lateral reference trajectory tracking.

Longitudinal and lateral autonomous flight performances of the UAV are improved by redesigning the main wing with morphing wingtips and simultaneous estimation of PID coefficients and wingtip dihedral angle with SPSA optimization.

This paper originally discusses the simultaneous design of innovative morphing wingtip and UAV flight control system for autonomous flight performance improvement. The proposed simultaneous design idea is conducted with the SPSA optimization and a machine learning algorithm as a novel approach.

Emerging literature on fintech has shown that consumers have been slow to adopt fintech-based products and services. However, limited literature is available regarding the factors associated with consumers' adoption of these products and services. This study aims to investigate the factors that are associated with consumer adoption of fintech-based products and services.

Data on the usage and perception of smartphone financial apps by US residents ages 18–70 was collected in the fall of 2020. Based on the Extended Post-Acceptance Model (EPAM) framework, Structural Equation Modeling and Confirmatory Factor Analysis were applied to inspect how financial capability, perceived security and perceived usefulness affect fintech adoption.

Fintech proficiency, investment risk tolerance and perceived safety are positively associated with the frequency of fintech application use upon adoption. Consumers are more likely to feel safer if they are more financially capable and technologically proficient. Consumers with higher risk tolerance tend to believe fintech apps are safe to use. Consumers with higher fintech proficiency are more likely to recognize the usefulness of fintech services.

The study introduces a revised EPAM framework with antecedent factors, fintech proficiency and risk tolerance to investigate the factors associated with consumer adoption of fintech-based products and services. The key findings of this study validate the EPAM in the American context. Additionally, this research is among the first to have confirmed the direct relationship between perceived security and fintech adoption. The results have practical implications for existing fintech companies, banks and financial institutions, policymakers and financial advisory practices considering adopting fintech-based services for their clients.

In the context of investment decisions, the intricate interplay between exogenous shocks and their influence on investor confidence significantly shapes their behaviors and, consequently, their outcomes. Investment decisions are influenced by uncertainties, exogenous shocks as well as the sentiments and confidence of investors, factors typically overlooked by decision-makers. This study will meticulously examine these multifaceted influences and discern their intricate hierarchical nuances in the sentiments of industrial entrepreneurs during the COVID-19 pandemic.

Employing the robust framework of the generalized linear latent and mixed models (GLLAMM), this research will thoroughly investigate individual and group idiosyncrasies present in diverse data compilations. Additionally, it will delve deeply into the exogeneity of disturbances across different sectors and regions.

Relevant insights gleaned from this research elucidate the adverse influence of exogenous forces, including pandemics and financial crises, on the confidence of industrial entrepreneurs. Furthermore, a significant discovery emerges in the regional analysis, revealing a notable homogeneity in the propagation patterns of industrial entrepreneurs' perceptions within the sectoral and regional context. This finding suggests a mitigation of regional effects in situations of global exogenous shocks.

Within the realm of academic inquiry, this study offers an innovative perspective in unveiling the intricate interaction between external shocks and their significant impacts on the sentiment of industrial entrepreneurs. Furthermore, the utilization of the robust GLLAMM captures the hierarchical dimension of this relationship, enhancing the precision of analyses. This approach provides a significant impetus for data-informed strategic directions.

In response to the lack of connection between employee development and employee engagement, this study explores the existing empirical findings regarding these two concepts. Based on the conservation of resources theory, the authors propose a novel theoretical framework that can better leverage the identified antecedents and relationships for future research.

An integrative literature review of 64 empirical studies published in peer-reviewed journals was conducted.

Three different levels of antecedents, including the work environment, social exchange and individual characteristics, are identified. Employee development and employee engagement exhibit reciprocal relationships. Considering the role of job performance as a catalyst, the authors propose an upward gain spiral model to advance both research and practice.

This integrative literature review aims to facilitate discussions based on three distinct sub-dimensions: physical, emotional and cognitive energies, relevant to both employee development and employee engagement. Through this distinction, a more comprehensive understanding of the connection between employee development and employee engagement can be cultivated.

Based on the trait activation theory, the current study systematically integrates how autonomy interacts with proactivity to influence the relationship between ethical leadership style and employee creativity.

Using simple random sampling and questionnaires, a sample of 475 engineering employees of 3 leading telecommunication companies in Ghana were obtained. The analysis was done using structured equation modeling (SEM), using SmartPLS.

The results showed that ethical leadership style provides employees with job autonomy which facilitates individual creativity. Employee proactivity also moderates a positive relationship between autonomy and creativity such that high-proactive employees are well placed to produce more creative outcomes when given autonomy. At the individual level, personal characteristics determine the degree of creativity.

The current study implies that telecommunication companies should put in more efforts to train and encourage leaders to be ethical in leaders' dealings with employees and employees must be rewarded for taking initiative.

With a focus on the integrative approach from a developing economy, this work is novel in exploring how contextual and personal features impact creativity.

The aim of this study is to examine the behavioral intention (BI) to adopt the Coinbase Wallet by Spanish users.

A survey was administered to individuals residing in Spain between March and April 2021. There were 301 questionnaires analyzed. This research applies a new predictive model based on technology acceptance model (TAM) 2, the unified theory of acceptance and use of technology (UTAUT) model, the theory of perceived risk and the commitment trust theory. A mixed partial least squares structural equation modeling (PLS-SEM)/fuzzy-set qualitative comparative analysis (fsQCA) methodology was employed for the modeling and data analysis.

The results showed that all the variables proposed have a direct and positive influence on the intention to use a Coinbase Wallet. The findings present clear directions for traders, investors and academics focused on improving their understanding of the characteristics of these markets.

First, this study addresses important concerns relating to the adoption of crypto-wallets during the global pandemic. Second, this research contributes to the existing literature by adding electronic word of mouth (e-WOM), trust, web quality and perceived risk as new drivers of the intention to use the Coinbase Wallet, providing unique and innovative insights. Finally, the study offers a solid methodological contribution by integrating linear (PLS) and nonlinear (fsQCA) techniques, showing that both methodologies provide a better understanding of the problem and a more detailed awareness of the patterns of antecedent factors.