Search results

People with visual impairments or blindness (PwVIB) are mostly excluded from tourism activities. Despite the rise of assistive technology (AT) solutions in Tourism, acceptance remains low because of the difficulty of providing the right functionality, effectiveness and usability. Arguably, it can be said that disability-oriented training can affect the latter two and, therefore, an AT solution’s acceptance. This paper aims to contribute to the theory development and conceptualization of technology acceptance of AT solutions in Tourism by studying, in the context of the Unified Theory of Acceptance and Use of Technology (UTAUT), the effects of training PwVIB on using AT solutions. This study presents the effects of training on the tourism behavior of PwVIB and provides valuable information to the stakeholders.

Questionnaire data collected from 128 PwVIB after evaluating an AT were subjected to exploratory and confirmatory factor analysis and structural equation modeling followed by post-evaluation interviews. The used application, called BlindMuseumTourer, enables high-precision autonomous indoor navigation for PwVIB in tourist places like museums and places of health care.

The results of this study indicate the partial satisfaction of the extended model validating the importance of performance expectancy and training (the new factor) in predicting the behavioral intention of PwVIB tourists toward using ATs during their tourist activities. This suggests that practitioners have to provide performant technological solutions accompanied by special training sessions for improved engagement and satisfaction.

This study contributes to the UTAUT theory in the context of Tourism for PwVIB by adding a new factor and replacing two moderator variables. To the best of the authors’ knowledge, no similar work is studying AT acceptance by PwVIB in the tourism literature. Furthermore, the validation process used a novel indoor navigation application, demonstrating its effectiveness in the Tactual Museum of Greece.

视障或失明人士（PwVIB）大多被排除在旅游活动之外。尽管辅助技术（AT）解决方案在旅游业中兴起, 但由于难以提供适当的功能、有效性和可用性, 接受度仍然很低。可以说, 以残疾为导向的培训会影响后两者, 从而影响对辅助技术解决方案的接受程度。本文旨在通过在技术接受和使用统一理论（UTAUT）的背景下, 研究培训 PwVIB 对使用 AT 解决方案的影响, 为旅游行业对 AT 解决方案的技术接受的理论发展和概念化做出贡献。本研究将介绍培训对 PwVIB 旅游行为的影响, 并为利益相关者提供有价值的信息。

对 128 名无身份者在评估了辅助设施后收集的问卷数据进行了探索性因素分析、确认性因素分析和结构方程模型分析, 然后进行了评估后访谈。所采用的应用程序名为 “BlindMuseumTourer”, 可在博物馆和医疗场所等旅游景点为 PwVIB 提供高精度的自主室内导航。

结果表明, 扩展模型的部分结果验证了性能预期和培训（新因素）在预测无障碍游客在旅游活动中使用自动视听设备的行为意向方面的重要性。这表明, 从业人员必须提供性能优越的技术解决方案, 并辅以专门的培训课程, 以提高参与度和满意度。

本研究通过增加一个新因素和替换两个调节变量, 为UTAUT 理论在无障碍游客旅游方面的应用做出了贡献。据我们所知, 在旅游文献中, 还没有类似的工作是研究 PwVIB 对 AT 的接受程度。此外, 在验证过程中还使用了一个新颖的室内导航应用程序, 并在希腊触觉博物馆展示了其有效性。

Las personas con discapacidad visual o ceguera (PwVIB) están excluidas en su mayoría de las actividades turísticas. A pesar del auge de las soluciones de tecnología de asistencia (TA) en el turismo, su aceptación sigue siendo baja debido a la dificultad de proporcionar la funcionalidad, eficacia y usabilidad adecuadas. Podría decirse que la formación orientada a la discapacidad puede afectar a las dos últimas y, por tanto, a la aceptación de una solución de TA. Este artículo pretende contribuir al desarrollo teórico y a la conceptualización de la aceptación de las soluciones de tecnología de apoyo en el turismo estudiando, en el contexto de la Teoría Unificada de la Aceptación y el Uso de la Tecnología (UTAUT), los efectos de la formación de las PwVIB en la utilización de las soluciones de tecnología de apoyo. El estudio presentará los efectos de la formación en el comportamiento turístico de las PwVIB y proporcionará información valiosa a las partes interesadas.

Los datos del cuestionario recogidos de 128 PwVIB tras la evaluación de una TA se sometieron a un Análisis Factorial Exploratorio y Confirmatorio y a un Modelado de Ecuaciones Estructurales, seguidos de entrevistas posteriores a la evaluación. La aplicación empleada, denominada BlindMuseumTourer, permite la navegación autónoma en interiores de alta precisión para PwVIB en lugares turísticos como museos y lugares de asistencia sanitaria.

Los resultados indican la satisfacción parcial del modelo ampliado validando la importancia de la expectativa de rendimiento y la formación en la predicción de la intención conductual de los turistas PwVIB por lo que sugieren a los profesionales que las sesiones especiales de formación son esenciales para el compromiso y la satisfacción de los usuarios.

Este estudio contribuye a la teoría UTAUT en el contexto del turismo para PwVIB añadiendo un nuevo factor y sustituyendo dos variables moderadoras. Hasta donde se sabe, no existe ningún trabajo similar que estudie la aceptación de la TA por parte de las PwVIB en la literatura sobre turismo. Además, en el proceso de validación se utilizó una novedosa aplicación de navegación en interiores que ha demostrado su eficacia en el Museo Táctico de Grecia.

Emissions have significant environmental impacts. Hence, minimizing emissions is essential. This study aims to use a hybrid neural network model to predict carbon monoxide (CO) and nitrogen oxide (NOx) emissions from gas turbines (GTs) to enhance emission prediction for GTs in predictive emissions monitoring systems (PEMS).

The hybrid model architecture combines convolutional neural networks (CNN) and bidirectional long-short-term memory (Bi-LSTM) networks called CNN-BiLSTM with modified extrinsic attention regression. Over five years, data from a GT power plant was uploaded to Google Colab, split into training and testing sets (80:20), and evaluated using test matrices. The model’s performance was benchmarked against state-of-the-art emissions prediction methodologies.

The model showed promising results for GT CO and NOx emissions. CO predictions had a slight underestimation bias of −0.01, with root mean-squared error (RMSE) of 0.064, mean absolute error (MAE) of 0.04 and R² of 0.82. NOx predictions had an RMSE of 0.051, MAE of 0.036, R² of 0.887 and a slight overestimation bias of +0.01.

While the model demonstrates relative accuracy in CO emission predictions, there is potential for further improvement in future research.

Implementing the model in real-time PEMS and establishing a continuous feedback loop will ensure accuracy in real-world applications, enhance GT functioning and reduce emissions, fuel consumption and running costs.

Accurate GT emissions predictions support stricter emission standards, promote sustainable development goals and ensure a healthier societal environment.

This paper presents a novel approach that integrates CNN and Bi-LSTM networks. It considers both spatial and temporal data to mitigate previous prediction shortcomings.

Recognizing construction workers' activities is critical for on-site performance and safety management. Thus, this study presents the potential of automatically recognizing construction workers' actions from activations of the erector spinae muscles.

A lab study was conducted wherein the participants (n = 10) performed rebar task, which involved placing and tying subtasks, with and without a wearable robot (exoskeleton). Trunk muscle activations for both conditions were trained with nine well-established supervised machine learning algorithms. Hold-out validation was carried out, and the performance of the models was evaluated using accuracy, precision, recall and F1 score.

Results indicate that classification models performed well for both experimental conditions with support vector machine, achieving the highest accuracy of 83.8% for the “exoskeleton” condition and 74.1% for the “without exoskeleton” condition.

The study paves the way for the development of smart wearable robotic technology which can augment itself based on the tasks performed by the construction workers.

This study contributes to the research on construction workers' action recognition using trunk muscle activity. Most of the human actions are largely performed with hands, and the advancements in ergonomic research have provided evidence for relationship between trunk muscles and the movements of hands. This relationship has not been explored for action recognition of construction workers, which is a gap in literature that this study attempts to address.

The current work investigates an unsteady squeezed flow of hybrid-nanofluid between two parallel plates in occurrence with a uniform transverse magnetic field. Water is used as base fluid mixed with Graphene Oxide (GO) and Copper (Cu) nanoparticles. The flow considered here is under slip boundary conditions.

The governing PDEs are transmuted into ODEs by applying an appropriate similarity transformation and then solved numerically using the 4th order R-K method with shooting technique. Graphical illustrations for velocity, temperature, entropy generation (NG), Bejan number (Be), streamline etc. are presented and discussed in detail from the physical point of view. The nature of the Nusselt number is also studied numerically through contour plots for different flow parameters.

There is no funding obtained for the research work.

This kind of study may be used in various fields including polymer processing, lubrication apparatus, compression including hydrodynamical machines compression, food processing etc.

It is observed that very little investigation has yet been made about the movement of hybrid nanofluid between two analogous plates.

COVID entered the world in 2019 as a pandemic and the intensity of this health crisis is only increasing in several regions. Therefore, it is critical to study and detect the public's frame of mind, government and economists' perception regarding the COVID crisis, as well as the primary worries that the public has expressed, and how this evolves over time. Responsive measures towards COVID-19 from the Indian economy have been explored as a key objective. Moreover, efforts have been made to explore recovery in India through economists and policymakers. Data have been explored through online interviews of key economists which were published in leading newspapers and covered through media channels such as NDTV, CNBC, etc. Moreover, various newspapers and reports were explored to understand government initiatives to address COVID-19 in India. The study's findings show how essential economic recovery from the second wave is in India, and how it may be achieved by strong fiscal and monetary policies, as well as specific attention to impoverished households, small and micro-businesses and increased employment. The short-term focus of the developing economic strategy must be on giving crisis relief to the most unprotected segments of society since long-term system stimulation is impossible.

Recently, mHealth apps for COVID-19 have emerged as a new research area due to the diverse efforts to control the COVID-19 epidemic. Although there are many bibliometric studies on mHealth and its applications, no bibliometric study sheds light on mHealth apps for COVID-19 as a new research area. To address the above-mentioned research gap, the current study conducts a bibliometric analysis of research in mHealth apps for COVID-19. It aims to provide a comprehensive overview of the new area and its directions.

The study uses a bibliometric approach to provide an analysis of the overall status of research in mHealth apps for COVID-19. The Scopus database provided by Elsevier was used to extract the analyzed data in this study. SciVal was used to perform the analyses, while VOSviewer was used for scientific mapping.

A total of 457 publications were published between 2020 and 2021 (until Tuesday, June 1) and cited 3,559 times. Publications were written by 2,375 authors, with an average of 5.20 authors per publication. Articles play a pivotal role in the literature on mHealth apps for COVID-19 in terms of production and impact. The research area of mHealth apps for COVID-19 is multidisciplinary. The United States made the largest contribution to this area, while the UK was the most influential. This study reveals the most productive and influential sources, institutions and authors. It also reveals the research hotspots and major thematic clusters in mHealth apps for COVID-19, highly cited publications and the international collaboration network.

mHealth apps for COVID-19 are gaining more and more importance due to their influential role in controlling the COVID-19 epidemic. Using bibliometric analysis, the study contributes to defining the knowledge structure of global research in mHealth apps for COVID-19 as a new, interdisciplinary area of research that has not previously been studied. Therefore, the study results and the comprehensive picture obtained about research in mHealth apps for COVID-19, especially at the level of Internet of Things (IoT) and artificial intelligence applications, make it an effective supplement to the expert evaluation in the field.

Despite their centrality to organizing – acknowledged by several theorists – unplanned conversations are often marginalized in organizational theory. To remedy this oversight, we recenter attention on this understudied aspect of organizing. We draw on the affordances perspective to elaborate on the spatial and temporal factors influencing unplanned conversations.

This conceptual paper integrates multidisciplinary literature on unplanned conversations to identify a range of spatiotemporal factors influencing unplanned conversations. Our approach emphasizes how various situational factors afford or hinder opportunities for unplanned conversations among organizational members.

Unplanned conversations were precisely defined as opportunistic or spontaneous conversations, characterized by the absence of pre-planning, that can be work or non-work-related. Then, the characteristics of unplanned conversations (emergent, episodic and brief, interrelated, convenient, and improvisational) were outlined, indicating their distinct organizing and structuring capabilities. The spatial (i.e. spatial proximity, visibility, legitimacy, and psychological safety) and temporal (i.e. work time pressure, work history, work expertise, and work routineness) factors identified in the study both afford and constrain individuals’ unplanned conversations. The empirically testable propositions offered in the study have significant theoretical and practical implications.

This study enriches our understanding of unplanned conversations by offering a precise conceptual definition, outlining their essential characteristics, and underscoring their theoretical and practical significance in organizing. The study highlights the need for organizations to consider the spatiotemporal factors that influence unplanned conversations.

The objective of this paper is to investigate how Industry 4.0 technologies can contribute to solve the problems of managing materials and medicines in the hospital supply chain, identifying opportunities for their adoption, evaluating their potential and impacts on this segment. This paper also plans to investigate the challenges involving change management, financial impacts and major changes in the process with the introduction of new technologies.

The research carried out a literature review using the CAPES Periodicals portal, which includes renowned scientific bases, like Scopus and Web of Science. The intention was to identify problems in the logistics of materials and medicines in hospital pharmacy. In addition, a second search was carried out in papers related to Industry 4.0 and the supply chain. Subsequently, it was listed how Industry 4.0 technologies could influence the management of the hospital supply chain, on materials and medicines.

The new technologies of Industry 4.0 identified in the research can contribute to the improvement of the hospital management supply chain, benefiting from a higher level of automation, control and security presented in the research. Thus, the article addresses a new perspective in the management of materials and medications in hospital pharmacy, adding value to the topic, as new technologies can provide more safety to patients, savings for hospital management, reducing waste and environmental impacts.

The automation of the supply chain, in the materials and medicines segment, in hospital environments, adopting Industry 4.0 techniques, will make it possible to differentiate hospital management, generating great added value, benefiting the entire chain. The combination of technologies such as IoT, BigData and artificial intelligence, applied to the management of materials and medicines, will allow real-time management; consumption and stock estimates; more agile and reliable decision-making process; greater economic efficiency, in addition to contributing to patient safety.

During everyday work, individuals often engage in unplanned conversations that help them develop relationships, share information and coordinate tasks. Unfortunately, the work-from-home mandates issued due to the pandemic have reduced the frequency of unplanned conversations among employees. So, as businesses reopen, organizations are considering post-pandemic workplace solutions that can facilitate unplanned conversations. To aid these efforts and move research and theory on unplanned conversations forward, this study proposes and tests a multi-factor model of the antecedents of unplanned conversations.

This study adopted a multi-methodological approach and collected data using automated sensing technology, structured observations and cross-sectional survey methods. A total of 5,297 unplanned conversations among 61 employees were recorded using the custom mobile application and structured observations. Cross-sectional survey data about these employees' work contexts were also collected.

The study results showed that the model significantly predicted the frequency of unplanned conversations. Notably, technical expertise, perceived time pressure and team psychological safety significantly predicted the frequency of unplanned conversations. The study findings have both theoretical and practical significance.

Previous research studies have primarily focused on the influence of workplace designs on unplanned conversations. However, this study demonstrates that several other factors facilitate unplanned conversations. This research theorizes and empirically tests the relationship between unplanned conversations and several individual, team and organizational factors.

The main purpose of this study is to identify the determinants that inhibit the adoption or usage of digital payment systems (DPSs) in India.

This study used a qualitative technique, including in-depth semi-structured interviews. Data analysis was conducted using thematic analysis, incorporating both deductive categorisation and inductive coding to identify factors responsible for the non-adoption or discontinuation of DPS use.

The findings are in the form of themes and sub-themes that were generated from the data analysis: digital divide (DD), which includes the digital access divide, digital capability divide and digital innovativeness divide; socio-demographic divide (SD), which includes education, geographical location, gender, age and income; psychological barriers, which include a lack of perceived ease of use, vulnerability to risks, technophobia and a lack of trust; and other barriers, which include a lack of awareness, a cash-dominated society and a lack of interoperability.

The factors identified in this research can be further validated and tested in future studies using quantitative data. This will enable stakeholders to better comprehend the impacts of these factors on DPS adoption or usage.

The study’s practical implications are specifically relevant to the Union Territory (UT) administration of Ladakh, as there is a DD and an SD among different sections of the population of the UT of Ladakh. UT administrations must prioritise efforts to eliminate these divides. The implications for banks and DPS providers are that they should conduct financial literacy training about DPSs in remote rural areas and invest in developing user-friendly and simplified DPS user interfaces to improve relationships with DPS users and their long-term retention.

The findings of this study reveal the three levels of the DD that determine DPS adoption or usage, which have not been discussed together in the literature in the DPS context and that must be addressed to expand DPS adoption, thus providing a more holistic view of the DD in the context of DPS.