Evolving uncertainty in healthcare service interactions during COVID-19: Artificial Intelligence - a threat or support to value cocreation?

2022 ◽  
pp. 93-116
Author(s):  
Sumit Saxena ◽  
Amritesh
Keyword(s):  
Artificial Intelligence ◽  
Healthcare Service ◽  
Value Cocreation ◽  
Service Interactions

scholarly journals 5G and intelligence medicine—how the next generation of wireless technology will reconstruct healthcare?

2019 ◽  
Vol 2 (4) ◽  
pp. 205-208 ◽  
Author(s):  
Dong Li
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Internet Of Things ◽  
Patient Experience ◽  
Healthcare Service ◽  
Healthcare Services ◽  
Wireless Transmission ◽  
Wireless Technology ◽  
Total Cost ◽  
5G Technologies

Abstract Despite intensive efforts, there are still enormous challenges in provision of healthcare services to the increasing aging population. Recent observations have raised concerns regarding the soaring costs of healthcare, the imbalance of medical resources, inefficient healthcare system administration, and inconvenient medical experiences. However, cutting-edge technologies are being developed to meet these challenges, including, but not limited to, Internet of Things (IoT), big data, artificial intelligence, and 5G wireless transmission technology to improve the patient experience and healthcare service quality, while cutting the total cost attributable to healthcare. This is not an unrealistic fantasy, as these emerging technologies are beginning to impact and reconstruct healthcare in subtle ways. Although the technologies mentioned above are integrated, in this review we take a brief look at cases focusing on the application of 5G wireless transmission technology in healthcare. We also highlight the potential pitfalls to availability of 5G technologies.

scholarly journals Customers’ evaluation of mechanical artificial intelligence in hospitality services: a study using online reviews analytics

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Marcello Mariani ◽  
Matteo Borghi
Keyword(s):  
Artificial Intelligence ◽  
Online Reviews ◽  
Service Robots ◽  
Specific Element ◽  
Content Type ◽  
Related Text ◽  
Online Ratings ◽  
Hotel Service ◽  
Hotel Managers ◽  
Service Interactions

Purpose This paper aims to analyze if and to what extent mechanical artificial intelligence (AI)-embedded in hotel service robots-influences customers’ evaluation of AI-enabled hotel service interactions. This study deploys online reviews (ORs) analytics to understand if the presence of mechanical AI-related text in ORs influences customers’ OR valence across 19 leading international hotels that have integrated mechanical AI – in the guise of service robots – into their operations. Design/methodology/approach First, the authors identified the 19 leading hotels across three continents that have pioneered the adoption of service robots. Second, by deploying big data techniques, the authors gathered the entire population of ORs hosted on TripAdvisor (almost 50,000 ORs) and generated OR analytics. Subsequently, the authors used ordered logistic regressions analyses to understand if and to what extent AI-enabled hospitality service interactions are evaluated by service customers. Findings The presence of mechanical AI-related text (text related to service robots) in ORs influences positively electronic word-of-mouth (e-WOM) valence. Hotel guests writing ORs explicitly mentioning their interactions with the service robots are more prone to associate high online ratings to their ORs. The presence of the robot’s proper name (e.g., Alina, Wally) in the OR moderates positively the positive effect of mechanical AI-related text on ORs ratings. Research limitations/implications Hospitality practitioners should evaluate the possibility to introduce service robots into their operations and develop tailored strategies to name their robots (such as using human-like and short names). Moreover, hotel managers should communicate more explicitly their initiatives and investments in AI, monitor AI-related e-WOM and invest in educating their non-tech-savvy customers to understand and appreciate AI technology. Platform developers might create a robotic tag to be attached to ORs mentioning service robots to signal the presence of this specific element and might design and develop an additional service attribute that might be tentatively named “service robots.” Originality/value The current study represents the first attempt to understand if and to what extent mechanical AI in the guise of hotel service robots influences customers’ evaluation of AI-enabled hospitality service interactions.

Voice assistants in hospitality: using artificial intelligence for customer service

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Dimitrios Buhalis ◽  
Iuliia Moldavska
Keyword(s):  
Artificial Intelligence ◽  
Customer Service ◽  
Hospitality Management ◽  
Human Computer Interactions ◽  
Content Type ◽  
Private Households ◽  
Value Cocreation ◽  
Perceptions And Expectations ◽  
Hotel Managers ◽  
Depth Interviews

Purpose Voice assistants (VAs) empower human–computer interactions by recognising human speech and implementing commands pronounced by users. This paper aims to investigate VA-enabled interactions between hotels and guests in the hospitality context. The research positions VAs within the artificial intelligence (AI)-enabled Internet of Things (IoT) context, disrupting old practices and processes. Smart hospitality uses VAs to support effortless value cocreation for guests cost-effectively. The research examines consumer perceptions and expectations of hospitality VAs and explores VA capabilities through expert technology providers. Design/methodology/approach This empirical paper investigates the current use and future implications of VAs for hotel environments. It uses qualitative, semi-structured in-depth interviews with 7 expert hospitality VA technology providers and 21 hotel guests who have VA experience. The research adopts a demand and supply approach, addressing the VAs in hospitality holistically. Findings The findings illustrate the requirements from both end-users’ sides, hotels and guests, exploring VA advantages and challenges. The analysis demonstrates that VAs increasingly become digital assistants. VA technology helps hotels to improve customer service, expand operational capability and reduce costs. Although in its infancy, VA technology has made progress towards optimising hotel operations and upgrading customer service. The study proposes a speech-enabled interactions model. Research limitations/implications This research stimulates the transformation of hospitality services by using VAs and the development of smart hospitality and tourism ecosystems. The study can benefit from further research with hotel managers, to reflect hoteliers’ points of view and investigate their perception of VAs. Further research can also explore different aspects of consumer–VA interaction in different contexts. Practical implications The paper makes a significant contribution to hospitality management and human–computer interaction best practices. It supports technology providers to reconsider how to develop suitable technology solutions towards improving their strategic competitiveness. It also explains how to use VAs cost-effectively and profitably while adding value to travellers’ experience. Originality/value VA studies are often focussed on the technology in private households, rather than in commercial or hotel spaces. This paper contributes to the emerging literature on AI and IoT in smart hospitality and explores the acceptance and operationalisation of VAs. The research contributes to the conceptualisation of VA-enabled hotel services and explores positive and negative features, as well as future prospects.

scholarly journals Artificial Intelligence (AI) and Cardiovascular Diseases: An Unexpected Alliance

2020 ◽  
Vol 2020 ◽  
pp. 1-8 ◽  
Author(s):  
Silvia Romiti ◽  
Mattia Vinciguerra ◽  
Wael Saade ◽  
Iñaki Anso Cortajarena ◽  
Ernesto Greco
Keyword(s):  
Artificial Intelligence ◽  
Healthcare Service ◽  
Information Technologies ◽  
Digital Health ◽  
Medical Knowledge ◽  
Relevant Information ◽  
Cardiovascular Medicine ◽  
Pervasive Healthcare ◽  
Chronic Disease Patients

Cardiovascular disease (CVD), despite the significant advances in the diagnosis and treatments, still represents the leading cause of morbidity and mortality worldwide. In order to improve and optimize CVD outcomes, artificial intelligence techniques have the potential to radically change the way we practice cardiology, especially in imaging, offering us novel tools to interpret data and make clinical decisions. AI techniques such as machine learning and deep learning can also improve medical knowledge due to the increase of the volume and complexity of the data, unlocking clinically relevant information. Likewise, the use of emerging communication and information technologies is becoming pivotal to create a pervasive healthcare service through which elderly and chronic disease patients can receive medical care at their home, reducing hospitalizations and improving quality of life. The aim of this review is to describe the contemporary state of artificial intelligence and digital health applied to cardiovascular medicine as well as to provide physicians with their potential not only in cardiac imaging but most of all in clinical practice.

Value cocreation in service interactions

2014 ◽  
Vol 15 (2) ◽  
pp. 221-242 ◽  
Author(s):  
Carmen Neghina ◽  
Marjolein C. J. Caniëls ◽  
Josée M. M. Bloemer ◽  
Marcel J. H. van Birgelen
Keyword(s):  
Value Cocreation ◽  
Service Interactions

scholarly journals Research on the Coordination Mechanism of Value Cocreation of Innovation Ecosystems: Evidence from a Chinese Artificial Intelligence Enterprise

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Yu Chen ◽  
Yantai Chen ◽  
Yanlin Guo ◽  
Yanfei Xu
Keyword(s):  
Artificial Intelligence ◽  
Evolutionary Game ◽  
Excess Return ◽  
Return Rate ◽  
Coordination Mechanism ◽  
Factors Affecting ◽  
Innovation Ecosystems ◽  
Coordination Costs ◽  
Value Cocreation ◽  
The Cost

This paper models the game process of the value cocreation of enterprises based on evolutionary game theory (EGT). The factors influencing value cocreation are found through mathematical analysis. Taking iFLYTEK as an example, a representative enterprise of artificial intelligence (AI) in China, six factors affecting value cocreation are verified, which are the excess return rate, the distribution coefficient of the excess return rate, coordination costs in the system, the cost-sharing coefficient, imitation costs, and penalties. These six factors have a profound impact on value cocreation in the ecosystem. Through the case study of iFLYTEK, it is concluded that innovation ecosystems can enable small- and medium-sized AI enterprises to grow. In order to build a sound ecosystem, we need to establish a mechanism to select partners, reduce the costs of cooperation, and strengthen the protection of intellectual property. At the beginning of the cooperation, it is necessary to establish a mechanism with clear responsibilities, rights, and interests. The conclusion is of great significance to the development of AI enterprises.

mHealth

2020 ◽  
pp. 1-21
Author(s):  
Gloria Ejehiohen Iyawa ◽  
Collins Oduor Ondiek ◽  
Jude Odiakaosa Osakwe
Keyword(s):  
Artificial Intelligence ◽  
Scoping Review ◽  
Text Messaging ◽  
Driving Force ◽  
Healthcare Service ◽  
Low Cost ◽  
Healthcare Services ◽  
Disease Diagnosis ◽  
Mobile Technologies ◽  
Present Information

Mobile health (mHealth), the application of mobile technologies for healthcare services, has been the driving force in healthcare in the last few decades; from healthcare service delivery to low-cost tools for effective disease diagnosis, prediction, monitoring, and management. The main purpose of this chapter was to identify the scope and range of studies on mHealth used as low-cost tools for effective disease diagnosis, prediction, monitoring, and management. The authors identified 55 papers that met the inclusion and exclusion criteria after searching different academic databases. The findings revealed that low-cost mHealth approaches such as text messaging and mobile applications developed using artificial intelligence algorithms have been used for disease diagnosis, prediction, monitoring, and management. The findings of this scoping review present information regarding different mHealth approaches that can be used by researchers and practitioners interested in the application of low-cost mHealth solutions in low-resource settings.

scholarly journals Proposal of a novel Artificial Intelligence Distribution Service platform for healthcare

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 245
Author(s):  
Antti Väänänen ◽  
Keijo Haataja ◽  
Katri Vehviläinen-Julkunen ◽  
Pekka Toivanen
Keyword(s):  
Artificial Intelligence ◽  
Open Access ◽  
Healthcare Service ◽  
Service Providers ◽  
Research Work ◽  
Health Assessment ◽  
Healthcare Services ◽  
Service Platform ◽  
Healthcare Evaluation ◽  
Multiple Health

In this paper, we focus on presenting a novel AI-based service platform proposal called AIDI (Artificial Intelligence Distribution Interface for healthcare). AIDI proposal is based on our earlier research work in which we evaluated AI-based healthcare services which have been used successfully in practice among healthcare service providers. We have also used our systematic review about AI-based healthcare services benefits in various healthcare sectors. This novel AIDI proposal contains services for health assessment, healthcare evaluation, and cognitive assistant which can be used by researchers, healthcare service provides, clinicians, and consumers. AIDI integrates multiple health databases and data lakes with AI service providers and open access AI algorithms. It also gives healthcare service providers open access to state-of-the-art AI-based diagnosis and analysis services. This paper provides a description of AIDI platform, how it could be developed, what can become obstacles in the development, and how the platform can provide benefits to healthcare when it will be operational in the future.

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