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Article
Publication date: 21 June 2023

Lilly Marie Baltruschat, Vikas Jaiman and Visara Urovi

Blockchain systems have been proposed as a solution for exchanging electronic health records (EHR) because they enable data sharing in decentralised networks. This paper aims to…

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Abstract

Purpose

Blockchain systems have been proposed as a solution for exchanging electronic health records (EHR) because they enable data sharing in decentralised networks. This paper aims to analyse the user acceptability of blockchain technology in enabling EHR exchange and to formulate practical implications for increasing user acceptability.

Design/methodology/approach

A technology acceptance model [extended Unified Theory of Acceptance and Use of Technology (UTAUT) model] was used as a framework to measure the effects of 13 factors. The authors conducted a survey and analysed data from 214 participants using partial least square path modelling.

Findings

The acceptance of blockchain for EHR sharing is positively influenced by performance expectancy, social influence and perceived trust. Effort expectancy and facilitating conditions do not influence acceptance. The UTAUT model explains the variance in acceptance at 58.4%. Self-efficacy influences effort expectancy, incentives influence facilitating conditions and security predicts perceived trust.

Practical implications

Three implications are drawn: (1) Users need to clearly understand system’s purpose, functions, security mechanism and environmental impacts. (2) Users are incentivised to share health data via a blockchain solution if the technology offers personalising options and health information. (3) Health personnel can socially impact patients to use blockchain-based solutions.

Originality/value

Studies have shown that blockchain technology is a valuable solution for exchanging EHR. The novelty of this work is to identify how and why patients may accept this emerging technology for EHR exchange.

Details

Journal of Systems and Information Technology, vol. 25 no. 3
Type: Research Article
ISSN: 1328-7265

Keywords

Available. Open Access. Open Access
Article
Publication date: 10 June 2020

Alexander P. Henkel, Stefano Bromuri, Deniz Iren and Visara Urovi

With the advent of increasingly sophisticated AI, the nature of work in the service frontline is changing. The next frontier is to go beyond replacing routine tasks and augmenting…

10743

Abstract

Purpose

With the advent of increasingly sophisticated AI, the nature of work in the service frontline is changing. The next frontier is to go beyond replacing routine tasks and augmenting service employees with AI. The purpose of this paper is to investigate whether service employees augmented with AI-based emotion recognition software are more effective in interpersonal emotion regulation (IER) and whether and how IER impacts their own affective well-being.

Design/methodology/approach

For the underlying study, an AI-based emotion recognition software was developed in order to assist service employees in managing customer emotions. A field study based on 2,459 call center service interactions assessed the effectiveness of the AI in augmenting service employees for IER and the immediate downstream consequences for well-being relevant outcomes.

Findings

Augmenting service employees with AI significantly improved their IER activities. Employees in the AI (vs control) condition were significantly more effective in regulating customer emotions. IER goal attainment, in turn, mediated the effect on employee affective well-being. Perceived stress related to exposure to the AI augmentation acted as a competing mediator.

Practical implications

Service firms can benefit from state-of-the-art AI technology by focusing on its capacity to augment rather than merely replacing employees. Furthermore, signaling IER goal attainment with the help of technology may provide uplifting consequences for service employee affective well-being.

Originality/value

The present study is among the first to empirically test the introduction of an AI-fueled technology to augment service employees in handling customer emotions. This paper further complements the literature by investigating IER in a real-life setting and by uncovering goal attainment as a new mechanism underlying the effect of IER on the well-being of the sender.

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Article
Publication date: 29 September 2020

Stefano Bromuri, Alexander P. Henkel, Deniz Iren and Visara Urovi

A vast body of literature has documented the negative consequences of stress on employee performance and well-being. These deleterious effects are particularly pronounced for…

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Abstract

Purpose

A vast body of literature has documented the negative consequences of stress on employee performance and well-being. These deleterious effects are particularly pronounced for service agents who need to constantly endure and manage customer emotions. The purpose of this paper is to introduce and describe a deep learning model to predict in real-time service agent stress from emotion patterns in voice-to-voice service interactions.

Design/methodology/approach

A deep learning model was developed to identify emotion patterns in call center interactions based on 363 recorded service interactions, subdivided in 27,889 manually expert-labeled three-second audio snippets. In a second step, the deep learning model was deployed in a call center for a period of one month to be further trained by the data collected from 40 service agents in another 4,672 service interactions.

Findings

The deep learning emotion classifier reached a balanced accuracy of 68% in predicting discrete emotions in service interactions. Integrating this model in a binary classification model, it was able to predict service agent stress with a balanced accuracy of 80%.

Practical implications

Service managers can benefit from employing the deep learning model to continuously and unobtrusively monitor the stress level of their service agents with numerous practical applications, including real-time early warning systems for service agents, customized training and automatically linking stress to customer-related outcomes.

Originality/value

The present study is the first to document an artificial intelligence (AI)-based model that is able to identify emotions in natural (i.e. nonstaged) interactions. It is further a pioneer in developing a smart emotion-based stress measure for service agents. Finally, the study contributes to the literature on the role of emotions in service interactions and employee stress.

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