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1 – 10 of 301Erica Falkenström and Anna T. Höglund
The purpose of this paper is to contribute knowledge on ethical issues and reasoning in expert reports concerning healthcare governance, commissioned by the Swedish healthcare…
Abstract
Purpose
The purpose of this paper is to contribute knowledge on ethical issues and reasoning in expert reports concerning healthcare governance, commissioned by the Swedish healthcare system.
Design/methodology/approach
An in-depth analysis of ethical issues and reasoning in 36 commissioned expert reports was performed. Twenty-seven interviews with commissioners and producers of the reports were also carried out and analysed.
Findings
Some ethical issues were identified in the reports. But ethical reasoning was rarely evident. The meaning of ethical concepts could be devalued and changed over time and thereby deviate from statutory ethical goals and values. Several ethical issues of great concern for the Swedish public healthcare were also absent.
Practical implications
The commissioner of expert reports needs to ensure that comprehensive ethical considerations and ethical analysis are integrated in the expert reports.
Originality/value
Based on an extensive data material this paper reveals an ethical void in expert reports on healthcare governance. By avoiding ethical issues there is a risk that the expert reports could bring about reforms and control models that have ethically undesirable consequences for people and society.
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Guang Zhu, Fengjing Li, Yi Yan and Hustin Guenis
The collection and use of personal medical information for mobile health (mHealth) service raise significant privacy concerns. In this context, this study aims to explore the…
Abstract
Purpose
The collection and use of personal medical information for mobile health (mHealth) service raise significant privacy concerns. In this context, this study aims to explore the privacy paradox and its impact from the perspective of paradox resolution.
Design/methodology/approach
Based on social support theory and privacy calculus theory, this study first studies the effect of social support on perceived benefits, and explores the moderating effect of perceived health status on the privacy trade-off process. Secondly, the study examines the path of “privacy concerns – disclosure intention – disclosure behavior” to verify the existence of the privacy paradox. Following this, based on rational choice theory, the rationality degree is introduced as a moderating variable to investigate both its impact on the central route and the strength of this impact on the privacy paradox.
Findings
Empirical results show that informational support and emotional support influence perceived benefits significantly. Perceived benefits significantly influence privacy concerns, and perceived health status has a significant positive moderating effect. The authors further find that there is a privacy paradox within the mHealth context, and the privacy paradox is moderated negatively by rationality degree. The findings indicate that the impact strength of the privacy paradox will decrease with increases in rationality degree.
Research limitations/implications
The findings indicate that it is crucial to evaluate the privacy paradox and its impact from the perspective of paradox resolution.
Originality/value
This study offers a complete comprehension of the privacy paradox in mHealth and provides several valuable recommendations for enhancing both mHealth services and privacy controls.
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Glenn W. Harrison and Don Ross
Behavioral economics poses a challenge for the welfare evaluation of choices, particularly those that involve risk. It demands that we recognize that the descriptive account of…
Abstract
Behavioral economics poses a challenge for the welfare evaluation of choices, particularly those that involve risk. It demands that we recognize that the descriptive account of behavior toward those choices might not be the ones we were all taught, and still teach, and that subjective risk perceptions might not accord with expert assessments of probabilities. In addition to these challenges, we are faced with the need to jettison naive notions of revealed preferences, according to which every choice by a subject expresses her objective function, as behavioral evidence forces us to confront pervasive inconsistencies and noise in a typical individual’s choice data. A principled account of errant choice must be built into models used for identification and estimation. These challenges demand close attention to the methodological claims often used to justify policy interventions. They also require, we argue, closer attention by economists to relevant contributions from cognitive science. We propose that a quantitative application of the “intentional stance” of Dennett provides a coherent, attractive and general approach to behavioral welfare economics.
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To explain how technology will replace a great deal of human labor in knowledge markets using a theory of reasoned action applied to demand and theories of procedural rationality…
Abstract
Purpose
To explain how technology will replace a great deal of human labor in knowledge markets using a theory of reasoned action applied to demand and theories of procedural rationality, cost structure and system dynamics applied to supply.
Design/methodology/approach
Two illustrative scenarios are presented. The first is a third-party Best Treatments site, and its effect on the expert advice pharmaceutical representatives provide doctors. The second scenario is an online higher education business course module with embedded AI.
Findings
Both scenarios demonstrate the advantages of online expertise and teaching platforms over the in-person alternative in variable and marginal cost, ease and convenience of use, quality conformance, scalability, knowledge reach and depth and most importantly, speed of evolutionary adaptability. Despite such overwhelming advantages, a number of reasons why the substitution might be slowed are presented, and some strategies firms might adopt are discussed. Opportunities for service scholars to confirm, challenge and extend the conclusions are presented throughout the paper.
Originality/value
Increasing cost structure and adaptability advantages of online technology and AI over in-person delivery of expertise and training services are demonstrated. It is also demonstrated that the innovation-imitation cycle is accelerating because of exogenous innovation in knowledge access and online influence networks and an endogenous effect where imitators accelerate their innovation that drives innovators to accelerate their innovation, which drives imitators to further accelerate their imitation.
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Diya Guha Roy, Sujoy Bhattacharya and Srabanti Mukherjee
This research theoretically proposed and empirically validated a Customer-Based Brand Equity (CBBE) scale specifically for Medical Tourism for emerging economies including recent…
Abstract
Purpose
This research theoretically proposed and empirically validated a Customer-Based Brand Equity (CBBE) scale specifically for Medical Tourism for emerging economies including recent findings from tourism theories such as gravity model and signalling theory, but more specifically accommodating political, cultural, economic, legal and social influences.
Design/methodology/approach
In-depth literature reviews from tourism, medical tourism, healthcare and hospitality domains are used to propose the theoretical model. The authors have used the lavaan package in R for the empirical analysis and model verification.
Findings
The research included, tested and verified the established latent variables such as “brand awareness”, “brand association”, “perceived quality” and “loyalty”, along with new observed variables for the CBBE scale from the theoretical perspectives of this research. “Infrastructure” has emerged as a new scale construct and “culture” was found to be a moderating variable for “perceived quality” in the CBBE scale, which are novel additions to the literature.
Originality/value
The research contributed to scale refining, latent construct assessment, and fine-tuning of the observed variables for the mentioned theoretical gaps.
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Mark Govers, Rachel Gifford, Daan Westra and Ingrid Mur-Veeman
Organizational change is a key mechanism to ensure the sustainability of healthcare systems. However, healthcare organizations are persistently difficult to change, and literature…
Abstract
Organizational change is a key mechanism to ensure the sustainability of healthcare systems. However, healthcare organizations are persistently difficult to change, and literature is riddled with examples of failed change endeavors. In this chapter, we attempt to unravel the underlying causes for failed organizational change. We distinguish three types of change with different levels of depth that require different change approaches. Transformations are the deepest forms of change where beliefs and principles need to be modified to successfully influence routines. Renewals are deep forms of change where principles need to be modified to successfully influence routines. Improvements are shallow forms of change where only modifications at the level of routines are needed. Using deoxyribonucleic acid (DNA) as our metaphor, we propose a theory of “organizational DNA” to understand organizations and these three types of organizational changes. We posit that organizations are made up of a double helix consisting of a so-called “social string,” which contains the “soft” interaction or communication among the organization's members, and a so-called “technical string,” which contains “hard” organizational aspects such as structure and technology. Ladders of organizational nucleotides (i.e., Routines, Principles, and Beliefs) connect this double helix in various combinations. Together, the double helix and accompanying nucleotides make up the DNA of an organization. Without knowledge of the architecture of organizational DNA and whether a change addresses beliefs, principles, and/or routines, we believe that organizational change is constrained and based on luck rather than change management expertise. Following this metaphor, we show that organizational change fails when it attempts to change one part of the DNA (e.g., routines) in a way that renders it incompatible with the connecting components (e.g., principles and beliefs). We discuss how the theory can be applied in practice using an exemplar case.
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Often linked to the New Public Management (NPM) doctrines, agencification has been on the priority list of policy makers for over two decades. This article proposes an analysis of…
Abstract
Purpose
Often linked to the New Public Management (NPM) doctrines, agencification has been on the priority list of policy makers for over two decades. This article proposes an analysis of the role of agencies in the French health system and the impact of government agency reform on physicians and the public.
Design/methodology/approach
The research analyzes the perceived implementation of a re-concentration of decision-making powers within public agencies as the declared goal of agencification at the French health care system, specifically primary care providers and hospitals. The assessment relies on secondary sources from ministerial bodies such as the Ministry of Health and Social Affairs, the Ministry of Labor, the Social Security and the General Accounting Office, and specialized French technical agencies.
Findings
Decentralization in France and the subsequent rise of public health care agencies had outcomes below expectations. Hence, a re-concentration of decision-making powers within the larger Regional Health Agencies; a streamlining of the public administration; and a re-appropriation of decision-making powers by the Ministry of Health are needed. The monitoring of health providers allows central health authorities to govern at a distance.
Originality/value
The analysis of health care agencies in France and of their use of efficiency-enhancing techniques may trigger a change of values within the medical profession.
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Sumaira Nazeer, Muhammad Saleem Sumbal, Gang Liu, Hina Munir and Eric Tsui
The purpose of this paper is to embark on evaluating the role of Chat Generative-Trained Transformer (ChatGPT) in personal knowledge management (PKM) practices of individual…
Abstract
Purpose
The purpose of this paper is to embark on evaluating the role of Chat Generative-Trained Transformer (ChatGPT) in personal knowledge management (PKM) practices of individual knowledge workers across varied disciplines.
Design/methodology/approach
The methodology involves four steps, i.e. literature search, screening and selection of relevant data, data analysis and data synthesis related to KM, PKM and generative artificial intelligence (AI) with a focus on ChatGPT. The findings are then synthesized to develop a viewpoint on the challenges and opportunities brought by ChatGPT for individual knowledge workers in enhancing their PKM capability.
Findings
This work highlights the prevailing challenges and opportunities experienced by knowledge workers while leveraging PKM through implying ChatGPT. It also encapsulates how some management theories back the cruciality of generative AI (specifically ChatGPT) for PKM.
Research limitations/implications
This study identifies the challenges and opportunities. from existing studies and does not imply empirical data/result. The authors believe that findings can be adjusted to diverse domains regarding knowledge workers’ PKM endeavors. This paper draws some conclusions and calls for further empirical research.
Originality/value
ChatGPT’s capability to accelerate organizational performance compelled scholars to focus in this domain. The linkage of ChatGPT to Knowledge Management is an under-explored area specifically the role of ChatGPT on PKM hasn't been given attention in the existing work. This is one of the earliest studies to explore this context.
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Zeping Wang, Hengte Du, Liangyan Tao and Saad Ahmed Javed
The traditional failure mode and effect analysis (FMEA) has some limitations, such as the neglect of relevant historical data, subjective use of rating numbering and the less…
Abstract
Purpose
The traditional failure mode and effect analysis (FMEA) has some limitations, such as the neglect of relevant historical data, subjective use of rating numbering and the less rationality and accuracy of the Risk Priority Number. The current study proposes a machine learning–enhanced FMEA (ML-FMEA) method based on a popular machine learning tool, Waikato environment for knowledge analysis (WEKA).
Design/methodology/approach
This work uses the collected FMEA historical data to predict the probability of component/product failure risk by machine learning based on different commonly used classifiers. To compare the correct classification rate of ML-FMEA based on different classifiers, the 10-fold cross-validation is employed. Moreover, the prediction error is estimated by repeated experiments with different random seeds under varying initialization settings. Finally, the case of the submersible pump in Bhattacharjee et al. (2020) is utilized to test the performance of the proposed method.
Findings
The results show that ML-FMEA, based on most of the commonly used classifiers, outperforms the Bhattacharjee model. For example, the ML-FMEA based on Random Committee improves the correct classification rate from 77.47 to 90.09 per cent and area under the curve of receiver operating characteristic curve (ROC) from 80.9 to 91.8 per cent, respectively.
Originality/value
The proposed method not only enables the decision-maker to use the historical failure data and predict the probability of the risk of failure but also may pave a new way for the application of machine learning techniques in FMEA.
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Jianping Zhang, Leilei Wang and Guodong Wang
With the rapid advancement in the automotive industry, the friction coefficient (FC), wear rate (WR) and weight loss (WL) have emerged as crucial parameters to measure the…
Abstract
Purpose
With the rapid advancement in the automotive industry, the friction coefficient (FC), wear rate (WR) and weight loss (WL) have emerged as crucial parameters to measure the performance of automotive braking systems, so the FC, WR and WL of friction material are predicted and analyzed in this work, with an aim of achieving accurate prediction of friction material properties.
Design/methodology/approach
Genetic algorithm support vector machine (GA-SVM) model is obtained by applying GA to optimize the SVM in this work, thus establishing a prediction model for friction material properties and achieving the predictive and comparative analysis of friction material properties. The process parameters are analyzed by using response surface methodology (RSM) and GA-RSM to determine them for optimal friction performance.
Findings
The results indicate that the GA-SVM prediction model has the smallest error for FC, WR and WL, showing that it owns excellent prediction accuracy. The predicted values obtained by response surface analysis are closed to those of GA-SVM model, providing further evidence of the validity and the rationality of the established prediction model.
Originality/value
The relevant results can serve as a valuable theoretical foundation for the preparation of friction material in engineering practice.
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