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Article
Publication date: 24 February 2020

Ahmad Khanijahani

The purpose of this viewpoint is to discuss and analyze three major governance tools that have been implemented in the United States to target tobacco smoking as a major public…

415

Abstract

Purpose

The purpose of this viewpoint is to discuss and analyze three major governance tools that have been implemented in the United States to target tobacco smoking as a major public health concern.

Design/methodology/approach

The author highlights the negative consequences of smoking as a global and U.S public health concern and discusses three categories of governance tools implemented in the U.S. Additionally, emerging challenges in the U.S. and different sides of story in developing countries are underscored.

Findings

Although some success has been reached in controlling smoking-related mortalities and morbidities in the U.S. and most of the countries, long-term and sustainable improvement require active surveillance and constant implementation of evidence-based policies and programs.

Practical implications

This viewpoint discusses the governance tools that can be implemented to decrease smoking-related preventable mortalities and morbidities. Similar tools with some tuning can be used to target smoking in other nations. Additionally, these tools can be modified to target other public health-related wicked problems such as obesity, alcohol consumption, and traffic accidents.

Originality/value

This viewpoint highlights the magnitude of smoking as a major public health concern and underscores the necessity of using governance tools in targeting this issue. Additionally, it provides application examples from the United States implementable in other countries with some contextual justifications and tuning.

Details

International Journal of Health Governance, vol. 25 no. 2
Type: Research Article
ISSN: 2059-4631

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Book part
Publication date: 28 August 2023

Caroline Wolski, Kathryn Freeman Anderson and Simone Rambotti

Since the development of the COVID-19 vaccinations, questions surrounding race have been prominent in the literature on vaccine uptake. Early in the vaccine rollout, public health…

Abstract

Purpose

Since the development of the COVID-19 vaccinations, questions surrounding race have been prominent in the literature on vaccine uptake. Early in the vaccine rollout, public health officials were concerned with the relatively lower rates of uptake among certain racial/ethnic minority groups. We suggest that this may also be patterned by racial/ethnic residential segregation, which previous work has demonstrated to be an important factor for both health and access to health care.

Methodology/Approach

In this study, we examine county-level vaccination rates, racial/ethnic composition, and residential segregation across the U.S. We compile data from several sources, including the American Community Survey (ACS) and Centers for Disease Control (CDC) measured at the county level.

Findings

We find that just looking at the associations between racial/ethnic composition and vaccination rates, both percent Black and percent White are significant and negative, meaning that higher percentages of these groups in a county are associated with lower vaccination rates, whereas the opposite is the case for percent Latino. When we factor in segregation, as measured by the index of dissimilarity, the patterns change somewhat. Dissimilarity itself was not significant in the models across all groups, but when interacted with race/ethnic composition, it moderates the association. For both percent Black and percent White, the interaction with the Black-White dissimilarity index is significant and negative, meaning that it deepens the negative association between composition and the vaccination rate.

Research limitations/implications

The analysis is only limited to county-level measures of racial/ethnic composition and vaccination rates, so we are unable to see at the individual-level who is getting vaccinated.

Originality/Value of Paper

We find that segregation moderates the association between racial/ethnic composition and vaccination rates, suggesting that local race relations in a county helps contextualize the compositional effects of race/ethnicity.

Details

Social Factors, Health Care Inequities and Vaccination
Type: Book
ISBN: 978-1-83753-795-2

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Article
Publication date: 24 September 2024

Verma Prikshat, Sanjeev Kumar, Parth Patel and Arup Varma

Drawing on the integrative perspective of the technology acceptance model (TAM) and theory of planned behaviour (TPB) and extending it further by examining the role of…

432

Abstract

Purpose

Drawing on the integrative perspective of the technology acceptance model (TAM) and theory of planned behaviour (TPB) and extending it further by examining the role of organisational facilitators and perceived HR effectiveness in this integrative perspective, we examine HR professionals’ AI-augmented HRM (HRM(AI)) acceptance in this research.

Design/methodology/approach

The data (N=375) were collected from HR professionals working in different organisations in India. Structural equation modelling (SEM) was employed to analyse the data.

Findings

The results of the study suggest that along with organisational facilitator antecedents to the relevant components of both TAM and TPB, perceived HR effectiveness also enhanced the HRM(AI) acceptance levels of HR professionals.

Practical implications

The research findings are expected to contribute to the understanding of the factors that influence the acceptance of AI-augmented HRM in organizations. The results may also help organisations to identify the facilitators that can enhance the adoption and implementation of AI-augmented HRM by HR professionals. Finally, the study provides a composite TAM-TPB theoretical framework that can guide future research on the acceptance of AI-augmented HRM.

Originality/value

To the best of our knowledge, this is one of the first attempts to factor in the effect of contextual factors (i.e. organisational facilitators and perceived HR effectiveness) in the TAM and TPB equations.

Details

Personnel Review, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0048-3486

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Article
Publication date: 8 January 2025

Ahmad A. Khanfar, Reza Kiani Mavi, Mohammad Iranmanesh and Denise Gengatharen

Despite the potential of artificial intelligence (AI) systems to increase revenue, reduce costs and enhance performance, their adoption by organisations has fallen short of…

163

Abstract

Purpose

Despite the potential of artificial intelligence (AI) systems to increase revenue, reduce costs and enhance performance, their adoption by organisations has fallen short of expectations, leading to unsuccessful implementations. This paper aims to identify and elucidate the factors influencing AI adoption at both the organisational and individual levels. Developing a conceptual model, it contributes to understanding the underlying individual, social, technological, organisational and environmental factors and guides future research in this area.

Design/methodology/approach

The authors have conducted a systematic literature review to synthesise the literature on the determinants of AI adoption. In total, 90 papers published in the field of AI adoption in the organisational context were reviewed to identify a set of factors influencing AI adoption.

Findings

This study categorised the factors influencing AI system adoption into individual, social, organisational, environmental and technological factors. Firm-level factors were found to impact employee behaviour towards AI systems. Further research is needed to understand the effects of these factors on employee perceptions, emotions and behaviours towards new AI systems. These findings led to the proposal of a theory-based model illustrating the relationships between these factors, challenging the assumption of independence between adoption influencers at both the firm and employee levels.

Originality/value

This study is one of the first to synthesise current knowledge on determinants of AI adoption, serving as a theoretical foundation for further research in this emerging field. The adoption model developed integrates key factors from both the firm and individual levels, offering a holistic view of the interconnectedness of various AI adoption factors. This approach challenges the assumption that factors at the firm and individual levels operate independently. Through this study, information systems researchers and practitioners gain a deeper understanding of AI adoption, enhancing their insight into its potential impacts.

Details

Management Decision, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0025-1747

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