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The author studies the determinants of public trust in government doctors and hospitals (DH) – a crucial indicator of the quality of a country's healthcare system – in India by analyzing the India Human Development Survey (IHDS) 2011–2012.

The author uses descriptive statistics and a set of ordered probit regression models controlling for a set of individual-specific, household-level and other covariates and analyze across heterogeneous contexts (national/rural/urban/male heads/female heads/social groups).

Across contexts, people reporting a great deal of trust in private DH (PDH) are significantly more likely to report a great deal of trust in government DH. Those people with a great deal of trust in government schools to provide good education (vis-à-vis people with only some trust in government school) have significantly higher likelihood of reporting a great deal of trust in government DH. Visiting a private doctor last time (vis-à-vis a government doctor) makes reporting higher trust levels in government DH less likely.

India's healthcare system is afflicted with several resource allocation problems and low public trust issues are indicative of misgovernance. In presence of limited state capacity, ubiquitous corruption and underwhelming institutional trust, understanding the factors influencing public trust in healthcare providers is critical to designing appropriate trust-enhancing public health policies.

Given the sparse empirical literature on public trust in healthcare systems in the developing countries such as India, this study is a pertinent contribution as the study explains the determinants of public trust in DH using a comprehensive unit-recorded household survey dataset.

This paper aims to explore students’ intention to use and actual use of the artificial intelligence (AI)-based chatbot such as ChatGPT or Google Bird in the field of higher education in an emerging economic context like Bangladesh.

The present study uses convenience sampling techniques to collect data from the respondents. It applies partial least squares structural equation modeling (PLS-SEM) for analyzing a total of 413 responses to examine the study’s measurement and structural model.

The results explore that perceived ease of use (PEOU) negatively affects intention to adopt AI-powered chatbots (IA), whereas university students’ perceived usefulness (PU) influences their IA positively but insignificantly. Furthermore, time-saving feature (TSF), academic self-efficacy (ASE) and electronic word-of-mouth (EWOM) have a positive and direct impact on their IA. The finding also reveals that students' IA positively and significantly affects their actual use of AI-based chatbot (AU). Precisely, out of the five constructs, the TSF has the strongest impact on students’ intentions to use chatbots.

Students who are not aware of the chatbot usage benefits might ignore these AI-powered language models. On the other hand, developers of chatbots may not be conscious of the crucial drawbacks of their product as per the perceptions of their multiple users. However, the findings transmit a clear message about advantages to users and drawbacks to developers. Therefore, the results will enhance the chatbots’ functionality and usage.

The findings of the study alert the teachers, students and policymakers of higher educational institutions to understand the positive outcomes and to accept AI-powered chatbots such as OpenAI’s ChatGPT. Outcomes also notify the AI-product developers to boost the chatbot’s quality in terms of timeliness, user-friendliness, accuracy and trustworthiness.

Technology serves as a key catalyst in shaping society and the economy, significantly altering customer dynamics. Through a deep understanding of these evolving behaviors, a service can be tailored to address each customer's unique needs and personality. We introduce a strategy to integrate customer complaints with their personality traits, enabling responses that resonate with the customer’s unique personality.

We propose a strategy to incorporate customer complaints with their personality traits, enabling responses that reflect the customer’s unique personality. Our approach is twofold: firstly, we employ the customer complaints ontology (CCOntology) framework enforced with multi-class classification based on a machine learning algorithm, to classify complaints. Secondly, we leverage the personality measurement platform (PMP), powered by the big five personality model to predict customer’s personalities. We develop the framework for the Indonesian language by extracting tweets containing customer complaints directed towards Indonesia's three biggest e-commerce services.

By mapping customer complaints and their personality type, we can identify specific personality traits associated with customer dissatisfaction. Thus, personalizing how we offer the solution based on specific characteristics.

The research enriches the state-of-the-art personalizing service research based on captured customer behavior. Thus, our research fills the research gap in considering customer personalities. We provide comprehensive insights by aligning customer feedback with corresponding personality traits extracted from social media data. The result is a highly customized response mechanism attuned to individual customer preferences and requirements.

In this article, we outline our views on the appropriateness and utility of Return on Investment (ROI) for the evaluation of the value of healthcare quality improvement (QI) programmes.

Our recent research explored the ROI concept and became the genesis of our viewpoint. We reflect on our findings from an extensive research project on the concept of ROI, involving a multidisciplinary global systematic literature review, a qualitative and Delphi study with mental healthcare leaders from the United Kingdom National Health Service. Research participants included board members, clinical directors and QI leaders. Our findings led to our conclusions and interpretation of ROI against the broad QI governance. We discuss our views against the predominant governance frameworks and wider literature.

ROI is in-line with top-down control governance frameworks based in politics and economics. However, there is evidence that to be of better utility, a tool for the assessment of the value of QI benefits must include comprehensive benefits that reflect broad monetary and non-monetary benefits. This is in-line with bottom-up and collaborative governance approaches. ROI has several challenges that may limit it as a QI governance tool. This is supported by wider literature on ROI, QI as well as modern governance theories and models. As such, we question whether ROI is the appropriate tool for QI governance. A more pragmatic governance framework that accommodates various healthcare objectives is advised.

This article highlights some of the challenges in adopting ROI as a QI governance tool. We signal a need for the exploration of a suitable QI governance approach. Particularly, are healthcare leaders to be perceived as “agents”, “stewards” or both. The evidence from our research and wider literature indicates that both are crucial. Better QI governance through an appropriate value assessment tool could improve clarity on QI value, and thus investment allocation decision-making. Constructive discussion about the utility and appropriateness of ROI in the evaluation of healthcare QI programmes may help safeguard investment in effective and efficient health systems.

The article raises awareness of QI governance and encourages discussions about the challenges of using ROI as a tool for healthcare QI governance.

The foremost objective of this chapter was to present an overview of the cooperative healthcare sector in Argentina by reviewing its brief history, components and the role these healthcare cooperatives play in the society. The second objective was to look at how these cooperatives have helped women and the local communities in which they operate.

This paper has used chiefly secondary data derived from various academic papers and official and government websites which publish cooperative sector-related information. The intention was to construct a concise yet detailed study that would be of help to other researchers in the field of healthcare cooperatives since the data related to Argentina is highly scattered and frequently found not up to date.

The research has found that health cooperatives in Argentina have aided in overcoming problems in the sector such as sectoral fragmentation, negligence and frequently inadequate standards of care management as well as operational and implementation failures that the private and government healthcare players have been accused of being fraught with. Furthermore, the cooperatives have frequently played a complementary or supplementary role rather than a competitive one with the private and government players.

Through the examples presented in this chapter, it is evident that health cooperatives in Argentina are making large impacts in the healthcare domain along with positively impacting women, marginalised and vulnerable sections of the society and the community. It only remains to be seen now how far this sector will grow in the future and how many more lives will be benefitted.

This paper aims to highlight the factors influencing the conceptualisation of return on investment (ROI) from healthcare quality improvement (QI) programmes.

In their previous work, the authors found that the concept of ROI from QI is broad and includes numerous internal and external benefits for organisations. In this paper, the authors developed a framework outlining the factors that influence this conceptualisation of QI-ROI from an institutional theory perspective. The framework is based on the synthesis of their serial studies on the determinants of the concept of ROI from QI. The research was performed from 2020–2023 and involved a global multidisciplinary systematic literature review (N = 68), qualitative interviews (N = 16) and a Delphi study (N = 23). The qualitative and Delphi studies were based on the publicly-funded mental healthcare in UK. Participants included board members, clinical and service directors, as well as QI leaders.

The authors outline a framework of internal and external institutional forces that influence the conceptualisation of ROI from QI programmes in mental healthcare and similar organisations. Based on these factors, the authors state several conjectures. In doing this, the authors highlight the ambiguities and uncertainties surrounding QI-ROI conceptualisation. These challenge leaders to balance various monetary and non-monetary benefits for organisations and health systems. This explains the broadness of the QI-ROI concept.

The authors developed a framework highlighting the forces underpinning the broad, ambiguous and sometimes uncertain nature of the QI-ROI concept. They raise awareness about dilemmas to be confronted in developing or applying any tool to evaluate the value for money of QI programmes. Specifically, the work highlights the limitations of the ROI methodology as a primary tool in the QI context and the need for a more comprehensive tool.

This chapter focuses on the COVID-19 pandemic’s impact on the recognition through discourses of essentiality, of low-status workers and more specifically of care aides as an occupational group that performs society’s ‘dirty work’. The pandemic appears as a privileged moment to challenge the normative hegemony of how work is valued within society. However, public recognition through political discourse is a necessary but insufficient element in producing social change. Based on the theory of performativity, this chapter empirically probes conditions and mechanisms that enable a transition from discourse of essentiality to substantive recognition of the work performed by care aides in healthcare organizations. The authors rely on three main sources of data: scientific-scholarly works, documents from government, various associations and unions, and popular media reports published between February 2020 and 1 July 2022. While discourse of essentiality at the highest level of politics is associated with rapid policy response to value the work of care aides, it is embedded in a system structure and culture that restrains the establishment of substantive policy that recognizes the nature, complexity, and societal importance of care aide work. The chapter contributes to the literature on performativity by demonstrating the importance of the institutionalization of competing logics in contemporary health and social care systems and how it limits the effectiveness of discourse in promulgating new values and norms and engineering social change.

This paper aims to explore the role of perceived organizational injustice (POI) leading to workplace conflict in determining organizational performance (OP) among healthcare employees. This paper also examines the serial mediating effects of moral disengagement (MD) and knowledge hiding (KH).

In all, 244 public and private hospital employees in Pakistan provided the data set.

According to partial least squares-structural equation modeling findings, the negative association between POI and OP was serially mediated by KH and MD. The recovery process underlying the linkage between POI and OP is tested and highlighted in this paper as a first step in unraveling it.

The findings highlight the significance of taking moral and KH models into account when attempting to understand the moral cognitive processes that employees go through when they see injustice. Organizations should guarantee the equitable distribution of incentives and resources, as distributive and procedural justices are concerned with organizations.

By directing actions meant to prevent MD and KH, the findings may potentially inspire new, more focused treatments to safeguard patient safety and avoid losses in the healthcare industry. One way to reduce unethical conduct and MD is to have people declare or agree to a code of ethics.

Drawing on conservation of resources (COR) theory, this paper aims to explore the effect of perceived organizational injustice (POI) on organizational performance (OPE) indirectly through knowledge hiding (KH). Moreover, this paper examines the boundary effects of perceived organizational politics (POP) and moral disengagement (MDS).

We employed a time-lag approach to collect data from 244 employees in Pakistani private hospitals. SmartPLS v. 4.4 was run to check the outer model. For testing the moderated-mediation model as an inner model, PROCESS v.4 was applied.

POI negatively and indirectly influenced OPE through KH, whereas high levels of POP and MDS may result in a stronger POI–KH linkage.

This paper adds to the literature related to COR that has been utilized to explain employee behaviors in the hospital context, where the antecedents of OPE were primarily the subject of empirical investigation.