Search results

The purpose of this paper is to examine patient engagement in remote consultation services, an increasingly important issue facing Healthcare Operations Management (HOM) given the significant expansion in this and other forms of telehealth worldwide over the last decade. We use our analysis of the literature to develop a comprehensive framework that incorporates the patient journey, multidimensionality, antecedents and consequences, interventions and improvement options, as well as the cyclic nature of patient engagement. We also propose measures suitable for empirical assessment of different aspects of our framework.

We undertook a comprehensive review of the extant literature using a systematic review approach. We identified and analysed 63 articles published in peer-reviewed scientific journals between 2003 and 2022.

We conceptualise patient engagement with remote consultation across three key aspects: dimensions, process, and the antecedents and consequences of engagement. We identify nine contextual categories that influence such engagement. We propose several possible metrics for measuring patient engagement during three stages (before service, at/during service and after service) of remote consultation, as well as interventions and possible options for improving patient engagement therein.

The primary contribution of our research is the development of a comprehensive framework for patient engagement in remote consultation that draws on insights from literature in several disciplines. In addition, we have linked the three dimensions of engagement with the clinical process to create a structure for future engagement assessment. Furthermore, we have identified impact factors and outcomes of engagement in remote consultation by understanding which can help to improve levels of adoption, application and satisfaction, and reduce healthcare inequality. Finally, we have adopted a “cyclic” perspective and identified potential interventions that can be combined to further improve patient engagement in remote consultation.

This paper does not concern with the “why” of ethics. Such questions are typically of interest to philosophers and are outside the scope of this work. In the next section, the authors offer a look into “what” of ethics, i.e. various types and subtypes of ethics. Subsequently, the authors explore “how” of ethics, by summarising various computational approaches to ethical reasoning offered by researchers in the field.

The approaches are classified based on the application domain, ethical theory, agent type and design paradigm adopted. Moreover, promising research directions towards ethical reasoning are also presented.

Since the field is essentially interdisciplinary in nature, collaborative research from such areas as neuroscience, psychology, artificial intelligence, law and social sciences is necessary. It is hoped that this paper offers much needed insight into computational approaches for ethical reasoning paving way for researchers to further engage with the question.

In this paper, the authors discussed vaious computational approaches proposed by researchers to implement ethics. Although none of the approaches adequately answer the question, it is necessary to engage with the research effort to make a substantial contribution to the emerging research area. Though some effort has been made in the design of logic-based systems, they are largely in stages of infancy and merit considerable research.

As artificial intelligence and machine learning become increasingly integrated into daily life, both individuals and institutions are growing dependent on these technologies. However, it's crucial to acknowledge that such advancements can introduce potential flaws or vulnerabilities. A case in point is the investigation conducted by the non-profit organization ProPublica into the COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) risk assessment tool – a tool widely used by US courts to assess the likelihood of a defendant reoffending. To address the issue of underlying biases, including racial biases, which can lead to inaccurate predictions and significant social harm, we are delving into the current literature on algorithmic bias in decision systems. We are also exploring the evolving considerations of fairness and accountability in machine learning. Specifically, within the realm of predictive policing algorithms employed in the criminal justice system, our focus is on recent studies aimed at mitigating biases in algorithmic decision-making. This involves reassessing recidivism rates and implementing adversarial debiasing in conjunction with fairness metrics.

This study examines the impact of the age of the chief executive officer (CEO) on the demand for audit quality, as reflected in auditor choice and audit fees. Furthermore, the study investigates whether CEO dominance moderates the association between CEO age, auditor choice and audit fees.

Using a sample of 14,066 firm-year observations from 2000 to 2017, the study employs logistic regression and ordinary least squares (OLS) regressions to estimate the research models. The study also employs various techniques to address the endogeneity issue in the findings.

Using industry specialist auditors and brand name (Big 4) auditors as proxies, the study finds that firms with older CEOs are more likely to appoint higher-quality auditors. The study also finds that firms with older CEOs pay higher audit fees than firms with younger CEOs, which is likely to be due to increased demand for higher-quality audits and to risk aversion among older CEOs. In addition, the study finds that CEO dominance attenuates the positive association of CEO age with auditor choice and audit fees. The findings are found to be robust in our analyses, which address the endogeneity issue with firm fixed effects, two-stage least squares (2SLS) regression and entropy balancing. In addition, the study provides evidence that the positive association between CEO age and audit pricing persists when firms replace younger CEOs with older CEOs.

The study’s findings suggest that the United States (US) Securities and Exchange Commission (SEC) and the Public Company Accounting Oversight Board (PCAOB) may need to be more cautious when monitoring financial statements from firms with younger CEOs.

This study contributes to a growing stream of research investigating the links between managers’ idiosyncratic age differences and the quality of financial reporting and corporate decisions.

This study aims to assess the effects of risk perception and management subject satisfaction on consumers' online meal food safety self-protection behavior during the COVID-19 pandemic.

This study uses 742 questionnaires collected via a two-stage online survey conducted during the COVID-19 pandemic, between December 2021 and January 2022. The entropy method, descriptive statistics, ordered logit model, stepwise regression models, interaction terms and decentralization method were used in the quantitative analysis. Respondents’ written responses to self-protection behavior were categorized into five groups.

Less than half of consumers were aware that online food products carry the risk of SARS-COV-2 (44.48%). Between 30 and 40% of consumers took insufficient or no self-protection measures. Risk perception significantly and positively affected self-protection behavior during the COVID-19 pandemic. Consumers' management subject satisfaction has a positive moderating effect on risk perception, with the moderating effect of the satisfaction of online retailers being significant at the 5% level. Risk perception significantly and positively influences consumer self-protection behavior in provinces not affected by the pandemic.

The findings stress the benefits of synergistic interventions by consumers and management subject to food safety measures and the inclusion of tailored interventions during events threatening public health to effectively address food safety. The study offers valuable insights contributing to the improvement of public health outcomes, customer trust and service quality within the online food delivery industry.

The rapid advancement of artificial intelligence (AI), particularly within the last decade and the application of ‘deep learning’, has simultaneously accelerated human fears of the changes AI provokes in human behaviour. The question is not any more if the new phenomena, like artificially-induced consciousness, empathy or creation, will be widely used, but whether they will be used in ethically acceptable ways and for ethically acceptable purposes.

Departing from a diagnosis of the state humans have brought themselves to by (ab)use of technology, the present chapter investigates the possibility of a systematic study of adaptations human society will have to consider in order to guarantee the obeyance to the fundamental ethical values and thus its spiritual survival. To that end, a new discipline – epharmology (from the Greek epharmozein = to adapt) is proposed, together with its aims and methodology.

Managers must make numerous strategic decisions in order to initiate and implement a business model innovation (BMI). This paper examines how managers perceive the management team interacts when making BMI decisions. The paper also investigates how group biases and board members’ risk willingness affect this process.

Empirical data were collected through 26 in-depth interviews with German managing directors from 13 companies in four industries (mobility, manufacturing, healthcare and energy) to explore three research questions: (1) What group effects are prevalent in BMI group decision-making? (2) What are the key characteristics of BMI group decisions? And (3) what are the potential relationships between BMI group decision-making and managers' risk willingness? A thematic analysis based on Gioia's guidelines was conducted to identify themes in the comprehensive dataset.

First, the results show four typical group biases in BMI group decisions: Groupthink, social influence, hidden profile and group polarization. Findings show that the hidden profile paradigm and groupthink theory are essential in the context of BMI decisions. Second, we developed a BMI decision matrix, including the following key characteristics of BMI group decision-making managerial cohesion, conflict readiness and information- and emotion-based decision behavior. Third, in contrast to previous literature, we found that individual risk aversion can improve the quality of BMI decisions.

This paper provides managers with an opportunity to become aware of group biases that may impede their strategic BMI decisions. Specifically, it points out that managers should consider the key cognitive constraints due to their interactions when making BMI decisions. This work also highlights the importance of risk-averse decision-makers on boards.

This qualitative study contributes to the literature on decision-making by revealing key cognitive group biases in strategic decision-making. This study also enriches the behavioral science research stream of the BMI literature by attributing a critical influence on the quality of BMI decisions to managers' group interactions. In addition, this article provides new perspectives on managers' risk aversion in strategic decision-making.

Perverse instantiation is one of many hypothetical failure modes of AI, specifically one in which the AI fulfils the command given to it by its principal in a way which is both unforeseen and harmful. A lot is already said about perverse instantiation itself, especially when such a failure mode presents an existential risk, as would be the case with a superintelligent AI. However novel these disaster scenarios may be, similar fictional cautionary tales already exist in many cultures: tragic stories about misinterpreted prophecies and grand wishes gone awry, from Croesus to Macbeth. Analysis of both old and new tales of perverse instantiation reveals that the core of the issue is an ancient philosophical and logical problem that even Socrates faced: the problem of defining terms. Unlike the Socratic problem, which focused on finding a good intensional definition, perverse instantiation encompasses problems that arise from both badly defined intension of terms (their internal content) and badly defined extension of terms (their range of applicability). However, models of machine learning that use vast amounts of training data hold the promise of resolving the issue of badly defined extension of terms. The issue of defining intension of terms remains. Further parallels can be found between scenarios of perverse instantiation and Socrates' dialogues with obstinate sophists, such as importance of philosophical reflection and discussion. This indicates that our future challenges in working with AI may still have a lot to do with retracing Socrates' steps.

As consumers interact with various small businesses, they develop a mental image, called a prototype, to represent what small businesses are as a generalized, conceptual category. However, prior research has said little about what this small business prototype entails. Thus, the aim of this study is to explore consumers’ perceptions of the prototypical small business by identifying common attributes among small businesses that differentiate them from large businesses.

This study undertakes a thorough review of the relevant consumer research literature for the attributes that consumers use to evaluate small businesses. Then, using a contemporary parallel analysis approach, it conducts an exploratory factor analysis (EFA) on a sample of 266 university students who were asked to evaluate how common those attributes are of small businesses. A second comparative EFA for large businesses is also conducted.

The EFA reveals two dimensions on which consumers evaluate small businesses: a sincere–authentic dimension and a disruptive–innovative dimension. Specifically, consumers view the prototypical small business to be relatively high on sincere–authentic and moderate on disruptive–innovative dimensions.

Through a comprehensive literature review and exploratory analysis, this study provides a novel understanding of consumers’ conceptualizations of small businesses. In studying the mental image consumers associate with the prototypical small business, this research fills a significant gap in the existing literature and provides important insights for practitioners and researchers alike.