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The primary purpose of this paper is to investigate the healthcare organization’s intention to use radio frequency identification (RFID) technology for improving efficiency. This paper also intends to identify various factors that influence the adoption of RFID in the healthcare organization. This paper develops and tests seven different hypotheses. These hypotheses are tested using structural equation modeling. Our results provide support for a number of relationships in the hypothesized model. These include direct relationships among the factors risk, resistance to change, supplier support and the factor perceived usefulness. However, the study did not find support for the relationship between the factors perceived ease of use and intention to use. The results provide support for several indirect relationships as well. These include indirect relationships between the factors perceived resistance to change, risk, suppliers’ support and perceived ease of use with the factor intention to adopt RFID technology in the healthcare organization. This research is grounded in the theory of reasoned action and applies the technology acceptance model (TAM) to the healthcare organization’s intention to use RFID technology.

The purpose of this paper is to identify the existing literature on the wide range of operations research (OR) studies applied to healthcare, and to classify studies based on application type and on the OR technique employed. The scope of the review is limited to studies which have been undertaken in the UK, and to papers published since the year 2000.

In total, 142 high‐quality journal and conference papers have been identified from ISI Web of Knowledge data base for review and analysis.

The findings categorise the OR techniques employed, and analyse the application type, publication trends, funding, and software packages used in the twenty‐first century in UK healthcare. Publication trends indicate an increasing use of OR techniques in UK healthcare. The findings show that, interestingly, the distribution of the OR techniques employed is not uniform; the majority of studies focus on simulation, either as the only technique employed or as one element of a multi‐method approach.

Several studies have focused on the use of simulation in healthcare modelling, but none has methodologically reviewed the use of the full range of OR techniques. This research is likely to benefit healthcare decision makers since it will provide them with an overview of the different studies that have utilised multiple OR techniques for investigating problems in the stated domain.

The purpose of this paper is to examine whether healthcare leaders use evidence-based management (EBMgt) when facing major decisions and what types of evidence healthcare administrators consult during their decision-making. This study also intends to identify any relationship that might exist among adoption of EBMgt in healthcare management, attitudes towards EBMgt, demographic characteristics and organizational characteristics.

A cross-sectional study was conducted among US healthcare leaders. Spearman’s correlation and logistic regression were performed using the Statistical Package for the Social Sciences (SPSS) 23.0.

One hundred and fifty-four healthcare leaders completed the survey. The study results indicated that 90 per cent of the participants self-reported having used an EBMgt approach for decision-making. Professional experiences (87 per cent), organizational data (84 per cent) and stakeholders’ values (63 per cent) were the top three types of evidence consulted daily and weekly for decision-making. Case study (75 per cent) and scientific research findings (75 per cent) were the top two types of evidence consulted monthly or less than once a month. An exploratory, stepwise logistic regression model correctly classified 75.3 per cent of all observations for a dichotomous “use of EBMgt” response variable using three independent variables: attitude towards EBMgt, number of employees in the organization and the job position. Spearman’s correlation indicated statistically significant relationships between healthcare leaders’ use of EBMgt and healthcare organization bed size (r_s = 0.217, n = 152, p < 0.01), attitude towards EBMgt (r_s = 0.517, n = 152, p < 0.01), and the number of organization employees (r_s = 0.195, n = 152, p = 0.016).

This study generated new research findings on the practice of EBMgt in US healthcare administration decision-making.

Several countries have been using internet of things (IoT) devices in the healthcare sector to combat COVID-19. Therefore, this study aims to examine the doctors’ intentions to use IoT healthcare devices in Iraq during the COVID-19 pandemic.

This study proposed a model based on the integration of the innovation diffusion theory (IDT). This included compatibility, trialability and image and a set of exogenous factors such as computer self-efficacy, privacy and cost into the technology acceptance model comprising perceived ease of use, perceived usefulness, attitude and behavioral intention to use.

The findings revealed that compatibility and image of the IDT factors, have a significant impact on the perceived ease of use, perceived usefulness and behavioral intention, but trialability has a significant impact on perceived ease of use, perceived usefulness and insignificant impact on behavioral intention. Additionally, external factors such as privacy and cost significantly impacted doctors’ behavioral intention to use. Moreover, doctors’ computer self-efficacy significantly influenced the perceived ease of use, perceived usefulness and behavioral intention to use. Furthermore, perceived ease of use has a significant impact on perceived usefulness and attitude, perceived usefulness has a significant impact on attitude, which, in turn, significantly impacting doctors' behavior toward an intention to use.

The limitations of the present study are the retractions of the number of participants and the lack of qualitative methods.

The finding of this study could benefit researchers, doctors and policymakers in the adaption of IoT technologies in the health sectors, especially in developing counties.

There is an increasing interest in employing e‐mail or other Internet‐based messaging systems in communication between patients and healthcare professionals. Many projects are put into practice, and numerous studies shed light on patients’ preferences regarding e‐messaging and their experience and use of e‐messaging. We argue in this paper that the conventional research in the field to some extent lacks a discussion about what kinds of knowledge an e‐messaging system generates among its users when it is put into practice. We suggest that placing the concept of knowledge as a discourse in focus, stressing how patients make judgements and distinctions in their use of e‐messaging, exposes important aspects not only regarding how patients relate to the system but also what e‐messaging in the healthcare sector means. We illustrate such a perspective with empirical material based on two focus groups of users of an e‐messaging system via a Swedish healthcare Web portal. Three kinds of knowledge formations are illuminated in this context: how patients develop knowledge by comparing e‐messaging services with traditional ways to contact healthcare; how the system generates a further demand for control by its users; and how the e‐messaging system helps users develop knowledge of the healthcare system in general.

The purpose of this paper is to determine the best measure among several alternatives (SERVQUAL, weighted SERVQUAL, SERVPERF, weighted SERVPERF) and develop a scale which healthcare providers can use for measurement of healthcare service quality.

The study involved two phases. The first phase was through a series of in-depth interviews with experts and patients followed by a pilot study. Subsequently, the second phase involved a quantitative phase through surveys with 384 patients. Alternative measures were analyzed using coefficient (Cronbach) α, composite reliability, factor analysis and logistic regression analysis.

Findings confirmed “Weighted SERVPERF” using an interactive methodology as the most appropriate for measurement of healthcare service quality.

Using the model and scale developed, healthcare providers will be able to measure healthcare service quality and identify areas of shortfall and act accordingly to improve delivery through allocating resources in service areas that would generate the greatest returns in customer satisfaction. Enhancing satisfaction will ultimately generate patient loyalty and positive recommendation behavior.

Consumer experiences of healthcare services are challenging for researchers to study because of the complex, intangible and temporal nature of service provision. The purpose of this paper is to introduce a novel longitudinal three-phase research protocol, which combines iterative interviewing with visual techniques. This approach is utilised to study consumer service experiences, dimensions of consumer value and consumer value co-creation in a transformational service setting: complementary and alternative medicine healthcare.

This research employed a three-phase qualitative longitudinal research protocol, which incorporated: an initial in-depth interview, implementation of the visual elicitation technique Zaltman Metaphor Elicitation Technique and a final interview to gain participant feedback on the analysis of data collected in the first two phases.

Four key benefits derived from using the three-phase protocol are reported: confirmation and elaboration of consumer value themes, emergence of underreported themes, evidence of transformation and refinement of themes, ensuring dependability of data and subsequent theory development.

The study provides evidence that a longitudinal multi-method approach using in-depth interviews and visual methods is a powerful tool that service researchers should consider, particularly for transformative service research settings with sensitive contexts, such as healthcare, and when studying difficult to articulate concepts, such as consumer value and value co-creation.

The purpose of this paper is to develop a framework for the application of evidence-based management to chronic disease healthcare.

Chronic healthcare is specially characterized by recursive patient-physician interactions in which evidence-based medicine (EBM) is applied. However, implementing evidence-based solutions to improve healthcare quality requires managers to effect changes in many different areas: organizational structure, procedures, technology and in physician/provider behaviors. To complicate matters further, they must achieve these changes using the tools of resource allocation or incentives. The literature contains many systematic reviews evaluating the question of physician and patient behavior under various types and structures of incentives. Similarly, systematic reviews have also been done regarding specific changes to the healthcare process and their effectiveness in improving patient outcomes. Yet, these reviews uniformly lament a lack of appropriate data from well-organized studies on the question of “Why?” solutions may work in one instance while not in another. The authors present a new theoretical framework that aids in answering this question.

This paper presents a new theoretical framework (Influence Model of Chronic Healthcare) that identifies: the critical areas in which managers can effect changes that improve patient outcomes; the influence these areas can have on each other, as well as on patient and physician behavior; and the mechanisms by which these influences are exerted. For each, the authors draw upon, and present the evidence in the literature. Ultimately, the authors recognize that this is a complex question that has not yet been fully researched. The contribution of this model is twofold: first, the authors hope to focus future research efforts, and second, provide a useful heuristic to managers who must make decisions with only the lesser-quality evidence the literature contains today.

This model can be used by managers as a heuristic either ex ante or ex post to determine the effectiveness of their decisions and strategies in improving healthcare quality. In addition, it can be used to analyze why actions or decisions taken achieved a given outcome, and how best to proceed to effect further improvements on patient outcomes. Last, the model serves to focus attention on specific questions for further research.

The purpose of the study is to investigate critical factors affecting individuals’ intention to adopt internet of things (IoT) products in healthcare.

An integrated model was developed based on technology acceptance model (TAM), innovation diffusion theory (IDT), technological innovativeness (TI), protection motivation theory and privacy calculus theory. The model was tested with 426 respondents (222 females, 204 males) using partial least square structural equation model with all data grouped by gender.

Based on the results of the complete model, perceived advantage (PA), image and perceived ease of use (PEOU) constructs have a significant effect on intention to adopt IoT healthcare technology products. The results show that for females, compatibility and trialability have more impact on PEOU whereas for males PA has more impact on PEOU. Image, perceived privacy risk, perceived vulnerability have more impact on males when compared to females.

Research conducted only among Turkish people.

This study investigated adoption of future technology, “internet of things”, products in healthcare from a behavioral perspective by integrating various theories. The reason is that before launching any technology into the market, its facilitative factors should be researched for the people who are going to use this in their daily routine.

The purpose of this article is to present a comprehensive structural equation based service quality and patient satisfaction model taking into account the patient's condition before and after discharge. The authors aim to test for causality in a sample of patients from United Arab Emirates public hospitals.

Data were collected using questionnaires completed by adults discharged (n=244) from UAE public hospitals. The proposed model consists of five main constructs. Three represent service quality: quality of care (four variables); process and administration (four variables) and information (four variables). There is also one construct that represents patient's status (two variables – health status before admission and after discharge). Finally, there is one construct that represents patient's satisfaction with care (two variables – general and relative satisfaction). Structural equation modeling and LISREL using maximum likelihood estimation was used to test hypothesized model(s)/parameters(s) derived deductively from the literature.

The structural equation modeling representation provides a comprehensive picture that allows healthcare constructs and patient satisfaction causality to be tested. The goodness‐of‐fit statistics supported the healthcare quality‐patient status‐satisfaction model.

The model has been found to capture attributes that characterize healthcare quality in a developing country and could represent other modern healthcare systems. Also, it can be used to evaluate other healthcare practices from patients' viewpoints. The study highlights the importance of healthcare quality as patient satisfaction predictors by capturing other effects such as patient status.