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The purpose of this paper is to establish a stylized model to solve the pricing strategy, resource allocation and consumer surplus problems of multichannel healthcare services.

This paper considers a two-stage decision model with different levels of consumers’ knowledge. Faced with physical problems, knowledgeable consumers can solve their problems by seeking online healthcare channels, while unknowledgeable consumers need to make a two-stage decision to try to solve their problems.

The effective diagnosis rate and the proportion of knowledgeable consumers positively impact the optimal pricing in online and offline channels. In addition, a higher proportion of knowledgeable consumers does not result in higher demand in the online and offline channels. Moreover, if service providers lower their prices a small amount, they will lose some profit, but the consumer surplus will be higher, which will encourage more consumers to access healthcare services.

Knowledge levels are simplified into two categories. Also, the authors assume the resources of online and offline healthcare services are comparable.

This paper incorporates the knowledge level and misdiagnosis rate into the model framework to study the most effective pricing strategy for multichannel healthcare services.

The purpose of this paper is twofold: (1) to understand the process and consequences of the two-way communication between consumers and businesses on online-to-offline (O2O) diagnosis-and-cure services platforms and (2) to examine how consumer request-specific factors and service quote-specific factors influence consumer decisions in the interactive marketing context.

The study analyzes a dataset of 17,878 service requests and 57,867 price quotes obtained from an O2O platform bridging consumers and automotive repair shops. On the platform, consumers request service quotes by uploading the description of automotive damage and multiple service providers suggest price quotes. The authors formulated a logit model to examine consumer decisions of responding service quotes.

This paper finds that (1) consumers receiving more severe diagnostic results are more likely to respond to the price quotes, and (2) diagnostic severity and inconsistency moderate the impacts of geographic distance, shop size, and quote price on consumers' responses to the service quotes.

This paper fills the gap in the literature by advancing the consumer decision processing model to address the interactive shopping experience on O2O diagnosis-and-cure services platforms. The findings are limited by the data and the research context.

For marketing practitioners, the empirical results imply specific positioning and targeting strategies for markets with informational and geographic barriers to expand the market scope and customer base.

The present work is the first to examine the consumer decision process on O2O diagnosis-and-cure service platforms. It adds value to the literature by investigating how consumers update their problem awareness through the service request-specific factors (i.e. diagnostic severity and diagnostic inconsistency) and how the request-specific factors moderate the impacts of the quote-specific factors (i.e. shop distance, shop size and quote price) on consumers' responses to price quote. The conceptual model and empirical findings provide theoretical and practical values for e-commerce researchers and practitioners.

This paper provides an effective study to detect and locate the inter-turn short-circuit faults (ITSC) in a three-phase induction motor (IM) using the support vector machine (SVM). The characteristics extracted from the analysis of the phase shifts between the stator currents and their corresponding voltages are used as inputs to train the SVM. The latter automatically decides on the IM state, either a healthy motor or a short-circuit fault on one of its three phases.

To evaluate the performance of the SVM, three supervised algorithms of machine learning, namely, multi-layer perceptron neural networks (MLPNNs), radial basis function neural networks (RBFNNs) and extreme learning machine (ELM) are used along with the SVM in this study. Thus, all classifiers (SVM, MLPNN, RBFNN and ELM) are tested and the results are compared with the same data set.

The obtained results showed that the SVM outperforms MLPNN, RBFNNs and ELM to diagnose the health status of the IM. Especially, this technique (SVM) provides an excellent performance because it is able to detect a fault of two short-circuited turns (early detection) when the IM is operating under a low load.

The original of this work is to use the SVM algorithm based on the phase shift between the stator currents and their voltages as inputs to detect and locate the ITSC fault.

To improve the speed and precise of online monitoring and diagnosis for satellite using satellite telemetry data.Design/methodology/approach – In monitoring system, a fuzzy range which gives the probability of alarm for telemetry channels using fuzzy reasoning is outlined. A failure confidence factor is presented to modify the traditional real‐time diagnosis algorithm based on multisignal model to describe the relative failure possibility for suspected components. According to the modified real‐time diagnosis algorithm based on multisignal model, it rapidly generates the states for all the components of the system such as good, bad, suspected and unknown. Then the failure probability for suspected components is obtained by Mamdani fuzzy reasoning algorithm.Findings – The experimental results reveal that the diagnosis system can not only improve diagnosis of speed but also can improve the diagnostic precision by giving failure probability for suspected fault components which may be potential failure components.Research limitations/implications – It requires the clear fault dependency relationship between components and tests.Practical implications – A very useful method for researchers and engineers who are engaged in satellite online monitoring and diagnosis.Originality/value – This paper presents a new method combining multisignal model and fuzzy theory to give the failure probability for suspected components which improves the speed and precision for fault diagnosis.

This article considers The Bradley Report and specifically its implications for dual diagnosis. This includes implications for dual diagnosis services, drug courts and mental health courts, role of liaison and diversion, and continuity of care on release.

This study aims to study the mechanisms and conditions of users' intention to continue to use online health platforms from an information technology (IT) affordance perspective.

b This research proposes that a critical affordance effect on an online health platform, users' intention to continue the use of the platform, is affected by five platform affordances via two actualized affordances (i.e. perceived benefits (PBs) and online engagement (OE)). Perceived health threat moderates the effect generated by affordance actualization. A dataset involving 409 users from the “Ping An Health” platform was collected through an online survey and analyzed to validate the research hypotheses.

The data analysis results confirm that the proposed online health platform affordances affect users' PBs and OE, which influence users' intentions to continue using the platform. Perceived threats (perceived vulnerability (PVU) and perceived severity (PSE)) moderate the relationship between PBs and continuance intention (CI) and between OE and CI.

The research provides important recommendations for online health platform designers to develop IT affordances that can support users' needs for healthcare services.

Limited studies investigated why users continue participating in online diagnosis and treatment. This study provides a new perspective to expand the affordance framework by combining technology features and user health behavior. The study also emphasizes the importance of perceived threats in IT use.

In recent years there has been a proliferation of computer-based psychotherapeutic interventions for common mental health difficulties. Building on this, a small number of such interventions have now been developed to address substance dependence, one of which is Breaking Free Online (BFO). A new “eTherapy” self-help service, which was set up by the UK mental health charity Self-Help Services, has provided access to BFO to service users presenting with comorbid mental health and substance misuse difficulties. The purpose of this paper is to evaluate a range of clinical outcomes in the first cohort of service users accessing this dual diagnosis service.

A number of standardised psychometric assessments were conducted with service users at baseline and post-treatment at discharge from the service. Outcome data were available for 47 service users out of an original cohort of 74.

Statistically significant improvements were found in terms of measures of social functioning, depression, anxiety, alcohol and drug use and social anxiety. Clinically relevant gains were also identified, with fewer service users reaching threshold scores for depression and anxiety at post-treatment compared to baseline. Effect sizes also indicated that the identified improvements across the psychometric measures were robust and significant.

These findings provide further support for the clinical effectiveness of BFO, and also provide evidence that an eTherapy self-help service may be appropriate for some individuals presenting with dual diagnosis. Further research is underway with larger and alternative clinical populations to examine the effectiveness of BFO and also this novel eTherapy self-help approach.

This paper has provided initial data to support effectiveness of a novel eTherapy service for dual diagnosis.

Considering both online and offline service scenarios, this study aims to explore the factors affecting doctors' intention to offer consulting services in eHealth and compare the factors between the free- and paid-service doctors. The theory of reasoned action and social exchange theory are integrated to develop the research model that conceptualizes the role of extrinsic motivations, intrinsic motivations, costs, and attitudes in doctors' behavioral intentions.

Partial least square structural equation modeling (PLS-SEM) was leveraged to analyze 326 valid sample data. To provide robust results, three non-parametric multigroup analysis (MGA) methods, including the PLS-MGA, confidence set, and permutation test approaches, were applied to detect the potential heterogeneity between the free- and paid-service doctors.

The results with overall samples reveal that anticipated rewards, anticipated associations, anticipated contribution, and perceived fee are all positively related to attitude, which in turn positively influences behavioral intention, and that perceived fee positively moderates the relationship between attitude and behavioral intention. Attitude's full mediation is also confirmed. However, results vary between the two groups of doctors. The three MGA approaches return relatively convergent results, indicating that the effects of anticipated associations and perceived fee on attitude are significantly larger for the paid-service doctors, while that of anticipated rewards is found to be significantly larger for the free-service doctors.

eHealth, as a potential contactless alternative to face-to-face diagnoses, has recently attracted widespread attention, especially during the continued spread of COVID-19. Most existing studies have neglected the underlying heterogeneity between free- and paid-service doctors regarding their motivations to engage in online healthcare activities. This study advances the understanding of doctors' participation in eHealth by emphasizing their motivations derived from both online and offline service scenarios and comparing the differences between free- and paid-service doctors. Besides, horizontally comparing the results by applying diverse MGA approaches enriches empirical evidence for the selection of MGA approaches in PLS-SEM.

People with dual diagnosis have complex needs and vulnerabilities that may lead to incarceration in prisons. Mental health and substance use services in prisons should have the capabilities to address their needs while incarcerated and facilitate the transfer of care to community services on release. In order to develop these capabilities, a training programme is required.A pilot training programme for dual diagnosis was developed and piloted in five London prisons. The training was based on a training needs assessment of prison staff and consultation with service users. It was delivered in two forms: a five‐day classroom based course, and a ‘blended learning’ method that comprised a manual and three sessions of supervision. The course was evaluated by a brief questionnaire that included items on attitudes, self‐efficacy and knowledge about working with dual diagnosis.The evaluation of the training revealed that all workers, no matter what method of training they received increased their perception of their skills (self‐efficacy) and increased their attitudes. Knowledge remained the same (although the scores pre‐training were high). There was no difference between the two types of training when mean scores were compared at post‐training. There was also no difference between the mental health and substance workers regarding their mean scores at follow‐up, apart from knowledge.The conclusion is that the training pilot was evaluated positively and did indicate that it has some effect on attitudes and self‐efficacy. More rigorous evaluation of the impact of the training is required, using a robust methodology and assessing the impact on clinical skills and service user outcomes.