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This study aims to investigate how dynamic tensions between performance measurement system (PMS) uses enable organizations to achieve both exploitation and exploration and enhance firm performance.

The authors collected survey data on 153 Italian companies. Scales for each construct were validated through an exploratory factor analysis. Data on firm performance were cross-validated by using lagged accounting data. The authors tested our hypotheses using hierarchical ordinary least squares regressions, together with bootstrapping procedures for the test on mediation.

A diagnostic use of PMS has a positive association with both exploitation – e.g. reductions in total costs and lead times – and exploration, e.g. introduction of new products and extension of product ranges. The dynamic tension created by a joint diagnostic and interactive use has the strongest association with organizational ambidexterity, measured as the multiplicative interaction between exploration and exploitation.

If an organization or business unit is mainly pursuing exploitative goals, a mainly diagnostic use of PMS would be most suitable. If goals are both exploitative and explorative, a mix of diagnostic and interactive uses would be most effective.

This research helps reconcile conflicting views in the literature. The diagnostic use of PMS, far from acting as a “negative force,” appears to be necessary to guide opportunity search and to establish an appropriate scope for exploration-related activities. The authors’ focus on the uses of PMSs shows that ambidexterity is achieved through managerial capability, rather than just through the introduction of systems and structures.

Value creation based on artificial intelligence (AI) can significantly change global healthcare. Diagnostics, therapy and drug discovery start-ups are some key forces behind this change. This article aims to study the process of start-ups' value creation within healthcare.

A multiple case study method and a business model design approach were used to study nine European start-ups developing AI healthcare solutions. Obtained information was performed using within and cross-case analysis.

Three unique design elements were established, with 16 unique frames and three unifying design themes based on business models for AI healthcare start-ups.

Our in-depth framework focuses on the features of AI start-up business models in the healthcare industry. We contribute to the business model and business model innovation by systematically analyzing value creation, how it is delivered to customers, and communication with market participants, as well as design themes that combine start-ups and categorize them by specialization.

A major source of failure for new ventures is the entrepreneurs℉ misunderstanding of the product-market fit. Recently, researchers have suggested that to get a better understanding of the product-market fit, entrepreneurs should “get out of the building” and interview many customers. This approach, while advantageous, is not without drawbacks. This article presents a conceptual model that incorporates the characteristics of “getting out of the building” to conduct customer interviews, and the biases that can arise to influence the entrepreneurs℉ misjudgment of the product-market fit. We provide recommendations to overcome these biases.

This paper proposes a progressive, multi-level framework for diagnosing maintenance performance: rapid performance health checks of key performance for different equipment groups and end-to-end process diagnostics to further locate potential performance issues. A question-based performance evaluation approach is introduced to support the selection and derivation of case-specific indicators based on diagnostic aspects.

The case research method is used to develop the proposed framework. The generic parts of the framework are built on existing maintenance performance measurement theories through a literature review. In the case study, empirical maintenance data of 196 emergency shutdown valves (ESDVs) are collected over a two-year period to support the development and validation of the proposed approach.

To improve processes, companies need a separate performance measurement structure. This paper suggests a hierarchical model in four layers (objective, domain, aspect and performance measurement) to facilitate the selection and derivation of indicators, which could potentially reduce management complexity and help prioritize continuous performance improvement. Examples of new indicators are derived from a case study that includes 196 ESDVs at an offshore oil and gas production plant.

Methodological approaches to deriving various performance indicators have rarely been addressed in the maintenance field. The proposed diagnostic framework provides a structured way to identify and locate process performance issues by creating indicators that can bridge generic evaluation aspects and maintenance data. The framework is highly adaptive as data availability functions are used as inputs to generate indicators instead of passively filtering out non-applicable existing indicators.

The study aims to summarise the literature on cancer care pathways at the diagnostic and treatment phases. The objectives are to find factors influencing the delivery of cancer care pathways; to highlight any interrelating factors; to find gaps in the literature concerning areas of research; to summarise the strategies and recommendations implemented in the studies.

The study used a qualitative approach and developed a causal loop diagram to summarise the current literature on cancer care pathways, from screening and diagnosis to treatment. A total of 46 papers was finally included in the analysis, which highlights the recurring themes in the literature.

The study highlights the myriad areas of research applied to cancer care pathways. Factors influencing the delivery of cancer care pathways were classified into different albeit interrelated themes. These include access barriers to care, hospital emergency admissions, fast track diagnostics, delay in diagnosis, waiting time to treatment and strategies to increase system efficiency.

As far as the authors know, this is the first study to present a visual representation of the complex relationship between factors influencing the delivery of cancer care pathways.

This paper aims to analyze the effects of web communities vs company websites in providing tactile information considering different types of product in terms of touch diagnosticity (low- vs. high-touch products).

Three experimental studies were conducted to examine the effect of online information sources (i.e. web communities vs. company websites) in providing tactile information on consumer responses, considering the moderation role of product type in terms of touch diagnosticity (low- vs. high-touch products, Study 1), the moderating role of type of information (tactile vs. generic, Study 2a); and the moderating role of need for touch (NFT) (Study 2a and 2b).

While previous research converges on the idea that the provision of a written description of tactile properties deriving from the product usage is particularly effective for products for which tactile information is diagnostic and for individuals high in NFT, the results demonstrated that the presence (vs. the absence) of the description of the tactile properties provided by web communities (vs. company websites) matters for those products for which touch is not diagnostic and for individuals low in NFT.

The findings have particular relevance for emerging brands intending to commercialize their products in the digital environment. These companies should be present in web communities to describe a product’s tactile characteristics, especially if not diagnostic.

This paper significantly contributes to a better understanding of a little studied area, namely, consumer responses toward haptic compensational strategies providing haptic cues (e.g. written description of tactile information along with pictures of products) aiming at compensating for the absence of touch, underlining the differential influence of online sources of tactile information on consumer responses across different types of products.

Professionals working with community populations are often presented with complicated cases where it is difficult to determine which diagnosis or diagnoses are appropriate. Differentiating among neurodevelopmental disorders such as autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder and intellectual disability can be a complex process, especially, as these disorders have some overlapping symptoms and often co-occur in young children. This series of case studies aims to present commonly overlapping symptoms in children who present to clinics with developmental concerns.

This paper presents three case studies that were completed at a free community ASD screening clinic in Southern California.

The case studies have common presenting behaviors and symptoms (e.g. social communication difficulties) that often co-occur across diagnoses; explanations for the final diagnoses are given in each case.

Conclusions from these three cases cannot generalize to all children being seen in clinics for neurodevelopmental concerns.

This series of case studies highlights commonly overlapping symptoms in children who present for differential diagnosis with social and/or behavioral concerns. Implications for educational placement and intervention are discussed.

These cases highlight the challenges involved in the differential and dual diagnostic process for young children with developmental concerns. Diagnostic considerations can affect later educational placement and opportunities for socialization.

This series of case studies provide practical information for clinicians about how to effectively differentiate between commonly occurring neurodevelopmental disorders, particularly given recent changes to the Diagnostic and Statistical Manual, 5th edition (DSM-5).

The primary objective of this paper is to show a systematic and methodological approach for the digitalization of critical clinical pathways (CPs) within the healthcare domain.

The methodology entails the integration of service design (SD) and action research (AR) methodologies, characterized by iterative phases that systematically alternate between action and reflective processes, fostering cycles of change and learning. Within this framework, stakeholders are engaged through semi-structured interviews, while the existing and envisioned processes are delineated and represented using BPMN 2.0. These methodological steps emphasize the development of an autonomous, patient-centric web application alongside the implementation of an adaptable and patient-oriented scheduling system. Also, business processes simulation is employed to measure key performance indicators of processes and test for potential improvements. This method is implemented in the context of the CP addressing transient loss of consciousness (TLOC), within a publicly funded hospital setting.

The methodology integrating SD and AR enables the detection of pivotal bottlenecks within diagnostic CPs and proposes optimal corrective measures to ensure uninterrupted patient care, all the while advancing the digitalization of diagnostic CP management. This study contributes to theoretical discussions by emphasizing the criticality of process optimization, the transformative potential of digitalization in healthcare and the paramount importance of user-centric design principles, and offers valuable insights into healthcare management implications.

The study’s relevance lies in its ability to enhance healthcare practices without necessitating disruptive and resource-intensive process overhauls. This pragmatic approach aligns with the imperative for healthcare organizations to improve their operations efficiently and cost-effectively, making the study’s findings relevant.

The purpose is using generative adversarial network (GAN) to solve the problem of sample augmentation in the case of imbalanced bearing fault data sets and improving residual network is used to improve the diagnostic accuracy of the bearing fault intelligent diagnosis model in the environment of high signal noise.

A bearing vibration data generation model based on conditional GAN (CGAN) framework is proposed. The method generates data based on the adversarial mechanism of GANs and uses a small number of real samples to generate data, thereby effectively expanding imbalanced data sets. Combined with the data augmentation method based on CGAN, a fault diagnosis model of rolling bearing under the condition of data imbalance based on CGAN and improved residual network with attention mechanism is proposed.

The method proposed in this paper is verified by the western reserve data set and the truck bearing test bench data set, proving that the CGAN-based data generation method can form a high-quality augmented data set, while the CGAN-based and improved residual with attention mechanism. The diagnostic model of the network has better diagnostic accuracy under low signal-to-noise ratio samples.

A bearing vibration data generation model based on CGAN framework is proposed. The method generates data based on the adversarial mechanism of GAN and uses a small number of real samples to generate data, thereby effectively expanding imbalanced data sets. Combined with the data augmentation method based on CGAN, a fault diagnosis model of rolling bearing under the condition of data imbalance based on CGAN and improved residual network with attention mechanism is proposed.