The delayed delivery, poor fitting and discomfort of customised orthoses are reported in rehabilitation clinics as resulting in more invasive interventions. The current practice of orthosis customisation relies heavily upon the experience and fabrication processes of therapists. In order to better understand the current practice, and thus identify data that is required for better comfort moving towards a data-driven customisation, this article describes a study generating working models of therapists. Customisations of hand and wrist orthoses for 18 patients were observed. Verbal protocol analysis was employed to extend the current understanding of fabrication processes. Working models of four therapists were established with quantitative evaluation on major phases, interactive activities and iterations of performing tasks during fabrication, revealing different working models between in- and out-patient departments (e.g. fabrication for in-patients was more complex and focussed on ergonomic fitting whereas fabrication for out-patients paid attention to durability) which were qualitatively explained. Practitioner summary: Fit and comfort are imperative for orthosis design and fabrication, however the current practice of customisation of an orthosis relies upon the experience of individual hand therapist. The article presents working models of hand therapists, and relevant data that would enable customisation of orthosis for better fit. Abbreviations: VPA: verbal protocol analysis; h&w: hand and wrist; LTT: low temperature thermoplastic; ANOVA: analysis of variance. 

Purpose: the objective of this work is to empirically investigate the design of a service delivery system that supports the provision of modular service logistics offerings. Design/methodology/approach: an in-depth single-case study relying on interview data and extensive documentary evidence is carried out in the business-to-business (B2B) logistics sector. Three main analytical techniques are used to make sense of the qualitative data: thematic analysis, process mapping and the application of modular operators. Findings: a modular service delivery system comprises three types of processes that collectively deliver modular offerings. The platform consists of core processes that enable the collection, transport and delivery of physical items for all offerings (modular and non-modular). Dedicated modular processes are mandatory and exclusive to individual modular offerings. Optional modular processes are shared across several modular offerings. Interfaces regulate physical (e.g. parcels or parts) and information (e.g. booking data) inputs provided by the customer in order to control the interdependencies within these different process types. Practical implications: the identification of three process types and their interdependencies provides detailed insights into how managers can design modular logistics services that benefit from economies of scale and meet increasingly variable customer requirements. The importance of well-designed interfaces among the customers, the service offering and the service delivery system is highlighted. Originality/value: This study extends previous modularity studies in service logistics. It is the first study to apply modular operators to determine the presence of modularity in the service delivery system and to establish the role of different process types in enabling modularity in the service delivery system. 

BACKGROUND: Generalized anxiety disorder (GAD) is characterized by excessive worry that is difficult to control and has high comorbidity with mood disorders including depression. Individuals experience long wait times for diagnosis and often face accessibility barriers to treatment. There is a need for a digital solution that is accessible and acceptable to those with GAD. OBJECTIVE: This paper aims to describe the development of a digital intervention prototype of acceptance and commitment therapy (ACT) for GAD that sits within an existing well-being app platform, BioBase. A pilot feasibility study evaluating acceptability and usability is conducted in a sample of adults with a diagnosis of GAD, self-referred to the study. METHODS: Phase 1 applied the person-based approach (creation of guiding principles, intervention design objectives, and the key intervention features). In Phase 2 participants received the app-based therapeutic and paired wearable for 2 weeks. Self-report questionnaires were obtained at baseline and posttreatment. The primary outcome was psychological flexibility (Acceptance and Action Questionnaire-II [AAQ-II]) as this is the aim of ACT. Mental well-being (Warwick-Edinburgh Mental Well-being Scale [WEMWBS]) and symptoms of anxiety (7-item Generalized Anxiety Disorder Assessment [GAD-7]) and depression (9-item Patient Health Questionnaire [PHQ-9]) were also assessed. Posttreatment usability was assessed via self-report measures (System Usability Scale [SUS]) in addition to interviews that further explored feasibility of the digital intervention in this sample. RESULTS: the app-based therapeutic was well received. of 13 participants, 10 (77%) completed the treatment. Results show a high usability rating (83.5). Participants found the digital intervention to be relevant, useful, and helpful in managing their anxiety. Participants had lower anxiety (d=0.69) and depression (d=0.84) scores at exit, and these differences were significantly different from baseline (P=.03 and. 008 for GAD-7 and PHQ-9, respectively). Participants had higher psychological flexibility and well-being scores at exit, although these were not significantly different from baseline (P=.11 and. 55 for AAQ-II and WEMWBS, respectively). CONCLUSIONS: This ACT prototype within BioBase is an acceptable and feasible digital intervention in reducing symptoms of anxiety and depression. This study suggests that this intervention warrants a larger feasibility study in adults with GAD. 

BACKGROUND: Given the established links between an individual's behaviors and lifestyle factors and potentially adverse health outcomes, univariate or simple multivariate health metrics and scores have been developed to quantify general health at a given point in time and estimate risk of negative future outcomes. However, these health metrics may be challenging for widespread use and are unlikely to be successful at capturing the broader determinants of health in the general population. Hence, there is a need for a multidimensional yet widely employable and accessible way to obtain a comprehensive health metric. OBJECTIVE: the objective of the study was to develop and validate a novel, easily interpretable, points-based health score ("C-Score") derived from metrics measurable using smartphone components and iterations thereof that utilize statistical modeling and machine learning (ML) approaches. METHODS: a literature review was conducted to identify relevant predictor variables for inclusion in the first iteration of a points-based model. This was followed by a prospective cohort study in a UK Biobank population for the purposes of validating the C-Score and developing and comparatively validating variations of the score using statistical and ML models to assess the balance between expediency and ease of interpretability and model complexity. Primary and secondary outcome measures were discrimination of a points-based score for all-cause mortality within 10 years (Harrell c-statistic) and discrimination and calibration of Cox proportional hazards models and ML models that incorporate C-Score values (or raw data inputs) and other predictors to predict the risk of all-cause mortality within 10 years. RESULTS: the study cohort comprised 420,560 individuals. During a cohort follow-up of 4,526,452 person-years, there were 16,188 deaths from any cause (3.85%). The points-based model had good discrimination (c-statistic=0.66). There was a 31% relative reduction in risk of all-cause mortality per decile of increasing C-Score (hazard ratio of 0.69, 95% CI 0.663-0.675). A Cox model integrating age and C-Score had improved discrimination (8 percentage points; c-statistic=0.74) and good calibration. ML approaches did not offer improved discrimination over statistical modeling. CONCLUSIONS: the novel health metric ("C-Score") has good predictive capabilities for all-cause mortality within 10 years. Embedding the C-Score within a smartphone app may represent a useful tool for democratized, individualized health risk prediction. A simple Cox model using C-Score and age balances parsimony and accuracy of risk predictions and could be used to produce absolute risk estimations for app users. 

Diabetes affects over 400 million people and is among the leading causes of
morbidity worldwide. Identification of high-risk individuals can support early
diagnosis and prevention of disease development through lifestyle changes.
However, the majority of existing risk scores require information about
blood-based factors which are not obtainable outside of the clinic. Here, we
aimed to develop an accessible solution that could be deployed digitally and at
scale. We developed a predictive 10-year type 2 diabetes risk score using 301
features derived from 472,830 participants in the UK Biobank dataset while
excluding any features which are not easily obtainable by a smartphone. Using a
data-driven feature selection process, 19 features were included in the final
reduced model. A Cox proportional hazards model slightly overperformed a
DeepSurv model trained using the same features, achieving a concordance index
of 0.818 (95% CI: 0.812-0.823), compared to 0.811 (95% CI: 0.806-0.815). The
final model showed good calibration. This tool can be used for clinical
screening of individuals at risk of developing type 2 diabetes and to foster
patient empowerment by broadening their knowledge of the factors affecting
their personal risk. 

Background: Cardiovascular diseases (CVDs) are among the leading causes of
death worldwide. Predictive scores providing personalised risk of developing
CVD are increasingly used in clinical practice. Most scores, however, utilise a
homogenous set of features and require the presence of a physician.
. Objective: the aim was to develop a new risk model (DiCAVA) using statistical
and machine learning techniques that could be applied in a remote setting. A
secondary goal was to identify new patient-centric variables that could be
incorporated into CVD risk assessments.
. Methods: Across 466,052 participants, Cox proportional hazards (CPH) and
DeepSurv models were trained using 608 variables derived from the UK Biobank to
investigate the 10-year risk of developing a CVD. Data-driven feature selection
reduced the number of features to 47, after which reduced models were trained.
Both models were compared to the Framingham score.
. Results: the reduced CPH model achieved a c-index of 0.7443, whereas DeepSurv
achieved a c-index of 0.7446. Both CPH and DeepSurv were superior in
determining the CVD risk compared to Framingham score. Minimal difference was
observed when cholesterol and blood pressure were excluded from the models
(CPH: 0.741, DeepSurv: 0.739). The models show very good calibration and
discrimination on the test data.
. Conclusion: We developed a cardiovascular risk model that has very good
predictive capacity and encompasses new variables. The score could be
incorporated into clinical practice and utilised in a remote setting, without
the need of including cholesterol. Future studies will focus on external
validation across heterogeneous samples. 

The burden of depression and anxiety in the world is rising. Identification
of individuals at increased risk of developing these conditions would help to
target them for prevention and ultimately reduce the healthcare burden. We
developed a 10-year predictive algorithm for depression and anxiety using the
full cohort of over 400,000 UK Biobank (UKB) participants without pre-existing
depression or anxiety using digitally obtainable information. From the initial
204 variables selected from UKB, processed into > 520 features, iterative
backward elimination using Cox proportional hazards model was performed to
select predictors which account for the majority of its predictive capability.
Baseline and reduced models were then trained for depression and anxiety using
both Cox and DeepSurv, a deep neural network approach to survival analysis. The
baseline Cox model achieved concordance of 0.813 and 0.778 on the validation
dataset for depression and anxiety, respectively. For the DeepSurv model,
respective concordance indices were 0.805 and 0.774. After feature selection,
the depression model contained 43 predictors and the concordance index was
0.801 for both Cox and DeepSurv. The reduced anxiety model, with 27 predictors,
achieved concordance of 0.770 in both models. The final models showed good
discrimination and calibration in the test datasets.We developed predictive
risk scores with high discrimination for depression and anxiety using the UKB
cohort, incorporating predictors which are easily obtainable via smartphone. If
deployed in a digital solution, it would allow individuals to track their risk,
as well as provide some pointers to how to decrease it through lifestyle
changes. 

Background: the emergence of COVID-19 resulted in postponement of nonemergent surgical procedures for cardiac patients in London. mHealth represented a potentially viable mechanism for highlighting deteriorating patients on the lengthened cardiac surgical waiting lists. Objective: to evaluate the deployment of a digital health solution to support continuous triaging of patients on a cardiac surgical waiting list. Method: an NHS trust utilized an app-based mHealth solution (Huma Therapeutics) to help gather vital information on patients awaiting cardiac surgery (valvular and coronary surgery). Patients at a tertiary cardiac center on a waiting list for elective surgery were given the option to be monitored remotely via a mobile app until their date of surgery. Patients were asked to enter their symptoms once a week. The clinical team monitored this information remotely, prompting intervention for those patients who needed it. Results: Five hundred and twenty-five patients were on boarded onto the app. of the 525 patients using the solution, 51 (9.71%) were identified as at risk of deteriorating based on data captured via the remote patient monitoring platform and subsequently escalated to their respective consultant. 81.7% of patients input at least one symptom after they were on boarded on the platform. Discussion: Although not a generalizable study, this change in practice clearly demonstrates the feasibility and potential benefit digital remote patient monitoring can have in triaging large surgical wait lists, ensuring those that need care urgently receive it. We recommend further study into the potential beneficial outcomes from preoperative cardiac mHealth solutions. 

The COVID-19 pandemic has created an urgent need for robust, scalable
monitoring tools supporting stratification of high-risk patients. This research
aims to develop and validate prediction models, using the UK Biobank, to
estimate COVID-19 mortality risk in confirmed cases. From the 11,245
participants testing positive for COVID-19, we develop a data-driven random
forest classification model with excellent performance (AUC: 0.91), using
baseline characteristics, pre-existing conditions, symptoms, and vital signs,
such that the score could dynamically assess mortality risk with disease
deterioration. We also identify several significant novel predictors of
COVID-19 mortality with equivalent or greater predictive value than established
high-risk comorbidities, such as detailed anthropometrics and prior acute
kidney failure, urinary tract infection, and pneumonias. The model design and
feature selection enables utility in outpatient settings. Possible applications
include supporting individual-level risk profiling and monitoring disease
progression across patients with COVID-19 at-scale, especially in
hospital-at-home settings. 

Interoception, perception of one's bodily state, has been associated with mental health and socio-emotional processes. However, several interoception tasks are of questionable validity, meaning associations between interoception and other variables require confirmation with new measures. Here we describe the novel, smartphone-based Phase Adjustment Task (PAT). Tones are presented at the participant's heart rate, but out of phase with heartbeats. Participants adjust the phase relationship between tones and heartbeats until they are synchronous. Data from 124 participants indicates variance in performance across participants which is not affected by physiological or strategic confounds. Associations between interoception and anxiety, depression and stress were not significant. Weak associations between interoception and mental health variables may be a consequence of testing a non-clinical sample. A second study revealed PAT performance to be moderately stable over one week, consistent with state effects on interoception. 

BACKGROUND: the prevalence of workplace-related stress and anxiety is high, resulting in stress-related physical and mental illness. Digital self-guided interventions aimed at key areas of workplace design may be able to provide remote anxiolytic effects. OBJECTIVE: the aim of this feasibility study is to assess changes in anxiety and mental well-being after use of the BioBase programme, a mobile phone platform for psycho-educational modules, tools, and real-time feedback of physiological data. METHODS: a 4-week observational study was carried out in 55 healthy adults who were screened for stress with the Depression Anxiety Stress Scale (DASS) Stress subscale. Participants completed anxiety (6-item State-Trait Anxiety Inventory [STAI]) and mental well-being (Warwick-Edinburgh Mental Well-being Scale [WEMWBS]) questionnaires at baseline and at 4 weeks. Feedback questionnaires were administered after 4 weeks. RESULTS: After 4 weeks of using the programme and controlling for any effect of being paid to take part in the study, STAI significantly decreased (baseline mean 45.52 [SD 13.2]; 4-week mean 39.82 [SD 11.2]; t54=-3.51; P 

BACKGROUND: University students in the United Kingdom are experiencing increasing levels of anxiety. A program designed to increase awareness of one's present levels of well-being and suggest personalized health behaviors may reduce anxiety and improve mental well-being in students. The efficacy of a digital version of such a program, providing biofeedback and therapeutic content based on personalized well-being metrics, is reported here. OBJECTIVE: the aim of this study was to test the efficacy and sustained effects of using a mobile app (BioBase) and paired wearable device (BioBeam), compared with a waitlist control group, on anxiety and well-being in university students with elevated levels of anxiety and stress. METHODS: the study employed a randomized, waitlist-controlled trial with assessments at baseline, 2 weeks, postintervention (4 weeks), and follow-up (6 weeks). Participants were eligible if they were current full-time undergraduate students and (1) at least 18 years of age, (2) scored >14 points on the Depression, Anxiety, and Stress Scale-21 items (DASS-21) stress subscale or >7 points on the DASS-21 anxiety subscale, (3) owned an iOS mobile phone, (4) did not have any previous psychiatric or neurological conditions, (6) were not pregnant at the time of testing, and (7) were able to read and understand English. Participants were encouraged to use BioBase daily and complete at least one course of therapeutic content. A P value ≤.05 was considered statistically significant. RESULTS: We found that a 4-week intervention with the BioBase program significantly reduced anxiety and increased perceived well-being, with sustained effects at a 2-week follow-up. Furthermore, a significant reduction in depression levels was found following the 4-week usage of BioBase. CONCLUSIONS: This study shows the efficacy of a biofeedback digital intervention in reducing self-reported anxiety and increasing perceived well-being in UK university students. Results suggest that digital mental health interventions could constitute a novel approach to treat stress and anxiety in students, which could be combined or integrated with existing therapeutic pathways. TRIAL REGISTRATION: Open Science Framework (OSF.io) 2zd45; https://osf.io/2zd45/. 

Background: Recovery from stress is a predictive factor for cardiovascular health, and heart rate variability (HRV) is suggested to be an index of how well people physiologically recover from stress. Biofeedback and mindfulness interventions that include guided breathing have been shown to be effective in increasing HRV and facilitating stress recovery. Objective: This study aims to assess the effectiveness of a brief app-based breathing intervention (BioBase) in enhancing physiological recovery among employees who were induced to cognitive and emotional stress. Methods: in total, we recruited 75 full-time employees. Interbeat (RR) intervals were recorded continuously for 5 min at baseline and during cognitive and emotional stress induction. The session ended with a 5-min recovery period during which participants were randomly allocated into 3 conditions: App-based breathing (BioBase), mindfulness body scan, or control. Subjective tension was assessed at the end of each period. Results: Subjective tension significantly increased following stress induction. HRV significantly decreased following the stress period. In the recovery phase, the root mean square of successive RR interval differences (P=.002), the percentage of successive RR intervals that differed by >50 ms (P=.008), and high frequency (P=.01) were significantly higher in the BioBase breathing condition than in the mindfulness body scan and the control groups. Conclusions: Biofeedback breathing interventions digitally delivered through a commercially available app can be effective in facilitating stress recovery among employees. These findings contribute to the mobile health literature on the beneficial effects of brief app-based breathing interventions on employees' cardiovascular health. 

Shared understanding is central to a design team's performance. While current literature describes general relationships between team interaction and the development of shared understanding, it is not known if or how dynamic patterns in team processes impact this. Using a comparative study, we describe dynamic process patterns that influence the relationship between collaborative design work and shared understanding development. We propose two major patterns that impact shared understanding development: taskwork-teamwork interdependency and inter-action heartbeat, and describe how what we term cross-level pattern alignment moderates their effect. We propose a conceptual model that integrates these insights and provides testable propositions. As such, this work has significant implications for both design researchers and practitioners. 

Distributed Ledger Technology (DLT), such as blockchain, has the potential to transform supply chains. It can provide a cryptographically secure and immutable record of transactions and associated metadata (origin, contracts, process steps, environmental variations, microbial records, etc.) linked across whole supply chains. The ability to trace food items within and along a supply chain is legally required by all actors within the chain. It is critical to food safety, underpins trust and global food trade. However, current food traceability systems are not linked between all actors within the supply chain. Key metadata on the age and process history of a food is rarely transferred when a product is bought and sold through multiple steps within the chain. Herein, we examine the potential of massively scalable DLT to securely link the entire food supply chain, from producer to end user. Under such a paradigm, should a food safety or quality issue ever arise, authorized end users could instantly and accurately trace the origin and history of any particular food item. This novel and unparalleled technology could help underpin trust for the safety of all food, a critical component of global food security. In this paper, we investigate the (i) data requirements to develop DLT technology across whole supply chains, (ii) key challenges and barriers to optimizing the complete system, and (iii) potential impacts on production efficiency, legal compliance, access to global food markets and the safety of food. Our conclusion is that while DLT has the potential to transform food systems, this can only be fully realized through the global development and agreement on suitable data standards and governance. In addition, key technical issues need to be resolved including challenges with DLT scalability, privacy and data architectures. 

There is a growing focus on achieving ‘openness’ in the design and transformation of organizations, in which the enabling role of ICTs is considered increasingly central. However, bureaucratic organizations with rigid structures continue to face significant challenges in moving towards more open forms of organizing. In this paper, we contribute to our understanding of these challenges by building on existing conceptualizations of openness as a form of boundary work that transforms by challenging both internal and external organizational boundaries. In particular, we draw on a performative view derived from actor-network theory to analyze a case study of ICT-based administrative reforms in a judicial system. Building on our case analysis, we develop a typology of the various roles that ICTs can play in both enabling and constraining ongoing boundary work within the context of their implementation. We thus present a view of ICT-enabled open organizing as a process where ICTs contribute to problematizing, shifting, and augmenting ongoing boundary work. This view highlights the inherently equivocal nature of the role of ICTs in transformations towards higher levels of openness. 

Departing from established research on entrepreneurship, design-based entrepreneurship places an explicit emphasis on the entrepreneurial process as evolutionary and emergent in which knowledge and understanding of an opportunity are acquired incrementally by means of design and evaluation of alternative solutions. This paper develops a use case of BioBeats Ltd. a UK-based university spin-off which has successfully managed to turn an opportunity in digital health into a commercially viable enterprise. Adopting a design-based paradigm, the company under study started by building a technical solution informed by a set of design principles which subsequently allowed the company to convert the socio-technical nature of the opportunity into technological artefacts that were further refined and tested by means of real-world experiments with third parties and citizens. 

In recent years, measures of cardiac interoceptive accuracy have been heavily scrutinised. The focus has been on potentially confounding physiological and psychological factors; little research has examined whether the device used to record objective heartbeats may influence cardiac interoceptive accuracy. The present studies assessed whether the device employed influences heartbeat counting (HCT) accuracy and the location from which heartbeats are perceived. In Study One, participants completed the HCT using a hard-clip finger pulse oximeter, electrocardiogram (ECG) and a smartphone application. In Study Two, an ECG, hard-clip and soft-clip oximeter were compared. Moderate-strong correlations were observed across devices, however, mean HCT accuracy and confidence varied as a function of device. Increased sensation in the finger when using a hard-clip pulse oximeter was related to increased accuracy relative to ECG. Results suggest that the device employed can influence HCT performance, and argue against comparing, or combining, scores obtained using different devices. 

BACKGROUND: the speed of physiological recovery from stress may be a marker for cardiovascular disease risk. Stress management programs that incorporate guided breathing have been shown to moderate the stress response and augment recovery. OBJECTIVE: the aim of this study was to examine the effectiveness of an app-based brief relaxation intervention (BioBase) for facilitating physiological recovery in individuals exposed to a brief psychological stressor. METHODS: a total of 75 participants (44 women) completed a stressor speech task and were randomly assigned to one of three conditions: control, rumination, or an app-based relaxation breathing (BioBase) conditions. Heart rate variability (HRV) was assessed as a measure of autonomic function at baseline (6 min), during stress (6 min), and during recovery (6 min). RESULTS: There was a significant increase in subjective stress following stress exposure, but the ratings returned to baseline after recovery in all three groups. In addition, there was a significant decrease in vagally mediated HRV in the poststress period. During recovery, the root mean square of successive differences (P50 ms (pNN50; P 

The user experience of a product is recognized as having an increasing importance in particular in consumer products. Current approaches to designing user experiences are not easily translated to languages that a computer can understand. This paper examines a particular aspect of user experience, namely perception of the aesthetics of a product, to formalize this to rules, which are embedded into a tool to generate design. Investigating the perception of consumers is key to designing for their aesthetic preferences. Previous research has shown that consumers and designers often perceive the same products differently. This paper aims to embed rules on perception into a tool to support designers during design synthesis. Aesthetic design rules connecting perceptions with aesthetic features were integrated into a set grammar and a parametric modeling tool, and applied to the particular case of vases. The generated tool targeted the creation of vases with the perception of beautiful, elegant, and exciting. Results show that it is possible to generate beautiful, elegant, and exciting vases following the three aesthetic design rules, i.e. tall, simple, and curvy. The main contribution of this paper is the method used to incorporate information on perception into the set grammar and the parametric model. The tool is additionally proposed for supporting designers during design synthesis of shapes. The results are valid for vases but the method can be applied to other perceptions and product categories. 

Wearable physiological monitors are becoming increasingly commonplace in the consumer domain, but in literature there exists no substantive studies of their performance when measuring the physiology of ambulatory patients. In this Letter, the authors investigate the reliability of the heart-rate (HR) sensor in an exemplar 'wearable' wrist-worn monitoring system (the Microsoft Band 2); their experiments quantify the propagation of error from (i) the photoplethysmogram (PPG) acquired by pulse oximetry, to (ii) estimation of HR, and (iii) subsequent calculation of HR variability (HRV) features. Their experiments confirm that motion artefacts account for the majority of this error, and show that the unreliable portions of HR data can be removed, using the accelerometer sensor from the wearable device. The experiments further show that acquired signals contain noise with substantial energy in the high-frequency band, and that this contributes to subsequent variability in standard HRV features often used in clinical practice. The authors finally show that the conventional use of long-duration windows of data is not needed to perform accurate estimation of time-domain HRV features. 

The concept of “Government as a Platform” (GaaP) (O'Reilly, 2009) is coined frequently, but interpreted inconsistently: views of GaaP as being solely about technology and the building of technical components ignore GaaP's radical and disruptive embrace of a new economic and organisational model with the potential to improve the way Government operates – helping resolve the binary political debate about centralised versus localised models of public service delivery. We offer a structured approach to the application of the platforms that underpin GaaP, encompassing not only their technical architecture, but also the other essential aspects of market dynamics and organisational form. Based on a review of information systems platforms literature, we develop a Platform Appraisal Framework (PAF) incorporating the various dimensions that characterise business models based on digital platforms. We propose this PAF as a general contribution to the strategy and audit of platform initiatives and more specifically as an assessment framework to provide consistency of thinking in GaaP initiatives. We demonstrate the utility of our PAF by applying it to UK Government platform initiatives over two distinct periods, 1999–2010 and 2010 to the present day, drawing practical conclusions concerning implementation of platforms within the unique and complex environment of the public sector. 

In 2016 a survey was conducted among Russian companies to discover the most common problems associated with flexibility of business process management. A gap between strict process formalization demands and unpredictable nature of many knowledge-intensive operations was identified. The article suggests an approach to facilitate process management via combined context-aware set of methods. Firstly, the key terms are selected to serve as special cause indicators of variation in a process instance based on risk profiles. Afterward a cloud service is called, which automatically analyzes semantic annotation of the concrete process instance. Risk detection service identifies potential operational risks and in case of unexpected process execution complexities notifies users. Finally, expert search service calls for an expert in an organization automatically to create expert community. This novel approach could be used for knowledge-intensive business sectors (such as Research and Development) or in any organization interested in increasing its agility in changing business environment. 

Big data has been considered to be a breakthrough technological development over recent years. Notwithstanding, we have as yet limited understanding of how organizations translate its potential into actual social and economic value. We conduct an in-depth systematic review of IS literature on the topic and identify six debates central to how organizations realize value from big data, at different levels of analysis. Based on this review, we identify two socio-technical features of big data that influence value realization: portability and interconnectivity. We argue that, in practice, organizations need to continuously realign work practices, organizational models, and stakeholder interests in order to reap the benefits from big data. We synthesize the findings by means of an integrated model. 

© 2017 John Wiley. &. Sons, Ltd. Distributed ledger technologies (DLTs) are rewriting conventional notions of business transacting, creating fresh opportunities for value creation and capture. Using qualitative interview data as a primary resource, the proposed five-point model synthesizes these possibilities, demonstrating how they may lead to “disruptive innovation.” a further conceptual model is subsequently provided with a view to assisting future problem solving in the area. 

Service research highlights the utility of adopting a service ecosystem approach to studying service innovation. It suggests that service innovations can arise from challenging and developing the institutions (i.e. norms, rules, practices, meanings, and symbols) which underpin an ecosystem. Also, recent emphasis on consumer well-being posits that studies of service provision to poor consumers are needed. Reflecting these research priorities, the context of this case study on service innovation is the food waste ecosystem, whereby service innovations can contribute to the alleviation of food poverty for thousands of citizens. The central actor of the ecosystem is the leading UK charity organization fighting food waste. The paper’s contribution lies in using data from ecosystem actors to clarify the distinctions between institutions, thereby enhancing understanding of the application of institutional theory within the ecosystem and highlighting some theoretical implications for service innovation both within- and between-system levels. An actor institutions matrix is offered as a fruitful outcome of the analysis of the institutions, and suggested recommendations for operationalizing service ecosystem studies are outlined.  

The objective of this study was to examine the association between perseverative cognition in the form of work-related rumination, and heart rate variability (HRV). We tested the hypothesis that high ruminators would show lower vagally mediated HRV relative to low ruminators during their leisure time. Individuals were classified as being low (n = 17) or high ruminators (n = 19), using the affective scale on the work-related rumination measure. HRV was assessed using a wrist sensor band (Microsoft Band 2). HRV was sampled between 8 pm and 10 pm over three workday evenings (Monday to Wednesday) while individuals carried out their normal evening routines. Compared to the low ruminators, high affective ruminators demonstrated lower HRV in the form of root mean square successive differences (RMSSDs), relative to the low ruminators, indicating lower parasympathetic activity. There was no significant difference in heart rate, or activity levels between the two groups during the recording periods. The current findings of this study may have implications for the design and delivery of interventions to help individuals unwind post work and to manage stress more effectively. Limitations and implications for future research are discussed. 

Blockchain technology provides an exciting application space for innovation in diverse domains but threatens disintermediation for organizations providing a trusted and auditable account of ownership and transactions. It needs, however, an appropriate regulation to keep pace with technological developments. Technology remains very young, akin to the Internet in the early 1990s. Use cases, practical demonstrators, standards, and lexical consistency are urgently required. 

The paper discovers potential human interactions with growing amount of internet of things (IoT) via proposed concept of Social Web of Services (classical social web with smart things-daily life objects connected to the internet). To investigate the impact of IoT on user behaviour patterns we modelled human-thing interactions using agent-based simulation (ABM). We have proved that under certain conditions SmartThings, connected to the IoT, are able to change patterns of Human behaviour. Results of this work predict our way of living in the era of caused by viral effects of IoT application (HCI and M2M connections), and could be used to foster business process management in the IoT era. 

Purpose – Improvement of reverse supply chains requires accurate and timely information about the patterns of consumption. In the consumer context, the ways to generate and access such use-visibility data are in their infancy. The purpose of this study is to demonstrate how the Internet of Things (IoT) may be operationalised in the domestic setting to capture data on a consumer’s use of products and the implications for reverse supply chains. Design/methodology/approach – This study uses an explorative case approach drawing on data from studies of six UK households. “Horizontal” data, which reveals patterns in consumers’ use processes, is generated by combining “vertical” data from multiple sources. Use processes in the homes are mapped using IDEF0 and illustrated with the data. The quantitative data are generated using wireless sensors in the home, and qualitative data are drawn from online calendars, social media, interviews and ethnography. Findings – the study proposes four generic measurement categories for operationalising the concept of use-visibility: experience, consumption, interaction and depletion, which together address the use of different household resources. The explorative case demonstrates how these measures can be operationalised to achieve visibility of the context of use in the home. The potential of such use-visibility for reverse supply chains is discussed. Research limitations/implications – This explorative case study is based on an in-depth study of the bathroom which illustrates the application of use-visibility measures (UVMs) but provides a limited use context. Further research is needed from a wider set of homes and a wider set of use processes and contexts. Practical implications – the case demonstrates the operationalisation of the combination of data from different sources and helps answer questions of “why?”, “how?”, “when?” and “how much?”, which can inform reverse supply chains. The four UVMs can be operationalised in a way that can contribute to supply chain visibility, providing accurate and timely information of consumption, optimising resource use and eliminating waste. Originality/value – IDEF0 framework and case analysis is used to identify and validate four UVMs available through IoT data – that of experience, consumption, interaction and depletion. The UVMs characterise IoT data generated from a given process and inform the primary reverse flow in the future supply chain. They provide the basis for future data collection and development of theory around their effect on reverse supply chain efficiency. 

Purpose – the purpose of this paper is to explore how financial services (FS) organizations manage the customer experience. It aims to establish what practices are used, to articulate the role of the FS context in influencing the choice of practices, and to identify how these practices support experience co-creation from the perspective of the organization. Design/methodology/approach – the authors adopt a multiple case study approach. In total, 23 cases provide a rich understanding of the phenomenon studied which permits grounding the findings on robust data. Findings – the authors identify five practices that are consistently used by FS organizations to manage the customer experience. The findings suggest that four industry-specific characteristics affect the choice of these practices. The results also reveal how these practices support the co-creation of the customer experience. Research limitations/implications – the authors focus on the FS context only, do not examine the impact of the practices on performance, and do not explore experience co-creation from the perspective of the customer. Practical implications – Adopting these practices can facilitate a more co-created customer experience, which in turn can provide FS organizations with a competitive differentiator. Originality/value – the paper advances current knowledge by revealing five customer experience management practices that are specific to the FS context. Moreover, this is one of the first studies to explore experience co-creation from the perspective of the organization and to identify ways in which organizations can support customers in co-creating the experiences. 

This paper addresses the call for the development of product service supply chains (PSSC) model in support of supply chain integration, focusing specifically on the inter-relationship of product and services in PSSC. Whilst the product aspects of supply chains have been extensively researched there is widespread recognition that the service elements have received inadequate attention and that is the focus of the early part of the paper. We examine the features of service environments using unified service theory concluding that supply chain operations reference is unsuitable for developing a PSSC model. We apply their Operate model to Talkphone, an internationally leading telecoms company with an integrated PSSC. Using the case research method, we develop an IDEF0 model of the core operate processes of Talkphone. We then use the IDEF0 model to highlight the importance of co-ordinating product and multiple service concepts for integrated PSSCs. An important observation is the importance of the model as a heuristic device for managing and co-ordinating PSSCs, particularly where there are multiple service concepts (see Figure 8). Finally, we consider the implications for future work including the potential for further empirical work into types of integrated PSSC which combine the four product and four service types into 16 possible combinations. 

This paper explores the process improvement approaches of organisations. It seeks to identify process redesign principles and the combinations of these principles that are used successfully in industry. We use Q-methodology to explore the viewpoints of a range of highly experienced process experts about the success of 16 improvement practices. Q-methodology enables the examination of the similarities and differences in the success of the improvement principles used by organisations in order to inform archetypes of process improvement. Overall, our findings suggest that process improvement is determined through the application of two foundational principles combined with one of four archetypes. Remove non-value-adding tasks and re-sequence tasks are described as foundational principles of process improvement, whilst outsourcing needs to be approached with caution. Furthermore, we articulate four distinctive archetypes comprising unique configurations of improvement principles that can be used to redesign operational processes. Based on this evidence we propose a typology of process improvement. This work suggests that rather than adopting generic improvement frameworks, managers should consult the typology to determine the archetype in closest proximity to their specific requirements. This study has several limitations including the small number of items populating the concourse and the fact that implementation problems are not taken into account. © 2013 Taylor & Francis. 

This article provides insights on how to manage collective innovation in the digital economy, an innovation regime which is riddled with complex regulatory challenges and increasing litigation over intellectual property rights. Private collective organizations face two main challenges: (1) to promote collective innovation while preserving the private interests of the firms within the collective, and (2) to ensure that collective innovation does not weaken healthy competition. Through a case study of the Third Generation Partnership Project (3GPP), an exemplary private collective federation of organizations composed of standardization bodies, industry consortia and technology producers, we identify organizational solutions to these challenges. We find that a combination of specific IP rights instruments is key to manage these trade-offs. We also find that the combined policies of essential patenting, FRAND, and maximum royalty rate help overcome the specific challenges associated with collective innovation within competitive contexts. Finally we discuss the implications of our findings for managers and for policy. © 2014 Elsevier Ltd. 

Modern urban performance depends not only on the city's endorsement of hard infrastructure (physical capital), but also on the availability and quality of knowledge communication and social infrastructure (intellectual capital and social capital). This is one of the clear reasons why the concept of Smart Cities recently attracted a great amount of attention, both from academia and city planners. One of the challenges of the Smart City concept is how to raise human capital among people, such as making them culturally sensitive, mobile and to improve other social characteristics. This challenge is especially valid for industrial cities that are facing economic turbulence and a demand for revitalizing their public spaces and economic specialties. The aim of this study is to examine the correlation between the amount of international students in Russian universities with the positive changes that occur in a Russian student's human capital, and their neighbourhood areas, especially in public spaces. We aim to support the hypothesis that a network of "internationalized" universities serves as a revitalization measure for a city, facilitating the development of its surrounding areas, and reducing political and social risks within a society. Research methods for gathering data are: deductive trend search, which uses a literature review from leading academic journals and the empirical study based on the created questionnaire. This questionnaire forms a dataset which consists of a number of master courses held in English from one of the leading Russian universities based in Moscow. In this paper, we explain the research design and the results of a long-term project which we expect to complete in Russia in 2016. 

Stem cell researchers labor in unpredictable circumstances, beset by uncertainties allied to the study of cellular signaling behaviors. STS research, based primarily on the work of Star (1985), has demonstrated that medical scientists often approach these vicissitudes using a type of phronesis that aims to better qualify the causes of experimental ambiguities, while also identifying optimistic reference points to help guide future research. Knowledge of this type of phronesis is extended by this article, which examines the composition of the three most popular citations allied to the regenerative cellular biology/human-induced pluripotent stem cells (hiPSC) literature. When analyzed, these papers afford evidence that the adducement of positive signals begins with sorting and categorizing. This article finds that, when representing the outcomes of this cataloging for peer-review consumption, the authors concerned predicated their observations in a perspicacious rhetoric, which serves to reinforce positive-leaning ascriptions by couching them in images of virtue, fortitude, and due diligence. The research findings presented herewith suggest that adducements of this kind may be anchored in a representational practice that could be described as “modal splicing.” This article contributes to the STS literature by observing connections between modal splicing, perspicacious representation, and knowledge affirmations in hiPSC contexts.  

This article argues that the future of public services will be shaped increasingly by the evolution of global, Internet-enabled, digital platforms, with two distinctive technical and commercial features. First, use of open standards and architectures that separate standard business logic from supporting applications will allow government to become technology- and vendor-agnostic, freeing it from its overdependence on proprietary systems and suppliers. Second, over time, open standards and increased market choice will drive both innovation and progressive convergence on cheaper, standard "utility" public services. These two features will combine to create a powerful dynamic situation, driving disintegration of traditional "black boxed" technologies and services, traditionally organized around "systems integrators" and departmental structures, and their reaggregation around the citizen in the form of services. Such reaggregation is allowing progressively sharp distinctions between niche/innovative and commodity/standard offerings, supplied by a plural, innovative, and more cost-effective marketplace, with unprecedented implications for the way in which the state buys and deploys technology. We draw on a range of data from across public and private sectors to illustrate our argument and identify some key policy and implementation recommendations. © 2013 the Author. Published by Oxford University Press on behalf of the Journal of Public Administration Research and Theory, Inc. All rights reserved. 

As video games have grown from crude and simple circuit-based artefacts to a multibillion dollar worldwide industry, video-game music has become increasingly adaptive. Composers have had to use new techniques to avoid the traditional, event-based approach where music is composed mostly of looped audio tracks, which can lead to music that is too repetitive. In addition, these cannot scale well in the design of today's games, which have become increasingly complex and nonlinear in narrative. This paper outlines the use of experience-driven procedural music generation, to outline possible ways forward in the dynamic generation of music and audio according to user gameplay metrics. © 2009-2012 IEEE. 

In this study we analyze co-adoption of several modern concepts of enterprise architecture creation and describe real-time business processes generation on global cloud-based self-generated business service basis to increase the agility of enterprise. We simulated the process of generating the business model started by particular business request with the support of subject-oriented business process management approach that results in particular business process architecture, which is approved or rejected/corrected by board of directors and architectural committee of the enterprise. During that generation all necessary requirements for supporting resources, such as information, know-how, intellectual and professional skills, inputs and outputs, quality and operational risk limitations, moderation, control and monitoring, are formed. On a next twist of the development all formed requirements are to be satisfied by appropriate selections from the cloud facilities and again approved. Finally after several iterations business model will be created in reality and could be executed with predicted results. Briefly that means that certain sets of valued and weighted business process replicas are located in Clouds. In addition to that, in this article we have provided the advanced view on the topic with attempt to install a virtual SOA torrent that catches services from the Internet and makes them available to customers and represents a business service basis for real-time business processes. © Global Institute of Flexible Systems Management 2013. 

This article contributes to the emerging discipline of service science through an empirical investigation of value propositions as connections between service systems. The starting point for our research is that service science is an interdisciplinary approach to the study, design and implementation of service systems, a service system being considered a dynamic configuration of resources (people, technology, organisations and shared information) that create and deliver value between the provider and the customer through service (IfM and IBM 2008). Specifically, this article investigates value propositions in the context of equipment-based service systems. Our qualitative findings identify three value proposition cycles: Recovery, Availability and Outcome. In so doing, showing that providers offer three distinct propositions of value with three different primary transformations. This research contributes to theory in service systems by identifying value propositions as multiple, simultaneous and iterative connections between provider and customer systems. © 2012 Copyright Taylor and Francis Group, LLC. 

AbstractDefining the aesthetic and emotional value of a product is an important consideration for its design. Furthermore, if several designers are faced with the task of creating an object that describes a certain emotion/perception (aggressive, soft, heavy, etc.), each is most likely to interpret the emotion/perception with different shapes composed of a set of different geometric features. The authors propose an automatic approach to formalize the relationships between geometric information of three-dimensional objects and the intended emotional content using fuzzy logic. In addition, the automatically generated fuzzy knowledge base was compared to the user's perceptions and to the manually constructed fuzzy knowledge base. The initial findings indicate that the approach is valid to formalize geometric information with perceptions and validate the author's manually developed fuzzy models. 

the Bristol Inquiry is arguably one of the most important cases of judicial medical investigation held in the United Kingdom (UK), which continues to raise important insights into the social construction of medical and scientific risks. As a way of marking the inquiry’s tenth anniversary year, this article returns to an important conversation held between noted pediatric cardiothoracic and cardiovascular specialists, on days 49 and 50 of the inquiry’s proceedings. Their conversance principally describes a pathway of scientific advancement across four decades (c. the mid-1960s to 1999). Risks, and the avoidance of medical error (iatrogenesis), represent important subtopics of the ensuing historical narrative, within which the experts describe opportunities for error as diminishing in-line with paradigmatic advancement. The telling of this story of risk mitigation involves the expurgation of considerable sensitivities to scientific uncertainty. In analyzing this example of expert scientific witness testimony, the article primarily considers how representations of uncertainty are formulated in language. The article concludes that, in this instance, the social construction of risk and uncertainty were heteroglossically premised upon language catalysts, cognitive metaphors, and logic-based mathematical rubrics.