The rapidly changing and increasingly complex processes enabled by artificial intelligence (AI) applications challenge the conventional concepts of innovation. In contrast to a general perception that AI adoption can augment innovation output, managers still lack empirical guidance on how to structure innovation processes with human-AI interaction across time and space. Drawing on observations from case studies in the aerospace, heavy engineering, information technology, and pharmaceutical sectors, this paper presents the development of a conceptual model for digital innovation to represent (i) Learning Processes (LPs) focusing on knowledge creation and knowledge reuse and (ii) Product Development Processes (PDPs) leading to radical and incremental changes. The conceptual model is inductively developed based on a theory building approach using multiple case studies. A set of transformative characteristics centralized on Originality, Reliability, Transferability, and Adaptability (ORTA) are identified to guide decision-making along multi-stage and cross-layer innovation processes involving cyclical handoffs between humans and machine agents. These ORTA characteristics form a base for strategic decision-making along the Human and AI decision spectrum suited to prepare companies for survival and prosperity in their journeys of digital transformation.

PurposeHow organisations interact with and respond to environmental pressures has been a long-term interest of organisational scholars. Still, it remains an under-theorised phenomenon from a project perspective. So far, there is limited understanding of how projects, which are composed by a constellation of organisations, "respond" to institutional pressures that are exerted on them. This research takes the perspective of projects as adopters/implementers of institutional pressures and analyses how they interact with, and respond to, such pressures. More specifically, this research explores how construction projects respond to the pressure of a Building Information Modelling (BIM) mandate.Design/methodology/approachMultiple in-depth case studies were conducted to explore the practical implementation of a BIM mandate in the UK and understand how the construction projects responded to the coercive pressures to implement a new policy mandate for process digitalisation. Multiple sources were employed for data collection and the data were analysed inductively. The findings identify a hybrid response comprising four distinct ways that projects might respond to an institutional pressure.FindingsWe find that projects decouple both from the content and from the intended purpose of a policy, i.e. there are two variance of a policy-practice decoupling phenomenon in projects. The findings also reveal the underlying conditions leading to decoupling.Originality/valueWe advance decoupling literature so that it better applies to the temporary, distributed and interdependent work conducted via projects. Second, we define decoupling in projects as a provisional and fragmented process of wayfinding through heterogeneous institutional spaces, and discuss the potential policy-practice assemblages in projects, influenced by how, if and when project members' activities decouple from the many and often contradicting institutional pressures they face. Third, we discuss how the qualitatively different forms of decoupling that we identified in our work may act as part of a legitimation process in ambiguous situations whereby projects might share a resemblance of conformity with institutional pressures when they are de facto only partially conforming to them.

In this study, we reviewed aircraft accidents in order to understand how autonomy and safety has been managed in the aviation industry, with the aim of transferring our findings to autonomous cyber-physical systems (CPSs) in general. Through the qualitative analysis of 26 reports of aircraft accidents that took place from 2016 to 2022, we identified the most common contributing factors and the actors involved in aircraft accidents. We found that accidents were rarely the result of a single event or actor, with the most common contributing factor being non-adherence to standard operating procedures (SOPs). Considering that the aviation industry has had decades to perfect their SOPs, it is important for CPSs not only to consider the actors and causes that may contribute to safety-related issues, but also to consider well-defined reporting practices, as well as the different levels of mechanisms checked by diverse stakeholders, in order to minimise the cascading nature of such events to improve safety. In addition to proposing a new definition of safety, in this study we suggest reviewing high-reliability organisations to offer further insights as part of future research on CPS safety.

On 11th March 2020, the UK government announced plans for the scaling of COVID-19 testing, and on 27th March 2020 it was announced that a new alliance of private sector and academic collaborative laboratories were being created to generate the testing capacity required. The Cambridge COVID-19 Testing Centre (CCTC) was established during April 2020 through collaboration between AstraZeneca, GlaxoSmithKline, and the University of Cambridge, with Charles River Laboratories joining the collaboration at the end of July 2020. The CCTC lab operation focussed on the optimised use of automation, introduction of novel technologies and process modelling to enable a testing capacity of 22,000 tests per day. Here we describe the optimisation of the laboratory process through the continued exploitation of internal performance metrics, while introducing new technologies including the Heat Inactivation of clinical samples upon receipt into the laboratory and a Direct to PCR protocol that removed the requirement for the RNA extraction step. We anticipate that these methods will have value in driving continued efficiency and effectiveness within all large scale viral diagnostic testing laboratories.

Transmission of SARS-CoV-2 without symptoms is well described, and may be mitigated by mass testing. Nonetheless, the optimal implementation and quantitative real-world impact of this approach remain unclear. During a period of rising SARS-CoV-2 prevalence, students at the University of Cambridge were enrolled in a voluntary programme of weekly PCR-based asymptomatic screening. Swab pooling by household reduced the total testing capacity required by five-fold, without affecting laboratory workflows or compromising test sensitivity. Participation remained >75% throughout the study period. 299/671 (45%) of students diagnosed with SARS-CoV-2 were either identified or pre-emptively quarantined because of the screening programme. After a negative screening test, the risk of developing COVID-19 over the following 7 days was decreased by 51%. Modelling transmission using parameters from our study suggests a reduction in R0 of up to 31% attributable to weekly screening. We therefore demonstrate the feasibility and efficacy of regular, voluntary mass testing for COVID-19.

AboutSectionsRequest Access ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked InEmail Go to Section HomeService ScienceVol. 13, No. 4 Special Issue on Reimagining the Science of Service in a Post-Pandemic VUCA WorldGuest Editors: Marshall Fisher, Andy Neely, Rohit Verma, Editor-in-Chief: Saif BenjaafarGuest Editors: Marshall Fisher, Andy Neely, Rohit Verma, Editor-in-Chief: Saif BenjaafarPublished Online:23 Dec 2021https://doi.org/10.1287/serv.2021.0292"Special Issue on Reimagining the Science of Service in a Post-Pandemic VUCA World." Service Science, 13(4), p. 193 Back to Top Next FiguresReferencesRelatedInformation Volume 13, Issue 4December 2021Pages 193-295, C3 Article Information Metrics Downloaded 20 times in the past 12 months Information Published Online:December 23, 2021 Copyright © 2021, INFORMSCite asGuest Editors: Marshall Fisher, Andy Neely, Rohit Verma, Editor-in-Chief: Saif Benjaafar (2021) Special Issue on Reimagining the Science of Service in a Post-Pandemic VUCA World. Service Science 13(4):193-193. https://doi.org/10.1287/serv.2021.0292

Grounded theory was first introduced more than 50 years ago, but researchers are often still uncertain about how to implement it. This is not surprising, considering that even the two pioneers of this qualitative design, Glaser and Strauss, have different views about its approach, and these are just two of multiple variations found in the literature. While studies using grounded theory in management research are becoming more popular, these are often mixed with the case study approach, or they provide contradictory guidelines on how to use it. The aim of this paper is to provide a clear guide for researchers who wish to use grounded theory in exploratory studies in management research. To support this goal, the methodology’s different terms and variations, as found in the literature, are also discussed. This study can support researchers using this methodology, but it is also useful for reviewers and examiners who wish to understand more about it and the different ways in which researchers have implemented it.

Building Information Modelling (BIM) has been widely seen as bringing a paradigm change to the construction industry. However, scholars have acknowledged that neither widespread BIM implementation nor the envisaged systemic changes within the sector have taken place. Despite acknowledging that the industry's conditions and embedded contexts shape innovation diffusion, existing studies have not explored in any depth "how" the context might influence the episode of change when a new technology is introduced and the new practices accompanying that technology and old practices co-evolve. By adopting activity theory, its concepts of contradictions and multiple layers within the activity system, in this paper, we explore the interaction between situated and existing practices, or the "how" of implementation; that is, how the activity system is questioned and redefined during an episode of technological change. Drawing on data from multiple case studies, our findings demonstrate that situated practices related to the definition of information requirements, and the production and the handover of information were re-enacted following institutionalised socio-historical constructs (e.g. norms, rules, division of labour) at the industry and organisational levels. The findings provide insights regarding the inertia in the transformation of the sector as also deriving from re-enactments of socio-historical constructs that mediate the institutionalisation of situated practices. Our findings reveal re-enactment as part of the transformation process and contribute to calls for more realistic views on BIM implementation.

Understanding the uncertainty of a neural network's (NN) predictions is essential for many purposes. The Bayesian framework provides a principled approach to this, however applying it to NNs is challenging due to large numbers of parameters and data. Ensembling NNs provides an easily implementable, scalable method for uncertainty quantification, however, it has been criticised for not being Bayesian. This work proposes one modification to the usual process that we argue does result in approximate Bayesian inference; regularising parameters about values drawn from a distribution which can be set equal to the prior. A theoretical analysis of the procedure in a simplified setting suggests the recovered posterior is centred correctly but tends to have an underestimated marginal variance, and overestimated correlation. However, two conditions can lead to exact recovery. We argue that these conditions are partially present in NNs. Empirical evaluations demonstrate it has an advantage over standard ensembling, and is competitive with variational methods.

PurposeDrawing on the literature on dynamic capabilities and digital transformation, this paper conceptualises and investigates the relevant antecedents of an essential capability for digital transformation – the digital transforming capability – and its effect on the competitive advantage of firms.Design/methodology/approachA framework with individual and organisational microfoundations of the digital transforming capability is proposed based on previous research. The digital transforming capability is conceptualised as a second-order construct. The model is tested using data from a broad spectrum of large US companies. Structural equation modelling (SEM) is applied to test the proposed framework.FindingsThe study identifies three main microfoundations that, when combined, build a digital transforming capability (digital-savvy skills, digital intensity and context for action and interaction); in addition, the study tests the relationship between digital transforming capability and firm performance. The results validate the proposed theoretical framework. In addition to proposing relevant microfoundations of the digital transforming capability, we advance knowledge on the performance effects of those microfoundations.Originality/valueThe paper contributes to advancing the understanding of the digital transformation phenomenon by revealing the role of the primary components underlying the digital transforming capability. Yet the mechanisms by which the micro-level aspects are important for digital transformation and organisational outcomes are only suggested by anecdotal evidence. The paper also contributes to ongoing calls for further investigation to extend the understanding of the microfoundations of dynamic capabilities. Finally, by drawing on archival data, this study also contributes to calls to broaden the toolkit used in dynamic capabilities research.

Most of the existing research on BIM implementation and benefits realisation has departed from a technocentric perspective and instead promoted the belief that the benefits of using BIM technologies are an inevitable outcome of adoption in and of itself. Recently, scholars have acknowledged that important links between implementation practices and realisation of benefits have received little attention in the literature. There have been calls for more research taking a more detailed account of how outcomes can be achieved. Thus, by adopting a practice-based perspective as a theoretical lens, in this paper we investigate the ‘what’, ‘who’ and ‘how’ of successful BIM processes implementation. We propose a conceptual framework on the underlying conditions of successful implementation and assert that the achievement of benefits is dependent on the interaction of those three aspects. Building on qualitative data from nine construction projects from three client organisations in the UK, we show that the relationship between BIM implementation as a set of technologies and processes and performance cannot be understood without taking into account not only ‘what’ new processes exist in a BIM project and its interdependencies but also ‘who’ implements and engages with them and ‘how’ existing structures are reconfigured when enacting those practices. In alignment with recent research challenging the perceptions of BIM enactment as a linear process, our findings provide new insights into why the proclaimed benefits of BIM have not always been realised as an outcome of a ‘symbolic’ implementation of information management processes and lack of reconfiguration of existing institutions.

Digitalization and the growth of big data promise greater customization as well as change in how manufacturing is distributed. Yet, challenges arise in applying these new approaches in consumer goods industries that often emphasize mass production and extended supply chains. We build a conceptual framework to explore whether big data combined with new manufacturing technologies can facilitate redistributed manufacturing (RDM). Through analysis of 24 consumer goods industry cases using primary and secondary data, we investigated evolving manufacturing configurations, their underlying drivers, the role of big data applications, and their impact on the redistribution of manufacturing. We find some applications of RDM concepts, although in other cases existing manufacturing configurations are leveraged for high volume consumer goods products through big data analytics and market segmentation. The analysis indicates that the framework put forward in the paper has broader value in organizing thinking about emerging interrelationships between big data and manufacturing. ARTICLE HISTORY Received 30 September 2016 Accepted 10 September 2017

A simple, flexible approach to creating expressive priors in Gaussian process (GP) models makes new kernels from a combination of basic kernels, e.g. summing a periodic and linear kernel can capture seasonal variation with a long term trend. Despite a well-studied link between GPs and Bayesian neural networks (BNNs), the BNN analogue of this has not yet been explored. This paper derives BNN architectures mirroring such kernel combinations. Furthermore, it shows how BNNs can produce periodic kernels, which are often useful in this context. These ideas provide a principled approach to designing BNNs that incorporate prior knowledge about a function. We showcase the practical value of these ideas with illustrative experiments in supervised and reinforcement learning settings.

Unaware of existing big data technologies, organizations fail to develop a big data capability despite its disruptive impact on today's competitive business environment. To determine the shortcomings and strengths of developing a big data architecture with open-source tools from technical and managerial perspectives, this study (1) systematically reviews the available open-source big data technologies to present a comprehensive picture, and (2) proposes an open-source architecture for businesses to take as a reference while developing big data analytics capabilities. Lastly, we discuss technical, domain-specific, and firm-specific soft challenges related to establishing a big data architecture in an organization, and how these challenges are reshaping the big data research domain.

Digitalization and the growth of big data promise greater customization as well as change in how manufacturing is distributed. Yet, challenges arise in applying these new approaches in consumer goods industries that often emphasize mass production and extended supply chains. We build a conceptual framework to explore whether big data combined with new manufacturing technologies can facilitate redistributed manufacturing (RDM). Through analysis of 24 consumer goods industry cases using primary and secondary data, we investigated evolving manufacturing configurations, their underlying drivers, the role of big data applications, and their impact on the redistribution of manufacturing. We find some applications of RDM concepts, although in other cases existing manufacturing configurations are leveraged for high volume consumer goods products through big data analytics and market segmentation. The analysis indicates that the framework put forward in the paper has broader value in organizing thinking about emerging interrelationships between big data and manufacturing.

PurposeServitized manufacturers can leverage close relationships with external providers of product-related services to mobilize value creation and improve the responsiveness of their offerings to customer needs. The purpose of this paper is to investigate the economic link between the relational embeddedness of external service providers, as arising from the key dimension of dependence, and firm performance.Design/methodology/approachThe study evaluates financial statement data pertaining to 190 dyadic relationships of servitized manufacturers with service providers operating in downstream channels and accounting for more than 10 per cent of their revenue.FindingsThe results indicate that service providers’ dependence has an inverted U-shaped relationship with manufacturers’ return-on-assets (ROA), via non-linear effects on return-on-sales and asset turnover. The results therefore suggest that the observed U-shaped relationship for ROA is driven by diminishing returns of dependence in terms of both differentiation ability and operational efficiency.Research limitations/implicationsFuture research could examine other dimensions of embeddedness, as well as contingency factors that may influence the embeddedness–performance relationship.Practical implicationsThe study conclusions suggest that managers of servitized firms should foster the embeddedness of external service providers, but they should also be careful to maintain an adequate level of dependence to maximize benefits and minimize liabilities.Originality/valueThe study adds to the limited research delving into inter-firm relationships between servitized manufacturers and external service providers. It empirically demonstrates the economic effects of service providers’ dependence-based embeddedness, challenging the general assumption about a monotonic positive effect of relational embeddedness.

Purpose The purpose of this paper is to investigate the effects of Blockchain on the customer order management process and operations. There is limited understanding of the use and benefits of Blockchain on supply chains, and less so at processes level. To date, there is no research on the effects of Blockchain in the customer order management process. Design/methodology/approach A twofold method is followed. First, a Blockchain is programmed and implemented in a large international firm. Second, a series of simulations are built based on three scenarios: current with no-Blockchain, 1-year and 5-year Blockchain use. Findings Blockchain improves the efficiency of the process: it reduces the number of operations, reduces the average time of orders in the system, reduces workload, shows traceability of orders and improves visibility to various supply chain participants. Research limitations/implications The research is based on a single in-depth case that has the scope to be tested in other contexts in future. Practical implications This is the first study that demonstrates with real data from an industrial firm the effects of Blockchain on the efficiency gains, reduction on the number of operations and human-processing savings. A detailed description of the Blockchain implementation is provided. Furthermore, this research shows a list of the resources and capabilities needed for building and maintaining a Blockchain in the context of supply chains. Originality/value This is the first study that demonstrates with real data from an industrial firm the effects of Blockchain on the efficiency gains, the reduction in the number of operations and human-processing savings. A detailed description of the Blockchain implementation is provided. This paper contributes to the resource-based view of the firm, by demonstrating two new competitive valuable capabilities and a new dynamic capability that organisations develop when implementing and using Blockchain in a supply–demand process. It also contributes to the information processing theory by highlighting the analytics capabilities required to sustain Blockchain-related operations.

Understanding the uncertainty of a neural network's (NN) predictions is essential for many applications. The Bayesian framework provides a principled approach to this, however applying it to NNs is challenging due to the large number of parameters and data. Ensembling NNs provides a practical and scalable method for uncertainty quantification. Its drawback is that its justification is heuristic rather than Bayesian. In this work we propose one modification to the usual ensembling process, that does result in Bayesian behaviour: regularising parameters about values drawn from a prior distribution. Hence, we present an easily implementable, scalable technique for performing approximate Bayesian inference in NNs.