Data-driven Innovation Research Articles

Predicting hydrogen storage capacity of V–Ti–Cr–Fe alloy via ensemble machine learning

The V–Ti–Cr–Fe quaternary alloy is a promising hydrogen storage material for excellent performances, but it is difficult to take reliable multi-factor synergistic effects into account by means of experiments. At present, using the data-driven innovation method of ensemble learning, the structure-property relationship of V–Ti–Cr–Fe alloy is built and the maximum hydrogen absorption capacity is accurately predicted as well through 19 features covering the composition and various crystal parameters with the mean square error of 0.187. The feature importance ranking indicates that valence electron concentration, lattice constant, and Z/r3 play a critical role in the prediction. The genetic algorithm is furtherly used to propose 3 optimal composition ranges, which are proved to be accurate by experiments with relative errors of around 1%. The present work could provide an effective way for accurate and rapid prediction of hydrogen storage capacity and rational design of high-performance alloys.

A European Approach to the Establishment of Data Spaces

Within a context defined by the rapid increase in the availability of data, combined with the complexity of data sources, infrastructures, technologies and actors involved in data sharing flows, the European Union (EU) is devising approaches that can reap the benefits of data-driven innovation [...]

New emerging capabilities for managing data-driven innovation in healthcare: the role of digital platforms

PurposeThis article aims to understand the role of intermediaries that manage innovation challenges in the healthcare scenario. More specifically, it explores the role of digital platforms in addressing data challenges and fostering data-driven innovation in the health sector.Design/methodology/approachFor exploring the role of platforms, the authors propose a theoretical model based on the platform’s dynamic capabilities, assuming that, because of their set of capabilities, platforms may trigger innovation practices in actor interactions. To corroborate the theoretical framework, the authors present a detailed in-depth case study analysis of Apheris, an innovative data-driven digital platform operating in the healthcare scenario.FindingsThe paper finds that the innovative data-driven digital platform can be used to revolutionize established practices in the health sector (a) accelerating research and innovation; (b) overcoming challenges related to healthcare data. The case study demonstrates how data and intellectual property sharing can be privacy-compliant and enable new capabilities.Originality/valueThe paper attempts to fill the gap between the use of the data-driven digital platform and the critical innovation practices in the healthcare industry.

Big Data Utilization in Innovation: Innovation Enhancing Activity or Capability-Enhancing Activity?

The potential benefit of big data utilization through the generation of data-driven knowledge raises questions about its role in firms’ innovation performance. This paper explores two roles of big data utilization on firms’ innovation: ‘innovation-enhancing activity’ and ‘capability-enhancing activity’. While the first role highlights big data utilization as one enabler of the data-driven innovation process, the latter emphasizes the role of big data utilization to complement firms’ external knowledge search activity to enable greater innovation performance. Using the matched data of the UK E-Commerce and ICT Survey 2015 and the UK Innovation Survey 2017 for over 1,300 firms in the UK, we use both probit and OLS regression models to investigate how big data utilization both directly and indirectly influence firms’ innovation performance, measured by innovation outcomes and innovation productivity. While we find no evidence for big data utilization as innovation-enhancing activity, our results support big data utilization as capability-enhancing activity. Our results indicate the indirect effect of big data utilization on firms’ innovation performance and emphasize the role of big data utilization to complement firms’ search behaviour by enhancing firms’ absorptive capacity and collaboration capability. Our results highlight both the capability-enhancing role of big data utilization and the importance of adopting a strategic approach to maximize the innovation benefits of big data utilization and external knowledge search activity.

How Does Big Data Capability Drive Firm Innovation?

As a key prerequisite for data-driven innovation, big data capability is crucial for firms to gain a competitive advantage. However, the role of big data capability on innovation is still in its infancy. Using the data from 228 Chinese firms, we investigated how and under what conditions big data capability affects radical innovation and incremental innovation. Our results show that big data capability is positively associated with radical/incremental innovation. Further, spontaneous improvisation plays a partial mediating role between big data capability and radical innovation, and creative improvisation plays a partial mediating role between big data capability and radical/incremental innovation. Additionally, data-driven culture strengthens the positive impact of big data capability on creative improvisation. These findings highlight novel pathways and contingencies through which improvisation may prove to be a potential means for big data capability to drive firm innovation.

Decision optimization in cooperation innovation: the impact of big data analytics capability and cooperative modes.

Data-driven innovation enables firms to design products that are more responsive to market needs, which greatly reduces the risk of innovation. Customer data in the same supply chain has certain commonality, but data separation makes it difficult to maximize data value. The selection of an appropriate mode for cooperation innovation should be based on the particular big data analytics capability of the firms. This paper focuses on the influence of big data analytics capability on the choice of cooperation mode, and the influence of their matching relationship on cooperation performance. Specifically, using game-theoretic models, we discuss two cooperation modes, data analytics is implemented individually (i.e., loose cooperation) by either firm, or jointly (tight cooperation) by both firms, and further discuss the addition of coordination contracts under the loose mode. Several important conclusions are obtained. Firstly, both firms’ big data capability have positive effects on the selection of tight cooperation mode. Secondly, with the improvement of big data capability, the firms’ innovative performance gaps between loose and tight mode will increase significantly. Finally, when the capability meet certain condition, the cost subsidy contract can alleviate the gap between the two cooperative models.

Does Big Data Drive Innovation In E-Commerce: A Global Perspective?

Objective: Literature indicates big data is a competitive edge, which boasts a firm’s overall performance. With the rise of big data (BD), e-commerce firms are using the tools to engage more with customers, offer better products, and innovate more to gain a competitive advantage. Nevertheless, past empirical studies have shown conflicting results. Design: Building on the capital-based perspective and the firm’s inertia concept, we created a model to explore how BD and BD analytics capability impact innovation results in e-commerce businesses. We carried out a two-year empirical investigation project to secure empirical data on 1703 data-driven innovation tasks from USA and Asia. Findings: We showed that there is a tradeoff between BD and BD analytics capability, in which the optimum balance of BD depends on the amount of BD analytics ability. BD analytics ability exerts a good moderating impact, that is, the better this capability is, the higher the effect of BD on gross margin and sales growth. For U.S. innovation tasks, BD has an inverted U-shaped relationship with sales innovation. For Asian innovation tasks, when major data capital is minimal, promoting big data analytics capability improves sales innovation and disgusting margin up to a specific point. Policy Implications: Establishing BD analytics capability over that time could prevent innovation efficiency. Our findings offer guidance to e-commerce firms on producing strategic choices about source allocations for BD and BD analytics ability. Originality: A limited research has been carried out to show the impact of using BD analytics tools to drive innovation. This is one of the first articles that dive into using BD to foster innovation in the e-commerce business.

Leveraging the Use of Online Marketplace for SMEs in Malaysia:

The purpose of this paper is to review the impact of SMEs that leverage online marketplace by examining the benefits of e-marketplace and explore how to successfully overcome the perceived barriers for SMEs to gain competitive advantage in Malaysia. SMEs need to recognise and fully embrace online marketplace opportunities to develop informed approaches to stay competitive. Malaysia, a thriving country within the South East Asia countries, has aggressively been increasing its efforts in aiding its SMEs to be digitally prepared to support the technological evolution by empowering a digital nation by 2022. This is to create commercial effectiveness and output synergy by leveraging automation processes that creates collaboration within digital skills usage across multiple businesses (Sharon, 2019). This study further delves into the adoption of online marketplace synergy created within the SME’s business model which enables data-driven innovations as business value by leveraging the vast data generated in digital transactions (Barann, Hermann, Cordes, Chasin, & Becker, 2019). Further synergy created via partnership with customers through online marketplace will significantly expand SMEs business’ inventive activities (Walsh, Przychodzen, & Przychodzen, 2017).

Exploring data-driven innovation: What’s missing in the relationship between big data analytics capabilities and supply chain innovation?

Data-driven innovations (DDI) have significantly impacted firms’ operations thanks to the massive exploitation of huge data. However, to leverage big data and achieve supply chain innovation, a variety of complementary resources are necessary. In this study, we hypothesise that supply chain innovation (SCI) is dependent on firms’ big data analytics capabilities (BAC). Furthermore, we propose that this relation is mediated by two crucial capabilities of agility and adaptability that enable firms to efficiently meet the challenges of supply chain ambidexterity. Finally, we also test the moderating role of technology uncertainty in our research model. We collected data from 386 manufacturing firms in Pakistan and tested our model using structural equation modelling. The results confirmed our initial hypotheses that agility and adaptability both mediated our baseline relationship of BAC and big data innovation in supply chains. We further found support for the moderating role of technology uncertainty. Furthermore, technology uncertainty moderates the relationship between BAC and SCI. This study extends the current literature on digital analytics capabilities and innovation along the supply chain. Practically, our research suggests that investment in big data can result in affirmative consequences, if firms cultivate capabilities to encounter supply chain ambidexterity through agility and adaptability. Accordingly, we suggest that managers belonging to manufacturing firms need to build up these internal capabilities and to monitor and assess technology uncertainty in the environment.

Strategic intelligence for the future of places: enabling inclusive economic growth through the Opportunity Areas Analysis Tool

ABSTRACT Based on the growing importance of place-based strategic intelligence, this article puts forward a conceptual framework for a new methodological approach to influence the shaping of subnational economic development. The novel Opportunity Areas Analysis Tool has been designed and deployed in the context of a City Region Deal in Scotland, UK. We define key trends, map core capabilities and assess exploitation capacities for data-driven innovation economic opportunities. We critically appraise the effectiveness of this approach for the creation of inclusive place-based future visions and narratives, and the alignment of technological opportunities within diverse geographical (e.g., semi-rural and rural) and sectoral conditions.

Design of IoT-Based and Data-Driven Mechanism to Drive Innovation in International Business and Finance Statistics

The 43 national cross-border e-commerce policies promulgated by the Chinese government in recent years are selected as the research samples, and the PMC index model is applied to evaluate the mechanism design based on the IoT and data-driven innovation mechanism of international business finance statistics. On this basis, the implementation effects of specific policies on cross-border e-commerce in China are evaluated and analyzed, and the role of national macropolicies in guiding local policies is also discussed, with the aim of providing a reference basis for the Chinese government to formulate relevant policies, as well as providing new ideas and methods for the evaluation of cross-border e-commerce policies.

A Matter of Trust: Online Proctored Exams and the Integration of Technologies of Assessment in Medical Education

Issue: Technology is pervasive in medicine, but we too rarely examine how it shapes assessment, learning, knowledge, and performance. Cultures of assessment also shape identities, social relations, and the knowledge and behavior recognized as legitimate by a profession. Therefore, the combination of technology and assessment within medical education is worthy of review. Online proctoring services have become more prevalent during the Covid-19 pandemic, as a means of continuing high-stakes invigilated examinations online. With criticisms about increased surveillance, discrimination, and the outsourcing of control to commercial vendors, is this simply “moving exams online”, or are there more serious implications? What can this extreme example tell us about how our technologies of assessment influence relationships between trainees and medical education institutions? Evidence: We combine postdigital and postphenomenology approaches to analyze the written component of the 2020 online proctored United Kingdom Royal College of Physicians (MRCP) membership exam. We examine the scripts, norms, and trust relations produced through this example of online proctoring, and then locate them in historical and economic contexts. We find that the proctoring service projects a false objectivity that is undermined by the tight script with which examinees must comply in an intensified norm of surveillance, and by the interpretation of digital data by unseen human proctors. Nonetheless, such proctoring services are promoted by an image of data-driven innovation, a rhetoric of necessity in response to a growing problem of online cheating, and an aversion, within medical education institutions, to changing assessment formats (and thus the need to accept different forms of knowledge as legitimate). Implications: The use of online proctoring technology by medical education institutions intensifies established norms, already present within examinations, of surveillance and distrust. Moreover, it exacerbates tensions between conflicting agendas of commercialization, accountability, and the education of trustworthy professionals. Our analysis provides an example of why it is important to stop and consider the holistic implications of introducing technological “solutions”, and to interrogate the intersection of technology and assessment practices in relation to the wider goals of medical education.

Enabling data-centric AI through data quality management and data literacy

Abstract Data is being produced at an intractable pace. At the same time, there is an insatiable interest in using such data for use cases that span all imaginable domains, including health, climate, business, and gaming. Beyond the novel socio-technical challenges that surround data-driven innovations, there are still open data processing challenges that impede the usability of data-driven techniques. It is commonly acknowledged that overcoming heterogeneity of data with regard to syntax and semantics to combine various sources for a common goal is a major bottleneck. Furthermore, the quality of such data is always under question as the data science pipelines today are highly ad-hoc and without the necessary care for provenance. Finally, quality criteria that go beyond the syntactical and semantic correctness of individual values but also incorporate population-level constraints, such as equal parity and opportunity with regard to protected groups, play a more and more important role in this process. Traditional research on data integration was focused on post-merger integration of companies, where customer or product databases had to be integrated. While this is often hard enough, today the challenges aggravate because of the fact that more stakeholders are using data analytics tools to derive domain-specific insights. I call this phenomenon the democratization of data science, a process, which is both challenging and necessary. Novel systems need to be user-friendly in a way that not only trained database admins can handle them but also less computer science savvy stakeholders. Thus, our research focuses on scalable example-driven techniques for data preparation and curation. Furthermore, we believe that it is important to educate the breadth of society on implications of a data-driven world and actively promote the concept of data literacy as a fundamental competence.

Theorising Data-Driven Innovation Capabilities to Survive and Thrive in the Digital Economy

ABSTRACT While data-driven innovation (DDI) is receiving substantial attention from academics and industry specialists, there is limited research on its dimensions and effects in marketing. We address this theoretical gap by examining the following research questions: ‘What are the necessary firm-level capabilities of DDI and its effects in a marketing environment?’ Drawing from the resource-based view (RBV), dynamic capabilities (DC), and market orientation theories, this study presents a data-driven innovation capabilities (DDIC) framework and proposes a new conceptualisation with new data product performance through new data product creativity. Based on a systematic literature review and thematic analysis, the findings offer seven propositions to enrich the emerging DDI theory in marketing and guide managerial decision making.

Key Challenges and Opportunities for Cloud Technology in Health Care: Semistructured Interview Study.

BackgroundThe use of cloud computing (involving storage and processing of data on the internet) in health care has increasingly been highlighted as having great potential in facilitating data-driven innovations. Although some provider organizations are reaping the benefits of using cloud providers to store and process their data, others are lagging behind.ObjectiveWe aim to explore the existing challenges and barriers to the use of cloud computing in health care settings and investigate how perceived risks can be addressed.MethodsWe conducted a qualitative case study of cloud computing in health care settings, interviewing a range of individuals with perspectives on supply, implementation, adoption, and integration of cloud technology. Data were collected through a series of in-depth semistructured interviews exploring current applications, implementation approaches, challenges encountered, and visions for the future. The interviews were transcribed and thematically analyzed using NVivo 12 (QSR International). We coded the data based on a sociotechnical coding framework developed in related work.ResultsWe interviewed 23 individuals between September 2020 and November 2020, including professionals working across major cloud providers, health care provider organizations, innovators, small and medium-sized software vendors, and academic institutions. The participants were united by a common vision of a cloud-enabled ecosystem of applications and by drivers surrounding data-driven innovation. The identified barriers to progress included the cost of data migration and skill gaps to implement cloud technologies within provider organizations, the cultural shift required to move to externally hosted services, a lack of user pull as many benefits were not visible to those providing frontline care, and a lack of interoperability standards and central regulations.ConclusionsImplementations need to be viewed as a digitally enabled transformation of services, driven by skill development, organizational change management, and user engagement, to facilitate the implementation and exploitation of cloud-based infrastructures and to maximize returns on investment.

Canada's Digital Charter becomes law

Canadians increasingly rely on digital technology to connect with each other, to work and innovate. That’s why the Government of Canada is committed to making sure Canadians can benefit from the latest technologies, knowing that their privacy is safe and secure, and that companies are acting responsibly. In June 2022, the government proposed the Digital Charter Implementation Act, 2022, which will significantly strengthen Canada’s private sector privacy law, create new rules for the responsible development and use of artificial intelligence (AI), and continue advancing the implementation of Canada’s Digital Charter. Canada's Digital Charter sets out principles to ensure that privacy is protected, data-driven innovation is human-centred, and Canadian organizations can lead the world in innovations that fully embrace the benefits of the digital economy. Canadians must be able to trust that their personal information is protected, that their data will not be misused, and that organizations operating in this space communicate in a simple and straightforward manner with their users. This trust is the foundation on which Canadian digital and data-driven economy will be built. This legislation takes a number of important steps to ensure that Canadians have confidence that their privacy is respected and that AI is used responsibly, while unlocking innovation that promotes a strong economy. The Digital Charter Implementation Act, 2022 will include three proposed acts: the Consumer Privacy Protection Act, the Personal Information and Data Protection Tribunal Act, and the Artificial Intelligence and Data Act.

Feedback Loops in Open Data Ecosystems

Public agencies are increasingly publishing open data to increase transparency and fuel data-driven innovation. For these organizations, maintaining sufficient data quality is key to continuous re-use but also heavily dependent on feedback loops being initiated between data publishers and users. This paper reports from a longitudinal engagement with Scandinavian transportation agencies, where such feedback loops have been successfully established. Based on these experiences, we propose four distinct types of data feedback loops in which both data publishers and re-users play critical roles.

Research on the characteristics of the animation industry and the innovation of marketing and communication strategies in the data era

The transformation of the animation industry in the context of the data era is characterized by the virtualization of the market, the nationalization of brands, and the linking of industries. The animation industry has entered a new data-driven era, and while the data-driven animation marketing model is empowered for the new era, the marketing model is facing difficulties such as unclear development direction, changes and impacts of mainstream media terminals, and unpredictable audience market, etc. The solution lies in the innovation of the animation marketing and communication model under the data-driven environment, i.e. creating issues in terms of topics, reasonably using the " The solution lies in the data-driven innovation of the animation marketing communication model, i.e. creating issues on topics, using the "halo effect" on the packaging, applying data to drive audience guidance, and relying on secondary communication to complete audience purchase.

How data-driven innovation capability is shaping the future of market agility and competitive performance?

• Data driven innovation is opening a new digitalization frontier. • We confirm the antecedents and effects of data driven innovation capability (DDIC). • We show how DDIC influences market agility and strategic performance. In the race to achieve market dominance and stay competitive in a disruptive and rapidly changing business environment, many firms have invested significantly in data-driven innovation capabilities (DDIC). Surprisingly, the role that strategic market agility plays in achieving a firm's competitiveness through DDIC is understudied. Addressing this gap, we conceptualize the DDIC research model using the resource-based view, dynamic capability, market orientation and disruptive innovation theory. We empirically test the model with survey data from 312 Australian managers. We illuminate the significance of strategic market agility as a key mediator between DDIC and strategic competitive performance. We discuss the implications of our findings for theoretical contributions and managerial implications.

Applying emerging data-driven technologies in social security. Country experiences and ISSA guidelines

The application of ICT is enabling the implementation of increasingly comprehensive social security systems throughout the world as well as the transformation of social security services. In particular, the so-called data-driven innovation enables social security institutions to improve products, processes and organisational methods. In this line, social security institutions are progressively applying emerging technologies, such as Analytics, Big Data, and Artificial Intelligence. While the pairing of analytics and big data allows for the performing of sophisticated analyses on increasingly large databases, Artificial Intelligence enables for automating processes and assisting staff in tasks requiring human decisions. However, the application of such emerging data-driven technologies brings with it many challenges, mainly the complexities of combining the adoption of not fully tested technologies with the required stability of critical operational processes and differences in the application of development processes. This paper addresses these issues and presents an overview of emerging data-driven technologies and their current application in social security institutions. It also presents guidelines supporting the application of data-driven technologies in social security developed by the International Social Security Association (ISSA).