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The purpose of this paper is to develop a model to estimate the value of information generated by and stored within vehicles to help people, businesses and researchers.

The authors provide a taxonomy for data within connected vehicles, as well as for actors that value such data. The authors create a monetary value model for different data generation scenarios from the perspective of multiple actors.

Actors value data differently depending on whether the information is kept within the vehicle or on peripheral devices. The model shows the US connected vehicle data market is worth between US$11.6bn and US$92.6bn.

This model estimates the value of vehicle data, but a lack of academic references for individual inputs makes finding reliable inputs difficult. The model performance is limited by the accuracy of the authors’ assumptions.

The proposed model demonstrates that connected vehicle data has higher value than people and companies are aware of, and therefore we must secure these data and establish comprehensive rules pertaining to data ownership and stewardship.

Estimating the value of data of vehicle data will help companies understand the importance of responsible data stewardship, as well as drive individuals to become more responsible digital citizens.

This is the first paper to propose a model for computing the monetary value of connected vehicle data, as well as the first paper to provide an estimate of this value.

This paper aims to define a conceptual framework for transforming Big Data into organizational value by focussing on the perspectives of service science and activity theory. In coherence with the agenda on evolutionary research on intellectual capital (IC), the study also provides momentum for researchers and scholars to explore emerging trends and implications of Big Data for IC management.

The paper adopts a qualitative and integrated research method based on a constructive review of existing literature related to IC management, Big Data, service science and activity theory to identify features and processes of a conceptual framework emerging at the intersection of previously identified research topics.

The proposed framework harnesses the power of Big Data, collectively created by the engagement of multiple stakeholders based on the concepts of service ecosystems, by using activity theory. The transformation of Big Data for IC management addresses the process of value creation based on a set of critical dimensions useful to identify goals, main actors and stakeholders, processes and motivations.

The paper indicates how organizational values can be created from Big Data through the co-creation of value in service ecosystems. Activity theory is used as theoretical lens to support IC ecosystem development. This research is exploratory; the framework offers opportunities for refinement and can be used to spearhead directions for future research.

The paper proposes a framework for transforming Big Data into organizational values for IC management in the context of entrepreneurial universities as pivotal contexts of observation that can be replicated in different fields. The framework provides guidelines that can be used to help organizations intending to embark on the emerging paradigm of Big Data for IC management for their competitive advantages.

The paper’s originality is in bringing together research from Big Data, value co-creation from service ecosystems and activity theory to address the complex issues involved in IC management. A further element of originality offered involves integrating such multidisciplinary perspectives as a lens for shaping the complex process of value creation from Big Data in relationship to IC management. The concept of how IC ecosystems can be designed is also introduced.

This study aims to construct mechanisms of big data-driven business model innovation from the market, strategic and economic perspectives and core logic of business model innovation.

The authors applied deductive reasoning and case analysis method on manufacturing firms in China to validate the mechanisms.

The authors have developed an integrated framework to deduce the elements of big data-driven business model innovation. The framework comprises three elements: perspectives, business model processes and big data-driven business model innovations. As we apply the framework on to three Chinese companies, it is evident that the mechanisms of business model innovation based on big data is a progressive and dynamic process.

The case sample is relatively small, which is a typical trade-off in qualitative research.

A robust infrastructure that seamlessly integrates internet of things, front-end customer systems and back-end production systems is pivotal for companies. The management has to ensure its organization structure, climate and human resources are well prepared for the transformation.

When provided with a convenient crowdsourcing platform to provide feedback and witness their suggestions being implemented, users are more likely to share insights about their use experience.

Extant studies of big data and business model innovation remain disparate. By adding a new dimension of intellectual and economic resource to the resource-based view, this paper posits an important link between big data and business model innovation. In addition, this study has contributed to the theoretical lens of value by contextualizing the value components of a business model and providing an integrated framework.

The purpose of this paper is to examine the possible implications of applying the infonomics methodology and measurement model within records and information management (RIM) to reduce organizations’ electronic footprint. By analyzing content using infonomics, it is possible for RIM managers in the private sector to keep only information with the highest value and change their behavior around keeping content beyond its infonomic value. This, in turn, may reduce the stress upon natural resources that are used in maintaining information data centers.

This paper examines different theories of evaluating information value and describes the role of infonomics in analyzing information as an asset to minimize its electronic footprint. Its focus is on the implications of applying a set of measurements that go beyond the information valuing models currently used in RIM; thereby, this study addresses how information that has superseded its business value may be eliminated.

This paper concludes that infonomics could elevate RIM function and alter how RIM managers within the private sector value information. Further, the inclusion of infonomics into RIM models may create new roles for RIM managers and extend the influence and reach of RIM. This may also lead to valuing all content and eliminating content that no longer has any business value. This may also eliminate the need for large data storage centers that harness and exhaust nonrenewable resources. Future developments must be watched and analyzed to see if this becomes a norm.

This paper will be of interest to stakeholders responsible for valuing information, appraisal of information, life-cycle management, records management, InfoSec and big data analytics.

The work is original but parts of this subject have been previously addressed in another study.

Medical research requires biological material and data collected through biobanks in reliable processes with quality assurance. Medical studies based on data with unknown or questionable quality are useless or even dangerous, as evidenced by recent examples of withdrawn studies. Medical data sets consist of highly sensitive personal data, which has to be protected carefully and is available for research only after the approval of ethics committees. The purpose of this research is to propose an architecture to support researchers to efficiently and effectively identify relevant collections of material and data with documented quality for their research projects while observing strict privacy rules.

Following a design science approach, this paper develops a conceptual model for capturing and relating metadata of medical data in biobanks to support medical research.

This study describes the landscape of biobanks as federated medical data lakes such as the collections of samples and their annotations in the European federation of biobanks (Biobanking and Biomolecular Resources Research Infrastructure – European Research Infrastructure Consortium, BBMRI-ERIC) and develops a conceptual model capturing schema information with quality annotation. This paper discusses the quality dimensions for data sets for medical research in-depth and proposes representations of both the metadata and data quality documentation with the aim to support researchers to effectively and efficiently identify suitable data sets for medical studies.

This novel conceptual model for metadata for medical data lakes has a unique focus on the high privacy requirements of the data sets contained in medical data lakes and also stands out in the detailed representation of data quality and metadata quality of medical data sets.

Media users daily exchange personal data for “free” personalised media. Is this a fair trade, or user “exploitation”? Do personalisation benefits outweigh privacy risks?

This study surveyed experts in three consecutive online rounds (e-Delphi). The authors explored personal data processing value for media, personalisation relevance, benefits and risks for users. The authors scrutinised the value-exchange between media and users and determined whether media communicate transparently, or use “dark patterns” to obtain more personal data.

Communication to users must be clear, correct and concise (prevent user deception). Experts disagree on “payment” with personal data for “free” personalised media. This study discerned obstacles and solutions to substantially balance the interests of media and users (fair value exchange). Personal data processing must be transparent, profitable to media and users. Media can agree “sector-wide” on personalisation transparency. Fair, secure and transparent information disclosure to media is possible through shared responsibility and effort.

This study’s innovative contribution is threefold: Firstly, focus on professional stakeholders’ opinion in the value network. Secondly, recommendations to clearly communicate personalised media value, benefits and risks to users. This allows media to create codes of conduct that increase user trust. Thirdly, expanding literature explaining how media realise personal data value, deal with stakeholder interests and position themselves in the data processing debate. This research improves understanding of personal data value, processing benefits and potential risks in a regional context and European regulatory framework.

Relatively little microcomputer software has been designed specifically for the storage and retrieval of bibliographic data. Information retrieval packages for mainframes and minicomputers have been scaled down to run on microcomputers, however, these programs are expensive, unwieldy, and inflexible. For this reason, microcomputer database management systems (DBMS) are often used as an alternative. In this article, criteria for evaluating DBMS used for the storage and retrieval of bibliographic data are discussed. Two popular types of microcomputer DBMS, file management systems and relational database management systems, are evaluated with respect to these criteria. File management systems are appropriate when a relatively small number of simple records are to be stored, and retrieval time for multi‐valued data items is not a critical factor. Relational database management systems are indicated when large numbers of complex records are to be stored, and retrieval time for multi‐valued data items is critical. However, successful use of relational database management systems often requires programming skills.

This paper aims to investigate the literacy corpus regarding the potential of big data to improve public decision-making processes and direct these processes toward the creation of public value. This paper presents a map of current knowledge in a sample of selected articles and explores the intersecting points between data from the private sector and the public dimension in relation to benefits for society.

A bibliometric analysis was performed to provide a retrospective review of published content in the past decade in the field of big data for the public interest. This paper describes citation patterns, key topics and publication trends.

The findings indicate a propensity in the current literature to deal with the issue of data value creation in the private dimension (data as input to improve business performance or customer relations). Research on data for the public good has so far been underestimated. Evidence shows that big data value creation is closely associated with a collective process in which multiple levels of interaction and data sharing develop between both private and public actors in data ecosystems that pose new challenges for accountability and legitimation processes.

The bibliometric method focuses on academic papers. This paper does not include conference proceedings, books or book chapters. Consequently, a part of the existing literature was excluded from the investigation and further empirical research is required to validate some of the proposed theoretical assumptions.

Although this paper presents the main contents of previous studies, it highlights the need to systematize data-driven private practices for public purposes. This paper offers insights to better understand these processes from a public management perspective.

The purpose of this paper is to examine how managers orchestrate, bundle and leverage resources from big data for value creation in emerging economies.

The authors grounded the theoretical framework in two perspectives: the resource management and entrepreneurial orientation (EO). The study utilizes an inductive, multiple-case research design to understand the process of creating value from big data.

The findings suggest that EO is vital through which companies based in emerging economies can create value through big data by bundling and orchestrating resources thus improving performance.

This is one of the first studies to have integrated resource orchestration theory and EO in the context of big data and explicate the utility of such theoretical integration in understanding the value creation strategies through big data in the context of emerging economies.

This paper aims to examine the interrelationships between user-centric measures and their impact on the firm’s perceived financial performance as the respondents’ decision-making role changes.

The data was collected jointly with SurveyMonkey, a marketing research company, from marketing professionals working in companies with at least limited experience deploying big data marketing analytics (BDMA) applications. The respondents originated from Canada and the USA, and out of 970 responses in the initial sample, 236 were working in companies with at least limited experience in BDMA deployment. The data analysis used partial least squares structural equation modeling and necessary condition analysis.

All hypotheses except one were accepted. Perceived value for money positively and significantly impacted user satisfaction, positively and significantly impacted perceived financial performance. Also, the decision-making role positively and significantly impacted the perceived value for money and user satisfaction but not the perceived financial performance.

The research contributes to understanding how the decision-maker’s role impacts the perceived user-related performance measures in the BDMA context.

Studies show that data quality (DQ) issues are extremely costly for companies. To address such issues, as a starting point, there is a need to understand what DQ is. In his context, the 1996 paper “Anchoring data quality dimensions ontological foundations” by Wand and Wang has been highly influential on the understanding of DQ. However, the present study demonstrates that some of the assumptions made in their paper can be challenged. On this basis, this study seeks to develop clearer definitions.

The assumptions behind Wand and Wang’s DQ classification are discussed, on which basis three counter-propositions are formulated. These are investigated through a representation theoretical approach involving analyses of deficient mappings between real-world and information system states. On this basis, an intrinsic DQ classification is derived. A case study is conducted to investigate the value of the developed DQ classification.

The representation theoretical analysis and the case study support the three propositions. These give rise to the development of a DQ classification that includes three primary intrinsic DQ dimensions (accuracy, completeness and conciseness), which are associated with six primary value-level DQ deficiencies (inaccuracy, incorrectness, meaninglessness, incompleteness, absence and redundancy). The case study supports the value of extending Wand and Wang’s DQ classification with the three additional data deficiencies.

By improving the conceptual clarity of DQ, this study provides future research with an improved basis for studies and discussions of DQ.

The study advances the understanding of DQ by providing additional clarity.

A more accurate comprehension of data elements and the exploration of new laws governing contemporary data in both theoretical and practical domains constitute a significant research topic.

Based on the perspective of evolutionary economics, this paper re-examines economic history and existing literature to study the following: changes in the “connotation of production factors” in economics caused by the evolution of production factors; the economic paradoxes formed by data in the context of social production processes and business models, which traditional theoretical frameworks fail to solve; the disruptive innovation of classical theory of value by multiple theories of value determination and the conflicts between the data market monopoly as well as the resulting distribution of value and the real economic society. The research indicates that contemporary advancements in data have catalyzed transformative innovation within the field of economics.

The research indicates that contemporary advancements in data have catalyzed disruptive innovation in the field of economics.

This paper, grounded in academic research, identifies four novel issues arising from contemporary data that cannot be adequately addressed within the confines of the classical economic theoretical framework.

This chapter outlines how the comprehensive North American and European datasets were collected and explains the ensuing data cleaning process outlining three alternative methods applied to deal with missing values, the complete case, the multiple imputation (MI), and the K-nearest neighbor (KNN) methods. The complete case method is the conventional approach adopted in many mainstream management studies. We further discuss the implied assumption underlying use of this technique, which is rarely assessed, or tested in practice and explain the alternative imputation approaches in detail. Use of North American data is the norm but we also collected a European dataset, which is rarely done to enable subsequent comparative analysis between these geographical regions. We introduce the structure of firms organized within different industry classification schemes for use in the ensuing comparative analyses and provide base information on missing values in the original and cleaned datasets. The calculated performance indicators derived from the sampled data are defined and presented. We show how the three alternative approaches considered to deal with missing values have significantly different effects on the calculated performance measures in terms of extreme estimate ranges and mean performance values.

This study assesses whether the strategic orientation of financial institutions to provide value to customers influences the dimensions of personal data privacy perceived by consumers of banking services. We also analysed whether these dimensions directly influence the value in use and, indirectly, the reputation of financial institutions.

Based on the literature, a model was developed to verify the proposed relationships. To test the model, we collected data via an online questionnaire from 2,422 banking customers, with analysis using structural equation modelling with partial least squares estimation.

The results suggest that strategic value orientation tends to have a direct positive influence on the constructs knowledge, control, willingness to value privacy and trust in sharing personal information and a direct negative influence on the personal data privacy experience. Three dimensions of personal data privacy (knowledge, willingness to value privacy and trust in sharing personal information) tend to have a direct positive influence on value in use. The results showed that the dimensions of personal data privacy experience and control had a significant and negative impact on the value in use construct. Another finding is the positive influence of value in use on organizational reputation. Investing in strategic value orientation can generate consumer perceptions of personal data privacy, which is reflected in the value in use and reputation of banks.

This study is theoretically original because it brings up the organizational reputation of financial institutions based on the strategic orientation to offer value to customers, personal data privacy and the value in use of banking services. The study of these relationships is unprecedented in the literature.

This study aims to investigate the impact of ex-ante regulatory interventions on emerging digital markets related to data sharing and combination practices. Specifically, it evaluates how such interventions influence market contestability by considering data network effects and the economic value of data.

The research uses agent-based modeling and simulations to analyze the dynamics of value generation and market competition related to the regulatory obligations on data sharing and combination practices.

Results show that while the promotion of data sharing through data portability and interoperability has a positive impact on the market, restricting data combination may damage value generation or, at best, have no positive impact even when it is imposed only on those platforms with very large market shares. More generally, the results emphasize the role of regulators in enabling the market through interoperability and service multihoming. Data sharing through portability fosters competition, while the usage of complementary data enhances platform value without necessarily harming the market. Service provider multihoming complements these efforts.

Although agent-based modeling and simulations describe the dynamics of data markets and platform competition, they do not provide accurate forecasts of possible market outcomes.

This paper presents a novel approach to understanding the dynamics of data value generation and the effects of related regulatory interventions. In the absence of real-world data, agent-based modeling provides a means to understand the general dynamics of data markets under different regulatory decisions that have yet to be implemented. This analysis is timely given the emergence of regulatory concerns on how to stimulate a competitive digital market and a shift toward ex-ante regulation, such as the regulatory obligations to large gatekeepers set in the Digital Markets Act.

This study aimed to present a model for open-data management for developing innovative information flow in Iranian knowledge-based companies (businesses).

The method was mixed (qualitative-quantitative) and data collection tools were interview and questionnaire. The qualitative part was done to identify the influential components in open data management (ecosystem) using the grounded theory method. A questionnaire was developed based on the results of the qualitative section and the theoretical foundations, and the quantitative section was conducted by analytical survey method and the model was extracted using factor analysis and the integration of the qualitative section.

Seven categories of entrepreneurial incentives, sustainable value, innovative features, challenges and barriers, actors, business model and requirements are the main categories that should be considered in open data management (ecosystem) with all categories of research have a significant relationship with open data management.

The study focused on open data management from an innovation paradigm perspective and its role in developing innovative information flow. The study aimed to identify the key components of the open data ecosystem, open-data value creation, and the need to use the “open data” approach to develop data-driven and knowledge-based businesses in Iran–an emerging approach largely ignored.

The purpose of this paper is to examine the relationship between trade integration and intra-regional business cycle synchronization using value-added trade data. Most empirical studies analyzing the relationship between trade integration and business cycle synchronization use gross trade data which suffer from double-counting. Double-counting distorts the empirical results on the estimated relationship between trade integration and business cycle synchronization. This paper explores the relationship using value-added trade data to be free from distortions caused by double-counting.

Gross trade data on exports and imports are decomposed into sub-categories following Koopman et al. (2014). Then, value-added data on exports and imports without double-counted terms are built to measure value-added bilateral trade intensity and value-added intra-industry trade intensity. Using this value-added trade intensities, the author run panel regressions for Europe and East Asian countries to examine how value-added trade intensities are correlated with output co-movements.

The paper finds that for European countries, the positive association between trade and business cycle co-movements is more evidently observed and the role of intra-industry trade increasing the business cycle synchronization is also more clearly revealed by value-added trade data. On the other hand, for East Asian countries, value-added trade data reveal that it is very uncertain whether increased trade contributes to stronger synchronization of business cycles and intra-industry trade is truly the major factor which deepens the business cycle co-movements.

First, the paper examines the relationship only by running static panel regression. There is a need to employ different methodologies such as instrumental variable regression or dynamic panel regression. Second, financial integration and policy coordination within a region are also other relevant factors which influence the intra-regional business cycle synchronization. There is a need to examine the relationship using value-added trade data with the variables measuring the degree of financial integration and policy coordination. Third, value-added trade data used in this paper has limited coverage of East Asian countries. There is also a need to extend the value-added data set to cover more countries and industries.

Most empirical literature studying the relationship between trade integration and business cycle synchronization rely on gross trade data. This paper would be the first attempt to study the relationship using value-added trade data. Duval et al. (2014) also use value-added data, but their value-added data are not supported by a solid accounting framework which decomposes a country’s gross exports into various value-added components by source and additional double-counted terms. Value-added data in this paper computed based on Koopman et al. (2014) are the total domestic value exports that are ultimately consumed abroad via final and intermediate exports. The author believes that value-added data in this paper are most relevant in estimating the relationship between trade integration and business cycle synchronization.

This study aims to assist organizations to protect the privacy of their users and the security of the data that they store and process. Users may be the customers of the organization (people using the offered services) or the employees (users who operate the systems of the organization). To be more specific, this paper proposes a privacy impact assessment (PIA) method that explicitly takes into account the organizational characteristics and employs a list of well-defined metrics as input, demonstrating its applicability to two hospital information systems with different characteristics.

This paper presents a PIA method that employs metrics and takes into account the peculiarities and other characteristics of the organization. The applicability of the method has been demonstrated on two Hospital Information Systems with different characteristics. The aim is to assist the organizations to estimate the criticality of potential privacy breaches and, thus, to select the appropriate security measures for the protection of the data that they collect, process and store.

The results of the proposed PIA method highlight the criticality of each privacy principle for every data set maintained by the organization. The method employed for the calculation of the criticality level, takes into account the consequences that the organization may experience in case of a security or privacy violation incident on a specific data set, the weighting of each privacy principle and the unique characteristics of each organization. So, the results of the proposed PIA method offer a strong indication of the security measures and privacy enforcement mechanisms that the organization should adopt to effectively protect its data.

The novelty of the method is that it handles security and privacy requirements simultaneously, as it uses the results of risk analysis together with those of a PIA. A further novelty of the method is that it introduces metrics for the quantification of the requirements and also that it takes into account the specific characteristics of the organization.

The aim of this study is to provide a conceptual framework to explain how museums sustain intellectual capital and promote value co-creation moving from designing virtual environments to introducing and managing Big Data.

This study is based on archival and qualitative data considering the literature related to the introduction of virtual environments and Big Data within museums.

Museums contribute to sustaining intellectual capital and in promoting value co-creation developing a Big Data-driven strategy and innovation.

By introducing and managing Big Data, museums contribute to creating a community by improving knowledge within cultural ecosystems while strengthening the users as active participants and the museum’s professionals as user-centred mediators.

As audience-driven and knowledge-oriented organisations moving from designing virtual environments to following a Big data-driven strategy, museums should select organisational and strategic choices for driving change.