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Article
Publication date: 6 May 2021

Salvador Barragan

The purpose of this paper is to examine the possible implications of applying the infonomics methodology and measurement model within records and information management…

Abstract

Purpose

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.

Design/methodology/approach

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go beyond the information valuing models currently used in RIM; thereby, this study addresses how information that has superseded its business value may be eliminated.

Findings

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.

Practical implications

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.

Originality/value

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

Details

Records Management Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0956-5698

Keywords

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Article
Publication date: 2 October 2018

Alexander M. Soley, Joshua E. Siegel, Dajiang Suo and Sanjay E. Sarma

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.

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Practical implications

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.

Social implications

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.

Originality/value

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.

Details

Digital Policy, Regulation and Governance, vol. 20 no. 6
Type: Research Article
ISSN: 2398-5038

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Article
Publication date: 22 August 2018

Lorna Uden and Pasquale Del Vecchio

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Practical implications

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.

Originality/value

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.

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Article
Publication date: 2 October 2017

Sarah Cheah and Shenghui Wang

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…

Abstract

Purpose

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.

Design/methodology/approach

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

Findings

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.

Research limitations/implications

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

Practical implications

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.

Social implications

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.

Originality/value

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.

Details

Journal of Chinese Economic and Foreign Trade Studies, vol. 10 no. 3
Type: Research Article
ISSN: 1754-4408

Keywords

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Article
Publication date: 1 April 1986

Richard Pollard

Relatively little microcomputer software has been designed specifically for the storage and retrieval of bibliographic data. Information retrieval packages for mainframes…

Abstract

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.

Details

The Electronic Library, vol. 4 no. 4
Type: Research Article
ISSN: 0264-0473

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Article
Publication date: 5 October 2018

Jing Zeng and Zaheer Khan

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

Abstract

Purpose

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

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Details

Management Decision, vol. 57 no. 8
Type: Research Article
ISSN: 0025-1747

Keywords

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Abstract

Details

Database Management Systems
Type: Book
ISBN: 978-1-78756-695-8

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Article
Publication date: 14 June 2021

Sergey Yablonsky

To be more effective, artificial intelligence (AI) requires a broad overall view of the design and transformation of enterprise architecture and capabilities. Maturity…

Abstract

Purpose

To be more effective, artificial intelligence (AI) requires a broad overall view of the design and transformation of enterprise architecture and capabilities. Maturity models (MMs) are the recognized tools to identify strengths and weaknesses of certain domains of an organization. They consist of multiple, archetypal levels of maturity of a certain domain and can be used for organizational assessment and development. In the case of AI, quite a few numbers of MMs have been proposed. Generally, the links between AI technology, AI usage and organizational performance stay unclear. To address these gaps, this paper aims to introduce the complete details of the AI maturity model (AIMM) for AI-driven platform companies. The associated AI-Driven Platform Enterprise Maturity framework proposed here can help to achieve most of the AI-driven platform companies' objectives.

Design/methodology/approach

Qualitative research is performed in two stages. In the first stage, a review of the existing literature is performed to identify the types, barriers, drivers, challenges and opportunities of MMs in AI, Advanced Analytics and Big Data domains. In the second stage, a research framework is proposed to align company value chain with AI technologies and levels of the platform enterprise maturity.

Findings

The paper proposes a new five level AI-Driven Platform Enterprise Maturity framework by constructing a formal organizational value chain taxonomy model that explains a vast group of MM phenomena related with the AI-Driven Platform Enterprises. In addition, this study proposes a clear and precise description and structuring of the information in the multidimensional Platform, AI, Advanced Analytics and Big Data domains. The AI-Driven Platform Enterprise Maturity framework assists in identification, creation, assessment and disclosure research of AI-driven platform business organizations.

Research limitations/implications

This research is focused on the basic dimensions of AI value chain. The full reference model of AI consists of much more concepts. In the last few years, AI has achieved a notable drive that, if connected appropriately, may deliver the best of expectations over many application sectors across the field. For this to occur shortly in machine learning, especially in deep neural networks, the entire community stands in front of the barrier of explainability. Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is widely acknowledged as a crucial feature for the practical deployment of AI models in industry. Our prospects lead toward the concept of a methodology for the large-scale implementation of AI methods in platform organizations with fairness, model explainability and accountability at its core.

Practical implications

AI-driven platform enterprise maturity framework can be used for better communicate to clients the value of AI capabilities through the lens of changing human-machine interactions and in the context of legal, ethical and societal norms.

Social implications

The authors discuss AI in the enterprise platform stack including talent platform, human capital management and recruiting.

Originality/value

The AI value chain and AI-Driven Platform Enterprise Maturity framework are original and represent an effective tools for assessing AI-driven platform enterprises.

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Article
Publication date: 18 May 2020

Eleni-Laskarina Makri, Zafeiroula Georgiopoulou and Costas Lambrinoudakis

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Details

Information & Computer Security, vol. 28 no. 4
Type: Research Article
ISSN: 2056-4961

Keywords

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Article
Publication date: 6 November 2019

Jan Michael Nolin

Principled discussions on the economic value of data are frequently pursued through metaphors. This study aims to explore three influential metaphors for talking about the…

Abstract

Purpose

Principled discussions on the economic value of data are frequently pursued through metaphors. This study aims to explore three influential metaphors for talking about the economic value of data: data are the new oil, data as infrastructure and data as an asset.

Design/methodology/approach

With the help of conceptual metaphor theory, various meanings surrounding the three metaphors are explored. Meanings clarified or hidden through various metaphors are identified. Specific emphasis is placed on the economic value of ownership of data.

Findings

In discussions on data as economic resource, the three different metaphors are used for separate purposes. The most used metaphor, data are the new oil, communicates that ownership of data could lead to great wealth. However, with data as infrastructure data have no intrinsic value. Therefore, profits generated from data resources belong to those processing the data, not those owning it. The data as an asset metaphor can be used to convince organizational leadership that they own data of great value.

Originality/value

This is the first scholarly investigation of metaphors communicating economic value of data. More studies in this area appear urgent, given the power of such metaphors, as well as the increasing importance of data in economics.

Details

Journal of Information, Communication and Ethics in Society, vol. 18 no. 1
Type: Research Article
ISSN: 1477-996X

Keywords

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