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information system , an integrated set of components for collecting, storing, and processing data and for providing information , knowledge, and digital products. Business firms and other organizations rely on information systems to carry out and manage their operations, interact with their customers and suppliers, and compete in the marketplace. Information systems are used to run interorganizational supply chains and electronic markets. For instance, corporations use information systems to process financial accounts, to manage their human resources, and to reach their potential customers with online promotions. Many major companies are built entirely around information systems. These include eBay , a largely auction marketplace; Amazon , an expanding electronic mall and provider of cloud computing services; Alibaba, a business-to-business e-marketplace; and Google , a search engine company that derives most of its revenue from keyword advertising on Internet searches. Governments deploy information systems to provide services cost-effectively to citizens. Digital goods—such as electronic books , video products, and software —and online services, such as gaming and social networking , are delivered with information systems. Individuals rely on information systems, generally Internet-based, for conducting much of their personal lives: for socializing, study, shopping, banking, and entertainment.

As major new technologies for recording and processing information were invented over the millennia, new capabilities appeared, and people became empowered. The invention of the printing press by Johannes Gutenberg in the mid-15th century and the invention of a mechanical calculator by Blaise Pascal in the 17th century are but two examples. These inventions led to a profound revolution in the ability to record, process, disseminate , and reach for information and knowledge. This led, in turn, to even deeper changes in individual lives, business organization , and human governance.

The first large-scale mechanical information system was Herman Hollerith ’s census tabulator. Invented in time to process the 1890 U.S. census, Hollerith’s machine represented a major step in automation , as well as an inspiration to develop computerized information systems.

One of the first computers used for such information processing was the UNIVAC I, installed at the U.S. Bureau of the Census in 1951 for administrative use and at General Electric in 1954 for commercial use. Beginning in the late 1970s, personal computers brought some of the advantages of information systems to small businesses and to individuals. Early in the same decade the Internet began its expansion as the global network of networks. In 1991 the World Wide Web , invented by Tim Berners-Lee as a means to access the interlinked information stored in the globally dispersed computers connected by the Internet, began operation and became the principal service delivered on the network. The global penetration of the Internet and the Web has enabled access to information and other resources and facilitated the forming of relationships among people and organizations on an unprecedented scale. The progress of electronic commerce over the Internet has resulted in a dramatic growth in digital interpersonal communications (via e-mail and social networks), distribution of products (software, music, e-books, and movies), and business transactions (buying, selling, and advertising on the Web). With the worldwide spread of smartphones , tablets , laptops, and other computer-based mobile devices, all of which are connected by wireless communication networks, information systems have been extended to support mobility as the natural human condition.

As information systems enabled more diverse human activities, they exerted a profound influence over society. These systems quickened the pace of daily activities, enabled people to develop and maintain new and often more-rewarding relationships, affected the structure and mix of organizations, changed the type of products bought, and influenced the nature of work. Information and knowledge became vital economic resources. Yet, along with new opportunities, the dependence on information systems brought new threats. Intensive industry innovation and academic research continually develop new opportunities while aiming to contain the threats.

Components of information systems

The main components of information systems are computer hardware and software , telecommunications, databases and data warehouses, human resources, and procedures. The hardware, software, and telecommunications constitute information technology (IT), which is now ingrained in the operations and management of organizations.

Today throughout the world even the smallest firms, as well as many households, own or lease computers. Individuals may own multiple computers in the form of smartphones , tablets , and other wearable devices. Large organizations typically employ distributed computer systems, from powerful parallel-processing servers located in data centres to widely dispersed personal computers and mobile devices, integrated into the organizational information systems. Sensors are becoming ever more widely distributed throughout the physical and biological environment to gather data and, in many cases, to effect control via devices known as actuators. Together with the peripheral equipment—such as magnetic or solid-state storage disks, input-output devices , and telecommunications gear—these constitute the hardware of information systems. The cost of hardware has steadily and rapidly decreased, while processing speed and storage capacity have increased vastly. This development has been occurring under Moore’s law : the power of the microprocessors at the heart of computing devices has been doubling approximately every 18 to 24 months. However, hardware’s use of electric power and its environmental impact are concerns being addressed by designers. Increasingly, computer and storage services are delivered from the cloud—from shared facilities accessed over telecommunications networks.

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1. INTRODUCTION

2. evaluating research infrastructures: a review, 3. the french study on research infrastructure metrics, 4. standard formats and metrics, 5. conclusion, acknowledgments, author contributions, competing interests, funding information, evaluating the scientific impact of research infrastructures: the role of current research information systems.

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Renaud Fabre , Daniel Egret , Joachim Schöpfel , Otmane Azeroual; Evaluating the scientific impact of research infrastructures: The role of current research information systems. Quantitative Science Studies 2021; 2 (1): 42–64. doi: https://doi.org/10.1162/qss_a_00111

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Research infrastructures (RIs) offer researchers a multitude of research opportunities and services and play a key role in the performance, innovative strength, and international competitiveness of science. As an important part of the generation and use of new knowledge and technologies, they are essential for research policies. Because of their strategic importance and their need for significant funding, there is a growing demand for the assessment of their scientific output and impact. Current research information systems (CRIS) have contributed for many years now to the evaluation of universities and research organizations. Based on studies on the application of CRIS to infrastructures and on a recent French report on the scientometric assessment of RI, this paper analyzes the potential of CRIS and their data models and standards (in particular the international CERIF format and the German RDC model) for the monitoring and evaluation of RIs. The interaction between functional specificities of RI and standards for their assessment is outlined, with reference to their own potential to stimulate and share innovation in the networks located inside and outside RIs. This societal challenge, more than an academic issue, is on the way to further harmonization and consolidation of shared and common RI metrics.

Research infrastructures (RIs) are facilities, resources, systems, and services needed by scientific communities to carry out large-scale research in cutting-edge fields. The European MERIL project defines RI as a “facility or (virtual) platform that provides the scientific community with resources and services to conduct research in their respective fields. These research infrastructures can be single-sited, distributed or an e-infrastructure, and can be part of a national or international network of facilities, or of interconnected scientific instrument networks” ( Beckers, Jägerhorn, & Höllrigl, 2012 ). Examples of RI are astronomical observatories, particle accelerators, synchrotrons, lasers, and intensive computing resources, as well as data production and management tools. These infrastructures are used by researchers from all disciplines, in astronomy, biology, physics, chemistry, human and social sciences, earth sciences, etc., who thus have access to high-performance equipment in a high-level scientific environment 1 .

The RI road map of the French Ministry of Higher Education, Research and Innovation (MESRI) enumerates 99 infrastructures: large national and international research facilities covering all disciplines, “incredible engines of knowledge, attractors of talent, catalysts for collaboration, bearers of scientific image and prestige […] not work tools like others,” because of their longevity, their ambitions, and their costs (largely over €1 billion per year) ( Ministère de l’enseignement supérieur, de la recherche et de l’innovation [MESRI], 2018 ). The French road map includes, among many others, the GENCI Company for high-performance computing, the CERN Large Hadron Collider, the CTA Cherenkov Telescope Array, the SOLEIL Synchrotron, the OpenEdition scientific publishing platform for the social sciences and humanities, and the Huma-Num digital humanities platform.

In 2018, the French Ministry of Higher Education, Research and Innovation commissioned a scientometric analysis for a shared assessment of the scientific impact measures of 24 very large RIs and international organizations 2 . The challenge is multiple: a better assessment of the scientific impact of each facility, at the level of disciplines and subdisciplines; a better identification of research collaboration at the national, institutional, and individual levels; the detection of emergent research topics; and a contribution to scientific foresight and advice, as part of the policy-making mechanism.

The results of this analysis were published in November 2019 ( Egret & Fabre, 2019 ). With regard to research information processing, the study reveals a high degree of diversity and specificity. Most RIs make use of some kind of current research information system (CRIS) to provide information about the use of their services, resources, and systems. CRISs are an instrument for the management of research information and are linked to various internal and external systems or databases (finance, SAP, HR systems, project management systems, open access repositories, Web of Science, Scopus, PubMed, national libraries, BASE, CrossRef, EVALuna, Ebsco, equipment management systems such as ULab, and others); they “collect and store metadata on research activities and outputs such as researchers and their affiliations; publications, data sets, and patents; grants and projects; academic service and honors; media reports; and statements of impact” ( Bryant, Clements et al., 2017 ), to support research institutions in the provision of funding information and reporting, in aggregating references for research outputs, and in producing indicators and assessment ( De Castro, 2018 ). With standardized formats and functionalities, they are first and foremost designed for academic institutions, research organizations, and authorities, not for infrastructures. So how can standard CRISs provide solutions to the particular needs and expectations of (especially large) RIs? What is the potential impact of the French approach to RI impact metrics on the further development and implementation of research information systems in this field?

First, we review published literature on the topic of research information systems for RI, especially from the euroCRIS seminars, meetings and conferences.

Second, we provide a summary of the French study on RI assessment, in particular of the specific demands and expectations and of the recommendations for further action in the field of RI metrics.

Third, we discuss how the Common European Research Information Format (CERIF) and the German Research Core Dataset (KDSF/RCD) meet the requirements of present standard information and metrics for RI.

This section reviews published literature on the real and potential interest of research information systems for RI, based especially on the papers presented during the euroCRIS seminars, meetings, and conferences. Building RI is one of the priorities of European research policy, to foster international cooperation and integration, to provide tools for the development of open science, and to improve the performance of academic research. For many years now, the European Commission has provided funds for a large variety of e-infrastructures ( Buhr, 2014 ), and similar strategies can be observed at national and regional levels, such as the German Excellence Initiative ( Spang-Grau, 2019 ) or the Finnish Research Information Hub ( Puuska & Rydman, 2018 ). However, funding implies awareness and a good knowledge of existing infrastructures, and it also implies follow-up, reporting, and monitoring; therefore, the need for the evaluation of the output of European or national research policies, in terms of performance indicators of RI, has been clearly identified by authorities and funding agencies.

The need for RI assessment has been highlighted by the OECD ( Organisation for Economic Co-operation and Development [OECD], 2019 ) and by the ESFRI Roadmap 3 , as well as by the Cour des Comptes (French Court of Audit) in its parliamentary report of May 2019 on the governance and funding of large French RIs. The OECD recommends backing up the socio-economic impact assessment of RIs with a catalog of “core impact indicators” of their scientific performance, such as those metrics developed by the European Spallation Source infrastructure 4 ; these metrics include the number of citations, the number of publications in high-impact journals, the number of projects granted, the number of scientific users, the number of patents with commercial use, the number of full-time equivalent (FTE) staff in the RI, and so on.

(a) accounting data and expert analysis of capital and operating expenditures, including in-kind contributions; (b) scientometric data to estimate trajectories of publications and their impact in a specific domain; (c) firms’ survey data on technological spillovers expressed in terms of increased sales and cost savings, or increased profits; expert analysis of the technological content of procurement; company accounting data for industries involved in procurement; and expert analysis of the cost savings or other quantifiable effects of open source software or other technological spillovers; (d) survey data and other statistical evidence of the expected or ex-post effects on salaries of former students and early career scientists; (e) statistics about on-site visitors, web access, use of social media, exposure to traditional media, and data on travel costs, opportunity costs of time, and other information related to cultural effects; (f) contingent valuation data through survey of samples of potential taxpayers about their WTP for potential discoveries related to a specific project” ( Florio et al., 2016 ).

It defines three basic quality criteria for the inclusion of existing RI in the European portal: They must offer scientific and/or technological performance and support that should be recognized as being of European relevance, they must offer access to scientific users from Europe and beyond through a transparent selection and admission process, and they must have a management structure. If an RI does not fulfil these criteria, it will not be included in the MERIL Portal. The debate over the meaning of “European relevance” has contributed to the definition of common features of European RI.

MERIL has defined a couple of elements as relevant for the description of RI, including information useful for evaluation issues, such as the number of users, research services, and equipment.

These basic elements have been developed in compliance with the European format for research information, CERIF. MERIL, for this reason, has been described as a “connected e-infrastructure”, interoperable in particular with existing research information management systems ( Brasse, 2012 ).

The interoperability allows the creation of tools and services on top of the MERIL directory, such as single national contact points to foster RI cooperation ( Houssos & Karaiskos, 2013 ) or, in the framework of the Research Infrastructures Consortium (RICH project), single entry and access points to information about RI ( Tzenou & Bonis, 2016 ).

As part of the MERIL-2 follow-up project ( Baginskaite, 2017 ) 5 , the European Science Foundation (ESF) introduced in September 2018 a new data visualization tool that allows users to discover and explore data on the European research landscape, such as the RI size and location, user profiles, and research capabilities of over 1,000 research facilities across the continent. Based on MERIL-2, the new catalog of RI services, CatRIS 6 , provides a kind of standard framework with minimal data for the description of the service providers and the services themselves, such as access to and use of facilities and instruments, user support and training, and other activities and resources that the RIs deliver to users and customers.

Assessing RI is also a challenge for specific research communities with significant scientific instruments, facilities, and equipment. We give three examples. First, research organizations in the field of solid earth sciences implemented some years ago a new project called European Plate Observing System (EPOS), designed “with the vision of creating a pan-European eResearch Infrastructure for solid Earth science to support a safe and sustainable society […] the EPOS mission is to integrate the diverse and advanced European Research Infrastructures for solid Earth science relying on new e-science opportunities to monitor and unravel the dynamic and complex Earth System” ( Bailo, Ulbricht et al., 2017 ). The main challenge of this project was the development of a common metadata model to describe and assess in an appropriate way the large variety of persons, services, data, equipment, software, organizations, web services (API), and RI. In particular, the EPOS project identified 12 mandatory and optional elements (attributes) of the IR entity, which are compliant (interoperable) with the CERIF format for research information management ( Figure 1 ).

The EPOS metadata baseline ( Bailo et al., 2017 ).

The second example is from the field of environmental research, where Boldrini, Luzi et al. (2014) demonstrated how to implement the CERIF data model to assess and describe RI especially in global and multidisciplinary contexts.

The third example is the model of the data continuum in photon and neutron facilities developed by the UK PaN-data ODI project ( Matthews, 2012 ), which provides a detailed mapping and description of the research and data life cycle of these facilities. The proposed elements, especially the actors and the stages of the experimental lifecycle, can be considered as basic elements for the evaluation of the performance of RI.

A quite different use case of the evaluation of RI is the European VRE4EIC project ( Ivanovic, Theodoridou, & Remy, 2018 ; Theodoridou, Patkos, & Doerr, 2016 ). Coordinated by the European Research Consortium for Informatics and Mathematics (ERCIM), this project builds on existing e-RIs providing services, software, data, and resources to develop an enhanced virtual research environment (VRE). The ingestion of information on research, data, and computing infrastructures requires interoperable (standard) metadata on RI. Again, the CERIF is selected as the target format, because of its flexibility, quality maintenance, and political support.

A last and more recent example is the implementation of the European Open Science Cloud (EOSC). One part of their activity is the inventory, description, and assessment of existing data, computational, networking, and thematic infrastructures ( Vancauwenbergh, 2019 ). Yet, so far, (August 2020), EOSC does not provide information on descriptive elements or formats for this assessment, except for general information on national open science and FAIR data policies.

However, in spite of the need for evaluation, most projects dealing with RI and evaluation assess the RI content (i.e., data and documents), and do not consider the RI performance as an object of evaluation on itself. For instance, the initiative for collaborative research information management by the UK Science and Technology Facilities Council (STFC) which “operates large scientific facilities to support experimental research in […] chemistry, materials science, and biochemistry (including) the ISIS neutron source, the Central Laser Facility and the Diamond synchrotron light source (with) large volumes of data and […] used each year by many thousands of experimental scientists from around the world” ( Crompton, Matthews et al., 2012 ) focused on the description and linking of data, and not on the assessment of the infrastructures and facilities themselves.

The UK Research Excellence Framework (REF) 2021 assesses RIs as institution-level resources and facilities available to support research, as part of the environment subprofile of universities and other research organizations, and among other supplemental criteria and similar to income, people, strategy, and contribution to economy and society ( Research Excellence Framework [REF 2021], 2019 ). The REF panel criteria consider the investment in RI as a contribution to (or dimension of) sustainability, to ensure the future development of the units and their disciplines. Therefore, evidence is requested for the existence and strategy of RI and about its usage, quality, operation, and benefits (REF5b, Section 3), but without specifying the expected evidence (data sources, indicators etc.).

A recent survey provides some empirical elements on the place of RI evaluation in German public research ( Schöpfel, Azeroual, & Saake, 2019 ). Universities and other research organizations are regularly evaluated and must report on their research activities. To improve the quality of this reporting, many of them have implemented some kind of CRIS, as a central database for the collection, presentation, and evaluation of data related to research. Yet, following a survey with 51 German institutions, only a small percentage (about 10%) make use of their CRIS to evaluate the performance of their own RI in terms of output and input, with appropriate metadata 7 .

This last survey raises two other issues: Insofar as RIs serve different purposes from different communities and institutions, should their performance be assessed differently, according to and for each community and institution, as is done by those German institutions with their institutional CRIS? In this case, assessing the global performance of an RI would require the aggregation of all performance metrics produced by relevant institutions and clearly identified as related to this specific RI—a tedious method whose success would require a high degree of standardization between the institutions involved. In fact, our approach is different, based on the reality of central funding and not on the reality of one community or many; instead of aggregating data and metrics from different institutions, the idea is to produce performance metrics upstream, by the RI itself.

The second issue is about the particularity of RIs. Why do they require a specific assessment, different from universities and other research institutions? CRISs are mainly designed for universities and research institutions; why do they need a specific adjustment for the assessment of RI? There are at least four reasons (i.e., four significant differences between RI and other academic and research institutions): RIs provide temporary hosting of scientists and projects (“hotel”); RI consist of a large equipment with an analysis output; RIs provide methodological support for the research and are not “neutral”; and RIs functioning is based on internal and external networking. We will come back to these characteristics in more detail in the following section, as part of the French study.

In summary, there is a general consensus that RIs are part of research evaluation and that they must be described and assessed. Also, because of the large variety of RIs, a standard and interoperable data model seems appropriate, in particular the only international standard format recommended by the European Commission for research information management system (CERIF). Section 4 will provide more information about the CERIF model and its potential for the evaluation of RI.

For the reasons mentioned above, the existing procedures and metrics should take into account the particular characteristics and functioning of RI to provide appropriate assessment of RI. Data models and systems made for universities and research organizations are useful but need adjustment for the specific needs of RI. Regarding CRIS in France and compared to other European countries, there is a relatively low degree of standardization among research structures, with few CERIF-compliant systems.

In France, large RIs, known as Very Large Research Infrastructures (TGIRs), are mainly defined by their scientific potential of a national or international nature. The distinction in France between RIs and TGIRs is currently being called into question: It stems, for the most part, from agreements of administrative or financial scope, and it has been recently observed by the Court of Audit that this distinction compromises the readability of national policy ( Cour des Comptes, 2019 ).

The TGIRs are originally French public goods, with a funding which is largely mutualized, around which the Court of Audit observes: “a historical trend towards the pooling of the support of the costs of these infrastructures in the world and, in particular in Europe.” Between 2012 and 2017, according to the Court's estimate, “the cumulative amount of TGIR resources reached €4.2 billion, half of which came from French budget appropriations.” This method of funding ensures strong international vitality for TGIR networks, but also requires a framework in which the CRIS have their strategic place: Faced with competition in Europe for scientific choices, the Court of Audit observes the need for “mastery of decision-making processes and the conception by France of genuine influence engineering” ( Cour des Comptes, 2019 ).

As mentioned above, the French Ministry of Higher Education, Research and Innovation 8 commissioned a study on the impact measures of large RIs, the results of which were published in 2019 ( Egret & Fabre, 2019 ). The report provides a review of current scientometric practices and describes the expectations and needs of infrastructure managers; moreover, it makes 15 proposals for the development of shared impact measures, and it discusses some general indicators (“publimetrics”) to contribute to a conceptual and methodological framework for further harmonization and standardization of existing metrics, to improve RI evaluation practice, and to develop a common evaluation culture, while respecting the specificities of each research facility and the requirements and standards of the European and international RI landscape, in particular the need for interoperability.

Temporary accommodation: The 24 TGIR, including the four major international organizations (OIs) in which France participates, host research teams from all institutional sources on scientific projects (universities and organizations with researchers from all countries, sometimes teams from private industrial research, etc.) according to quotas and rules defined at the level of each TGIR with the agreement of the major national scientific authorities (e.g., CEA or CNRS). With the exception of EMBL, which in itself constitutes a special category, TGIRs do not provide any permanent reception beyond a project, which generally spans a short period (less than or equal to 6 months).

“Self-service” experimentation on a project: The experiment is carried out in “self-service” when the TGIR accepts the scientific project and validates the conditions for its realization, while defining the allocation of reception resources (technicians' time, adaptation of installations to experience, instrument time, computing time), means of transport (EURO ARGO, oceanographic vessels), beam time (SOLEIL, ESRF, LLB, …), computing resources (GENCI high-performance computing), etc. All output data are systematically made publicly available after a limited embargo period, and most often result from standard analysis pipelines.

Technical assistance to experimenters: The survey data, which cannot be developed here, show a very systematic adaptation of the TGIR to the needs for advice, expertise, and scientific support by the teams of permanent researchers of the TGIR to all kinds of scientific projects, from the Humanities (TGIR HUMA-NUM and Progedo) to astrophysics, oceanography, climatology, etc.

Networking of means and results: Technical assistance is frequently associated with networking. In terms of resources, this is carried out by internationalization of similar resources (e.g., LIGO and EGO VIRGO) or additional resources (neutron lines and X-ray lines coupling the experiments in a mixed program between SOLEIL and the LLB); there are also many opportunities for mutualization of instruments, software, and other resources in astronomy and climate sciences. In addition, pooling is also frequent and currently developing in the sharing of results (standardization of the presentation of acknowledgments, data, affiliations, databases, scientific publications; standardization of the presentation of platforms, European key performance indicators, ERC nomenclature for indexing disciplines, etc.).

3.1. Expectations, Needs and Interests

The 2019 survey reveals the broad interest of large RIs in the development of functions and tools to analyze and share scientific results through new metrics, new software approaches to current metrics, and emerging tools to build numerical functions to support research. Several large research organizations in France have implemented systems to make their research data publicly available 9 . RI managers want to assess the output of their infrastructure in terms of data and publication, and its impact in terms of citations, but also in terms of new knowledge, concepts, ideas, etc. The French-Italian Antarctic Station Concordia, for instance, is very interested in all analytical services that can focus on the implementation of metrics to ensure the traceability of scientific production, its thematic semantic analysis, and the genealogy of concepts. The goal is to extend beyond metrics to the analysis of the value of scientific work for all publics.

There is a need, in particular to trace the genealogy of scientific ideas, and to analyze the ruptures and reorientations of programs, such as those coming from the French community (optimal control, variational assimilation…)

an interest in a future platform of metrics tools, notably for sorting publications and developing analyses, in relation with the publication committees of major collaborations, and for adopting coherent positions towards funding agencies. The interest is clear on the institutional side (IN2P3 10 ), but less obvious on the side of the researchers themselves.

How to organize an RI access route? This question contains that of the associated services, which can be shared or dedicated according to a “map” that is not yet sketched out … This publimetric map can be declined according to its various vocations discussed above, and include discovery support services that identify relevant links between work in progress and published in accessible forms in an open science framework.

an important aspect of new needs: the implementation of DOI on data is now underway and […] the RI has launched an OCT (Open Citations Tools) program with all the DOI reservoirs, to build an Observatory, with an “appropriate metric” and this is a hot topic for INSHS 11 , but also for the French-speaking world, with the development of scientific French.

Genealogical analysis of scientific ideas would ”bring a lot”. One could also better know and trace the French participation: two French researchers per expedition, this means that a dozen French scientists embark each year on IODP expeditions and then ”interact with about a hundred of their colleagues” to process the data from the campaigns. One onboard scientist per year has a knock-on effect on 70 to 100 researchers concerned in one way or another by his approach. The genealogy of the communities in question would undoubtedly be interesting and profitable for the work of the RI.

It is certainly necessary to follow the current developments in research on new metrics tools, and, at the same time, make an in-depth analysis of the uses of the RI. In particular, it will be necessary to assess how metrics actually contribute to the scientific options chosen by public policy. In this way, it will be possible to evaluate the precise contribution of science to public policy actions, as in the case of “evidence-based policies.”

The coordinators of the French contribution to the European Southern Observatory (ESO) and ( Stocker, Darroch et al., 2020 ) point out that they have “no current practice of text mining or semantic analysis” and admit that there is, “on the other hand, obvious scientific interest, and it is necessary to follow the advances in the corresponding fields of STI research.” The question of resources is raised by the French oceanographic fleet (FOF): “We are ready to develop sharing with other large RIs, particularly in the field of climate. But on condition that we have the associated resources.”

to organize the traceability of the RI results

to build a catalog of shared strategic indicators (publimetrics)

to create a network of these new metrics

These general recommendations are broken down into 15 detailed issues. We present the key points here, as summarized in the report ( Egret & Fabre, 2019 ).

3.2. Recommendation A: Organize the Traceability of Results

Generalize the use of DOI and global traceability

Harmonize the main performance indicators (domains, partnerships, equipment)

Harmonize the terminology (classification) of research areas

Develop new metrics for emerging research fields and monitor the genealogy of ideas

Develop open science metrics for publications and data

This first group of recommendations is particularly sensitive for TGIRs, which, unlike university or research institutions, lack visibility in the large bibliometric databases referencing scientific production.

The actors concerned by the recommendations are the persons in charge of the TGIR who must define, in an operational way, the contours of their scientific production: Indeed, this is most often not restricted to that of their teams but must also include that of their users, or even consider more broadly the production of knowledge that has directly benefited from the existence of the infrastructure.

The publishers of large databases are also concerned by these recommendations, who may seek to include specific metadata for instruments and infrastructures, and to develop the nomenclatures of research fields.

3.3. Recommendation B: Catalog Shared Strategic Indicators

The second group of recommendations is based on the current practices and expectations regarding metrics of the RI activities (performance) and their scientific impact.

List the rules for identifying publications and reaffirm the requirement of an explicit mention of the RI (affiliation)

Collect the shared scientific impact indicators based on the prior establishment of a Guide for publimetrics

Design an architecture of metrics practices by major purposes and build a typology of current metrics practices

Participate in a global modeling of the uses of publimetrics at a European scale

Specify the organization and standards of publimetrics services

The production of this Guide aims to encourage the pooling and wide dissemination of good practices, the use and quality of which should be tested at national and European levels. Such a collection of recognized and recommended standards, coproduced with the large RIs, will also facilitate the construction of relevant and flexible digital architectures, appropriate for each infrastructure experiencing the need to complete its digital master plan. To meet the needs of research communities in terms of scientific impact metrics, the diversity of needs and the pluralism of practices must be recognized and supported: These are among the first lessons learned.

This second group of recommendations concerns the deployment of shared indicators. Here again, the main actors concerned are those in charge of the TGIR, but also the supervisory bodies (research organizations, ministry) who will seek to use these indicators in the service of strategic reflection. Finally, these developments must take into account the European and international context in which the TGIRs are deployed and be carried out jointly with the partners of the other countries concerned.

3.4. Recommendation C: Create a Network of Shared Metrics

Display the reference charters and support large RIs in their efforts to adhere to international declarations of good practice for the evaluation of scientific results

Develop new metrics to support scientific foresight

Consolidate the scientific and professional deployment of publimetrics

Initiate a national metrics orientation approach

Set up a first experimentation process with a few large RIs

This third group of recommendations aims to promote a national network with the knowhow and skills for the implementation and monitoring of tracers and indicators of scientific impact. The actors directly concerned are therefore, in addition to those responsible for the TGIRs (and potentially for the RIs), the national research organizations (such as the CNRS, the CEA, and the IFREMER), the ministerial authorities, as well as the national evaluation and control bodies.

The publimetrics guide, mentioned in recommendation B, would be the means of bringing about the networking of metrics practices common to RIs, universities, research organizations, and other academic institutions.

It can be recalled on this point that, in 2016–2017, on the initiative of the CNRS Department of Scientific and Technical Information (DIST), and in association with the information professionals of Couperin (French academic library consortium), ADBU (Association of academic library directors) and EPRIST (Association of STI directors of research organizations), the higher education (HE) and research institutions had taken the initiative to assess the feasibility of networking the digital objectives and practices of scientific work, particularly in terms of metrics and analysis of scientific publications ( Centre national de la recherche scientifique [CNRS], 2017 ) 12 . This former study has shown the potential synergies of resources and projects that can be expected from networking all the approaches, based on the stronger and more detailed recommendations obtained in the survey of large RIs.

Obviously, there is a growing interest and demand for the assessment of RI by the RI management, and also by funding bodies, research organizations, and authorities ( Stocker et al., 2020 ). The French publimetrics initiative reveals different dimensions of such an approach, including the scientometric evaluation of the RI performance in terms of output and impact as well as the discovery of emerging research trends and the assessment of partnerships, communities, and knowledge production. Large RIs have importance in terms of national and international research strategy, and they need significant, recurrent long-term funding; for both reasons, the French initiative recommends a shared, concerted and mutualized approach to evaluation, based on flexible standard metrics.

In fact, as the French study shows, many infrastructures already do some kind of assessment, often without appropriate tools or models, specific and not standard, and not interoperable. The published projects in the field of research information management show that CRISs, with their standard data models, may be an option for the assessment of RI. Yet, as mentioned above, CRISs are generally designed for the evaluation of research institutions and organizations, not of infrastructures, which are usually considered and assessed by such systems as part of institutional resources, similar to other facilities, services, and equipment. Therefore, the following section analyzes how the main standard CRIS format (i.e., the Common European Research Information Format [CERIF]), and the new German Research Core Dataset (KDSF/RCD) meet the requirements of the present standard information and metrics for RI. Our focus is on the mapping of RCD attributes, RCD entities, and CERIF elements and compare this information with the recommended requirements (metrics) of the French publimetrics initiative. Do RCD and CERIF provide an appropriate solution for the need for evaluation of RI? Are they compliant with the publimetrics recommendations?

Developed with the support of the European Commission and recommended for use by the EU member states, CERIF 13 is a generic and standard model for organizing and exchanging research information, the research domain and their relationships to each other on conceptual, logical, and physical levels. CERIF is intended to serve as a model for homogeneous access to heterogeneous data systems and as a definition of a data exchange format. The aim of CERIF is to serve as an interoperability level between the digital infrastructure and the research data, and to promote integration and exchange through standardization.

The CERIF data model includes persons, organizations, their projects, funding, and generally everything that arises from or is connected to the research process. At the very heart of the CERIF model are three interconnected core elements: persons, organizations, and projects; all the other elements—outputs, activities, metrics etc. and on another level, identifiers, geographical origin, addresses etc.—are connected with these elements through the semantic layer, in a rich, highly complex but standard network of relations ( Figure 2 ).

CERIF data model (source: OpenAIRE 14 ).

We will not, in this context, describe and comment the CERIF data model in detail. Relevant for our study is the fact that the CERIF data model contains three infrastructure entities (i.e., facility, equipment, and service [ Figure 3 ]), with semantic links to all base entities (project, person, organization unit) and result entities (publication, patent, product) and to some second level and link entities, such as funding, event, postal address, measurement, and indicator ( Dvorak, 2013 ).

The CERIF data model infrastructure entities (source: CERIF 1.3 Full Data Model 15 ).

This data model allows a flexible description (multilingual fields for name, description, and keywords) and assessment of RI and bears the potential for specific extensions, especially for identities ( Jörg, Höllrigl, & Sicilia, 2012 ), classification, and typologies, which may be added and stored in the semantic layer of the CERIF data model. Through the semantic interconnection of the different element levels, CERIF is able to handle RI identifiers, RI classifications and/or typologies, and an RI directory, and to link specific outcome (result) data such as publications and research data sets.

The OpenAIRE Guidelines for CRIS Managers define “equipment” as an “instrumentality needed for an undertaking or to perform a service,” with one mandatory attribute (internal identifier) and six optional attributes or elements (type of equipment, acronym, name, identifier, description, owner), whereas “service” is defined as a research information management system (CRIS).

4.2. KDSF/RCD

More recently, the German Council of Science and Humanities has funded the development and promotion of the Research Core Dataset (KDSF/RCD) 16 , which describes information on research activities in a standardized form ( Azeroual, Saake et al., 2019a ; Biesenbender, 2019 ; Biesenbender & Herwig, 2019 ). This should enable quality-assured research activities for research reports to be compared with little effort and be used multiple times ( Azeroual, Schöpfel, & Ivanovic, 2020 ). The goal is to provide a standard for Germany; the target groups for this are universities and nonuniversity research institutions. As there has been no standardized recording of research activities by institutions in Germany up to now, the RCD standard is intended to contribute to the standardization of research reporting. According to the RCD, research information in the areas of researchers employed by the institutions, young researchers, third-party funded projects, patents and spin-offs, publications, and RIs are to be collected.

These are converted into so-called core data and their characteristics and aggregation measures on the basis of existing definitions and standardization, including CERIF; the mapping between RCD and CERIF shows that RCD is a specialized version of CERIF. The implementation of the RCD standard is supported by the provision of a technical data model based on CERIF in XML format, which describes both basic and aggregate data formats and their respective relationships. The basic data model corresponds to the objects, the description of the objects, and the relationships and properties. The aggregate data model only defines the core data, without characteristics or specializations. Further details about the RCD specification (version 1.0) and the RCD XML schema can be found publicly on the RCD website. Figure 4 shows the semantic linking of the RCD areas as an Entity Relation Model (ERM). This contains the objects on which the specification is based, their attributes, and the relationships between them.

RCD data model ( Azeroual & Herbig, 2020 ).

large/costly instruments, resources or service facilities for research in all scientific fields, which are characterized by at least supraregional importance for the respective scientific field as well as by a medium to long-term lifetime (more than 5 years) and are available for external use for which access or use regulations have been established 17 .

Operator: organizational unit

Operating personnel: employer/employee

Coordinator: organizational unit

Use: use/intensity of use

Publication: publication

Type: type of RI

Access type: type of access

4.3. Mapping CERIF and RCD

The RCD is compliant with the European CERIF format, and the RCD team provides a mapping between CERIF and RCD, to enable the exchange between different CRIS ( Azeroual & Herbig, 2020 ) 18 . The RCD core data “ Forschungsinfrastruktur ” (RI) is mapped against the CERIF infrastructure entity “equipment” but not to the other entities “service” and “facility.” In comparison, CERIF appears more detailed, complete, and flexible for the description and assessment of RI than the German RCD.

RCD and CERIF serve as guidelines for scientific institutions that want to represent RCD and CERIF in their CRIS systems. Implementation can take place at both institutional and CRIS provider level. Both cases can be observed in institutions. The XML schema from CERIF and RCD can be used as a data source before importing into CRIS and/or as an export format to simplify reporting ( Azeroual et al., 2020 ). The use of CERIF and RCD in CRIS can be illustrated using Figure 5 .

Use of CERIF and RCD in CRIS ( Azeroual & Herbig, 2020 ).

The data quality is somewhat dependent on the standard application and this will likely improve the data quality. A standardized data model such as CERIF and RCD is an essential prerequisite for data management in terms of monitoring and strengthening data management in institutions. This enables the introduction and permanent quality assurance in institutions as an overarching goal for research information ( Azeroual & Herbig, 2020 ).

In the field of infrastructures, this means that if the data on RI are created directly in the RI-CRIS, care must be taken to index the RI in an appropriate way (with identifier, classification, etc.) and to link the RI to each relevant element (publication, data, patent, domain, person, etc.). If the information on RI is ingested from other, internal or external sources, such as repositories, bibliographic databases, or RI systems, care must be taken to control the data quality and to cleanse, enrich, and standardize the integrated data for further RI assessment.

4.4. The Compliance of CERIF and RCD with the Publimetrics Report

The French report makes 15 recommendations for the development of scientometric assessment of RI, summarized above ( Egret & Fabre, 2019 ). How can research information systems cope with these requirements, and to what extent are the standard format CERIF and the German RCD consistent with them? Table 1 provides some elements.

Compliance of CRIS data models with French publimetrics recommendations

Some comments. First, some recommendations have been excluded because they are not really relevant for research information management systems. In particular, recommendations B2–B5 regarding the usage of metrics (best practices, etc.) will contribute to the development and design of CRIS reporting functionalities, downstream of the ingestion and processing of research information (cf. Figure 4 ) but should have no impact on the data model itself. Also, recommendations C1, C3, and C4 on networking appear less relevant for data models; even if CRIS will produce useful information and thus support the implementation of a national or international strategy of RI metrics.

4.4.1. A1 Generalized use of unique identifiers for publications

Both CERIF and RCD include identifiers as an attribute of the entity publication. The CERIF attribute ID is for local identifiers, whereas for persistent identifiers such as DOI the link to the CERIF FedID entity should be used.

4.4.2. A2 Harmonization (convergence) of performance indicators (domains, partnerships, equipment)

CERIF handles standard or controlled vocabularies in the semantic layer (classification…). Relationships are established by identifiers of persons, organizations, or projects (attribute column), and fractions are indicated in the classification column, where each value belongs to a scheme.

4.4.3. A3 Harmonization (convergence) of scientific domains

CERIF supports controlled terminology in the semantic layer.

4.4.4. A4 Development of new metrics for emergent research fields

CERIF appears flexible enough to represent and report new indicators, based on semantic relations between entities and attributes and on measurement extensions, elaborated on infrastructure entities and semantics.

4.4.5. A5 Indicators of open science (open access publications, open repository deposits, paywall publications)

CERIF can handle this as semantics and attributes of the result entity publication.

4.4.6. B1 Generalization of RI affiliation

CERIF would represent this as a semantic link between a person (author), an organization (OrgUnit), and an equipment, facility, or service (infrastructure entity).

4.4.7. C2 Development of new metrics for scientific foresight

Depends on the development. Research information systems would at least be able to produce useful information for such new metrics. The CERIF model appears flexible enough for the definition of new metrics, with the entities Indicator, Metrics, and Measurement. Moreover, there is a flexible semantic layer and links between almost all CERIF entities, which can be classified and time framed using the startDate and endDate attributes of link entities.

4.4.8. C5 Experimental approaches

Both data models can handle information about the number of publications, scientific domains, impact metrics (citations), international partnerships, and open science-related metrics (open access publications).

RIs are facilities, equipment, and services needed by the scientific communities from all disciplines; they provide high-performance equipment in a high-level scientific environment. Because of their strategic importance, and also because of their need for significant, recurrent long-term funding, there is a growing demand for the monitoring and assessment of their performance in terms of research outcomes (publications, data, patents, etc.). A couple of national and international projects from the last decade show that research information management systems, with their standard data models, formats, and procedures may be an option for RI assessment. They also do appear to be consistent with requirements and recommendations of a shared approach to RI outcome and impact metrics, as suggested by the French publimetrics initiative.

significant coordination effort (…) at an international level to raise and share emerging best practice case studies, since research is a deeply international endeavour and research facilities used in international projects may be based in any of the partnering countries 19 .

Beyond the scientific evaluation, whose instruments are partly in place, and the socio-economic evaluation, which is still in progress, a large field of study has not been tackled to date: the evaluation of the positive qualitative externalities linked to the development of knowledge enabled by large RI. However, this impact should be taken into consideration, in order to guide public policies and ultimately to extend the reflection on risks, natural or medical, for example, and the improvement of living conditions 20 .

Above all, more mutual understanding and coordination between RI management and CRIS development seems required to address this challenge.

As long as RIs have their own specific performance indicators, produced with their own specific systems and for internal use only, it will be difficult to harmonize or consolidate these metrics to assess the overall performance of the different RIs, which is necessary for the development of a reasonable national or international policy. The French publimetrics initiative provides a strategy on how to progress on the way to further harmonization and consolidation of shared and common RI metrics. Research information management systems or CRIS, designed to support research institutions in the provision of funding information and reporting, in aggregating references for research outputs, and in producing indicators and assessment ( De Castro, 2018 ), bear the potential to contribute to this strategy. They have proven their worth in complex research environments, they are based on standards, and they consider the issue of data quality as a critical factor of success ( Azeroual, Saake et al., 2019b ).

Moreover, these systems would also contribute to a better understanding of scientific discovery and knowledge. At the crossroads of information sciences and bibliometrics, research is advancing towards the construction of “global” traceable document paths ( Cabanac, Frommholz, & Mayr, 2020 ): In this sense, navigation between all the databases accessible on the Web is recognized as possible ( Brickley, Burgess, & Noy, 2019 ). Furthermore, it is essential for the progress of routes and maps that information search behaviors are modeled and that the semantics of the documentary choices made are stabilized by a solid “topic modeling,” based on a “topic analysis-based approach” built through innovative and exhaustive methods ( Tsatsaronis, 2020 ). The search for these solutions is encouraged by a context of rapid and diverse editorial changes, open to innovation ( Conrad, Richardson, & Rinehart, 2020 ). These evolutions lead directly to the creation of tools for comparing navigation routes; in the words of Atanassova, Bertin, and Mayr (2019) , it is necessary to produce “annotated corpora and shared evaluation protocols to enable the comparison between different tools and methods.”

In this environment of query “paths” under construction, a common requirement towards more traceability appears. The paths allow discoverers and users of science to represent their path of hypotheses, discoveries, and ideas, in a more readable and traceable way, through a structured sequence of all published scientific results, based on valid analyses of new maps of documentary choices ( Aria & Cuccurullo, 2017 ). These mappings display their results using new ergonomic and user-friendly tools: This vision is nothing less than the current grail of industries contributing to the exploitation of scientific documentation. A dynamic global offer is thus being developed, with the slogan: “solving the problem of problem solving.” 21 Some research institutes and infrastructures, such as the European Bioinformatics Institute (EMBL-EBI 22 ) already do this discovery mapping, which constitutes an initial response to the needs expressed by most large RIs. Improved international standards and cooperation for such marking out of the routes (i.e., a global markup of open routes, such as sea or land air routes) would ensure the scientific integrity of navigation choices and their coherent sharing, and would optimize navigation in digital scientific databases. RI evaluation with research information management systems could be an opportunity for further progress.

Alongside the current community, domain, and institutional platforms, new multiactor, agent, and object infrastructures are now emerging, using a combination of computing and analysis resources to carry out relevant data groupings on a very large scale. The Directory of Research Information Systems 23 shows a broad and structured pool of research information systems, which can enhance research intelligence and contribute to the notion of knowledge infrastructure, as a place for sharing and experimenting with RI publimetrics and for the preparation of what the National Academies of Sciences had called some years ago The future of scientific knowledge discovery in open networked environments ( Uhlir, 2012 ). In this dynamic and strategic environment, international synergy and cooperation between the different stakeholders and projects from the communities of RIs (such as the ESFRI working group on the monitoring of RI performance with Key Performance Indicators or the JISC equipment data project), euroCRIS, research information management systems, research organizations, and funding bodies would be extremely useful for the development of relevant standard indicators for the reporting, monitoring, and assessment of the performance of RIs, to meet the academic and societal challenge.

Share scientific results on scientific themes common to several RIs: The French Publimetric Survey recorded this objective in a majority of complementary TGIR around a global scientific objective (examples: climate change sciences creating an articulation of interest for the structured exchange of data between glaciology, analysis of marine temperatures, traces of carbon, meteorological conditions, etc.).

Share the technical and scientific resources and practices of the same type of equipment between and in TGIR (astronomy, synchrotron radiation, mainframe computers, large interdisciplinary scientific analysis networks, etc.). As such, TGIRs already practice many ULAB-type procedures to build their different interfaces (uses, experimenters, partners).

Develop an expression of global interest in research approaches and scientific analysis tools: Even more than others familiar with macroevolutions in concepts and work directions, all TGIRs feel the need to federate approaches on the new semantics of discovery, on the itineraries and maps of knowledge renewed by the present innovations which surround the human sciences and the information sciences. The Bibliometric Survey has collected many testimonies in this direction. In this sense, the analysis graphs of scientific choice routes ( Fabre, 2019 ) are present in our present reflection, as in that of most of the TGIR, which are ready to share experiences on innovative devices on the current orientations of the work of science, as reported in the survey.

The authors are engaged in further work to test the proof of concept of a bipartite Scientific Knowledge Graph (SKG), which was discussed as a research question in Fabre (2019) . This SKG compares “routes” of networked users querying scientific information for discovery purposes and uses. Various studies in the literature ( Aryani, Fenner et al., 2020 ; Brack, Hoppe et al., 2020 ) confirm that SKGs offer powerful means of representation of scholarly knowledge and assessment of research impact. This work will include applications of SKGs to RI uses.

The authors are most grateful for insightful advice and comments from Guillaume Cabanac (University of Toulouse) and Dragan Ivanovic (University of Novi Sad) and for the constructive critics from two anonymous reviewers.

Renaud Fabre: Writing—review & editing. Daniel Egret: Writing—review & editing. Joachim Schöpfel: Supervision, Writing—original draft, Writing—review & editing. Otmane Azeroual: Writing—original draft, Writing—review & editing.

The authors have no competing interests.

The French Publimetrics study on the scientific impact of large IR was supported by the French Ministry of Higher Education, Research and Innovation (DGRI).

See the interview with Gabriel Chardin, former president of the CNRS RI committee http://www.cnrs.fr/cnrsinfo/dans-les-tgir-se-construit-la-societe-du-futur-gabriel-chardin-president-du-comite-tres .

TGIR (Très Grandes Infrastructures de Recherche) and OI (Organisations Internationales).

European Strategy Forum on Research Infrastructures https://www.esfri.eu/esfri-roadmap-2021 .

ESS https://europeanspallationsource.se/ .

MERIL-2 https://portal.meril.eu/meril/ .

CatRIS https://www.portal.catris.eu/ .

Unpublished data from Azeroual O. (in preparation). Untersuchungen zur Datenqualität und Nutzerakzeptanz von Forschungsinformationssystemen . PhD dissertation.

Direction générale de la recherche et de l'innovation (DGRI).

See, for instance, the MINnD project at the French Geological Survey BRGM (Monitoring of changes in practices and knowledge around the digital model) https://www.minnd.fr/ .

The French National Institute of Nuclear and Particle Physics.

The French National Institute of Social Sciences and Humanities.

See the COPIST reports at https://adbu.fr/les-etudes-du-copist-catalogue-doffres-partagees-en-ist/ .

For more information see https://www.eurocris.org/cerif/main-features-cerif .

OpenAIRE Guidelines for CRIS Managers https://openaire-guidelines-for-cris-managers.readthedocs.io/en/latest/cris_elements_openaire.html .

https://www.eurocris.org/Uploads/Web%20pages/CERIF-1.3/Specifications/CERIF1.3_FDM.pdf .

In German: Kerndatensatz Forschung (KDSF). For more information see https://kerndatensatz-forschung.de/ .

See https://kerndatensatz-forschung.de/version1/technisches_datenmodell/index.html#http://kerndatensatz-forschung.de/owl/Basis#Forschungsinfrastruktur .

See https://kerndatensatz-forschung.de/version1/technisches_datenmodell/Mapping.html .

Persistent identifiers for research instruments and facilities? June 25, 2020 https://www.eurocris.org/blog/persistent-identifiers-research-instruments-and-facilities .

Sénat, Commission des Finances Audition TGIR du 17 juillet 2019, Exposé de Sophie Moati, présidente de la troisième chambre, http://www.senat.fr/rap/r18-675/r18-675_mono.html .

See https://www.lens.org/ .

See https://www.ebi.ac.uk/services .

DRIS https://dspacecris.eurocris.org/cris/explore/dris .

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Research Basics

• What Is Research?
• Types of Research
• Secondary Research | Literature Review
• Developing Your Topic
• Primary vs. Secondary Sources
• Evaluating Sources
• Responsible Conduct of Research
• More Information

A good working definition of research might be:

Research is the deliberate, purposeful, and systematic gathering of data, information, facts, and/or opinions for the advancement of personal, societal, or overall human knowledge.

Based on this definition, we all do research all the time. Most of this research is casual research. Asking friends what they think of different restaurants, looking up reviews of various products online, learning more about celebrities; these are all research.

Formal research includes the type of research most people think of when they hear the term “research”: scientists in white coats working in a fully equipped laboratory. But formal research is a much broader category that just this. Most people will never do laboratory research after graduating from college, but almost everybody will have to do some sort of formal research at some point in their careers.

So What Do We Mean By “Formal Research?”

Casual research is inward facing: it’s done to satisfy our own curiosity or meet our own needs, whether that’s choosing a reliable car or figuring out what to watch on TV. Formal research is outward facing. While it may satisfy our own curiosity, it’s primarily intended to be shared in order to achieve some purpose. That purpose could be anything: finding a cure for cancer, securing funding for a new business, improving some process at your workplace, proving the latest theory in quantum physics, or even just getting a good grade in your Humanities 200 class.

What sets formal research apart from casual research is the documentation of where you gathered your information from. This is done in the form of “citations” and “bibliographies.” Citing sources is covered in the section "Citing Your Sources."

Formal research also follows certain common patterns depending on what the research is trying to show or prove. These are covered in the section “Types of Research.”

• Next: Types of Research >>
• Last Updated: Jun 18, 2024 4:19 PM
• URL: https://guides.library.iit.edu/research_basics

Part I: What is an information system?

Chapter 1: What Is an Information System?

Learning Objectives

Upon successful completion of this chapter, you will be able to:

• define what an information system is by identifying its major components;
• describe the basic history of information systems; and
• describe the basic argument behind the article “Does IT Matter?” by Nicholas Carr.

Introduction

Welcome to the world of information systems, a world that seems to change almost daily. Over the past few decades information systems have progressed to being virtually everywhere, even to the point where you may not realize its existence in many of your daily activities. Stop and consider how you interface with various components in information systems every day through different electronic devices. Smartphones, laptop, and personal computers connect us constantly to a variety of systems including messaging, banking, online retailing, and academic resources, just to name a few examples. Information systems are at the center of virtually every organization, providing users with almost unlimited resources.

Have you ever considered why businesses invest in technology? Some purchase computer hardware and software because everyone else has computers. Some even invest in the same hardware and software as their business friends even though different technology might be more appropriate for them. Finally, some businesses do sufficient research before deciding what best fits their needs. As you read through this book be sure to evaluate the contents of each chapter based on how you might someday apply what you have learned to strengthen the position of the business you work for, or maybe even your own business. Wise decisions can result in stability and growth for your future enterprise.

Information systems surround you almost every day. Wi-fi networks on your university campus, database search services in the learning resource center, and printers in computer labs are good examples. Every time you go shopping you are interacting with an information system that manages inventory and sales. Even driving to school or work results in an interaction with the transportation information system, impacting traffic lights, cameras, etc. Vending machines connect and communicate using the Internet of Things (IoT). Your car’s computer system does more than just control the engine – acceleration, shifting, and braking data is always recorded. And, of course, everyone’s smartphone is constantly connecting to available networks via Wi-fi, recording your location and other data.

Can you think of some words to describe an information system? Words such as “computers,” “networks,” or “databases” might pop into your mind. The study of information systems encompasses a broad array of devices, software, and data systems. Defining an information system provides you with a solid start to this course and the content you are about to encounter.

Defining Information Systems

Many programs in business require students to take a course in information systems . Various authors have attempted to define the term in different ways. Read the following definitions, then see if you can detect some variances.

• “An information system (IS) can be defined technically as a set of interrelated components that collect, process, store, and distribute information to support decision making and control in an organization.” [1]
• “Information systems are combinations of hardware, software, and telecommunications networks that people build and use to collect, create, and distribute useful data, typically in organizational settings.” [2]
• “Information systems are interrelated components working together to collect, process, store, and disseminate information to support decision making, coordination, control, analysis, and visualization in an organization.” [3]

The Components of Information Systems

Information systems can be viewed as having five major components: hardware, software, data, people, and processes. The first three are technology . These are probably what you thought of when defining information systems. The last two components, people and processes, separate the idea of information systems from more technical fields, such as computer science. In order to fully understand information systems, you will need to understand how all of these components work together to bring value to an organization.

Technology can be thought of as the application of scientific knowledge for practical purposes. From the invention of the wheel to the harnessing of electricity for artificial lighting, technology has become ubiquitous in daily life, to the degree that it is assumed to always be available for use regardless of location. As discussed before, the first three components of information systems – hardware, software, and data – all fall under the category of technology. Each of these will be addressed in an individual chapter. At this point a simple introduction should help you in your understanding.

Hardware is the tangible, physical portion of an information system – the part you can touch. Computers, keyboards, disk drives, and flash drives are all examples of information systems hardware. How these hardware components function and work together will be covered in Chapter 2.

Software comprises the set of instructions that tell the hardware what to do. Software is not tangible – it cannot be touched.  Programmers create software by typing a series of instructions telling the hardware what to do. Two main categories of software are: Operating Systems and Application software. Operating Systems software provides the interface between the hardware and the Application software. Examples of operating systems for a personal computer include Microsoft Windows and Ubuntu Linux. The mobile phone operating system market is dominated by Google Android and Apple iOS. Application software allows the user to perform tasks such as creating documents, recording data in a spreadsheet, or messaging a friend. Software will be explored more thoroughly in Chapter 3.

The third technology component is data. You can think of data as a collection of facts. For example, your address (street, city state, postal code), your phone number, and your social networking account are all pieces of data. Like software, data is also intangible, unable to be seen in its native state. Pieces of unrelated data are not very useful. But aggregated, indexed, and organized together into a database, data can become a powerful tool for businesses. Organizations collect all kinds of data and use it to make decisions which can then be analyzed as to their effectiveness. The analysis of data is then used to improve the organization’s performance. Chapter 4 will focus on data and databases, and how it is used in organizations.

Networking Communication

Besides the technology components (hardware, software, and data) which have long been considered the core technology of information systems, it has been suggested that one other component should be added: communication. An information system can exist without the ability to communicate – the first personal computers were stand-alone machines that did not access the Internet. However, in today’s hyper-connected world, it is an extremely rare computer that does not connect to another device or to a enetwork. Technically, the networking communication component is made up of hardware and software, but it is such a core feature of today’s information systems that it has become its own category. Networking will be covered in Chapter 5.

When thinking about information systems, it is easy to focus on the technology components and forget to look beyond these tools to fully understand their integration into an organization. A focus on the people involved in information systems is the next step. From the front-line user support staff, to systems analysts, to developers, all the way up to the chief information officer (CIO), the people involved with information systems are an essential element. The people component will be covered in Chapter 9.

The last component of information systems is process. A process is a series of steps undertaken to achieve a desired outcome or goal. Information systems are becoming more integrated with organizational processes, bringing greater productivity and better control to those processes. But simply automating activities using technology is not enough – businesses looking to utilize information systems must do more. The ultimate goal is to improve processes both internally and externally, enhancing interfaces with suppliers and customers. Technology buzzwords such as “business process re-engineering,” “business process management,” and “enterprise resource planning” all have to do with the continued improvement of these business procedures and the integration of technology with them. Businesses hoping to gain a competitive advantage over their competitors are highly focused on this component of information systems. The process element in information systems will be discussed in Chapter 8.

The Role of Information Systems

You should now understand that information systems have a number of vital components, some tangible, others intangible, and still others of a personnel nature. These components collect, store, organize, and distribute data throughout the organization. You may have even realized that one of the roles of information systems is to take data and turn it into information, and then transform that information into organizational knowledge. As technology has developed, this role has evolved into the backbone of the organization, making information systems integral to virtually every business. The integration of information systems into organizations has progressed over the decades. 

The Mainframe Era

From the late 1950s through the 1960s, computers were seen as a way to more efficiently do calculations. These first business computers were room-sized monsters, with several machines linked together. The primary work was to organize and store large volumes of information that were tedious to manage by hand. Only large businesses, universities, and government agencies could afford them, and they took a crew of specialized personnel and dedicated facilities to provide information to organizations.

Time-sharing allowed dozens or even hundreds of users to simultaneously access mainframe computers from locations in the same building or miles away. Typical functions included scientific calculations and accounting, all under the broader umbrella of “data processing.”

In the late 1960s, Manufacturing Resources Planning (MRP) systems were introduced. This software, running on a mainframe computer, gave companies the ability to manage the manufacturing process, making it more efficient. From tracking inventory to creating bills of materials to scheduling production, the MRP systems gave more businesses a reason to integrate computing into their processes. IBM became the dominant mainframe company.  Continued improvement in software and the availability of cheaper hardware eventually brought mainframe computers (and their little sibling, the minicomputer) into most large businesses.

Today you probably think of Silicon Valley in northern California as the center of computing and technology. But in the days of the mainframe’s dominance corporations in the cities of Minneapolis and St. Paul produced most computers. The advent of the personal computer resulted in the “center of technology” eventually moving to Silicon Valley.

The PC Revolution

In 1975, the first microcomputer was announced on the cover of Popular Mechanics : the Altair 8800. Its immediate popularity sparked the imagination of entrepreneurs everywhere, and there were soon dozens of companies manufacturing these “personal computers.” Though at first just a niche product for computer hobbyists, improvements in usability and the availability of practical software led to growing sales. The most prominent of these early personal computer makers was a little company known as Apple Computer, headed by Steve Jobs and Steve Wozniak, with the hugely successful “Apple II.” Not wanting to be left out of the revolution, in 1981 IBM teamed with Microsoft, then just a startup company, for their operating system software and hurriedly released their own version of the personal computer simply called the “PC.” Small businesses finally had affordable computing that could provide them with needed information systems. Popularity of the IBM PC gave legitimacy to the microcomputer and it was named Time  magazine’s “Man of the Year” for 1982.

Because of the IBM PC’s open architecture, it was easy for other companies to copy, or “clone” it. During the 1980s, many new computer companies sprang up, offering less expensive versions of the PC. This drove prices down and spurred innovation. Microsoft developed the Windows operating system, with version 3.1 in 1992 becoming the first commercially successful release. Typical uses for the PC during this period included word processing, spreadsheets, and databases. These early PCs were standalone machines, not connected to a network.

Client-Server

In the mid-1980s, businesses began to see the need to connect their computers as a way to collaborate and share resources. Known as “client-server,” this networking architecture allowed users to log in to the Local Area Network (LAN) from their PC (the “client”) by connecting to a central computer called a “server.” The server would lookup permissions for each user to determine who had access to various resources such as printers and files. Software companies began developing applications that allowed multiple users to access the same data at the same time. This evolved into software applications for communicating, with the first popular use of electronic mail appearing at this time.

This networking and data sharing all stayed mainly within the confines of each business. Sharing of electronic data between companies was a very specialized function. Computers were now seen as tools to collaborate internally within an organization. These networks of computers were becoming so powerful that they were replacing many of the functions previously performed by the larger mainframe computers at a fraction of the cost. It was during this era that the first Enterprise Resource Planning (ERP) systems were developed and run on the client-server architecture. An ERP system is an application with a centralized database that can be used to run a company’s entire business. With separate modules for accounting, finance, inventory, human resources, and many more, ERP systems, with Germany’s SAP leading the way, represented the state of the art in information systems integration. ERP systems will be discussed in Chapter 9.

The Internet, World Wide Web and E-Commerce

The first long distance transmission between two computers occurred on October 29, 1969 when developers under the direction of Dr. Leonard Kleinrock sent the word “login” from the campus of UCLA to Stanford Research Institute in Menlo Park, California, a distance of over 350 miles. The United States Department of Defense created and funded ARPA Net (Advanced Research Projects Administration), an experimental network which eventually became known as the Internet. ARPA Net began with just four nodes or sites, a very humble start for today’s Internet. Initially, the Internet was confined to use by universities, government agencies, and researchers. Users were required to type commands (today we refer to this as “command line”) in order to communicate and transfer files. The first e-mail messages on the Internet were sent in the early 1970s as a few very large companies expanded from local networks to the Internet. The computer was now evolving from a purely computational device into the world of digital communications.

In 1989, Tim Berners-Lee developed a simpler way for researchers to share information over the Internet, a concept he called the World Wide Web . [4] This invention became the catalyst for the growth of the Internet as a way for businesses to share information about themselves. As web browsers and Internet connections became the norm, companies rushed to grab domain names and create websites.

The digital world also became a more dangerous place as virtually all companies connected to the Internet. Computer viruses and worms, once slowly propagated through the sharing of computer disks, could now grow with tremendous speed via the Internet. Software and operating systems written for a standalone world found it very difficult to defend against these sorts of threats. A whole new industry of computer and Internet security arose. Information security will be discussed in Chapter 6.

As the world recovered from the dot-com bust, the use of technology in business continued to evolve at a frantic pace. Websites became interactive. Instead of just visiting a site to find out about a business and then purchase its products, customers wanted to be able to customize their experience and interact online with the business. This new type of interactive website, where you did not have to know how to create a web page or do any programming in order to put information online, became known as Web 2.0. This new stage of the Web was exemplified by blogging, social networking, and interactive comments being available on many websites. The new Web 2.0 world, in which online interaction became expected, had a major impact on many businesses and even whole industries. Many bookstores found themselves relegated to a niche status. Video rental chains and travel agencies simply began going out of business as they were replaced by online technologies. The newspaper industry saw a huge drop in circulation with some cities such as New Orleans no longer able to support a daily newspaper. Disintermediation is the process of technology replacing a middleman in a transaction. Web 2.0 allowed users to get information and news online, reducing dependence of physical books and newspapers.

As the world became more connected, new questions arose. Should access to the Internet be considered a right? Is it legal to copy a song that had been downloaded from the Internet? Can information entered into a website be kept private? What information is acceptable to collect from children? Technology moved so fast that policymakers did not have enough time to enact appropriate laws. Ethical issues surrounding information systems will be covered in Chapter 12.

The Post-PC World, Sort of

Ray Ozzie, a technology visionary at Microsoft, stated in 2012 that computing was moving into a phase he called the post-PC world. [5] Now six years later that prediction has not stood up very well to reality. As you will read in Chapter 13, PC sales have dropped slightly in recent years while there has been a precipitous decline in tablet sales. Smartphone sales have accelerated, due largely to their mobility and ease of operation. Just as the mainframe before it, the PC will continue to play a key role in business, but its role will be somewhat diminished as people emphasize mobility as a central feature of technology. Cloud computing provides users with mobile access to data and applications, making the PC more of a part of the communications channel rather than a repository of programs and information. Innovation in the development of technology and communications will continue to move businesses forward.

Can Information Systems Bring Competitive Advantage?

It has always been the assumption that the implementation of information systems will bring a business competitive advantage. If installing one computer to manage inventory can make a company more efficient, then it can be expected that installing several computers can improve business processes and efficiency.

In 2003, Nicholas Carr wrote an article in the Harvard Business Review  that questioned this assumption. Entitled “I.T. Doesn’t Matter.” Carr was concerned that information technology had become just a commodity. Instead of viewing technology as an investment that will make a company stand out, Carr said technology would become as common as electricity – something to be managed to reduce costs, ensure that it is always running, and be as risk-free as possible.

The article was both hailed and scorned. Can I.T. bring a competitive advantage to an organization? It sure did for Walmart (see sidebar). Technology and competitive advantage will be discussed in Chapter 7.

Sidebar: Walmart Uses Information Systems to Become the World’s Leading Retailer

Walmart is the world’s largest retailer, earn  8.1 billion for the fiscal year that ended on January 31, 2018. Walmart currently serves over 260 million customers every week worldwide through its 11,700 stores in 28 countries. [6] In 2018 Fortune magazine for the sixth straight year ranked Walmart the number one company for annual revenue as they again exceeded $500 billion in annual sales. The next closest company, Exxon, had less than half of Walmart’s total revenue. [7] Walmart’s rise to prominence is due in large part to making information systems a high priority, especially in their Supply Chain Management (SCM) system known as Retail Link. ing $14.3 billion on sales of $30

This system, unique when initially implemented in the mid-1980s, allowed Walmart’s suppliers to directly access the inventory levels and sales information of their products at any of Walmart’s more than eleven thousand stores. Using Retail Link, suppliers can analyze how well their products are selling at one or more Walmart stores with a range of reporting options. Further, Walmart requires the suppliers to use Retail Link to manage their own inventory levels. If a supplier feels that their products are selling out too quickly, they can use Retail Link to petition Walmart to raise the inventory levels for their products. This has essentially allowed Walmart to “hire” thousands of product managers, all of whom have a vested interest in the products they are managing. This revolutionary approach to managing inventory has allowed Walmart to continue to drive prices down and respond to market forces quickly.

Today Walmart continues to innovate with information technology. Using its tremendous market presence, any technology that Walmart requires its suppliers to implement immediately becomes a business standard. For example, in 1983 Walmart became the first large retailer to require suppliers to the use Uniform Product Code (UPC) labels on all products. Clearly, Walmart has learned how to use I.T. to gain a competitive advantage.

In this chapter you have been introduced to the concept of information systems. Several definitions focused on the main components: technology, people, and process. You saw how the business use of information systems has evolved over the years, from the use of large mainframe computers for number crunching, through the introduction of the PC and networks, all the way to the era of mobile computing. During each of these phases, new innovations in software and technology allowed businesses to integrate technology more deeply into their organizations.

Virtually every company uses information systems which leads to the question: Does information systems bring a competitive advantage? In the final analysis the goal of this book is to help you understand the importance of information systems in making an organization more competitive. Your challenge is to understand the key components of an information system and how it can be used to bring a competitive advantage to every organization you will serve in your career.

Study Questions

• What are the five major components that make up an information system?
• List the three examples of information system hardware?
• Microsoft Windows is an example of which component of information systems?
• What is application software?
• What roles do people play in information systems?
• What is the definition of a process?
• What was invented first, the personal computer or the Internet?
• In what year were restrictions on commercial use of the Internet first lifted?
• What is Carr’s main argument about information technology?
• Suppose that you had to explain to a friend the concept of an information system. How would you define it? Write a one-paragraph description  in your own words  that you feel would best describe an information system to your friends or family.
• Of the five primary components of an information system (hardware, software, data, people, process), which do you think is the most important to the success of a business organization? Write a one-paragraph answer to this question that includes an example from your personal experience to support your answer.
• Everyone interacts with various information systems every day: at the grocery store, at work, at school, even in our cars. Make a list of the different information systems you interact with daily. Can you identify the technologies, people, and processes involved in making these systems work.
• Do you agree that we are in a post-PC stage in the evolution of information systems? Do some original research and cite it as you make your prediction about what business computing will look like in the next generation.
• The Walmart sidebar introduced you to how information systems was used to make them the world’s leading retailer. Walmart has continued to innovate and is still looked to as a leader in the use of technology. Do some original research and write a one-page report detailing a new technology that Walmart has recently implemented or is pioneering.
• Examine your PC. Using a four column table format identify and record the following information: 1st column: Program name, 2nd column: software manufacturer, 3rd column: software version, 4th column: software type (editor/word processor, spreadsheet, database, etc.).
• Examine your mobile phone. Create another four column table similar to the one in Lab #1. This time identify the apps, then record the requested information.
• In this chapter you read about the evolution of computing from mainframe computers to PCs and on to smartphones. Create a four column table and record the following information about your own electronic devices: 1st column – Type: PC or smartphone, 2nd column – Operating system including version, 3rd column – Storage capacity, 4th column – Storage available.
• Laudon, K.C. and Laudon, J. P. (2014) Management Information Systems , thirteenth edition. Upper Saddle River, New Jersey: Pearson.
• Valacich, J. and Schneider, C. (2010). Information Systems Today – Managing in the Digital World , fourth edition. Upper Saddle River, New Jersey: Prentice-Hall.
• Laudon, K.C. and Laudon, J. P. (2012). Management Information Systems , twelfth edition. Upper Saddle River, New Jersey: Prentice-Hall.
• CERN . (n.d.) The Birth of the Web. Retrieved from http://public.web.cern.ch/public/en/about/web-en.html
• Marquis, J. (2012, July 16) What is the Post-PC World? Online Universities.com. Retrieved from https://www.onlineuniversities.com/blog/2012/07/what-post-pc-world/
• Walmart . (n.d.) 2017 Annual Report. Retrieved from http://s2.q4cdn.com/056532643/files/doc_financials/2017/Annual/WMT_2017_AR-(1).pdf
• McCoy, K. (2018, May 21). Big Winners in Fortune 500 List. USA Today . Retrieved from http://https://www.usatoday.com/story/money/2018/05/21/big-winners-fortune-500-list-walmart-exxon-mobil-amazon/628003002/

Information Systems for Business and Beyond (2019) by David Bourgeois is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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What is information systems? Definition, uses, and examples

I'll admit it—"information systems" might be one of the vaguest terms I've ever heard. What first came to mind was huge quantities of data, and after doing some research, I found that my guess wasn't too far off. Like many vague terms in the business world, it encompasses a lot of mechanisms that contribute to organizational success.

So what is information systems? In this guide, I'll unpack what goes into this essential set of tools and walk through how to build your own information system strategy.

Table of contents:

What is information systems?

Every decision an organization makes should be data-driven, so the uses of information systems are practically limitless—human resource management, financial account management, customer outreach and advertising, competitive landscape analysis, you name it.

Information systems examples

Information systems can improve nearly any business operation, but here are a few valuable ways you can put them to work.

Expert systems: AI is becoming more advanced every day, and it's leveraged in information systems to simulate human problem-solving (think Siri!). Expert systems use knowledge that would otherwise need to be provided by a subject matter expert to tackle problems and make decisions. In a business context, it can solve accounting problems or identify malware.

Process control systems: If you're looking for a way to apply information systems to product manufacturing, process control systems are your solution. They rely on inputs from sensors to generate specific outputs and are frequently used to ensure a product meets specific criteria. A simple example is a thermostat—when the temperature dips below a certain level, the heat turns on. If you produce a physical product that's regularly criticized by customers, you may want to tweak your process controls.

5 components of information systems

So what goes into information systems? Nearly everything you need for a functional modern office: hardware, software, data, communication, and people. Virtually every information system includes these components in some capacity.

You can break hardware down by its components as well: hard drives for storage, microprocessors for processing power, graphics cards for generating graphics, monitors for displaying them, and so on.

Computers are just shiny black mirrors without the programs running behind the scenes telling the hardware what to do. Software can be broken down into two types:

System software , which allows you to manage the computer's files and overall interface (think operating systems like Windows 10).

Application software , the programs that take care of specific tasks (think Google Sheets and Microsoft Outlook). System software creates a starting point from which application software can build.

Data sources

Telecommunications.

Telecommunications is how computers share information with each other. The first thing that may come to mind is the internet, and you're correct. But telecommunications can be broken down further.

Some connections are physical: coaxial and fiber-optic cables are physical wires used by telephone, internet, and cable providers to carry data. Others are wireless: think networks like local area networks (LANs) and wide area networks (WANs). Microwaves and radio waves are also invisible channels that transmit data across devices.

Telecommunications makes it possible to access data via the cloud—without these systems in place, all data would have to be stored on one device.

Human resources

Automation is replacing a lot of tedious tasks with robots, but we haven't quite reached a Westworld-esque android takeover. Human experts capable of understanding and manipulating data are essential to any information systems strategy. 

How to build an information system strategy

Curating a cohesive information system strategy can't be done with the click of a mouse—it takes time and effort.

1. Determine your business's objectives and information needs

You should build your strategy around your goals. When in doubt, turn to your KPIs. Which benchmarks are you failing to hit? For example, maybe you actively market yourself as a customer-friendly solution, but a survey shows customer satisfaction falling 20% below your benchmark. 

2. Plan how you'll improve your existing system

3. design and implement your new system.

During this phase, you'll create a list of specifications and requirements that your system will have to meet, which will vary depending on your company's needs. For example, you may consider the following questions:

How will you collect, consolidate, and access data?

What software do you need, and how will you customize it?

Should hardware be updated to accommodate new software?

How will your applications integrate?

What parts of your system will be automated vs. managed by human resources?

Who will head your information systems? The CIO, CTO, or another role?

Your team should then build the functions that will bring your system to life. Once you've designed everything, it's time to purchase and install your new mechanisms. This process can be expensive and time-consuming—after all, you're supplanting your entire organization's status quo. Be sure to test that the system is functioning as planned before rolling it out across your organization.

Why your organization needs information systems

For example, by improving its information systems strategy, an organization can centralize its information resources, minimizing confusion and turning a scattered office into a well-oiled machine. This makes both your employees and your customers happier.

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Based in sunny San Diego, Luke is a digital marketer with 3+ years of experience developing and executing content strategy for eCommerce startups and SaaS enterprises alike—Airtable, Zoom, and yes, Zapier—to name a few. When he isn’t diving into a keyword research rabbit hole, you can find him at a music festival, thrifting, or spending time with his friends and family.

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Home / Learning / What is an Information System?

Combining hardware, software, human power and processes, an information system refers to a network used to collect, store, process, analyze and distribute data. Information systems and professionals with  advanced degrees in information systems  can help businesses and other organizations improve their efficiency, maximize revenue and streamline their operations.

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Information System Definition

Many people think of information systems as computer-based technology. While information systems often incorporate computers to help manage data and achieve business objectives, they do not necessarily have to include computers.

There are different types of information systems that can serve a variety of purposes depending on an organization’s needs. Examples include:

• Data warehouses.  Data warehouses are data management systems that support analytics and other business intelligence activities. They consolidate and analyze data from a large variety of sources. Data warehouses can provide insight into businesses to help improve decision-making.
• Enterprise systems.  Enterprise systems, also known as enterprise resource planning (ERP) systems, are integrated systems that combine all the hardware and software a business uses for different functions in its operations. These organization-wide systems help information flow between departments and allow processes from different parts of the business to be integrated across a company.
• Expert systems.  Expert systems use artificial intelligence to mimic human decision-making. The software uses human knowledge to solve problems that would typically require a person’s expertise. Expert systems can be applied in areas such as medical diagnoses, accounting and coding.
• Geographic information systems.  Geographic information systems (GIS) are tools that gather, organize, map and analyze data with a spatial component. GIS can improve analysis and decision-making by allowing users to visualize data on a map. Global information systems are a type of GIS that synthesize worldwide data.
• Office automation systems.  Office automation systems combine communication technology, people and computers to help perform office activities, such as preparing written communication, printing, scheduling or creating reports. 

Components of Information Systems

Every information system includes several key components: hardware, software, telecommunications, people and data. Hardware refers to the physical pieces of the information system; software is the programming that controls the information system; telecommunication transmits information through the system; humans manage and interact with the information system; and data is information stored within and processed by the system.

The hardware component of an information system comprises the physical elements of the system. People can touch and feel pieces of hardware. These mechanisms, equipment and wiring allow systems like computers, smartphones and tablets to function.

Input and output devices are essential pieces of technology that allow humans to interact with computers and other information systems. Keyboards, mice, microphones and scanners are all examples of input devices. And output devices might include printers, monitors, speakers and sound and video cards.

Pieces of hardware including microprocessors, hard drives, electric power supply units, and removable storage also allow computers to store and process data.  

Software are the intangible programs that manage information system functions, including input, output, processing and storage.

System software – such as the MacOS or Microsoft Windows operating systems – provides a base for application software to run.

Application software operates programs geared toward particular uses in information systems. For example, word processing applications are used to create and edit text documents.  Graphical user interface (GUI) software is among the most common application software ; it presents the information stored in computers and allows users to interact with computers through digital graphics – such as icons, buttons and scroll bars – rather than through text-based commands.

Software can be either open source or closed source. Open source software coding is publicly available for users and programmers to manipulate, whereas closed source software is proprietary. 

Telecommunications

Telecommunications systems connect computer networks and allow information to be transmitted through them. Telecommunications networks also allow computers and storage services to access information from the cloud.

There are a number of methods telecommunications networks use to convey information. Coaxial cables and fiber optic cables are used by telephone, internet and cable providers to transmit data, video and audio messages.

Local-area networks (LANs) connect computers to create computer networks in a designated space, like a school or home. Wide-area networks (WANs) are collections of LANs that facilitate data-sharing across large areas.  A virtual private network (VPN) allows a user to protect their online privacy by encrypting data on public networks. 

Microwaves and radio waves can also be used to transmit information in telecommunications networks.

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Data are intangible, raw facts that are stored, transmitted, analyzed and processed by other components of information systems. Data are often stored as numerical facts, and they represent quantitative or qualitative information.

Data can be stored in a database or data warehouse, in a form that best suits the organization using it.

Databases house collections of data that can be queried or retrieved for specific purposes. Databases allow users to perform fundamental operations, such as storage and retrieval. Data warehouses, on the other hand, store data from multiple sources for analytical purposes. They allow users to assess an organization or its operations.

Human Resources

Human resources are a crucial part of information systems. The human component of information systems encompasses the qualified people who influence and manipulate the data, software and processes in information systems. Humans involved in information systems may include  business analysts ,  information security analysts  or system analysts.

Business analysts work to elevate an organization’s operations and processes. They often focus on improving efficiency and productivity or streamlining distribution. Information security analysts work to prevent data breaches and cybersecurity attacks. And system analysts use information technology to help organizations optimize their user experiences with programs.

The Role of Information Systems

Information systems allow users to collect, store, organize and distribute data—functions that can serve a variety of purposes for companies. Many businesses use their information systems to manage resources and improve efficiency. And some rely on information systems to compete in global markets. Huawei researchers found that in 2016, the  digital economy worldwide was worth $11.5 trillion dollars or 15.5% of the global GDP [PDF, 22.8 MB] . By 2025, that number is projected to grow further, to about 24% of the global GDP.

There are a variety of applications for different types of information systems. For example, GIS can help researchers track the movement of sea ice, help inform agricultural decisions, or offer insight into crime patterns. Email software, such as Microsoft Outlook, is a common type of office automation system that can automatically sort, prioritize, file and respond to messages. And Apple’s SIRI is a well-known expert system that works to replicate human decision-making when prompted by speech from users. From internet browsing to online banking, information systems are becoming increasingly integrated in daily life.

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Basic Concepts of Information Systems

Submitted: 10 November 2020 Reviewed: 08 April 2021 Published: 15 July 2021

DOI: 10.5772/intechopen.97644

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This chapter covers the basic concepts of the information systems (IS) field to prepare the reader to quickly approach the book’s other chapters: the Definition of information, the notion of system, and, more particularly, information systems. We also discuss the typology of IS according to the managerial level and decision-making in the IS. Furthermore, we describe information systems applications covering functional areas and focusing on the execution of business processes across the enterprise, including all management levels. We briefly discuss the aspects related to IS security that ensure the protection and integrity of information. We continue our exploration by presenting several metrics, mainly financial, to assess the added value of IS in companies. Next, we present a brief description of a very fashionable approach to make the information system evolve in all coherence, which is the urbanization of IS. We conclude this chapter with some IS challenges focusing on the leading causes of IS implementation’s failure and success.

• information
• information system
• IS typology
• Decision-making
• IS applications
• IS security
• IS evaluation
• IS evolution
• and IS challenges

Author Information

Leila zemmouchi-ghomari *.

• National Superior School of Technology, Algiers, Algeria

*Address all correspondence to: [email protected]

1. Introduction

Data represents a fact or an event statement unrelated to other things. Data is generally used regarding hard facts. This can be a mathematical symbol or text used to identify, describe, or represent something like temperature or a person. The data simply exists and has no meaning beyond its existence (in itself). It can exist in any form, usable or not. The data exists in different formats, such as text, image, sound, or even video.

Information is data combined with meaning. Information embodies the understanding of a relationship as the relationship between cause and effect [ 2 ]. Ex: The temperature dropped 15 degrees, then it started to rain. A temperature reading of 100 can have different meanings when combined with the term Fahrenheit or with the term Celsius. More semantics can be added if more context for the temperature read is added, such as the fact that this temperature concerns a liquid or a gas or the seasonal norm of 20°. In other words, information is data that has meaning through relational connection. According to Ackoff, information is useful data; it provides answers to the questions: “who,” “what,” “where,” and “when.”

Knowledge can be seen as information combined with experience, context, and interpretation. Knowledge constitutes an additional semantic level derived from information via a process. Sometimes this process is observational. Ackoff defines it as applying data and information; knowledge provides answers to the question “how” For example, what happens in cold weather for aircraft managers? Observational knowledge engineers interpret cold by its impact, which is the ice that can form on an aircraft by reducing aerodynamic thrust and potentially hampering the performance of its control surfaces [ 2 ].

IF temperature < = 0° C THEN cold = true;

Cold IF == right THEN notify personnel to remove ice from aircraft.

Indeed, knowledge is the appropriate collection of information such that it intends to be useful. Knowledge is a deterministic process. Memorization of information leads to knowledge. Knowledge represents a pattern and provides a high level of predictability regarding what is being described or will happen next.

Ex: If the humidity is very high and the temperature drops drastically, the atmosphere is unlikely to hold the humidity so that it rains.

This knowledge has a useful meaning, but its integration in a context will infer new knowledge. For example, a student memorizes or accumulates knowledge of the multiplication Table. A student can answer 2 × 2 because this knowledge is in the multiplication table. Nevertheless, when asked for 1267 × 300, he cannot answer correctly because he cannot dip into the multiplication table. To answer such a question correctly requires a real cognitive and analytical capacity that exists in the next level … comprehension. In computer jargon, most of the applications we use (modeling, simulation, etc.) use stored knowledge.

2. System definition

The system is an aggregated “whole” where each component interacts with at least one other component of the system. The components or parts of a system can be real or abstract.

All system components work toward a standard system goal. A system can contain several subsystems. It can be connected to other systems.

Input is the activity of collecting and capturing data.

Processing involves the transformation of inputs into outputs such as computation, for example.

Output is about producing useful information, usually in the form of documents and reports. The output of one system can become the input of another system. For example, the output of a system, which processes sales orders, can be used as input to a customer’s billing system. Computers typically produce output to printers and display to screens. The output can also be reports and documents written by hand or produced manually.

Finally, feedback or feedback is information from the system used to modify inputs or treatments as needed.

3. Information system definition

An information system (IS) is a set of interrelated components that collect, manipulate, store and disseminate information and provide a feedback mechanism to achieve a goal. The feedback mechanism helps organizations achieve their goals by increasing profits, improving customer service [ 3 ], and supporting decision-making and control in organizations [ 4 ].

Companies use information systems to increase revenues and reduce costs.

Technology : The IT (Information Technology) of an IS includes the hardware, software, and telecommunications equipment used to capture, process, store and disseminate information. Today, most IS are IT-based because modern IT enables efficient operations execution and effective management in all sizes.

Task : activities necessary for the production of a good or service. These activities are supported by the flow of material, information, and knowledge between the different participants.

Person : The people component of an information system encompasses all the people directly involved in the system. These people include the managers who define the goals of the system, the users, and the developers.

Structure : The organizational structure and information systems component refers to the relationship between individuals people components. Thus, it encompasses hierarchical structures, relationships, and systems for evaluating people.

Leavitt’s diamond: A socio-technical view of IS.

4. Typology of information systems

A company has systems to support the different managerial levels. These systems include transaction processing systems, management information systems, decision support systems, and dedicated business intelligence systems.

Companies use information systems so that accurate and up-to-date information is available when needed [ 5 ].

On the lowest level , staff perform routine day-to-day operations such as selling goods and issuing payment receipts.

Operational management in which managers are responsible for overseeing transaction control and deal with issues that may arise.

Tactical management, which has the prerogative of making decisions on budgets, setting objectives, identifying trends, and planning short-term business activities.

Strategic management is responsible for defining its long-term objectives and positioning concerning its competitors or its industry.

Information Systems types according to managerial level.

4.1 Transaction processing system (TPS)

At the operational level, managers need systems that keep track of the organization for necessary activities and operations, such as sales and material flow in a factory. A transaction processing system is a computer system that performs and records the routine (daily) operations necessary for managing affairs, such as keeping employee records, payroll, shipping merchandise, keeping records, accounting and treasury.

At this level, the primary purpose of systems is to answer routine questions and monitor transactions flow through the organization.

At the operational level, tasks, resources, and objectives are predefined and highly structured. The decision to grant credit to a customer, for example, is made by a primary supervisor according to predefined criteria. All that needs to be determined is whether the client meets the criteria.

4.2 Management information systems (MIS)

Middle managers need systems to help with oversight, control, decision making, and administrative activities. The main question that this type of system must answer is: is everything working correctly?

Its role is to summarize and report on essential business operations using data provided by transaction processing systems. Primary transaction data is synthesized and aggregated, and it is usually presented in reports produced regularly.

4.3 Decision support systems (DSS)

DSS supports decision-making for unusual and rapidly evolving issues, for which there are no fully predefined procedures. This type of system attempts to answer questions such as: What would impact production schedules if we were to double sales for December? What would the level of Return on investment be if the plant schedule were delayed by more than six months?

While DSSs use internal information from TPS and MIS systems, they also leverage external sources, such as stock quotes or competitor product prices. These systems use a variety of models to analyze the data. The system can answer questions such as: Considering customer’s delivery schedule and the freight rate offered, which vessel should be assigned, and what fill rate to maximize profits? What is the optimum speed at which a vessel can maximize profit while meeting its delivery schedule?

4.4 Executive support system (ESS)

ESS helps top management make decisions. They address exceptional decisions requiring judgment, assessment, and a holistic view of the business situation because there is no procedure to be followed to resolve a given issue at this level.

ESS uses graphics and data from many sources through an interface that senior managers easily understand. ESS is designed to integrate data from the external environment, such as new taxes or competitor data, and integrate aggregate data from MIS and DSS. ESSs filter, synthesize and track critical data. Particular attention is given to displaying this data because it contributes to the rapid assimilation of these top management figures. Increasingly, these systems include business intelligence analysis tools to identify key trends and forecasts.

5. Decision making and information systems

Decision-making in companies is often associated with top management. Today, employees at the operational level are also responsible for individual decisions since information systems make information available at all company levels.

So decisions are made at all levels of the company.

Although some of these decisions are common, routine, and frequent, the value of improving any single decision may be small, but improving hundreds or even thousands of “small” decisions can add value to the business.

Not all situations that require decisions are the same. While some decisions result in actions that significantly impact the organization and its future, others are much less important and play a relatively minor role. A decision’s impact is a criterion that can differentiate between decision situations and the degree of the decision’s structuring. Many situations are very structured, with well-defined entrances and exits. For example, it is relatively easy to determine the amount of an employee’s pay if we have the appropriate input data (for example, the number of hours worked and their hourly wage rate), and all the rules of relevant decision (for example, if the hours worked during a week are more than 40, then the overtime must be calculated), and so on. In this type of situation, it is relatively easy to develop information systems that can be used to help (or even automate) the decision.

In contrast, some decision situations are very complex and unstructured, where no specific decision rules can be easily identified. As an example, consider the following task: “Design a new vehicle that is a convertible (with a retractable hardtop), has a high safety rating, and is esthetically pleasing to a reasonably broad audience. No predefined solution to this task finalizing a design will involve many compromises and require considerable knowledge and expertise.

Examples of Types of decisions, according to managerial level, are presented in Table 1 .

Types of decisions according to managerial level.

Generally speaking, structured decisions are more common at lower levels of the organization, while unstructured problems are more common at higher business levels.

The more structured the decision, the easier it is to automate. If it is possible to derive an algorithm that can be used to make an efficient decision and the input data to the algorithm can be obtained at a reasonable cost, it generally makes sense to automate the decision.

Davenport and Harris [ 6 ] proposed a framework for the categorization of applications used for decision automation. Most of the systems they describe include some expert systems, often combined with DSS and/or EIS aspects. The categories they provided include Solution Configuration, Optimization of Performance, Routing or Segmentation of Decisions, Business Regulatory Compliance, Fraud Detection, Dynamic Forecasting, and Operational Control.

Many business decision situations are not very structured, and therefore cannot (or should not) be fully automated.

5.1 A particular type of decision support system: geographic information systems

Data visualization tools allow users to see patterns and relationships in large amounts of data that would be difficult to discern if the data had been presented in tabular form, for example.

Geographic Information Systems (GIS) helps decision-makers visualize issues requiring knowledge about people’s geographic distribution or other resources. GIS software links the location data of points, lines, and areas on a map. Some GIS have modeling capabilities to modify data and simulate the impact of these modifications. For example, GIS could help the government calculate response times to natural disasters and other emergencies or help banks identify the best replacement for installing new branches or ATMs of tickets.

Geographic (or geospatial) information refers not only to things that exist (or are being planned) on specific locations on the Earth’s surface but also to events such as traffic congestion, flooding, and other events such as an open-air festival [ 7 ].

Location, extent, and coverage are essential aspects of geographic information.

Granularity, for example, geometric information, can be concise or fuzzy depending on the application.

It is a computer system with a database observing the spatial distribution of objects, activities, or events described by points, lines, or surfaces.

It is a comprehensive collection of tools for capturing, storing, extracting, transforming, and visualizing real-world spatial data for applications.

It is an information system containing all the data of the territory, the atmosphere, the surface of the Earth, and the lithosphere, allowing the systematic capture, the update, the manipulation, and the analysis of these data standardized reference framework.

It is a decision support system that integrates spatial data into a problem-solving environment.

A collection of spatial data with storage and retrieval functions

A collection of algorithmic and functional tools

A set of hardware and software components necessary for processing geospatial data

A particular type of information technology

A gold mine for answers to geospatial questions

A model of spatial relations and spatial recognition.

Typically, a GIS provides functions for the storage and retrieval, interrogation and visualization, transformation, geometric and thematic analysis of information.

Indeed, geographic/geospatial information is ubiquitous, as seen on mobile devices such as cell phones, maps, satellite images, positioning and routing services, and even 3D simulations, gaining popularity from increasingly essential segments of the consumers.

Web-based and service-oriented approaches have led to a client–server architecture.

Mobile technology has made GIS ubiquitous in smartphones, tablets, and laptops (opening up new markets).

6. Information systems applications

IS applications cover functional areas and focus on the execution of business processes across the enterprise, including all management levels.

There are several categories of business applications: Enterprise Resource Planning (ERP), Supply Chain Management systems (SCM), Customer Relationship Management systems (CRM), electronic commerce or e-commerce, Knowledge Management systems or KM, and Business Intelligence or BI. The categories of business applications dealt with in this section cover all managerial levels since KMS are mainly intended for top management (ESS), SCMs, CRMs, and BI for mid-level management (MIS and DSS), ERP and e-commerce dedicated to the transactional level (TPS or basic or operational).

However, it is useful to specify that some ERP systems, such as the global giant SAP, offer versions of its software package covering these different categories, including SCM and CRM.

6.1 ERP, Enterprise resource planning

ERPs allow business processes related to production, finance and accounting, sales and marketing, and human resources to be integrated into a single software system. Information that was previously fragmented across many different systems is integrated into a single system with a single, comprehensive database that multiple business stakeholders can use.

An ERP system centralizes an organization’s data, and the processes it applies are the processes that the organization must adopt [ 8 ]. When an ERP provider designs a module, it must implement the rules of the associated business processes. ERP systems apply best management practices. In other words, when an organization implements ERP, it also improves its management as part of ERP integration. For many organizations, implementing an ERP system is an excellent opportunity to improve their business practices and upgrade their software simultaneously. Nevertheless, integrating an ERP represents a real challenge: Are the processes integrated into the ERP better than those currently used? Furthermore, if the integration is booming, and the organization operates the same as its competitors, how do you differentiate yourself?

ERPs are configurable according to the specificities of each organization. For organizations that want to continue using their processes or even design new ones, ERP systems provide means for customizing these processes. However, the burden of maintenance falls on the organizations themselves in the case of ERP customization.

Organizations will need to consider the following decision carefully: should they accept the best practice processes embedded in the ERP system or develop their processes? If the choice is ERP, process customization should only concern processes essential to its competitive advantage.

6.2 E-commerce, electronic commerce

Electronic commerce is playing an increasingly important role in organizations with their customers.

E-commerce enables market expansion with minimal capital investment, improves the supply and marketing of products and services. Nevertheless, there is still a need for universally accepted standards to ensure the quality and security of information and sufficient telecommunications bandwidth.

Business-to-Consumer (B2C) e-commerce involves the retailing of products and services to individual customers. Amazon, which sells books, software, and music to individual consumers, is an example of B2C e-commerce.

Business-to-Business (B2B), e-commerce involves the sale of goods and services between businesses. The ChemConnect website for buying and selling chemicals and plastics is an example of B2B e-commerce.

Consumer-to-Consumer (C2C), this type of e-commerce involves consumers selling directly to consumers. For example, eBay, the giant web-based auction site, allows individuals to sell their products to other consumers by auctioning their goods, either to the highest bidder or through a fixed price.

6.3 SCM, Information systems for supply chain management

Information systems for the management of the supply chain or SCM make it possible to manage its suppliers’ relations. These systems help suppliers and distributors share information about orders, production, inventory levels, and delivery of products and services so that they can source, produce and deliver goods and services efficiently.

The ultimate goal is to get the right amount of products from their suppliers at a lower cost and time. Additionally, these systems improve profitability by enabling managers to optimize scheduling decisions for procurement, production, and distribution.

Anomalies in the supply chain, such as parts shortages, underutilized storage areas, prolonged storage of finished products, or high transportation cost, are caused by inaccurate or premature information. For example, manufacturers may stock an excessive amount of parts because they do not know precisely the dates of upcoming deliveries from suppliers. Alternatively, conversely, the manufacturer may order a small number of raw materials because they do not have precise information about their needs. These supply chain inefficiencies squander up to 25 percent of the company’s operating costs.

If a manufacturer has precise information on the exact number of units of the product demanded by customers, on what date, and its exact production rate, it would be possible to implement a successful strategy called “just in time” (just-in-time strategy). Raw materials would be received precisely when production needed them, and finished products would be shipped off the assembly line with no need for storage.

However, there are always uncertainties in a supply chain because many events cannot be predicted, such as late deliveries from suppliers, defective parts or non-conforming raw materials, or even breakdowns in the production process. To cope with these kinds of contingencies and keep their customers happy, manufacturers often deal with these uncertainties by stocking more materials or products than they need. The safety stock acts as a buffer against probable supply chain anomalies. While managing excess inventory is expensive, a low stock fill rate is also costly because orders can be canceled.

6.4 CRM, Information systems for customer relationship management

CRM aims to manage customer relationships by coordinating all business processes that deal with customers’ sales and marketing. The goal is to optimize revenue, customer satisfaction, and customer loyalty. This collected information helps companies identify, attract and retain the most profitable customers, and provide better service to existing customers and increase sales.

The CRM captures and integrates the data of the company’s customers. It consolidates data, analyzes it, and distributes the results to different systems and customer touchpoints throughout the company. A point of contact (touchpoint, contact point) is a means of interaction with the customer, such as telephone, e-mail, customer service, conventional mail, website, or even a sales store, by retail.

Well-designed CRM systems provide a single view of the company’s customers, which is useful for improving sales and customer service quality. Such systems also provide customers with a single view of the business regardless of their contact point or usage.

CRM systems provide data and analytical tools to answer these types of questions: “What is the value of a customer to the business” “Who are the most loyal customers?” “Who are the most profitable customers” and “What products are profitable customers buying?”

Businesses use the answers to these questions to acquire new customers, improve service quality, support existing customers, tailor offerings to customer preferences, and deliver escalating services to retain profitable customers.

6.5 KM, knowledge management

Some companies perform better than others because they know how to create, produce, and deliver products and services. This business knowledge is difficult to emulate, is unique, and can be leveraged and deliver long-term strategic benefits. Knowledge Management Systems or KMS enable organizations to manage processes better to collect and apply knowledge and expertise. These systems collect all the relevant knowledge and experiences in the company and make them available to everyone to improve business processes and decision management.

Knowledge management systems can take many different forms, but the primary goals are: 1) facilitating communication between knowledge workers within an organization, and 2) to make explicit the expertise of a few and make it available to many.

Consider an international consulting firm, for example. The company employs thousands of consultants across many countries. The consultancy team in Spain may be trying to resolve a client’s problem, very similar to a consultancy team in Singapore that has already been solved. Rather than reinventing the solution, it would be much more useful for the Spain team to use the Singapore team’s knowledge.

One way to remedy this situation is to store case histories from which employees worldwide can access (via the Internet) and search for cases (using a search engine) according to their respective needs. If the case documentation is of good quality (accurate, timely, complete), the consultants will share and benefit from each other’s experiences, and the knowledge gained.

Unfortunately, it is often difficult to get employees to contribute meaningfully to the knowledge base (as they are probably more concerned with moving forward on their next engagements with customers rather than documenting their past experiences). For such systems to have any chance of success, the work organization must change, such as establishing a reward system for cases captured and well documented.

6.6 BI, business intelligence

The term Business Intelligence (BI) is generally used to describe a type of information system designed to help decision-makers learn about trends and identify relationships in large volumes of data. Typically, BI software is used in conjunction with large databases or data warehouses. While the specific capabilities of BI systems vary, most can be used for specialized reporting (e.g., aggregated data relating to multiple dimensions), ad-hoc queries, and trend analysis.

As with knowledge management systems, the value of business intelligence systems can be hampered in several ways. The quality of the data that is captured and stored is not guaranteed. Besides, the database (or data warehouse) may lack essential data (for example, ice cream sales are likely to correlate with temperature; without the temperature information, it may be difficult to identify why it is. There has been an increase or decrease in sales of ice cream). A third challenge is the lack of mastery of data analysts over the context of the organization’s operations, even if they are proficient in BI software. In contrast, a manager has mastery of the organization but does not know how to use BI software. As a result, it is common to have a team (a manager associated with a data analyst) to get the most information (and/or knowledge) from a business intelligence system.

7. Information systems security

Unlike physical assets, the information does not necessarily disappear when it has been stolen. If an organization holds confidential information such as a new manufacturing process, it may be uploaded by an unauthorized person and remain available to the organization.

Exposing information to unauthorized personnel constitutes a breach of confidentiality.

Another type of system failure happens when the integrity of information is no longer guaranteed. In other words, rather than unauthorized exposure of information, there are unauthorized changes of information. A corporate website containing documentation on how to configure or repair its products could suffer severe financial harm if an intruder could change instructions, leading to customers misconfigure or even ruin the purchased product.

Finally, the denial of access to information or the unavailability of information represents another type of information failure. For example, if a doctor is prevented from accessing a patient’s test results, the patient may suffer needlessly or even die. A commercial website could lose significant sales if its website were down for an extended period.

Understanding the potential causes of system failure enables appropriate action to be taken to avoid them. There are a wide variety of potential threats to an organization’s information systems.

Accidental behavior by members of the organization, technical support staff, and customers of the organization

Malicious behavior by someone inside or outside the organization

Other categories of threats include:

A natural event: flood, fire, tornado, ice storm, earthquake, pandemic flu

Environmental elements: chemical spill, gas line explosion.

Technical Threat: Hardware or software failure

Operational Threat: a faulty process that unintentionally compromises the confidentiality, integrity, or availability of information. For example, an operational procedure that allows application programmers to upgrade software without test or notification system operators can result in prolonged outages.

Management controls management processes that identify system requirements such as confidentiality, integrity, and availability of information and provide for various management controls to ensure that these requirements are met.

Operational controls: include the day-to-day processes associated with the provision of information services.

Technical controls: concern the technical capacities integrated into the IT infrastructure to support the increased confidentiality, integrity, and availability of information services.

A widely cited Gartner research report concludes that “people directly cause 80% of downtime in critical application services. The remaining 20% are caused by technological failures, environmental failure or a natural disaster”.

Often, these failures are the result of software modifications such as adding new features or misconfiguring servers or network devices.

IT professionals should ensure that system changes are prioritized and tested and that all interested parties are notified of proposed changes.

8. Information systems assessment

Perceptible benefits can be quantified and assigned a monetary value. Imperceptible benefits, such as more efficient customer service or improved decision making, cannot be immediately quantified but can lead to quantifiable long-term gain [ 4 ].

System performance can be measured in different ways.

8.1 Efficiency

Efficiency is often referred to as “doing the things right” or doing things right. Efficiency can be defined as the ratio of output to input. In other words, a company is more efficient if it produces more with the same amount of resources or if it produces the same amount of output with a lower investment of resources, or - even better - produces more with less input. In other words, the company achieves improvements in terms of efficiency by reducing the waste of resources while maximizing Productivity.

Each time an item is sold or ordered, the manager updates the quantity of the item sold in the inventory system. The manager needs to check the sales to determine which items have been sold the most and restocked. This considerably reduces the manager’s time to manage his stock (limit input to achieve the same output). So efficiency is a measure of what is produced divided by what is consumed [ 3 ].

8.2 Effectiveness

Effectiveness is measured based on the degree achieved in achieving system objectives. It can be calculated by dividing the objectives achieved by the total of the objectives set.

Effectiveness is denoted as “doing the right thing” or doing the things necessary or right. It is possible to define effectiveness as an organization’s ability to achieve its stated goals and objectives. Typically, a business more significant is the one that makes the best decisions and can carry them out.

For example, to better meet its various customers’ needs, an organization may create or improve its products and services founded on data collected from them and information accumulated from sales activities. In other words, information systems help organizations better understand their customers and deliver the products and services that customers desire. Collecting customer data on an individual basis will help the organization provide them with personalized service.

The manager can also ask customers what kind of products and services customers would like to buy in the future, trying to anticipate their needs. With the information gathered, the manager will order the customers’ products and stop ordering unpopular products.

In what follows, we present several formulas established to measure efficiency and effectiveness resulting from the information systems use. Indeed, the impact of an information system on an organization can be assessed using financial measures.

8.3 Financial measures of managerial performance

When the information system is implemented, management will certainly want to assess whether the system has succeeded in achieving its objectives. Often this assessment is challenging to achieve. The business can use financial metrics such as Productivity, Return On Investment (ROI), net present value, and other performance metrics explained in the following:

8.3.1 Return on investment

Return on investment, denoted as a Return rate, is a financial ratio that measures the amount gained or lost compared to the amount initially invested.

An information system with a positive return on investment indicates that this system can improve its efficiency.

The advantage of using Return on investment is that it is possible to quantify the costs and benefits of introducing an information system. Therefore, it is possible to use this metric to compare different systems and see which systems can help the organization be more efficient and/or more effective.

8.3.2 Productivity

Developing information systems that measure Productivity and control is a crucial element for most organizations. Productivity is a measure of produced output divided by required input. A higher production level for a given entry-level means greater Productivity; a lower output level for a given entry-level means lower Productivity. Values assigned to productivity levels are not always based on hours worked. Productivity may be based on the number of raw materials used, the quality obtained, or the time to produce the goods or services. According to other parameters and with other organizations in the same industry, Productivity’s value has to mean only compared to other Productivity periods.

8.3.3 Profit growth

Another measure of the SI value is the increase in profit or the growth in realized profits. For example, a mail-order company installs an order processing system that generates 7 percent growth in profits over the previous year.

8.3.4 Market share

Market share is the percentage of sales of a product or service relative to the overall market. If installing a new online catalog increases sales, it could help increase the company’s market share by, for example, 20 percent.

8.3.5 Customer satisfaction

Although customer satisfaction is difficult to quantify, many companies measure their information systems performance based on internal and external feedback. Some companies use surveys and questionnaires to determine whether investments have resulted in increased customer satisfaction.

8.3.6 Total cost of ownership

Another way to measure the value of information systems has been developed by the Gartner Group and is called the Total Cost of Ownership (TCO). This approach allocates the total costs between acquiring the technology, technical support, and administrative costs. Other costs are added to the TCO, namely: retooling and training costs. TCO can help develop a more accurate estimate of total costs for systems ranging from small computers to large mainframe systems.

9. Information systems evolution

The evolution of information technologies leads to the reflection on new approaches that set up more flexible, more scalable architectures to meet its agility needs. The urbanization of information systems is one such approach.

9.1 Definition of the urbanization of information systems

The company’s information system’s urbanization is an IT discipline consisting of developing its information system to guarantee its consistency with its objectives and business. By taking into account its external and internal constraints while taking advantage of the opportunities of the IT state of the art.

This discipline is based on a series of concepts modeled on those of the urbanization of human habitat (organization of cities, territory), concepts that have been reused in IT to formalize or model the information system.

Town planning defines rules and a coherent, stable, and modular framework, to which the various stakeholders refer for any investment decision relating to the management of the information system.

In other words, to urbanize is to lead the information systems’ continuous transformation to simplify it and ensure its consistency.

The challenges of urbanization consist of managing complexity, communicating and federating work, considering organizational constraints, and guiding technological choices.

9.2 Stages of urbanization

9.2.1 definition of objectives.

Define and frame the objectives of the project, define the scope, develop the schedule.

9.2.2 Analysis of the existing situation

Business Architecture

Identify “business processes”: Who does what and why? The description of the processes is done with BPMN, EPC formalisms, etc. This step is tricky and may require the use of exploration methods. However, it does improve the overall understanding and increase the possibilities for optimization

Functional architecture

Identify the “functional block”: What do we need to carry out the business processes? Here, we are based on a classic division into zones (exchanges, core business, reference data, production data, support activities, management). This step’s difficulty lies in choosing the right level of detail and remaining consistent with business processes. However, it provides a hierarchical presentation and makes it easier to break down the work.

Application Architecture

Identify the applications: How to achieve the functionalities? This step is based on a classic N-Tiers division. However, it is not easy to provide value and solutions compared to functional architecture. This stage lays the foundations for the realization (major technological choices, etc.).

System Architecture

Identify the technical components: With what and where the applications work, it is based on a classic division into technical areas (security, storage, etc.). It is not easy to make the connection between applications and servers. This step brings concrete and structuring and is essential to assess the cost of the system.

9.2.3 Identification of the target IS

Impact on the different layers, consideration of constraints (human, material, etc.), design of costed scenarios, and arbitration of the choice of a target.

9.2.4 Development of the trajectory

How to organize the work, frame and then refine the budgets, design and plan projects, define the support strategy, set up an organization, contributions, roles, and responsibilities of actors.

Summaries of the orientations chosen as well as the justifications for the options selected.

A definition of areas, neighborhoods, and blocks.

Existing and target maps (process, functional, application, and technical mapping).

Additional documents (interview reports, list of people and organizational entities, etc.)

The goal is to identify the gaps between the existing and the principles of urbanization and establish changes by describing the actions and their corresponding cost.

In practice, the urbanization process is very cumbersome to implement. On the one hand, it requires the participation of many actors in the organization, and on the other hand, the analysis is very long. As a result, needs to change, and LUP is no longer necessarily suitable.

10. Information systems challenges

The reasons for a successful or unsuccessful IS implementation are complex and contested by different stakeholders and from the various perspectives involved. Developers tend to focus on the system’s technical validity in terms of execution, operation, and evolution. Other qualities are often considered, such as security, maintainability, scalability, stability, and availability. All of these criteria are considered to be signs of successful IS Development.

The failure of an IS can be defined as: either the system put in place does not meet the user’s expectations or does not function properly. The reasons for failure are as divergent as the projects.

The perspective of project management, on the other hand, tends to focus on the consumption of resources. The project delivered with the initial budget and within the allotted time is considered a successful project. Nelson [ 9 ] analyzed 99 SI projects and identified 36 classic errors. He categorized these errors into four categories: process, people, product, and technology. The last category concerns the factors leading to IS failures based on the misuse of modern technologies.

The seminal article by DeLone and McLean [ 10 ] suggested that IS success should be the preeminent dependent variable for the IS domain. These researchers proposed a taxonomy of six interdependent variables to define the IS’ success as the system’s quality, the quality of information, the IS, user satisfaction, individual impact, and organizational impact.

One of the significant extensions to this proposition is the dimension of the IT department’s quality of service [ 11 ].

Either way, the use of the system is seen as a sign of its success. The IS use level is incorporated into most IS success models [ 11 , 12 ]. These models show the complexity of measuring user satisfaction because, even in the same organization, some user groups may be more or less enthusiastic than others to use the new information system.

In the current global context of the covid pandemic, it appears clear that information systems that integrate web and mobile technologies can positively contribute to the monitoring of contaminated cases and therefore minimize the risks of contamination provided that users adhere to this movement for the benefit of all [ 13 ]. A truly global, rapid, and efficient decision-making process is enabled by the integration of information systems from distributed sources [ 14 ].

11. Conclusion

Levels of information are data, information, and knowledge.

The system is an aggregated “whole” where each component interacts with at least one other system component to achieve a goal.

An information system can be defined as a set of interconnected components that gather, process, store and dispense information to support decision making and control in an organization. An IS can be seen as a socio-technical system. The technical part includes the technology and the processes, while the social part includes the people and the structure.

The role of information systems is to solve an organization’s problems concerning its information needs

A company has systems to support the different managerial levels: transaction processing systems, management information systems, decision support systems, and systems dedicated to business intelligence.

Decisions can be operational or strategic.

There are several categories of business applications: enterprise resource planning, supply chain management systems, customer relationship management systems, knowledge management systems, and business intelligence.

Among the failures that can affect IS a violation of confidentiality, integrity, and availability of information.

The controls intended to avoid the IS’s security failures include management controls, operational controls, and technical controls.

The information system’s performance can be measured according to efficiency, effectiveness, Return on investment, Productivity, customer satisfaction, etc.

Urbanizing an information system means directing its continuous transformation to guarantee its consistency

The reasons for a successful or unsuccessful implementation of an IS are complex and contested by the various stakeholders and from the various perspectives involved.

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© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Health Information System

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• Branko Jakovljević 2  

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Healthcare information system; Patient-care information system; Medical information system

A health information system (HIS) is an information system for processing data, information, and knowledge in health care environments. It can be defined as an integrated effort to collect, process, report, and use health information and knowledge to influence policy-making, program action, and research.

Basic Characteristics

Development of health information system.

Branches of health information systems are primary care information systems (information systems supporting primary health care), hospital information systems (information systems supporting clinical work), public health information systems, geographic information systems, medical research information systems , medical education information systems , medical management information systems , etc. ( health information ).

To initiate the development of new information systems, the World Health Organization proposes the...

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Jakovljević, B. (2008). Health Information System . In: Kirch, W. (eds) Encyclopedia of Public Health. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-5614-7_1425

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