Information and Communication Technologies (ICTs) affect the environment in various ways. Their energy consumption is growing exponentially, with and without the use of ‘green’ energy. Increasing environmental awareness within information science has led to discussions on sustainable development. ‘Green Computing’ has been introduced: the study and practice of environmentally sus- tainable computing. This can be defined as ‘designing, manufacturing, using, and disposing of com- puters, servers, and associated subsystems - such as monitors, printers, storage devices, and net- working and communications systems - efficiently and effectively with minimal or no impact on the en- vironment’. Nevertheless, the data deluge makes it not only necessary to pay attention to the hard- and software dimensions of ICTs but also to the value of the data stored. We explore the possibilities to use information and archival science to reduce the amount of stored data. In reducing this amount of stored data, it’s possible to curb unnecessary power consumption. The objectives of this paper are to develop a model (and test its viablility) to [1] increase awareness in organizations for the environ- mental aspects of data storage, [2] reduce the amount of stored data, and [3] reduce power consump- tion for data storage. This model integrates the theories of Green Computing, Information Value Chain (IVC) and Archival Retention Levels (ARLs). We call this combination ‘Green Archiving’. Our explora- tory research was a combination of desk research, qualitative interviews with information technology and information management experts, a focus group, and two exploratory case studies. This paper is the result of the first stage of a research project that is aimed at developing low power ICTs that will automatically appraise, select, preserve or permanently delete data based on their value. Such an ICT will automatically reduce storage capacity and curb power consumption used for data storage. At the same time, data disposal will reduce overload caused by storing the same data in different for- mats, it will lower costs and it reduces the potential for liability.
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The development of the World Wide Web, the emergence of social media and Big Data have led to a rising amount of data. Information and Communication Technologies (ICTs) affect the environment in various ways. Their energyconsumption is growing exponentially, with and without the use of ‘green’ energy. Increasing environmental awareness has led to discussions on sustainable development. The data deluge makes it not only necessary to pay attention to the hard- and software dimensions of ICTs but also to the ‘value’ of the data stored. In this paper, we study the possibility to methodically reduce the amount of stored data and records in organizations based on the ‘value’ of information, using the Green Archiving Model we have developed. Reducing the amount of data and records in organizations helps in allowing organizations to fight the data deluge and to realize the objectives of both Digital Archiving and Green IT. At the same time, methodically deleting data and records should reduce the consumption of electricity for data storage. As a consequence, the organizational cost for electricity use should be reduced. Our research showed that the model can be used to reduce [1] the amount of data (45 percent, using Archival Retention Levels and Retention Schedules) and [2] the electricity consumption for data storage (resulting in a cost reduction of 35 percent). Our research indicates that the Green Archiving Model is a viable model to reduce the amount of stored data and records and to curb electricity use for storage in organizations. This paper is the result of the first stage of a research project that is aimed at developing low power ICTs that will automatically appraise, select, preserve or permanently delete data based on their ‘value’. Such an ICT will automatically reduce storage capacity and reduce electricity consumption used for data storage. At the same time, data disposal will reduce overload caused by storing the same data in different formats, it will lower costs and it reduces the potential forliability.
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The development of the World Wide Web, the emergence of social media and Big Data have led to a rising amount of data. Infor¬mation and Communication Technol¬ogies (ICTs) affect the environment in various ways. Their energy consumption is growing exponentially, with and without the use of ‘green’ energy. Increasing envi¬ronmental aware¬ness has led to discussions on sustainable development. The data deluge makes it not only necessary to pay attention to the hard‑ and software di¬mensions of ICTs but also to the ‘value’ of the data stored. In this paper, we study the possibility to methodically reduce the amount of stored data and records in organizations based on the ‘value’ of informa¬tion, using the Green Archiving Model we have developed. Reducing the amount of data and records in organizations helps in allowing organizations to fight the data deluge and to realize the objectives of both Digital Archiving and Green IT. At the same time, methodi¬cally deleting data and records should reduce the con¬sumption of electricity for data storage. As a consequencs, the organizational cost for electricity use should be reduced. Our research showed that the model can be used to reduce [1] the amount of data (45 percent, using Archival Retention Levels and Retention Schedules) and [2] the electricity con¬sumption for data storage (resulting in a cost reduction of 35 percent). Our research indicates that the Green Ar¬chiving Model is a viable model to reduce the amount of stored data and records and to curb electricity use for storage in organi¬zations. This paper is the result of the first stage of a research project that is aimed at devel¬oping low power ICTs that will automa¬tically appraise, select, preserve or permanently delete data based on their ‘value’. Such an ICT will automatically reduce storage capacity and reduce electricity con¬sumption used for data storage. At the same time, data dispos¬al will reduce overload caused by storing the sa¬me data in different for¬mats, it will lower costs and it reduces the po¬tential for liability.
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Computers create environmental problems. Their production requires electricity, raw materials, chemical materials and large amounts of water, and supplies (often toxic) waste. They poison dumping sites and pollute groundwater. In addition, the energy consumption in IT is growing exponentially, with and without the use of ‘green’ energy. Increasing environmental awareness within information science has led to discussions on sustainable development. ‘Green Computing’ has been introduced: the study and practice of environmentally sustainable computing or IT. It is necessary to pay attention to the value of the information stored. In this paper, we explored the possibilities of combining Green Computing components with two theories of archival science (Archival Retention Levels and Information Value Chain respectively) to curb unnecessary power consumption. Because in 2012 storage networks were responsible for almost 30 % of total IT energy costs, reducing the amount of stored information by the disposal of unneeded information should have a direct effect on IT energy use. Based on a theoretical analysis and qualitative interviews with an expert group, we developed a ‘Green Archiving’ model, that could be used by organizations to 1] reduce the amount of stored information, and 2] reduce IT power consumption. We used two exploratory case studies to research the viability of this model.
MULTIFILE
Within eGovernment, trust in electronic stored information (ESI) is a necessity. In the last decades, most organizations underwent substantial reorganization. The integration of structured data in relational databases has improved documentation of business transactions and increased data quality. That integration has improved accountability as well. Almost 90% of the information that organizations manage is unstructured (e.g., e-mail, documents, multimedia files, etc.). Those files cannot be integrated into a traditional database in an easy way. Like structured data, unstructured ESI in organizations can be denoted as records, when it is meant to be (and used as) evidence for organizational policies, decisions, products, actions and transactions. Stakeholders in eGovernment, like citizens, governments and courts, are making increasing demands for the trustworthiness of this ESI for privacy, evidential and transparency reasons. A theoretical analysis of literature of information, organization and archival science illustrates that for delivering evidence, reconstruction of the past is essential, even in this age of information overload. We want to analyse how Digital Archiving and eDiscovery contribute to the realization of trusted ESI, to the reconstruction of the past and to delivering evidence. Digital Archiving ensures (by implementing and managing the ‘information value chain’) that: [1] ESI can be trusted, that it meets the necessary three dimensions of information: quality, context and relevance, and that [2] trusted ESI meets the remaining fourth dimension of information: survival, so that it is preserved for as long as is necessary (even indefinitely) to comply to privacy, accountability and transparency regulations. EDiscovery is any process (or series of processes) in which (trusted) ESI is sought, located, secured and searched with the intent of using it as evidence in a civil or criminal legal case. A difference between the two mechanisms is that Digital Archiving is implemented ex ante and eDiscovery ex post legal proceedings. The combination of both mechanisms ensures that organizations have a documented understanding of [1] the processing of policies, decisions, products, actions and transactions within (inter-) organizational processes; [2] the way organizations account for those policies, decisions, products, actions and transactions within their business processes; and [3] the reconstruction of policies, decisions, products, actions and transactions from business processes over time. This understanding is extremely important for the realization of eGovernment, for which reconstruction of the past is an essential functionality. Both mechanisms are illustrated with references to practical examples.
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Archives are, more than ever, organizational and technological constructs, based on organizational demands, desires, and considerations influencing configuration, management, appraisal, and preservation. For that reason, they are, more than ever, distortions of reality, offering biased (and/or manipulated) images of the past and present an extremely simplified mirror of social reality. The information objects within that archive are (again: more than ever), fragile, manipulable, of disputable provenance, doubtful context, and uncertain quality. Their authenticity is in jeopardy.The “Allure of Digital Archives” will be more about finding knowledge about the archive as a whole than about finding knowledge hidden in the information objects that are its constituents. It will be about determining the value of a digital archive as a “trusted” resource for historical research. To be successful in that endeavour, it will be necessary to assess the possibility to “reconstruct the past” of the digital archive. That assessment would allow historians to understand quality, provenance, context, content, and accessibility of the digital archive, not only in its design stage but also in its life cycle.In this chapter, I present the theoretical framework of the “Archive–as–Is” as an instrument for such an assessment. It is possible for historians to use this framework as a declarative model for the way archives have been designed, configured, managed, and maintained. It will allow historians to understand why archives are as they are, and why records are part of it (or not). Using the framework, historians can determine the research value of a digital archive as a historical resource.
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Purpose As a step toward more firmly establishing factors to promote retention among younger employees in the hospitality industry, this study aims to focuses on fun in the workplace (fun activities, manager support for fun and coworker socializing) and training climate (organizational support, manager support and job support) as potential antecedents of turnover in a European context. Design/methodology/approach Logistic regression was used to analyze the impact of fun and training climate on turnover with a sample of 902 employees from Belgium, Germany and The Netherlands. Data on fun and training climate were obtained through surveys, which were paired with turnover data from organizational records. Findings With respect to fun in the workplace, group-level manager support for fun and coworker socializing were significantly related to turnover, but not fun activities. With respect to training climate, individual-level job support was significantly related to turnover, but not organizational support and manager support. Research limitations/implications As the data were obtained from employees from one organization, further research would be valuable with additional samples to substantiate the generalizability of the results. Practical implications Given the challenge of turnover, organizations should foster informal aspects of fun in the workplace and learning opportunities to promote retention. Originality/value The study examined the fun–turnover relationship in a context outside of the USA where previous fun–turnover research has been conducted, and it examined fun relative to training climate, which has not been studied heretofore. This study also investigated group- and individual-level effects of both fun and training climate on turnover.
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There is a wide range of literature suggesting that implicit learning is more effective than explicit learning when acquiring motor skills. However, the acquisition of nursing skills in educational settings continues to rely heavily on detailed protocols and explicit instructions. This study aimed to examine the necessity for comprehensive protocols in the acquisition of nursing skills. In the context of bandaging techniques, three studies were conducted to investigate whether students who practiced with an instruction card containing minimal instructions (implicit group) performed comparably to the students who practiced with a protocol containing step-by-step instructions (explicit group). Study 1 was designed to determine whether both groups performed equally well in applying a bandage during training. Study 2 and 3 were designed to determine if both groups performed equally well during a retention and transfer (multitasking) test, administered after a series of three training sessions. In comparison with the explicit group, the implicit group demonstrated comparable performance with their practice attempts in Study 1 and performed equally well during the retention and transfer test in Study 2. Furthermore, several results from Study 3 indicated better performance of the implicit group. In conclusion, the use of protocols with explicit step-by-step instructions may not be essential for the acquisition of nursing skills. Instead, instructional methods that facilitate implicit learning may be preferable, as students in the implicit group demonstrated at least comparable performance in all studies and tended towards greater consistency when multitasking.
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More than 80 % of all information in an organization is unstructured, created by knowledge workers engaged in peer-to-peer networks of expertise to share knowledge across organizational boundaries. Enterprise Information Systems (EIS) do not integrate unstructured information. At best, they integrate links to unstructured information connected with structured information in their databases. The amount of unstructured information is rising quickly. Ensuring the quality of this unstructured information is difficult. It is often inaccessible, unavailable, incomplete, irrelevant, untimely, inaccurate, and/or incomprehensible. It becomes problematic to reconstruct what has happened in organizations. When used for organizational policies, decisions, products, actions and transactions, structured and unstructured information are called records. They are an entity of information, consisting out of an information object (structured or unstructured) and its metadata. They are important for organizational accountability and business process performance, for without them reconstruction of past happenings and meaningful production become an impossibility. Organization-wide management of records is not a common functionality for EIS, resulting in [1] a fragmentation in the management of records, where structured and unstructured information objects are stored in a variety of systems, unconnected with their metadata; [2] a fragmentation in metadata management, leading to a loss of contextuality because metadata are separated from their information objects; and [3] a declining quality or records, because their provenance, integrity, and preservation are in peril. Organizational accountability is based on records and their context to reconstruct the past. Because records are not controlled by EIS, they can only marginally be used for accountability. The challenge for organizational accountability is to generate trusted records, fixed and contextual information objects inseparately linked with metadata that capture context to regain evidential value and to allow for the reconstruction of the past. The research question of this paper is how to capture records and their context within EIS to regain the evidential value of records to allow for a more robust organizational accountability. To find an answer, we need to pay attention to the concept of context, on how to capture context in metadata, and how to embed and manage records in EIS.
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