Understanding the management of personal records at home: a virtual guided tour
Introduction. This paper considers how we can better manage personal records in the home by addressing questions such as how and why personal records are retained in an electronic form and how they are managed.
Method. A qualitative method with semi-structured interviews was used. Participants were recruited through social media. The interviews included virtual guided tours of personal records. There were thirty participants in twenty-two interviews (some interviews were with couples).
Analysis. Each stage of the personal records management process described by participants was observed and categorised, resulting in an inclusive flow diagram.
Results. The management of personal records at home can be categorised and described in terms of a flow. Some commonalities were found between personal information management in the workplace and at home, such as the frequent use of e-mail to manage records and the use of micro-notes and reminders.
Conclusion. Personal records management at home can be described as a flow through which records progress. The fact that the study of personal information management has rarely addressed personal information management at home offers many opportunities for fruitful future research.
Practices and pain points in personal records
Introduction. This paper reports the findings of a survey on personal electronic records management practices focussing on records that people deal with in their everyday lives at home. The aim of this research was to determine which personal electronic records practices were most effective in averting oversights and generating satisfaction in participant's records management practices. This paper presents one stage of a broader design science research program.
Method. The research for this paper was conducted by means of an online questionnaire using Qualtrics software and participants were recruited through social media.
Analysis. Analysis was conducted using tabular analysis in SPSS, and Principal Component Analysis in R.
Results. The research found that there is a statistical relationship between the practices that respondents adopted with their personal electronic records management and their level of satisfaction with that process. For example, respondents who saved records on a computer or in the cloud reported higher levels of satisfaction with how they managed their personal records and experienced fewer adverse incidents such as losing documents or failing to pay bills on time.
Conclusion. The paper concludes by identifying some specific personal records management practices that are likely to improve satisfaction with that task, such as saving and sorting records that need to be retained outside of email in a structured filing system.
Attributes of Personal Electronic Records
The purpose of this article is to identify the key attributes of personal electronic records in order to develop systems that may enable people to manage them in the home. As more personal information becomes electronic, this is increasingly necessary. Personal electronic records were identified and categorised using interviews and virtual guided tours. Three main attributes were identified: primary user-subjective categories; attributes which identify the circumstances that give rise to the records; and attributes which describe the legal validity of each record. In addition to providing an improved understanding of personal electronic records in the home, these attributes are developed into a set of potential metadata fields.
How Smart is Artificial Intelligence?
In the world of computing, intelligence means 'human-like'. Intelligent systems are designed to behave as much like people would in the same circumstances.
Computers are programmed to execute smart algorithms: 'if this, then that.' Computers do this immediately and without question – far better than humans. If you apply an algorithm when programming an aircraft, the program says, 'If the plane is at X altitude and is not in landing or taking off mode, sound a warning.' Apply the same programming to a self-driving car, and the program will instruct the car to do things safely, such as stop when there is a red light. The programming team have time to figure out the best course of action, and program the machine to make that happen automatically. This is using a computer to be smart.