Heuristic over Social Informatics

A heuristic is a replicable method or approach for directing one's attention in learning, discovery, or problem-solving. It is commonplace, and a good heuristic for inquiry, especially with complex technologies. References to technologies and social entities and to the interactions between them are made largely for analytical convenience. This is where Social informatics comes in, from the beginning in Europe modern studies by Dr. Robert Kling.

Social informatics refers to the interdisciplinary study of the design, uses and consequences of information and communication technologies that takes into account their interactions with institutional and cultural contexts. Social informatics research may be done at group, departmental, organizational, national and/or societal levels of analysis, focused on the relationships among information, information systems, and the people who use them and the context, including the difficulties, of use. [1]

Historically, social informatics research has been strong in Europe, although in past few decades within the United States, the field is represented largely through independent research efforts at a number of diverse institutions. [1] In North America, Dr. Robert Kling was considered the father of Social Informatics [2].

Robert Kling was born in August 1944 and grew up in Northern New Jersey. He completed his undergraduate studies at Columbia University (1965) and his graduate studies, specializing in Artificial Intelligence, at Stanford University (1967, 1971). Between 1966 and 1971 he held a research appointment in the Artificial Intelligence Center at the Stanford Research Institute. He held his first professorship in Computer Science at the University of Wisconsin-Madison from 1970 to 1973. He was on the faculty of UC-Irvine 1973-1996 and held professorial appointments at UCI's Center for Research on Information Technology and Organizations and Graduate School of Management. [3]

In the 1970s, Dr. Kling's was already a leading expert on the study of social informatics which then investigated aspects of computerization and organizational change and the ways that the social organization of information technology is influenced by social forces and social practices. He early observed that complex information and computer systems are integrated into the social life of organizations and conducted studies in numerous kinds of environments, including local government, insurance companies, pharmaceutical firms, and high-tech manufacturing. Dr. Kling studied how intensive computerization transforms work practices and how computerization entails many social choices. [4]

According to Dr. Kling, social informatics is [5]:

"A serviceable working conception of ‘social informatics’ is that it identifies a body of research that examines the social aspects of computerization. A more formal definition is ‘the interdisciplinary study of the design, uses and consequences of information technologies that takes into account their interaction with institutional and cultural contexts."

Social Informatics would tell us that many of the difficulties that researchers have in quantifying the benefits of Information Technology would also affect managers. As a result, they may have difficulty in bringing the benefits to the bottom line if output targets, work organization and incentives are not appropriately adjusted. The result is that Information Technology might increase organizational slack instead of output or profits (i.e., employees misusing the internet). This is consistent with arguments by Roach that manufacturing has made better use of Information Technology than has the service sector because manufacturing faces greater international competition, and thus tolerates less slack. [6]

Sometimes the benefits do not even appear in the most direct measures of Social Informatics effectiveness. This stems not only from the difficulty of system design and software engineering, but also because the rapidly-evolving technology leaves little time for time-tested principles to diffuse before being supplanted.[7] A related argument derives from evolutionary models of organizations. The difficulties in measuring the benefits