Centralized vs. decentralized computing : organizational considerations and management options

The long-standing debate over whether to centralize or decentralize computing is examined in terms of the fundamental organizational and economic factors at stake. The traditional debate is examined and found to focus predominantly on issues of efficiency vs. effectiveness, with solutions based on a rationalistic strategy of optimizing in this tradeoff. A more behavioralistic assessment suggests that the driving issues in the debate are the politics of organization and resources, centering on the issue of control. The economics of computing deployment decisions is presented as an important issue, but one that often serves as a field of argument that is based on more political concerns. The current situation facing managers of computing, given the advent of small and comparatively inexpensive computers, is examined in detail, and a set of management options for dealing with this persistent issue is presented

Balance of Trade in the Marketplace of Ideas

If the Information Systems (IS) field is to exist with other fields in some kind of balance of trade in a marketplace of ideas, the scheme is not working too well, at least when comparing IS with Computer Science (CS). The trade tends to be one-way, from CS to IS. This paper explores why that is the case, and what might be done to change things

Policy and Imprecise Concepts: The Case of Digital Transformation

The name digital transformation is widely used, even though its meaning is imprecise. This constructive ambiguity signifies the growing importance of information technology in organizational life rather than a specific thing. Most organizations can and do use the name freely. They do not need policy for digital transformation as long as they recognize the utility of watchful waiting and ongoing policy analysis

Nothing At The Center?: Academic Legitimacy in the Information Systems Field

Researchers in the information system (IS) field have recently called for the field to legitimate itself by erecting a strong theoretical core at its center. This paper examines this proposition, and concludes that it is logically invalid and does not recognize ample evidence to the contrary from the history of other disciplines. We construct a broader concept of academic legitimacy around three drivers: the salience of the issues studied, the production of strong results, and the maintenance of disciplinary plasticity. This analysis suggests that to remain successful, the IS field needs intellectual discipline in boundary spanning across a ¡°market of ideas¡± concerning the application of information technology in human enterprise

Evol uti on and Organizational Information Systems: An Assessment of Nol an\u27s Stage Model

The stage model of Richard Nolan, as published between 1973 and 1979, is the best known model of evolution related to organizational information systems. The model has been accepted as a sound description of this evolution, but has never been subjected to careful conceptual assessment. This paper eval uates the model in 1 ight of its logical structure and its pl ace within the larger realm of evol ution expl anatlons in the social sciences. The model evolved over a period of years. The original 1973 version derived from the S shaped logistic curve of growth in computing budgets. The three points of directional change in the curve were taken as a surrogate of major changes in the environment and management of computing within the organization, dividing the total curve into four sections Nol an called stages: initiation (beginning of use); contagion (rapid expansion of use); control (constraining response from top management to restrict growth); and integration (refinement of controls to accomplish organizational objectives in computing use). This basic descriptive hypothesis was elaborated in the 1974 version (with Cyrus Gibson) which added two significant features: definition of the primary driving agent in computing growth as change in technol ogy; and the devel opment of the model as an equilibrium model . The state of computing at any time was the result of an equilibrium between the stimulating forces of technical change and the constraining forces of manageri al control policies. The model was elaborated in 1977 and 1979 to include two new stages. Management policies were characterized as either slack policies (lack of control s, encouragement of innovation) or control policies (constraints on growth, encouragement of efficiency). The S curve was said to illustrate the organization\u27s learning curve in dealing with computing, in which management policy improves over time in its effectiveness at achieving desired results. A basic change was said to be underway in management attitude toward computing, from concentration on control of computing resources to control of organizational data resources, stimulated in part by the emerging technol ogy of database management systems. A new stage called data administration was added to the model, which would eventually give way to a sixth stage called maturity. In maturity managers woul d be sufficiently knowledgeable to effect a productive balance or equilibrium between sl ack (encouraging innovation) and control (encouraging efficiency). Our evaluation of the model reveal s probl ems with its assumptions. First, the empirical foundation of the model is questionable. Computing budgets are not likely to be effective surrogates for the wide range of variables they are said to represent, and, as subsequent empirical research has shown, do not necessarily conform to the S curve. Moreover, predictions made .using the model \u27s .assumptions have proven inaccurate. Second, the focus on technological change as the basic driving force in computing growth is probably too simplistic. It does not adequately deal with the many demand-related contextual factors of change that have been shown emplrically to be important. Third, the model implicitly assumes that there is cl arity and congruity on organizational goals for computing use among top managers, but this expectation is seldom uphel d. A 1 ack of congruity in goals weakens the assumption that acquisition of knowledge will automatically result in the development of appropriate management controls. Fourth, we doubt that knowledge of appropriate means for deal ing with computing will be as easy to acquire as the model suggests. There are many competi ng theories about how best to manage computing, and differences in organizational actors\u27 abilities to acquire knowledge and dispositions about how to use it. There is no specification in the model regarding how knowledge of appropriate policies leading to maturity will be found and applied. Fifth, balancing control vs. slack policles implies that managers have some idea of the di recti on computing use is headed. In fact, most policies are reactive, and the notion that balance can be deliberately achieved is questionable. Finally, the assumption that change actually proceeds in a continuous manner is not upheld either by the history of computing development in organizations or by other studies of organizational or social change. Within the context of evol ution expl anati ons in the social sciences, Nolan\u27 s model is an exampl e of evol utionist models, which assume same a priori direction of change and an expected end state of change, but seldom precisely specify the mechanisms whereby change takes pl ace. Nol an\u27 s model posits a definite end state (integration in the early versions, maturity in the 1 ater versions), but does not provide a detail ed account of how change takes pl ace. As such, Nolan\u27s model offers some useful insights, but suffers from problems common to evolutionist models: it is difficult to test empi rically, and does not offer a good account of why specific changes occur the way they do. Most importantly, the only empi rical test avall abl e for such model s (waiting to see whether predictions made using them prove to be correct) has not supported the Nolan model to date. The model remains an insightful organizing framework for thinking about computing change in organizations, but is not the empirically validated model of change some of its proponents claim it to be

Policy: An Information Systems Frontier

The information systems community can contribute more, not just to “public policy” but to the broader notion of policy that guides decisions toward desired outcomes. Policy entails politics. It requires knowing about policy promulgation, implementation, and effect. It requires some understanding of policy analysis. The policy analyst takes a scientific and systematic view of policy issues. Much policy is focused on the unglamorous issues of efficiency and effectiveness. The goal is to speak truth to power. This is the first in a series of papers to address policy

Advanced Technologies: Health Care Anytime... Anywhere?

Advanced technology in the medical profession has had a significant impact on the access, efficiency, and cost of health care delivery services over the past decade. Technological advancements in the medical profession can be bucketed into two main categories: mobile and biological/physiological. Some examples of mobile technology include web apps that can monitor a patient’s vital signs remotely and mobile phone attachments that can provide medical imaging data for doctors in the most remote areas of the globe. Remote patient monitoring and the use of mobile health apps to deliver timely, useful information to the patient about their health decision represent a significant shift in health care information delivery. Research conducted with a biological/physiological intent ranging from nanotechnology to molecularly modified proteins and genes designed to provide personalized medicine based on the “context of a patient’s unique biological state.” The health care industry is among the first to develop the semantic web through WC3 which launched the Health Care Life Sciences Interest Group to improve interaction and collaboration through adaptive data mining using the semantic web. “Connected devices” refers to the premise that the semantic web will make the meaningful connections between disparate bits of information through smart and connected devices. EHRs already use APIs (application programming interfaces) to securely share clinical content.https://fuse.franklin.edu/forum-2013/1019/thumbnail.jp

Governance in the Blockchain Economy: A Framework and Research Agenda

Blockchain technology is often referred to as a groundbreaking innovation and the harbinger of a new economic era. Blockchains may be capable of engendering a new type of economic system: the blockchain economy. In the blockchain economy, agreed-upon transactions would be enforced autonomously, following rules defined by smart contracts. The blockchain economy would manifest itself in a new form of organizational design—decentralized autonomous organizations (DAO)—which are organizations with governance rules specified in the blockchain. We discuss the blockchain economy along dimensions defined in the IT governance literature: decision rights, accountability, and incentives. Our case study of a DAO illustrates that governance in the blockchain economy may depart radically from established notions of governance. Using the three governance dimensions, we propose a novel IT governance framework and a research agenda for governance in the blockchain economy. We challenge common assumptions in the blockchain discourse, and propose promising information systems research related to these assumptions