Godoe, P. & Johansen, T.S., (2012). Understanding adoption of new technologies: Technology readiness and technology acceptance as an integrated concept. Journal of European Psychology Students. 3(1), pp.38–52.
Organisations adopt new technologies to improve the efficiency and effectiveness of various work processes. Unfortunately, many technology-based products and services never reach their full potential, and some are simply rejected (Burton-Jones & Hubona, 2006). Failed investments in technology may not only cause financial losses, but also lead to dissatisfaction among employees (Venkatesh, 2000).
The technology acceptance model (TAM) has come to be one of the most widely used models. The two primary predictors in TAM that affect technology usage are perceived usefulness and perceived ease of use.
The other paradigm focuses on latent personality dimensions to explain the use and acceptance of new technologies (Porter & Donthu, 2006). In other words, an individual's personality influences the potential acceptance of technology in general. The technology readiness index (TRI) (Parasuraman, 2000) follows this approach. Technology readiness can be viewed as a gestalt resulting from four personality dimensions: optimism, innovativeness, discomfort, and insecurity. According to Parasuraman (2000) these personality dimensions affect people's tendency to embrace and use new technologies. In this respect, optimism and innovativeness function as mental enablers, while discomfort and insecurity function as mental inhibitors to accepting new technologies.
In the last decade, research has emerged combining the two paradigms by integrating the TRI and TAM into one model. Lin, Shih, Sher, and Wang (2005) and Lin, Shih, and Sher (2007) included technology readiness as an antecedent of perceived usefulness and perceived ease of use in TAM. Walczuch, Lemmink, and Streukens (2007) took a somewhat different approach by investigating how each dimension of technological readiness affects the predictors in TAM.
Beating Murphy's Law in New Technology Adoption
MIT Sloan Management Review, Spring 1991
W. Bruce Chew, Dorothy Leonard-Barton and Roger E. Bohn
Beating Murphy’s Law - Steps
Rule #1. Think of Implementation as R&D
Acquisition of new technolgy should be considered an ongoing process of data gathering and learning that evolves over time. Initially, an organization must focus on technical data regarding equipment options and costs and a study of existing applications. Eventually, the technology goes through startup and data is generated in-house. But in every phase the goal is to learn all that can possibly be learned at that point in time. In effect, the introduction of technology should be considered less as an investment issue or technical issue and more as a question of research design.
The experiments should address both technical and organizational questions. Managers who understand that they are managing organizational change, not just technical change, are better positioned to direct the learning process. The work of technology managers should include: working very closely with users, whose role should be as codevelopers rather than receivers of the technology; constantly redefining the necessary support structure in the user organization, identifying and targeting potentially weak links; enlarging the definition of the technology to include the delivery system or other linkages on which the technology is critically dependent; and experimenting as consciously and productively with organizational forms as with technical ones, capitalizing wherever possible on experiments occurring naturally in the company.
Rule #2. Ask “What made it hard?” Not “How well did it work?”
The firm must look for answers to questions of technology implementation like “How did you make this technology work? What had to be changed? What was hardest?”
Our studies suggest that technical knowledge, about the hardware itself, transfers more easily than organizational knowledge,
Rule #3. Learn in Many Ways at Once
Broadly speaking, there are four methods of learning that a firm can use:
vicarious learning—learning from the experience of others;
simulation—constructing artificial models of the new technology and experimenting with it;
prototyping—actually building and operating the new technology on a small scale in a controlled environment; and
on-line learning—examining the actual, full-scale technology implementation while it is operating as part of the normal production process. A clear hierarchy exists among these four methods: costs get higher moving down the list, but so does fidelity.
Managers in our studies consistently underinvest in preimplementation learning, choosing in effect to do most of their learning later, when it is most expensive.
Many managers appear unwilling to invest in learning by methods that offer less than perfect fidelity. They fail to recognize that learning need not be all or nothing. Another plant’s experience will not be completely relevant, but it is still possible to identify some issues that can be addressed vicariously. Similarly, simulation and prototyping can be effectively targeted at specific questions.
The consequence of these two competing hierarchies is important: use a mixed strategy for learning. Learn as much as possible using the low-cost, low-fidelity methods, but realize that some learning will probably be necessary from all four methods.
Furthermore, the ideal learning strategy includes parallel and simultaneous use of all methods, not just sequential use. For example, opportunities for improvements that are not uncovered until die prototype is running may become the target of simulation. Prototype pilot lines should be kept going in parallel with the main production line, as test beds for diagnostic experiments and trials of changes. Both technical and organizational learning must be documented and remembered. As noted earlier, one plant’s Murphy’s Law disaster is another plant’s opportunity for vicarious learning.
Rule #4. Simulate and Prototype Everything
A simulation of a new technology is a model of how it will work. Simulations can range from simple mathematical models such as spreadsheets, through elaborate Monte Carlo computer models, to physical models of the entire plant, before and after the new technology is introduced. For example, in the steel industry it is common to simulate changes using scaled-down models with water in place of molten steel.
Simulation technology has improved dramatically in recent years due to advances in computer hardware (engineering workstations and personal computers are more than adequate for simulations of most production processes) and especially in easy-to-use, special purpose simulation languages. It is often possible to do a crude but useful initial simulation in less than a week of effort. In fact, we recommend that any new technology involving more than a few person months of total effort should probably be simulated in some way. Complex or large technology should usually receive several simulations targeted at different levels of detail and different aspects of the total system.
A prototype is a small-scale construction or isolated version of the final system for learning purposes, using methods as close as possible to the final technological target. The purpose of prototyping is to learn about problems and opportunities that were not found during the simulation but that will cause delays or expense if they are left for on-line learning.
Rule #5. “Everything” Includes the Organization
Simulation is equally applicable to organizational changes, though it is rarely applied. To implement a new MRPII system, the implementation manager persuaded representatives from all the potentially affected functions (shipping, purchasing, inventory, etc.) to come together in one room for several days to go through a noncomputerized simulation of the information flows The simulation served to educate the various functions about the coming system. The supervisors got to know each other and talked about the process interdependencies that the new system was going to cause or exacerbate. Because they came to understand the needs of other functions, whose representatives they often had not even known before, the participants negotiated compromises and agreements that forestalled problems when the actual system was implemented.
Organizational prototyping, like technical prototyping, is the execution of a design on a small scale for the express purpose of evaluating that design from an organizational viewpoint. With organizational prototypes managers can anticipate needed alterations, potential pitfalls, and opportunities for additional benefits by observing the technology-organization interaction in microcosm before launching the full-scale production change.
Pilot runs of a new technology offer the opportunity for organizational prototyping, but they are rarely used for that purpose. Usually test runs are conducted by technical staff to learn about potential problems in the physical system. Litde attention is paid to the possibility of learning about organizational effects and opportunities, such as changes in roles, conflicts with existing rewards and incentives, differing responses to and use of the new technology depending upon operator background and skill, the different meaning that the technology has for different groups of users, and the most effective organizational structure.
Rule #6. Follow Lewis and Clark
The problem is not with planning per se but with the substance of a plan.
When Lewis and Clark headed west from St. Louis they did not attempt to specify in advance their exact trail and how they would cope with each expected contingency. They realized that the wilderness ahead was too unknown and the contingencies too many. Rather, they set out with a general sense of their route (up the Missouri River and over the Rockies), a good store of resources, and a team that had familiarized itself with everything known about the wilderness ahead.
Too often managers forget that new technologies have more in common with Lewis and Clark’s wilderness than today’s travel. thing go awry. Planning must provide a guiding structure for discovering and solving problems. It should focus more on what to look for and think about than on what to do. It should plan for an expedition of discovery, not a drive to a relative’s house; it should be a research design, not a recipe.
Rule #7. Produce Two Outputs: Salable Products and Knowledge
Eventually, the new technology is up and running. The new process produces not only salable products, but also usable knowledge. Production time, management time, labor, and materials should be budgeted for making both types of output.
Budgeting Time for the Seven Rules
The core of our argument has been that to beat Murphy’s Law it is necessary to plan for and manage directed learning. Anything you don’t learn about early will hurt you later.
One simple solution is to budget for this learning time throughout the project schedule. In particular, keep a reserve of production time for on-line learning in the first several months of startup. This is over and above the planned lower output during startup. Ten percent of production time is a realistic amount if vicarious learning, simulation, and prototyping have been done thoroughly.
The Rules in Practice: Different Kinds of Knowledge
In summary, managers typically underinvest in learning both before and after startup. This is particularly true of the organizational changes relating to new technologies.
The seven rules reflect a different vision of what it means to implement technology.
In all the most successful sites, investment was made in the creation of local user-experts, whose job it became to anticipate, model, prototype, and teach the new behavior necessitated by the technology, especially during the critical period of change from the old to the new system.