[Introduction]

Since a very long time, many researches are being done to observe the organizational behaviour, all having a more or less common motto – “Optimization of performance”. Effectiveness and efficiency are the most important factors to determine the company’s performance. In present day views, the fit between the technology and structure has become the focus of the discussion where as earlier theories used to have a different focal point. This study is more about regularizing the analysis level to each organization and measurement of contingency relationship between technology and structure. Studies which considered large units led to more enigma than studies which considered smaller sub-units. The research paper concentrates on highlighting the link between technology and structure; and how they can be utilized to improve the performance of an organization. The central disagreement revolves around the theory that instead of technology or structure or technology and structure, a fit between both the structure and technology is a better measurement of performance. One of authors Judith W.Alexander served as an assistant professor in college of nursing, University of South Carolina, the other author W. Alan Randolph is a professor of International business and leadership. They present the argument that performance can be better studied in sub-units than in large units(organization), considering the fact the factors influencing the performance would affect the result in terms of complexity.

[Assumptions]
The purpose of the study ‘The Fit Between Technology and Structure as a Predictor Of Performance in Nursing Subunits’ is to find out whether a simplified measure of fit between technology and structure will affect the quality of care in nursing units. This study has also specified contingency relationship between different pair of technologies and structures to reach a quantifiable theory. Researchers have long argued that a fit between these two is a better predictor of quality of care than technology alone, structure alone and technology and structure as separate predictors in the same model. In doing so, a number of assumptions have been made that are mentioned below. The below mentioned hypothesis depend on some characteristics of Technology (Uncertainty, Instability and Variability), Structure (Vertical Participation, Horizontal Participation and Formalization) and three forms of Congruency (Effect, General and Functional).
Uncertainty refers to complexity of the work. Instability refers to unpredictability and deviation from work techniques and practices. Variability measures how much workers engage in different tasks. Horizontal participation is the degree of involvement of among peers in decision making. Vertical participation defines hierarchical consultation in decision making and job tasks. Formalization refers to existence of rules, procedures and instructions and abiding by them.
Hypothesis 1:The fit between technology and structure will be a better predictor of performance than technology, structure or technology and structure.
This means that the study uses absolute value of difference between the values of technology and structure to determine the fit. This approach is simple yet it shows non-monotonic relationship between the two. This point makes an assumption that for every value of technology, there is a particular value of structure that decides high effectiveness.
Hypothesis 2: The fit between instability and vertical participation will be a significant predictor of performance. High (low) levels of instability when matched with high (low) levels of vertical participation will yield high performance.
This means that instability, which is a characteristic of technology, is linked to unpredictability at work. This hypothesis again draws support from previous studies done by Commstock& Scott, 1977. It also means that if the predictability of technology is low, then the technology instability is more. If the technology is instable, then with an increase in supervisory process of vertical participation, the yield will be better output.
Hypothesis 3:The fit between variability and horizontal participation will be a significant predictor of performance. High (low) levels of variability when matched with high (low) levels of horizontal participation will yield high performance.
This means that the more variability is there in the tasks that people perform in their various sub-units, the more is the need for horizontal participation. To help people achieve their targets easily, we must have increased fit between horizontal participation and variability. This hypothesis derives tentative support from the studies of Schoonhoven (1981) and Randolph and Finch (1977).
Hypothesis 4: The fit between uncertainty and formalization will be a significant predictor of performance. High (low) levels of uncertainty when matched with high (low) levels of formalization will yield low performance.
Although uncertainty is dealt with formalization in most places, high levels of uncertainty with high levels of formalisation lead to an overall decrease in performance. For example, in case of this study, with incomplete and uncertain patient information, standard rules under formalisation cannot be followed. Patients cannot be treated immediately if there is uncertainty about their medical condition.
Another important fact is that this model relies very heavily on previously done researches. It assumes that all the previous research work still hold true although they were performed with different objectives and were based on different parameters. For example, the three characteristics of technology and structure on which the above hypotheses are made by Alexander (1982) is based on research done by Overton (1977) and Liefer, Huber and Duncan (1971). Hence, these researches are now 40 years old and may not be valid in today’s far more advanced technology and today’s dynamic and hybrid organisation structures.

[Evidences]
There are evidences in the research paper about four empirical studies that were conducted in different settings using different dependant variables and different operational definitions of fit. These empirical studies aimed at establishing the effect of the fit between technology and structure on the performance of an organization.
Empirical Study 1: Dewar and Webbel (1979) studied 52 departments of 13 organizations and found that fit between technology and centralization was an important predictor of internal conflict. Similarly, employee satisfaction could be predicted through the fit between technology, surveillance and enforcement actions.
Empirical Study 2: Schoonhoven (1981) studied 17 operating rooms and established negative prediction of severe morbidity through the fit between technological uncertainty and destandardization and technological uncertainty and decentralization.
Empirical Study 3: Argote (1982) studied 30 emergency rooms and found that fit between input uncertainty and means of coordination was a significant predictor of promptness of care, quality of nursing care and quality of medical care.
Empirical Study 4: Fry and Scolum (1984) studied 35 police units and discovered the significance of interaction between search behaviour and specialization was significant for workgroup performance but other interactions studied were not.

[Analysis]
This study was based on an examination of 27 nursing subunits in 3 hospitals through a questionnaire survey addressing the relevant technologies and structures on a five point scale. The methods employed in the process involved measure of technology and structure and measure of fit. Two 21-item instruments were used to measure technology and structure with a 5-point scale each – 1 through 5 for routine to non-routine for the former and 1 through 5 for mechanistic to organic for the latter. The measure of fit applied in the process made use of the general congruency model, which states that an organization’s performance is derived from the 4 elements - task, people, culture and structure. This involves an assumption that each value of a technology variable is best fit with of structure with regards to high performance. Fit is thus the absolute difference between the values for technology and structure. By analysing the variance of the subunit scores assigned to the 3 technology and structure scales, it was found that there was a considerable amount of difference in variance for the technology measures than the structure measures. However, the standard deviations of the variables tested suggested levels of variance high enough to proceed with the experiment. Fig. 1 - Iso-performance: Fit, misfit and performance
An analysis of the results of the questionnaire revealed that a routine technology demands a mechanistic structure for an efficient organization with high performance. The same applies to a non-routine technology demanding an organic structure. This helped in arriving at the inverse relationship between fit and performance. This is further explained through the graph in Fig.1 which represents the best fit line by mapping the 5-point values of technology and structure.
There is high relevance of the central argument of this study – to measure the performance of an organization through the fit between structure and technology –in various real time scenarios. For example, an organization with a very complex or non-routine technology, a more flexible structure is desired such that the management can easily address any unprecedented situation. On the other hand, for an organization where technology is routine, a formal mechanistic structure is feasible and more appropriate. A cross combination of a non-routine technology with a mechanistic structure may lead to undesirable outcomes by increasing response times.

[Conclusion]
Technology comprises of the processes and methods that produces outputs from inputs in an organisation. The author has recognised the fit between technology and structure but neither of them alone is sufficient to determine the quality of care in nursing subunits. Two major contributions to understanding this technology-structure fit were made by Joan Woodword and Charles Perrow. Woodword proposed three types of production technology- unit, mass and process. She identified distinct relationships among these technology classes and the subsequent structure of the firms. She also clearly indicated that the effectiveness of firms is strictly related to the fit between structure and technology. We conclude that the technology imperative is supported best by job level research, applies well only to small organisations and to the structural arrangement of the organisation that works at or very near to the operating core of it.
Taking an example of the beverage giant Coca Cola. Its core operation is manufacturing and distributing syrups to other beverage manufacturers and manufacturing finished beverages and distributing the same to bottlers and distributers for dispatching to the customers. A business case study conducted on Coca Cola found that the company’s bottling subunits are completely automated in and around its core operation of bottling beverages. There is hardly any manual labour that works near these bottling machines. The major part of its workforce forms the higher hierarchy of employees. Due to the technology that Coca Cola employs, its organisational structure is an Inverted Pyramid with most employees at the top hierarchy and very few employees at the bottom. Hence, as a result of the fit between routine technology and mechanistic structure of the Coca Cola company, it has been able to establish and maintain itself as the biggest brand in non-alcoholic beverages worldwide.
Hence, in smaller subunits, due to lesser formalisation and decentralisation, handling of emergency situations can be better planned. This is because due to lesser time available to handle such situations, decision making is faster and quicker. Also, due to lesser formalisation, process adherence can be delayed and people can make the extra effort required to solve the problems as well as work in a dynamic and innovative manner.
In our analysis of the research paper by Judith W. Alexander and W. Alan Randolph, we concur with the authors’ central argument that the fit between technology and structure is of utmost importance and really acts as a predictor of performance. We conclude that for optimisation of performance in smaller sub-units, all the above discussed points hold true.

[References]
Alexander. J. W. 1982, ‘The relationship of Technology and structure to quality of care on nursing subunits’. Unpublished doctoral dissertation, University of South Carolina, Columbia.
Perrow, C., 1967, A framework for comparative analysis of organization. American Sociologistical review, 32: 194-208.
Woodward, J. 1965. Industrial organization. London: Oxford University Press. http://en.wikipedia.org/wiki/ http://businesscasestudies.co.uk/coca-cola-great-britain/creating-an-effective-organisational-structure/structure-and-culture.html http://www.scribd.com/doc/20200601/Coca-Cola-Organizational-Theory Lex Donaldson, ‘The Contingency Theory Of Organizational Design: Challenges And Opportunitie’