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IMAGES OF ORGANIZATION PDF

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PDF | This research paper focuses on the analysis of the “Morgan's Images of Organizations”, Morgan's eight metaphors of the “Images of Organizations were. Gareth Morgan, Images of Organization. by. Thousand Oaks, CA: Sage Publications Inc., xv +. pp. ISBN 1 9. In this revised management . Since its first publication over twenty years ago, Images of Organization has become a classic in the canon of management literature. The book is based on a .


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Introduction. In his best-selling book, Images of Organization, Gareth. Morgan ( ) set out what has subsequently become known as “the eight metaphors”. Professor Morgan, I only recently received a copy of Images of Organization ( updated edition) as a desk copy to review for the I/O Psych class that I teach. Pre Order Free PDF Images of Organization Books Online Original book Click to download myavr.info Since its first publication over.

Metaphors we lead by: Understanding leadership in the real world.

Burrell, G. Sociological Paradigms and Organisational Analysis.

41: Images of Organization – Gareth Morgan

Huq, J. Reay, and S. Lakoff, G.

Metaphors we live by. University of Chicago press.

Tsoukas, H. Turco, C. Columbia University Press.

Weick, K. Whyte, W. The potential of clustering algorithms is to reveal the underlying structures in data and it can be exploited in a wide variety of applications, including classification, image processing and pattern recognition, modeling and identification. In particular, data mining techniques can be used to identify categories or behavioral patterns in organizations.

Images of Organization (eBook, PDF)

Many clustering algorithms have been introduced in the literature Pedrycz, A widespread accepted classification scheme subdivides these techniques into two main groups: hard crisp or soft fuzzy clustering. In hard clustering, data is divided into distinct clusters, where each data element belongs to exactly one cluster, however in fuzzy clustering, data elements can belong to more than one cluster, and associated with each element is a set of membership levels that indicate the strength of the association between that data element and a particular cluster.

Due to the fuzzy nature of many practical problems, a number of fuzzy clustering methods have been developed following the general fuzzy set theory strategies outlined by Zadeh, Fuzzy set theory deals with the representation of classes whose boundaries are not well defined. The key idea is to associate a membership function that takes values in the interval [0,1], with 0 corresponding to non membership in the class and 1 corresponding to full membership.

Thus, membership is a notion intrinsically gradual instead of abrupt as in conventional Boolean logic. The concept of fuzzy partition is essential for cluster analysis and identification techniques that are based on fuzzy clustering. The most known method of fuzzy clustering is the Fuzzy c-Means method FCM , initially proposed by Dunn and generalized by Bezdek and other authors; in Kruse, Hoppner, Klawonn and Runkler an overview is presented.

Finally, the parameter m is a real number greater than 1 that is a weighting factor called fuzzifier. One of the drawbacks of FCM is the requirement for the number of clusters, c, to be specified before the algorithm is applied. Fuzzy partitioning is carried out through an iterative minimization of the objective function under the following fuzzy constraints: In the approach proposed by Bezdek in each iteration membership levels uij and centroid positions cj are updated applying the technique of Lagrange multipliers.

The algorithm stops when a maximum number of iterations is reached, or when the algorithm is unable to reduce the current value of the objective function.

Given the fact that different organizational images can often be linked to an organization, in this work a soft clustering approach is considered more appropriate. Using the FCM technique, each organization is allowed to belong to many clusters with different degrees of membership and therefore they have multiple images or metaphors linked.

In the paper the results of the analysis are presented.

Any data mining process is composed of the following basic phases or stages: data compilation; data processing in which it is cleaned, transformed and reduced ; application of data mining determining the model touse, carrying out statistical analysis, and graphically visualizing data to obtain a first approximation ; and finally, interpretation and evaluation of results obtained. Following the previous stages, in the next sections we will show the practical application of data mining techniques to identify behavioral features in a sample of Brazilian companies.

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This instrument is a questionnaire with 35 questions on organizational aspects that are grouped into 7 blocks; each block is associated with one of the images considered. In order to identify characteristics of the images in an organization, a set of employees can make a quantitative assessment on each of the 35 questions of the questionnaire.

The evaluation uses a discrete scale with values between 1 and 4, according to the following criteria: 4 if there is a strong presence, 3 if there is a reasonable presence, 2 if there is little impact and 1 if there is virtually no presence. The Appendix shows the 35 questions selected and Table 1 shows the relationship of each question with one of the 7 images defined by Morgan. With the answers to 35 questions, 7 numerical values can be generated with the sum of the scores for each of the 5 questions related to each of the 7 images.

These 7 values can be taken into account in determining the most relevant image in the company, according to the opinions of the employee interviewed.

'Morgan's four images of organisation applied to the James Hardie case study'

An example of the tabulation of answers to the questionnaire is shown in Table 2. The sums of the scores associated with each of the images are shown in the last row, for example, in the case presented in Table 2 , the most visible organizational images are those of the "political system SP ", but images M, C and ID also obtain high scores. To analyze the organizational images with greater presence in the state of Rio Grande do Sul Brazil , a sample of 61 companies from various sectors and sizes was selected.

In each company a group of up to 4 employees were interviewed, resulting in a total of responses to the questionnaire mean of 3.

All data were pre-processed for analysis with data mining techniques. This software implements a great variety of clustering algorithms; the Fuzzy C-Means FCM algorithm, implemented in package 'e', was selected. In our case 7 variables were considered with the average values corresponding to the sum of the scores of the 5 questions from each of the 7 blocks given by each employee in the company. The data matrix has 61 rows companies.

In our case we decided to give 7 initial cluster centers. The center of cluster i was initially defined as: Note that 5 is the minimum value and 20 the maximum in a block of 5 questions. The algorithm needed a total number of iterations to converge, and the final error was 3, After the execution of the iterations, the cluster centers were updated as shown in Table 3.

Bold identifies the highest values in each centroid, that is, the images obtained higher scores in each group. Table 4 shows a ranking of the images with the greatest presence in each group.

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As can be seen, image O is clearly the most relevant in most groups. Another image with a high presence in the groups is FT. In relation to the images of smaller presence in the sample, they are those corresponding to PP and SP. As a result of the algorithm we obtained a matrix with the degrees of membership of each company for each of the 7 groups identified.If you continue browsing the site, you agree to the use of cookies on this website.

They have to embrace the idea that in rapidly changing circumstances with high degrees of uncertainty, problems and error are inevitable. To overcome this limitation this study presents an approach using Data Mining and Soft Clustering techniques to understand what can happen in an organizational culture environment through images in a large number of companies. Key Points In hard clustering, data is divided into distinct clusters, where each data element belongs to exactly one cluster, however in fuzzy clustering, data elements can belong to more than one cluster, and associated with each element is a set of membership levels that indicate the strength of the association between that data element and a particular cluster.

This instrument is a questionnaire with 35 questions on organizational aspects that are grouped into 7 blocks; each block is associated with one of the images considered. Lakoff, G. Jennifer Norcutt.

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