The physical environment and organizational behavior

Research into organizational behaviour has indicated that there is an inevitable conflict between the needs of the individual and organizational demands. Psychologists have given insights into basic individual needs and contend that satisfaction of these needs constitutes a motivating force which enhances desired behavioural patterns. Behaviouralists have suggested that a basic and pervasive individual need is the culturally determined need for privacy. Anthropologists and environmental psychologists have shown that man's spatial behaviour is observable and predictable and that changes in the physical environment or the way it is perceived are accompanied by concommitant changes in behaviour. Research findings from each of the disciplines have been reviewed in an attempt to show that the physical environment is a significant factor in satisfying the needs of the individual organizational member, hence, a significant influence on organizational behaviour. A model has been generated to show the relationship between the physical setting and behaviour and to underscore the importance of making provisions within the physical setting for the attainment of a culturally determined optimal level of privacy. The physical setting, by providing for this need, becomes a significant factor in reducing the conflict between the individual and the organization and makes for acceptable role behaviour and the fulfilment of organizational goals.

Understanding value congruence : a sport organization case study

With the recent growth in cultural complexity, many organizations are faced with increasingly diverse employee pools. Gaining a greater understanding of the values that employees possess is the first step in effectively satisfying their needs and achieving a more productive workforce (lung & Avolio, 2000). Values playa significant role in influencing individual behaviours. It is therefore necessary to assess the qualities of employee value systems and directly link them to the values of the organization. The importance of values and value congruence has been emphasized by many organizational behaviour researchers (cf. Adkins & Caldwell, 2004; Erdogan, Kraimer, & Liden, 2004; Jung & Avolio, 2000; Rokeach, 1973); however the emphasis on value studies remains fairly stagnant within the sport industry (Amis, Slack, & Hinings, 2002). In order to examine the realities that were constructed by the participants in this study a holistic view of the impact of values within a specific sport organization were provided. The purpose of this case study was to examine organizational and employee values to understand the effects of values and value congruence on employee behaviours within the context of a large Canadian sport organization. A mUltiple methods case study approach was adopted in order to fully serve the purpose and provide a comprehensive view of the organization being examined. Document analysis, observations, surveys, as well as semi-structured interviews were conducted. The process allowed for triangulation and confirmability of the findings. Each method functioned to create an overarching understanding of the values and value congruence within this organization. The analysis of the findings was divided into qualitative and quantitative sections. The qualitative documents were analyzed twice, once manually by the researcher and once via AtIas.ti Version 4 (1998). The a priori and emergent coding that took place was based on triangulating the findings and uncovering common themes throughout the data. The Rokeach Value Survey (1973) that was incorporated into the survey design of the study was analyzed using descriptive statistics, as well as Mann-Whitney U, and Kruskal Wallis formulas. These were deemed appropriate for analysis given the non-parametric nature of the survey instrument (Kinnear & Gray, 2004). The quantitative survey served to help define the values and value congruence that was then holistically examined through the qualitative interviews, document analyses, and observations. The results of the study indicated incongruent value levels between employees and those stated or perceived as the organization's values. Each finding demonstrated that varying levels of congruence may have diverse affects on individual behaviours. These behaviours range from production levels to interactions with fellow employees to turnover. In addition to the findings pertaining to the research questions, a number of other key issues were uncovered regarding departmentalization, communication, and board relations. Each has contributed to a greater understanding of the organization and has created direction for further research within this area of study.

Automatic Inference of Graph Models for Complex Networks with Genetic Programming

Complex networks can arise naturally and spontaneously from all things that act as a part of a larger system. From the patterns of socialization between people to the way biological systems organize themselves, complex networks are ubiquitous, but are currently poorly understood. A number of algorithms, designed by humans, have been proposed to describe the organizational behaviour of real-world networks. Consequently, breakthroughs in genetics, medicine, epidemiology, neuroscience, telecommunications and the social sciences have recently resulted. The algorithms, called graph models, represent significant human effort. Deriving accurate graph models is non-trivial, time-intensive, challenging and may only yield useful results for very specific phenomena. An automated approach can greatly reduce the human effort required and if effective, provide a valuable tool for understanding the large decentralized systems of interrelated things around us. To the best of the author's knowledge this thesis proposes the first method for the automatic inference of graph models for complex networks with varied properties, with and without community structure. Furthermore, to the best of the author's knowledge it is the first application of genetic programming for the automatic inference of graph models. The system and methodology was tested against benchmark data, and was shown to be capable of reproducing close approximations to well-known algorithms designed by humans. Furthermore, when used to infer a model for real biological data the resulting model was more representative than models currently used in the literature.