Organisational climate and its influence upon performance: A study of Australian hotels in South East Queensland
Michael Cameron Gordon Davidson B.A., M.Ed.Admin., Cert. Ed., Diploma Hotel and Catering Management
Faculty of Commerce and Management School of Marketing and Management Griffith University
Submitted in fulfillment of the requirements of the degree of Doctor of Philosophy
August 2000 Abstract
This study gathered data from 14 four to five-star hotels in South-East
Queensland, Australia, in an attempt to examine the nature and degree of influence organisational climate has upon the performance of hotels. Employee perception of customer satisfaction was studied both as an index of performance and as an intervening variable between organisational climate and financial performance as indexed by revenue per available room (REVPAR). The data provided a description of a young, relatively gender balanced, well educated and trained work force which received relatively low levels of financial remuneration and displayed very high levels of turnover.
A new instrument was used to measure the dimensions of organisational climate across the hotels. This instrument represented a modification of that presented by Ryder and Southey (1990), which itself was a modification of the 145 item psychological climate questionnaire of Jones and James (1979). The instrument represented a subset of
70 items of the Ryder and Southey instrument. Responses to all items within the instrument were on a 7 point anchored scale. Principal components analysis (PCA) produced results consistent with earlier versions of the instrument, which had been reported elsewhere. This analysis described organisational climate within the sample to be composed of 7 underlying dimensions; Leader facilitation and support, Professional and organisational esprit, Conflict and ambiguity, Regulations, organisation and pressure, Job variety, challenge and autonomy, Workgroup co-operation, friendliness and warmth, and Job standards. These dimensions were judged to be consistent with those reported earlier by Jones and James, and by Ryder and Southey. Poor support was found for the first structural model that proposed that employee demographic variables would affect organisational climate and that organisational climate would affect customer satisfaction (although the latter link was quite strong). The most important
I finding of the study was the support for a second structural model when it was found that variation in the 7 dimensions of organisational climate accounted for 30% of the variation in Employee Perception of Customer Satisfaction. Furthermore, that Employee
Perception of Customer Satisfaction accounted for 23% of the variation in REVPAR between the hotels. Possible extensions of this study using direct measures of customer satisfaction and expanding it to include hotels of different star ratings are discussed.
II Table of Contents
ABSTRACT...... I
TABLE OF CONTENTS...... III
LIST OF TABLES ...... VI
LIST OF FIGURES ...... VIII
STATEMENT OF ACKNOWLEDGEMENT...... IX
1.0 INTRODUCTION ...... 1
1.1 BACKGROUND TO THE RESEARCH...... 1 1.2 RESEARCH PROBLEM ...... 3 1.3 JUSTIFICATION OF THE RESEARCH...... 4 1.4 METHOD ...... 8 1.5 ORGANISATIONAL AND OPERATIONAL STRUCTURE OF A HOTEL ...... 10 1.6 DEFINITIONS ...... 13 1.7 OUTLINE OF THE THESIS...... 14 2.0 LITERATURE REVIEW OF ORGANISATIONAL CLIMATE...... 17
2.1 AN INTRODUCTION TO ORGANISATIONAL CLIMATE LITERATURE ...... 17 2.2 EARLY FORMULATIONS OF THE CLIMATE CONSTRUCT ...... 19 2.4 THE DISTINCTION BETWEEN CULTURE AND CLIMATE...... 23 2.5 DEVELOPMENT OF CLIMATE INSTRUMENTS ...... 25 2.6 DIMENSIONS OF ORGANISATIONAL CLIMATE ...... 27 2.7 A CRITIQUE OF CLIMATE THEORY ...... 32 2.8 MEASUREMENT ISSUES OF THE MULTILEVEL CLIMATE CONSTRUCT...... 33 2.9 ORGANISATIONAL CLIMATE AS A VARIABLE IN THEORY AND RESEARCH ...... 39 2.10 ORGANISATIONAL CLIMATE AND MODELS OF ORGANISATIONAL FUNCTIONING...... 43 2.11 CLIMATE, SERVICE QUALITY AND ORGANISATIONAL PERFORMANCE...... 48 2.12 UTILISATION OF THE CLIMATE CONSTRUCT WITHIN A SERVICE QUALITY PERSPECTIVE...... 51 2.13 CUSTOMER AND EMPLOYEE PERCEPTIONS OF CUSTOMER SATISFACTION ...... 60 2.14 CLIMATE AND INNOVATION...... 61 2.15 ORGANISATIONAL CLIMATE AND IMPLICATIONS FOR THE HOTEL INDUSTRY...... 62 3.0 THEORETICAL MODELS AND HYPOTHESES...... 68
3.1 THE RESEARCH QUESTION...... 68 3.2 THE DIMENSIONS OF ORGANISATIONAL CLIMATE WITHIN THE HOTELS ...... 68 3.3 THE RELATIONSHIPS BETWEEN EMPLOYEE DEMOGRAPHIC VARIABLES, ORGANISATIONAL CLIMATE, AND EMPLOYEE PERCEPTIONS OF CUSTOMER SATISFACTION...... 72 3.4 THE RELATIONSHIPS BETWEEN THE DIMENSIONS OF ORGANISATIONAL CLIMATE, EMPLOYEE PERCEPTIONS OF CUSTOMER SATISFACTION, AND PERFORMANCE OF HOTELS...... 74 4.0 METHOD...... 76
4.1 INTRODUCTION ...... 76 4.2 JUSTIFICATION FOR THE PARADIGM AND METHOD ...... 77 4.3 IDENTIFICATION AND RATIONALE FOR THE SAMPLE...... 79 4.4 GAINING CO-OPERATION OF THE HOTELS...... 82 4.5 FORMULATION OF THE SURVEY INSTRUMENTS ...... 86 4.5.1 The organisational climate questionnaire...... 86 4.5.2 Hotel profile and hotel manager's questionnaire ...... 93 4.6 PERCEPTIONS OF CUSTOMER SATISFACTION MEASURE...... 95 4.7 ORGANISATIONAL PERFORMANCE–REVENUE PER AVAILABLE ROOM (REVPAR)...... 96 4.7.1 Occupancy percentage ...... 97 4.7.2 AVERAGE DAILY ROOM RATE ...... 97 4.7.3 Revenue per Available Room (REVPAR) ...... 99 4.8 PILOT AND PRE-TESTING PROCEDURE ...... 99
III 4.9 ADMINISTRATION OF CLIMATE SURVEY ...... 102 4.10 DATA COLLECTION AND SORTING PROCEDURES ...... 104 5.0 HOTEL GENERAL OPERATING STATISTICS AND STAFF DEMOGRAPHIC DATA ...... 106
5.1 INTRODUCTION ...... 106 5.2 ANALYTICAL PROCEDURES...... 106 5.3 HOTEL LEVEL DATA...... 108 5.4 STAFF LEVEL DATA...... 118 5.5 SUMMARY AND DISCUSSION...... 142 6.0 THE DIMENSIONS OF ORGANISATIONAL CLIMATE IN 14 AUSTRALIAN HOTELS ...... 146
6.1 INTRODUCTION ...... 146 6.2 RELIABILITY ANALYSIS...... 146 6.2.1 Approaches to the estimation of reliability of a test instrument...... 146 6.2.2 Reliability analysis of responses to the 70 item version of the psychological climate questionnaire...... 147 6.3 STATISTICAL TECHNIQUES TO IDENTIFY UNDERLYING DIMENSIONS IN A DATA MATRIX OF PARTICIPANT RESPONSES ...... 153 6.3.1 Factor analysis...... 153 6.3.2 Principal components analysis...... 153 6.4 PRINCIPAL COMPONENTS ANALYSES OF ORGANISATIONAL CLIMATE DATA ...... 154 6.5 VARIABLES ENTERED INTO THE PCA...... 157 6.6 PROPORTION OF VARIANCE EXPLAINED BY PRINCIPAL COMPONENTS...... 157 6.7 ROTATED PRINCIPAL COMPONENT LOADINGS ...... 158 6.8 INTERPRETATION OF MEANING OF THE PRINCIPAL COMPONENTS ...... 163 6.9 VARIATION IN CLIMATE DIMENSIONS BETWEEN HOTELS...... 166 6.9.1 Generating climate dimension sores ...... 166 6.9.2 Comparison of Climate Dimensions between the 14 Hotels in the Study...... 168 6.10 SUMMARY AND DISCUSSION...... 170 7.0 ANALYSES OF THE RELATIONSHIPS BETWEEN; EMPLOYEE DEMOGRAPHIC VARIABLES, ORGANISATIONAL CLIMATE, CUSTOMER SATISFACTION, AND REVPAR...... 177
7.1 OVERVIEW...... 177 7.2 STATISTICAL ANALYSES AND MODELLING TECHNIQUES USED IN THIS CHAPTER ...... 178 7.2.1 Multiple linear regression ...... 178 7.2.2 Structural equation modelling...... 179 7.3 STRUCTURAL MODEL A: RELATIONSHIP BETWEEN DEMOGRAPHIC VARIABLES, ORGANISATIONAL CLIMATE AND CUSTOMER SATISFACTION...... 181 7.3.1. Multiple linear regression analysis examining the relationship between employee demographic variables and organisational climate proposed by structural model A...... 182 7.3.2 An examination of the relationship between organisational climate and employee perception of customer satisfaction as proposed by Structural Model A...... 184 7.3.3 Structural equation modeling...... 186 7.3.4 Summary of analysis of Structural Model A...... 188 7.4 STRUCTURAL MODEL B: THE RELATIONSHIP BETWEEN ORGANISATIONAL CLIMATE, CUSTOMER SATISFACTION, AND REVPAR...... 188 7.4.1 Multiple linear regression analysis examining the relationship between organisational climate dimensions and employee perception of customer satisfaction proposed by Structural Model B...... 190 7.4.2 An examination of the relationship between REVPAR and employee perception of customer satisfaction as proposed by Structural Model B...... 192 7.4.3 Structural equation modeling...... 193 7.4.4 Summary of analysis of Structural Model B...... 194 7.5 SUMMARY AND DISCUSSION ...... 194 8.0 GENERAL DISCUSSION AND CONCLUSIONS...... 198
8.1 OVERVIEW OF STUDY...... 198 8.2 HOTEL OPERATING STATISTICS ...... 198 8.3 STAFF DEMOGRAPHIC DATA...... 199 8.4 VARIATION IN STAFF DEMOGRAPHIC VARIABLES BETWEEN HOTELS ...... 201 IV 8.5 THE MEASUREMENT OF ORGANISATIONAL CLIMATE WITHIN THE HOTELS OF THE SAMPLE...... 202 8.6 TESTING STRUCTURAL MODEL A ...... 205 8.7 TESTING STRUCTURAL MODEL B ...... 207 8.8 IMPLICATIONS OF THE RESULT THAT STRUCTURAL EQUATION MODEL B IS SUPPORTED ...... 208 8.9 THE VALIDITY OF MEASURES USED IN THIS STUDY...... 210 8.9.1 The index of financial performance REVPAR ...... 210 8.9.2 Organisational climate...... 210 8.9.3 Customer satisfaction...... 213 8.10 THE ISSUE OF MULTILEVEL VARIABLES AND THE INTERPRETATION OF RELATIONSHIPS ...... 214 8.11 GENERALISING RESULTS...... 217 8.12 FUTURE RESEARCH ...... 218 8.13 SUMMARY AND CONCLUSION...... 218 TABLE OF APPENDICES ...... 220
APPENDIX A ...... 221
ORGANISATIONAL CLIMATE QUESTIONNAIRE, EMPLOYEE DEMOGRAPHICS, AND EMPLOYEE PERCEPTION OF OPERATIONS AND CUSTOMER SATISFACTION . 221
APPENDIX B ...... 231
HOTEL PROFILE INSTRUMENT...... 231
APPENDIX C ...... 233
HOTEL MANAGERS' DEMOGRAPHICS, OPERATION PERFORMANCE AND PERCEPTION OF CUSTOMER SATISFACTION...... 234
APPENDIX D ...... 240
STAFF DEMOGRAPHIC DATA AND CONTINGENCY TABLE ANALYSES ...... 240
APPENDIX E ...... 253
RELIABILITY ANALYSIS AND ...... 253
PRINCIPAL COMPONENTS ANALYSIS OF EMPLOYEE ORGANISATIONAL CLIMATE DATA...... 253
APPENDIX F...... 282
MODEL TESTING...... 282
PART 1 STRUCTURAL EQUATION MODEL A...... 283
PART 2 STRUCTURAL EQUATION MODEL B ...... 291
REFERENCES...... 300
V List of Tables
Table 2.1 Contrasting: Organisational culture and organisational climate ...... 24 Table 2.2 Comparison of climate dimensions across studies ...... 30 Table 2.3 Climate for service (in banks) ...... 55 Table 2.4 Comparison of HRM climate dimensions ...... 57 Table 4.1 70 items of the modified version of the PCQ used in this study and 35 the ‘a priori’ scales used by Jones and James (1979)...... 89 Table 5.3.1 Hotel operational statistics ...... 109 Table 5.3.2 Rooms business mix...... 112 Table 5.3.3 Hotel revenue mix percentages and key employment percentages ...... 114 Table 5.3.4 Comments by hotels on what affected trading conditions...... 117 Table 5.4.1 Response rate for the survey of the 14 hotels...... 119 Table 5.4.2 Gender – employees and managers ...... 120 Table 5.4.3 Gender of employees for each of the 14 hotels ...... 122 Table 5.4.4 Age profile of employees and managers ...... 124 Table 5.4.5 Age profile of employees for each of the 14 hotels...... 125 Table 5.4.6 Educational level of employees and managers...... 126 Table 5.4.7 Educational level of employees for each of the 14 hotels ...... 127 Table 5.4.8 Organisational tenure for employees and managers...... 128 Table 5.4.9 Organisational tenure for employees for each of the 14 hotels ...... 130 Table 5.4.10 Job tenure for employees and managers...... 131 Table 5.4.11 Job tenure for employees for each of the 14 hotels...... 132 Table 5.4.12 Gross salary for employees and managers ...... 133 Table 5.4.13 Gross salary for employees of the 14 hotels...... 134 Table 5.4.14 Mode of employment for employees...... 135 Table 5.4.15 Mode of employment for employees for the 14 hotels...... 136 Table 5.4.16 Hours worked by employees ...... 137 Table 5.4.17 Hours worked by employees for the 14 hotels ...... 138 Table 5.4.18 Time since last training session for employees and managers ...... 139 Table 5.4.19 Time since last training session for employees for the 14 hotels...... 141 Table 5.4.20 Employees and managers were asked do you need training?...... 142 Table 6.1 Statistics for each of the 70 items of the modified version of the Psychological Climate Questionnaire entered into the reliability analysis...... 149
VI Table 6.2 Percentage of variance explained by Principal Components with Eigenvalues greater than 1...... 158 Table 6.3 Primary Rotated Component loadings for items of the modified version of the PCQ. Also included for comparison purposes are the factors upon which those items loaded in the earlier studies of Jones and James (1979) and Ryder and Southey (1990)...... 160 Table 6.4 Relationship between principal components (Factors) found in this study, and those found by Jones and James (1979) and Ryder and Southey (1990). The proportion of items falling on the corresponding factor in each of the earlier studies is also indicated...... 164 Table 6.5 Mean scores on climate dimensions and for the composite measure of organisational climate across the 14 hotels in the study...... 168 Table 6.6 Summary of results of 7 oneway ANOVAs. Each ANOVA compared the 14 means of each of the hotels on one of the 7 dimensions of organisational climate...... 169 Table 7.1 Regression coefficients and associated probabilities for multiple linear regression using demographic variables to predict composite measure of Organisational Climate...... 184 Table 7.2 Pearson r correlation coefficients examining the relationship between the Composite Measure of Organisational Climate and Employee Perceptions of Customer Satisfaction for each of the Hotels participating in the study...... 185 Table 7.3 Mean Composite Measure of Organisational Climate, Mean Employee Perception of Customer Satisfaction and REVPAR for each of the 14 Hotels...... 186 Table 7.4 Goodness of fit and parsimony of fit indices for structural equation analysis...... 187 Table 7.5 Correlations between Employee Perception of Customer Satisfaction and each of the 7 dimensions of Organisational Climate...... 190 Table 7.6 Regression coefficients and associated probabilities for Multiple Linear Regression using Organisational Climate Dimensions to predict Employee Perception of Customer Satisfaction...... 191 Table 7.7 Goodness of fit and parsimony of fit indices for structural equation analysis...... 193
VII List of Figures
Figure 1.1 General Hotel Organisational Chart ...... 11 Figure 2.1 Climate Formation (from Ashforth, 1985) ...... 41 Figure 2.2 Moran and Volkwein (1992) depiction of culture and climate ...... 42 Figure 2.3 Jones and James 1976 Model Of Organisational Functioning ...... 45 Figure 2.4 A Model of Climate, Culture and Productivity (Adapted from Kopelman, Brief And Guzzo 1990) ...... 47 Figure 2.5 General Factor of Psychological Climate (Reproduced from James and James (1989)) ...... 58 Figure 3.1 Organisational Climate Model A: The dimensions of organisational climate from the study of Jones and James (1979)...... 70 Figure 3.2 Organisational Climate Model B: The dimensions of organisational climate from the study of Ryder and Southey (1990)...... 71 Figure 3.3 Structural Model A...... 73 Figure 3.4 Structural Model B ...... 75 Figure 6.1 Organisational Climate Model A: The dimensions of Organisational Climate from the study of Jones and James (1979)...... 155 Figure 6.2 Organisational Climate Model B: The dimensions of Organisational Climate from the study of Ryder and Southey (1990)...... 156 Figure 6.3 Organisational Climate Model C: The dimensions of Organisational Climate of the 14 Hotels participating in the current study...... 174 Figure 7.1 Structural Model A...... 182 Figure 7.2 Structural Model B ...... 189 Figure 8.1 Structural Model A...... 205 Figure 8.2 Structural Model B...... 207
VIII Statement of Acknowledgement
In the preparation of this thesis I would like to acknowledge the assistance and support of a number of people: Firstly, my supervisors, Dr Nils Timo for his constant encouragement and support, Professor Peter Brosnan who has given detailed feedback and advice, Professor Paul Ryder who helped set up the study and survey instrument, and Dr David Kennedy for his advice.
Apart from my supervisors the author is also indebted to Dr Mark Manning for his detailed advice and guidance on the statistical procedures and modelling used.
I would also like to thank Debbie Amsler for the work in data input, formatting and presentation, Lorraine Hauser in assisting in the data collection phases and my colleagues in the School of Tourism and Hotel Management for their support.
Finally, I must thank my wife, Rosalind, and my family for their encouragement and support over all the stages of the study.
M.C.G. Davidson
August 2000
IX This work has not previously been submitted for a degree or diploma in any university.
To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made in the thesis itself.
M.C.G. Davidson
August 2000
X 1.0 Introduction
1.1 Background to the research
The hospitality industry is rich in many traditions that the word hospitality itself conjures up. The Macquarie Dictionary defines hospitality as:
the reception and entertainment of guests or strangers with liberality and
kindness.
The history of the hospitality industry can be traced back to the beginning of the major civilisations and the need for people to travel, trade and communicate. Religion played an early part as religious orders saw it as one of their responsibilities to provide rest, food and shelter for travellers. Hospitality gradually moved from the responsibility to host travellers for altruistic reasons to a commercial basis with ‘Inns’ being set up at major cross-roads and places of commerce and governance. The development process of the travellers’ inns took place in Europe, Asia Minor and Asia, and has continued over the centuries. The advent of mass affordable travel in the last half of the 20th century has meant that tourism and hospitality has become the major industry of the world (WTO,
1996).
Despite this huge growth it remains one of the least researched of the major industries in the world today. This lack of basic research has been recognised not only by the World Tourism Organisation (WTO) but also many individual governments worldwide. In Australia the federal government has in 1997 provided funds for a Co- operative Research Centre in Sustainable Tourism that is headquartered at Griffith
University’s Gold Coast campus. It should be noted that the word tourism encompasses
1 the hospitality industry that is seen as a major sector within the tourism industry as a whole.
Integral to the concept of hospitality is the notion of service. For service in a hospitality setting we must have a service delivery process, and that is provided by employed staff. A predominant factor in the hospitality industry’s economic importance is the number of people employed to provide the service. Current estimates by the
Bureau of Tourism Research (BTR) and the World Travel and Tourism Council
(WTTC), reported by the Tourism Forecasting Council (TFC, June 1997) put tourism’s worth at 7 to 11.5 % of Australian jobs, and between 6 and 10.5 % of gross domestic product. These estimates vary because of the methodology and approach taken as to what precisely constitutes tourism and its attendant processes and segments such as hospitality.
Hospitality as a service industry is provided within various physical structures
(hotels, motels, resorts, clubs, restaurants, etc.), and has a plethora of management structures and ownership arrangements ranging from independent owner operators to chain operator’s, e.g., Hilton. The hospitality industry, whilst not seen as a great user of technology, nonetheless, is reliant upon fairly sophisticated computer equipment for reservations, accounting and monitoring of energy consumption. Operational management systems, marketing and finance vary in their sophistication depending upon, principally, the size of the company. Most importantly it is the staff and customers which have the biggest impact upon how the process of hospitality service is carried out. The use of tacit skills, those that interpret the contextual framework and acknowledge the shared perception of customer and staff member, are crucial to the enhancement of the service experience (Lammont and Lucas, 1999). Of crucial importance to the success of an enterprise is the employee perceptions of their
2 organisation as expressed through the concept of ‘organisational climate’, and the employees’ relationship with customers (Schneider, 1994; Francese, 1993; James &
James, 1989; Jones and James, 1976; Kopelman, Brief & Guzzo, 1990; Sinclair, 1996;
Price & Chen, 1993; Shea, 1996, and others).
Organisational climate, as represented by the aggregation of the perceptions of individual employees within the organisation, has been the focus of considerable empirical research that can be traced back to the work of Lewin, Lippitt and White
(1939). The large body of climate research - much of this has been included in the literature review in chapter 2 - has been subjected to very considerable theoretical debate. This debate concentrates on the methodological issue of how the construct of climate can be translated into an indicator of organisational effectiveness. Schneider and
Bowen (1985) and Cole, Bacayan and White (1993) have provided evidence that a good organisational climate does have a positive effect upon service outcomes and hence improves organisational success.
1.2 Research problem
Many organisational climate studies have been conducted across a range of industries. Yet no specific academic study of organisational climate in the hospitality industry has been undertaken to ascertain what effect this construct has on performance.
Therefore, the research question to be addressed by the current research is:
What is the nature and degree of influence that organisational climate has upon the performance level of organisations within the Australian hotel industry?
3 1.3 Justification of the research
The largest single item of operating expenditure for international four or five star hotels is the cost of labour. By definition, such hotels (four or five stars) rely upon their reputation for service and customer satisfaction to be profitable. The major resource component in service delivery is the hotel employee, the deliverer of the service. It is, therefore, crucial for operational managers to seek an understanding of the hotel employees’ perceptions of their jobs and satisfaction derived. The emphasis upon employee motivation and satisfaction is within the broad management theoretical framework of human relations theory. These theories were first espoused by such theorists as Mayo and colleagues from the Chicago School in the United States and Trist from the Tavistock Institute in the United Kingdom, who concentrated on the human aspects of the work and production process (Mullins, 1996).
Information gathered through research on an organisation's employees can be used as a basis for assessing how operational and strategic goals are to be achieved. This information should also be utilised in the design of appropriate procedures and systems that are needed to ensure an individual organisation, such as a hotel, is able to deliver the service excellence expected by its customers.
Organisational climate surveys are an excellent tool to supply information about employee perceptions and have been successfully used in a range of organisational settings (many of which are discussed at some length in chapter 2). Although the hotel industry represents the largest employment sector in the world's largest industry - tourism (Olsen, 1996) - when organisational climate research is examined within this industry, only one major international hotel chain regularly uses this research methodology and the information gained as a management tool for improving its
4 management and operational systems. It is noteworthy that this company, Marriott
Hotels, was the only hotel company to be named in the Fortune Magazine’s top 100
American companies (Branch, 1999). Many individual enterprises within the tourism industry carry out individual employee surveys but they are seen as one off - identify a problem, fix it and carry on! Many of the companies have achieved success and international reputations but what might have been, if they were more cognisant of their employee perceptions?
The scope and importance of the tourism industry in terms of physical structures such as hotels in Australia, particularly in Queensland, has seen dramatic growth over the last 15 years. In the survey area of Southeast Queensland there were 5 international four and five star hotels in the early to mid-1980’s, whereas today there are in excess of
40 (Queensland Tourist and Travel Corporation, 1997). This level of dramatic growth has been seen in many other tourism destinations ‘that have been discovered’ e.g., the growth of the Spanish tourism industry in the 1950’s and 1960’s. Butler (1980) has described this growth phenomenon in his ‘resort destination life cycle’ that analyses the development, consolidation, maturity and decline or re-invention of any tourism destination.
Australia has had the additional impediment in its growth cycle of being geographically isolated from even its nearest neighbour (Papua New Guinea) with the majority of the Australian east-coast tourism destinations being at least 7 hours flying time. Much of Australia’s initial tourism growth in the early 1980’s was based upon the
Asian economic expansion and in particular the Japanese ‘economic miracle’ where an increasingly affluent middle class saw Australia as an ideal alternative to the U.S. and
Europe. The flying time was less than many U.S. and European destinations and with a
5 unique flora and fauna, wonderful natural attractions, and suitable destinations with appropriate accommodation, Australia became an international tourist destination.
The expansion of the Australian hotel industry saw a substantial increase in the supply of hotel accommodation accounting for over $1 billion in capital investment during 1997 - 1998 (Industry Commission, 1996). By the end of 1998, Australian hotels provided 191,147 beds with an annual turnover of in excess of A$2 billion (Australian
Bureau of Statistics, 1998).
Whilst international visitors proved to be the catalyst for major hotel growth, it must be remembered that most of the destinations were already in existence serving the domestic tourism market providing a sound base for the growth. The inflow of Japanese visitors and the huge strength of the Japanese economy, plus in Australia and certainly in Queensland a very pro development stance taken by the government, was the catalyst for large amounts of Japanese investment in hotels and tourism ventures. The actual number of Japanese visitors to Australia grew from 100,000 in 1985 to 800,000 in 1995
(Tourism Forecasting Council - TFC, 1997).
Japan remains a major source of international visitors despite the recent Asian economic crisis, and this is especially so for Queensland. The QTTC (1997) reported that in the year ended August 1997, Queensland received 453,500 Japanese visitors,
31% of Australian international visitors. All other Asian countries accounted for another
28%.
The geographical area that has been surveyed for this study includes the Gold
Coast, Brisbane, the Sunshine Coast and Wide Bay. This area accounted for 6,017,000
(77.7%) of the total Queensland visitors and 25,239,000 (62.3%) of total Queensland visitor nights in 1997 (QTTC, June 1998) 6 This level of significance of the hotel industry was not achieved by capital investment alone. To run the many new establishments that were planned and built during the 1980’s and early 1990’s required an enormous expansion in the training and education systems to provide people with the skills to operate these new properties. The
Technical and Further Education (TAFE) system was expanded and, the then, Colleges of Advanced Education offered higher education courses in the area of tourism and hospitality. The skill shortage also meant that a considerable number of people were recruited from overseas. This included many tourism and hospitality educators who were required to staff the expanded training and education system (Davidson, 1991).
The overview presented above of Australian and Queensland tourism, very briefly describes some of the activity that accompanied the expansion of the tourism and hospitality industry in the 1980’s. The numbers of visitors reinforces the economic importance that the industry has now assumed. In Butler’s life cycle model, a major tourism area such as the Gold Coast can be said to have reached its maturity stage. If stagnation and decline are not to be experienced, new markets, attractions, and levels of professionalism will be needed for the re-invention process. It is in the area of professionalism and operational performance that the study of employee attitudes and perceptions can play a major role. Organisational climate is a management tool that, if used appropriately, can identify how performance can be enhanced, and this is the crucial issue for the Australian and Queensland hotel industry into the 21st century.
South East Queensland was selected for this study because it represented a complete cross section of locations and styles of hotels, e.g., city-centre, golf/sport/leisure resort, seaside resort, boutique resort, eco-resort and casino complexes. The area is a major tourism destination for both domestic and international
7 visitors and is second only to Sydney in actual international visitor nights (QTTC, June
1998).
1.4 Method
Chapters 2 and 3 will provide a full explanation of the literature, research method and statistical techniques used. The aim here is to provide only a brief overview of the main research methods and research strategies.
The research has been carried out in the southern coastal fringe of Queensland bordered by Wide Bay in the north and the Gold Coast in the south. All of the hotels studied are of an international four to five star standard and cover a cross-section of operations that includes business, resort, leisure, group and conference market. The data was collected by a combination of visits, personal interviews, telephone interviews, 3 composite survey questionnaires and reference to secondary data sources for confirmation of certain factual and performance indicators. Data collection took place in the period from August 1997 to February 1998.
The data collection for the main research thrust of examining organisational climate necessitates a fairly complex survey instrument that is capable of capturing employee perceptions. Many theorists have worked in this area and a number of underlying dimensions had been proposed. Among the dimensions proposed for organisational climate are leadership facilitation and support; job variety, challenge and autonomy; conflict and pressure; organisational planning to achieve workgroup effectiveness; workgroup reputation; co-operation, friendliness and warmth; and interdepartmental co-operation. Prominent in the process of identification of the various organisational climate dimensions during the 1960’s and 1970’s were Kahn,
Wolfe, Quinn, Snoek and Rosenthal (1964); Taguiri (1966); Litwin and Stringer (1966); 8 Schneider and Bartlett (1968); Campbell, Dunnette, Lawler and Weick (1970); Pritchard and Karasick (1973); James and Jones (1974); and Jones and James (1979).
For this study, the Jones and James (1979) organisational climate questionnaire, developed originally for the use in the US Navy and subsequently called psychological climate, was used as the base. This questionnaire has been used by a number of researchers in different settings and has been proposed to be a reliable and valid measure through a factor analysis over a range of settings. However, for use in the hospitality setting there was a need to modify both the language and length. Details of the modification of the instrument are provided in chapter 3.
Fourteen hotel properties in the study area took part in the project. In addition to the organisational climate data of the modified Jones and James questionnaire, data collected from the hotel staff sought their employment perceptions, demographic profile and employment details. Staff were asked to complete an organisational performance questionnaire that addressed their view on how the hotel was performing in a number of key service areas.
Managers at each property were asked to complete an organisational performance questionnaire. This survey instrument included financial performance broken down into departmental areas, demographic information, employment detail and a customer satisfaction rating. The last aspect of the data collection was confidential property information for both financial and operational performance indicators which either the general manager or the financial controller was asked to extract from the audited accounts.
Each property was visited at least twice to explain the details of the research and the method by which the questionnaires should be distributed, collected and returned. It 9 also provided an opportunity to deal with concerns raised by employees, departmental managers and senior management.
Descriptive and inferential statistics were computed using the SPSS computer package. Tabulations of frequencies were calculated to compare employee and staff demographic data between hotels. An exploratory Factor Analysis (Principal
Components) was conducted on the responses to the modified organisational climate questionnaire used in this study. This analysis enabled the comparisons of underlying dimensions of the sample with organisational climate dimensions described elsewhere using different versions of the instrument in Australia and overseas. Correlation and
Multiple Linear Regression were used to examine the relationship between a number of predictor variables and hotel performance (as indexed by revenue per available room –
REVPAR). To test a number of models proposing explicit causal relationships between variables, the AMOS structural equation modelling program was used.
1.5 Organisational and operational structure of a hotel
It is appropriate at this point to offer a brief overview of the typical organisational and operational structure of a hotel in order to provide a context and reference point for readers of this thesis. Much of the subsequent discussion and the analysis can only be interpreted if some understanding of the industrial setting is known.
Whilst many would argue that there are as many organisational and operational systems as there are hotels, nonetheless, there are certain generic similarities which can be applied to all hotels. A simplified typical hotel organisational chart is provided using the hierarchical model as an example. (See Figure 1.1)
10 These departments and areas of responsibility are typical of a large international four or five star hotel of 200 rooms plus. Of course, the number of departments and the naming can and does vary enormously according to the hotel, its location and style of operations. The organisational chart does provide an indication of the scope of
General Manager
Directors of: Marketing - Food & Beverage – Rooms Division –
Finance - Engineering – Human Resources
Departments and Areas of Responsibility:
Dir. Marketing Dir. F & B Dir. Rooms Dir. Finance Sales Restaurants Housekeeping Cashier Marketing Kitchen Front Office Control P.R. Bars Reservations Stores Publicity Rooms Service Telephonists Accounts Statistics Banqueting Concierge Inventory Conference Laundry Payroll Linen Security Cleaning
Dir. Eng. Dir. HRM Maintenance Personnel Landscape Recruitment Gardens Training Decoration Industrial Relations Systems Support Other (Sports) Figure 1.1 General Hotel Organisational Chart operations that are carried out routinely by any large hotel. It can be readily seen that a hotel is indeed a large and complex operation and as such is often seen as a microcosm of a small community.
11 From an operational standpoint, the technological and human systems are diverse and complex. Many large hotel companies seek to codify the operational procedures. However, it is patently obvious that when there are 200 plus hotel bedrooms, numerous food and beverage outlets, recreational and conference facilities; all the departments listed; the staff to run the operation; and the customers that this is indeed a complex operation.
Whilst standard operational procedures are used in many operations it is impossible to fully codify what happens in a hotel. So how does a hotel deliver and maintain a quality operation? It can only be achieved by training and giving employees the power to make decisions on service actions, within laid down parameters, that impact upon the guest. This view in supported by Peters (1997) who so aptly points out, it is the empowerment of the staff that ensures a hotel can deliver the type of service customers are looking for and increasingly demanding. The only way to deliver high quality service is to ensure that staff have the appropriate training and are able to make the required operational decisions.
If the Australian hotel industry can improve its reputation for service and professionalism and thus be more attractive to both international and domestic visitors, it will also have the opportunity to improve its profitability. However, this improvement in reputation and professionalism of the industry as a whole must also be accompanied by a commensurate increase in the employment image for the industry’s staff. Unless this improvement takes place and the career path planning is significantly improved, it will still not be seen as a worthwhile career choice for school leavers and individuals being displaced in other more traditional manufacturing and agricultural industries. If this is achieved the hotel industry will have ‘come of age’ in Australia but that state will
12 not be attained without a far better understanding of the industry’s employees, which is where organisational climate research can play a significant role.
1.6 Definitions
The definitions adopted by researchers are often not uniform. Therefore, this section will outline the definitions used throughout the thesis.
Organisational Climate and Psychological Climate: a full definitional discussion is provided in chapter 2. Organisational climate will be used for both constructs. In essence, organisational climate is an individual attitude toward the organisation and can be subject to change when circumstances change.
Organisational Culture is the framework that is engendered by the organisational systems and beliefs. It is relatively slow to form but has a high degree of permanency. In today’s management consultancy parlance, the notion of ‘Change
Management’ is often thought of as cultural change. In many cases, in fact, it should be organisational climate change. Again, a full discussion is provided in chapter 2.
Construct can be defined in varying degrees of specificity from narrow concepts to more abstract and complex concepts. No matter what the level of its specificity a construct cannot be directly measured but it needs to be approximated by a range of indicators (Hair, Anderson, Tatham and Black, 1995).
Tourism Industry has almost as many definitions as it has parts. Broadly speaking it encompasses any activity that businesses and/or governments engage in that provide travel, accommodation, sustenance, recreational and leisure activities outside of a person's home. Its economic flow-on effect is considerable in not only those directly
13 employed but in the many service based industries that also benefit from the tourism dollar.
Hospitality Industry is a defined sector of the tourism industry that principally concentrates upon services such as accommodation, food and beverage in all their forms.
Organisational performance: for the purposes of this thesis, 2 principal indicators of organisational performance will be used. Those indicators are customer satisfaction as measured by the perceptions of the hotel employees, and revenue generated per available bedroom for each of the hotels (REVPAR).
1.7 Outline of the thesis
This introduction chapter provides a general overview of the research question and the aims of the study. It briefly deals with justification and methodology employed as well as giving various definitions that have been used.
Chapter 2 is a detailed literature review that addresses the origins and research traditions of organisational climate. It traces the various debates that have surrounded organisational climate and how these link with the construct of culture. The effect that climate has within an organisation and what the processes are that contribute to its formation are examined. Various contentious issues amongst leading theorists, such as the aggregation of climate dimensions and what climate actually measures, are analysed and discussed in some depth.
The focus of this chapter examines climate and service quality, climate and innovation and the implications of organisational climate for the performance of the
14 hotel industry. The question ‘Can climate be used a predictor of organisational performance?’ is also raised.
Chapter 3 firstly proposes causal models to test the relevance of organisational climate as a predictor of customer satisfaction as measured by employee perceptions.
One model, labelled Structural Model A, uses demographic variables as predictors of organisational climate. Another model, Structural Model B, uses the dimensions of organisational climate to ascertain the veracity of an aggregated measure of organisational climate as a predictor of organisational performance. A number of hypotheses are posed based upon the theoretical models presented.
Chapter 4 then provides a fully detailed account of the research methodology, the reasons that the various research strategies were selected, and the rationale for the sample selection process. It also deals with the issue of why the particular survey instrument was selected as being appropriate for the current study. Considerable detail will be provided of the reasons for selection and the modifications from the original instrument initially designed for US Navy personnel to one that is suitable for the hotel industry.
Detail is provided of the pilot study that was undertaken to assist in the design of the instruments used to obtain performance and financial data as well as demographic and employment details. This chapter will also describe the administration and collection process that was used for the survey of the hotels.
Chapter 5 will report the Hotel Level Data of the 14 hotels concentrating upon the demographic data, general operational data, general operational statistics and the ranking each hotel’s performance. The key performance indicator for the hotels is interpreted in the context of the other operational and market data. The second part of 15 the chapter will examine the Staff Level Data for employees and managers. This will concentrate upon the various demographic data including gender, age, educational level, organisational tenure, job tenure, gross salary, mode of employment, hours worked, training frequency, and training needs.
Chapter 6 provides an analysis of the dimensions of organisational climate using the multivariate technique of Principal Components Analysis (PCA). It examines the organisational climate diversion across the 14 hotels using a one way analysis of variance (ANOVA).
Chapter 7 reports upon the relationships between the dimension of organisational climate, customer satisfaction and the key performance indicator of
REVPAR. The data again fall into the 2 classifications of Hotel Level and Individual
Level. The analysis presented in this chapter is guided by the structural models and uses the multivariate technique of structural equation modelling.
Chapter 8 will discuss the conclusions that can be drawn and addresses the issue of the hypotheses and whether they have been supported or rejected by the data collected. Finally, the results will be put into the context of the hotel industry for South
East Queensland and Australia addressing the questions of what implications the research has for the industry and future research in this area.
16 2.0 Literature Review of Organisational Climate
2.1 An introduction to organisational climate literature
In this study organisational climate is defined as the following.
Organizational climate is a relatively enduring characteristic of an organization which distinguishes it from other organizations: (a) and embodies members collective perceptions about their organization with respect to such dimensions as autonomy, trust, cohesiveness, support, recognition, innovation, and fairness: (b) is produced by member interaction; (c) serves as a basis for interpreting the situation; (d) reflects the prevalent norms, values and attitudes of the organisations culture; and (e) acts as a source of influence for shaping behavior. (Moran and Volkwein, 1992, p. 2)
Although this is the definition used to guide this research, many researchers have presented different definitions of organisational climate, and there has been some confusion as to the manner in which organisational climate is distinct from the notion of organisational culture. This chapter will, in part, provide a review of the evolution of this definition of organisational climate and provide an explanation of its relationship to the concept of organisational culture.
Not only is it important to clarify the construct of organisational climate, but it is also important to understand its usefulness for the service industries as a possible tool in seeking to improve the effectiveness and quality of their service provision. The importance of climate for the hospitality industry has been highlighted by a number of theorists including, Francese (1993) who examined the effect of climate in service responsiveness; Meudell and Gadd (1994) who studied climate and culture in short life organisations; and Vallen (1993) who was concerned about organisational climate and service staff burnout. However, the investigation of these themes further becomes very
17 difficult when consensus on the definition of climate has proved elusive, and there are many conceptual issues that need to be addressed.
Organisational climate has much to offer in terms of its ability to explain the behaviour of people in the workplace. Ashforth (1985, p. 838) put forward the view that ‘climate has the potential to facilitate a truly integrative science of organisational behaviour’. Schneider later discussed climate in terms of:
the atmosphere that employees perceive is created in their organisations by practices, procedures and rewards … Employees observe what happens to them (and around them) and then draw conclusions about the organisation's priorities. They then set their own priorities accordingly. (Schneider, 1994, p. 18)
Schneider, Brief and Guzzo (1996, p. 9) argue that ‘sustainable organisational change is most assured when both the climate - what the organisations’ members experience - and the culture - what the organisations’ members believe the organisation values - change’. Other empirical studies have claimed that climate has a considerable impact upon organisational effectiveness (Campion, Medsker & Higgs, 1993; Drexler, 1977; Franklin, 1975; Fredrickson, Jensen & Beaton, 1972; James & Jones, 1989; Likert, 1961, 1967; Furnham & Drakeley, 1993; Lawler, Hall & Oldham, 1974; Kanter, 1983; Mudrack, 1989; Schneider, Brief & Guzzo, 1996; Schneider, Gunnarson & Niles –Jolly, 1994, and others).
The role of climate is crucial in any organisational improvement process that requires the implementation of a major organisational change, or innovation. Much of the following review will be definitional. This is necessary for two reasons:
1) in the context of performance and quality management, the term climate has been used loosely to the extent that the terms culture and climate have been used interchangeably; and
18 2) the literature on climate itself contains multiple definitions, factors, dimensions, research methods and aetiologies.
The review will examine the major theories and models that have formed the basis of climate research.
2.2 Early formulations of the climate construct
The concept of climate can be traced back to the work of Lewin, Lippitt and White (1939) and a work entitled ‘Patterns of aggressive behaviour in experimentally created social climates’ (Denison, 1996; Schneider, 1990). The Lewin et. al. (1939) study investigated the relationship between leadership style and climate, a factor that has remained central to the concept. Joyce and Slocum (1982) trace the concept back to the studies of Koffka (1935) on ‘behaviour environment’; Lewin’s (1936) study on ‘life space’; and Murray’s (1938) work on organisational climate.
Lewin’s concept of life space, has been explained by Krech and Crutchfield as:
the individual’s total conception of the worlds in which he exists ... It includes his knowledge, beliefs and memories and his view of the past and future as well as of the present; and it may include domains of life reached after mortal ‘death’ - heaven and hell paradise and purgatory. It is not, of course, the same as the actual physical and social environments described by the outside observer. It is what exists subjectively for the person. His life space may correspond in some way with the actual external environment but it also deviates from them in radical degree, and varies markedly from life spaces of other people. (Krech & Crutchfield, 1961, p. 210)
In the understanding of the differences between culture and climate, Lewin’s (1951) approach to climate was conceptualised by the relationship between individuals, their social environment and how that is set in a framework. Lewin expressed this in
19 terms of the simple equation:
B = f (P.E.) in which B= Behaviour, E = Environment, and P = the person
It is clear from Lewin’s equation that the concept of climate takes a psychological approach, focussing upon the individual and seeking to understand the cognitive processes and behaviour. Lewin’s conceptualisation of the theory provides the underpinnings of many studies and approaches to climate research.
2.3 Three approaches to the climate construct
Following the seminal work of Lewin et. al. (1939), obtaining consensus as to the definition of climate has been difficult as the climate construct is complex and many different researchers have used the same terminology to mean different things to the extent that providing a definitive description of climate has been likened to ‘nailing jello to the wall’ (Schneider, 1990, p. 1). Others have argued that if the use of the same term to mean different things continues, climate research will ‘grind to a stop in an assemblage of walled in hermits each mumbling to himself words in a private language that only he can understand’ (Boulding, cited in Glick, 1988, p. 133).
James and Jones (1974) conducted a major review of the theory and research on organisational climate and identified climate in three separate ways that were not mutually exclusive, (a) multiple measurement - organisational attribute approach, (b) perceptual measurement – organisational attribute approach, and (c) the perceptual measurement – individual attribute approach. In the multiple measurement organisational approach they cite Forehand and Gilmer (1964) as defining organisational climate as a
set of characteristics that describe an organization and that (a) distinguish the organization from other organizations (b) are relatively enduring over time, and
20 (c) influence the behavior of people in the organization. (Forehand & Gilmer, 1964 p. 3621 cited in James & Jones , 1974)
The perceptual measurement organisational attribute approach seeks to define climate in terms of individual perceptions of the organisation and it is these perceptions that influence behaviour. James and Jones (1974) reported that the Campbell, Dunnette, Lawler & Weick (1970) study which itself had synthesised Kahn, Wolfe, Quinn, Snoek & Rosenthal (1964), Litwin & Stringer (1968) and Schneider & Bartlett (1968) had proposed four organisational climate dimensions,
! Individual autonomy – based on the factors of individual responsibility, agent independence, rules orientation and opportunities for exercising individual initiative.
! The degree of structure imposed upon the position – based on the factors of structure, managerial structure and the closeness of supervision.
! Reward orientation – based upon the factors of reward, general satisfaction, promotional-achievement orientation, and being profit minded and sales oriented.
! Consideration, warmth and support – based upon the factors of managerial support, nurturing of subordinates, and warmth and support.
It must be remembered that such dimensions of climate are not always clearly distinguishable from other variables that might fit into categories such as organisational structure, process, system values and norms. The reliance upon perceptual measurement may mean that organisational climate also includes some situational characteristics as well as individual perceptual differences and attitudes. Whilst James and Jones (1974) note considerable criticism of this approach, they reaffirm that there is both rational and empirical evidence to support that which is being measured by the perceptual questions are variables related to different levels of explanation.
21 In reviewing psychological climate as a set of perceptually based, psychological attributes (rather than the conceptualised independent or structural variable) Jones and James (1979) noted that the process reflected the developments that had occurred in the conceptualisation of climate and the nature of its major influences. They propose that psychological climate,
(a) refers to the individual’s cognitively based description of the situation; (b) involves a psychological processing of specific perceptions into more abstract depictions of the psychologically meaningful influences in the situation; (c) tends to be closely related to situational characteristics that have relatively direct and immediate ties to the individual experience; and (d) is multi-dimensional, with a central core of dimensions that apply across a variety of situations (though additional dimensions might be needed to better describe particular situations. (Jones and James, 1979, p. 205)
However, Schneider and Hall (1972) describe climate as a global perception held by individuals about their own organisational environment. Schneider and Snyder (1975) further clarified the approach by defining climate as a summary perception which individuals form of (or about) an organisation. For them it is a global impression of the organisation. The global nature of organisational climate does not suggest that the concept is uni-dimensional. Many different types of events, practices and procedures may contribute to the global or summary perception individuals have of their organisation.
Within the current study, organisational climate is conceptualised as a construct created by the activities of the organisation. It is not the activities themselves, which is a distinction that is not always clear in some of the earlier works. Schneider (1975) refined the definition of climate to include meaningful apprehensions of order for the perceiver that are based on the equivalent of psychological cues. Whilst this definition has some common elements with James & Jones’ (1974) and Jones & James’ (1979)
22 constructs, its focus shows a movement from definitional issues toward a concern for people and their view of climate and what impact it has for the organisation. It is a way of apprehending order and a way of judging the appropriateness of behaviour.
2.4 The distinction between culture and climate
Trice and Beyer (1993) define culture in terms of what it is not. It is not climate, which is measured with researcher-based data, whereas culture is measured by intense data collection of an emic (contrastive) nature. Reflecting the concerns of both Schneider (1990) and Glick (1988), Trice and Beyer state:
So many different variables have been subsumed under the climate concept by various researchers that it overlaps with most constructs in organisational behaviour as well as with structure, technology, formalisation and effectiveness … The appeal of the climate construct was that it seemed to give the researchers a way to combine a broad array of variables already studied into a single omnibus concept that would simplify the process of characterising and comparing the psychological environments. (1993, pp. 19-20)
The definition of culture put forward by Trice and Beyer (1993) noted that it has many unique indicators like myths, symbols, rites and stories. Denison (1996) took what he considered to be a more controversial view in arguing that it is not clear that culture and climate are examining distinct organisational phenomena. However, the literature refers to culture as being deeply rooted in the structure of an organisation and based upon values, beliefs and assumptions held by the members. Climate, however, tends to present social environments in relatively static terms measured by a broad set of dimensions and can be considered as temporary and subject to a range of controls. Table
2.1 gives an outline of differences between the literatures using an epistemological
23 approach, the point of view taken, methodology used, temporal orientation, level of analysis and the discipline area.
Table 2.1 Contrasting: Organisational culture and organisational climate
Contrasting: Organisational Culture and Organisational Climate
Research Perspective Cultural Literature Climate Literature
Epistemological Contextualised and Comparative and idiographic nomothetic
ViewPoint Emic (native view) Etic (researcher's view)
Methodological Qualitative observation Quantitative data
Temporal Orientation Historical evolution Ahistorical snapshot
Level of Analysis Underlying values and Surface level assumptions manifestations
Discipline Sociology Psychology
Source: Denison (1996, p. 625)
Culture studies were searching for that which is unique in each setting and used qualitative methods whereas climate studies in contrast, used quantitative methods and looked for factors that were generalisable across different settings. Many of the difficulties that seem to have plagued researchers in the climate area can be traced to this desire to find generalisable factors that are applicable to all environments, to the extent that a multiplicity of dimensions, climate instruments and underlying theoretical assumptions have been produced by various researchers. Denison summed up this paradox thus,
Culture researchers were more concerned with the evolution of social systems over time ... whereas climate researchers were generally less concerned with evolution but more concerned with the impact that organisational systems have on groups and individuals … Culture researchers argued for the importance of 24 deep underlying assumptions ... Climate researchers in contrast, typically placed greater emphasis on organisational members perceptions of observable practices and procedures that are closer to the surface of organisational life ... and categorisation of these practices and perceptions into analytic dimensions defined by the researchers. (Denison, 1996, pp. 621- 622).
2.5 Development of climate instruments
James and Jones (1976) developed the items for their questionnaire after an extensive review of the literature. From the literature they identified 35 concepts related to organisational climate. Eleven concepts related to job and role characteristics, eight related to leadership characteristics, four to work-group characteristics and 12 comprised sub-system and organisational level characteristics. Many of these had been shown to be internally consistent, psychologically meaningful measures of the work environment. For each of these concepts, between two and seven items were generated. This procedure produced a 145 item questionnaire. Responses to each individual item consisted of a stem with a variable scaled response of either three or five. Thirty-five a priori composite variables were produced by summing across the relevant item responses.
This was done to support their choice of climate composites, as they called them, and the individual question items or scales that comprised each composite. In 1989 James and James reported that the items and scales that comprised the dimensions of climate that had shown factorial invariance were developed using interviews, observations and literature reviews. They outlined a number of measures for the job or role, leader orientation, workgroup environment and variables that relate to the overall organisational climate.
Schneider argues that neither, interviews or questionnaires are necessarily preferable to each other in collecting data, but are useful for different purposes. The qualitative information yielded from interviews is particularly useful for providing 25 managers with ‘the precise practices and procedures that inhibit service delivery [for instance] rather than merely identifying the fact that there are some inhibitory practices and procedures’ (1990, p. 404). This stands in the way of change agents dealing with the manifestations of particular climates in particular settings. Low levels of supervisory support, for instance, don’t reveal precisely what needs to be changed. Schneider proposes that intermediate positions may be useful;
one alternative would be to have a survey that contained items assessing generic themes that could be used across settings but for each setting the generic items could be supplemented by tailor made items. The latter items would require some in depth exploration of issues in a specific organization to identify the ways in which generic concepts become manifest there. (Schneider, 1990 p. 404)
This discussion reaffirms that there is still much work to be done in the area of developing appropriate climate instruments. Current instruments include Patterson, Payne & West (1996) Business Organisation Climate Index that consists of 28 item scales however only eight were used because of the length. Kozlowski & Doherty’s (1989) instrument uses 55 measures consisting of 11 sub-scales that overlaps with Jones & James (1979). Joyce & Slocum (1982) used the same measure as Pritchard & Karasick (1973) with 10 dimensions that were factor analysed and reduced to six. Drexler’s (1977) survey of operations that was based upon Taylor & Bowers (1972), a composite of several other instruments. Likert’s (1967) profile of organisational characteristics and Pritchard & Karsick (1973) instrument were both based upon Campbell et al., (1970) using eleven of their original 22 measures. James and Jones (1976) developed their psychological climate questionnaire (PCQ) which used 35 a priori scales derived from the literature, to that point. This questionnaire was administered to a large US Navy sample as discussed above and the results were then factor analysed. The components that resulted were then compared to other samples to derive the generalised dimensions.
26 Ryder and Southey (1990) used the James and Jones (1979) questionnaire as the basis for their instrument which they applied to employees within a large public building construction and maintenance authority in Australia. Modifications to the original instrument were threefold, consisting of modifications to the wording, scaling, and presentation format. Items were reworded to remove culturally specific terminology, to enable the use of non-sexist language, and to make the items applicable, non-military employees. Ryder and Southey judged the scaling of the original instrument to be unsatisfactory. The original instrument employed between three and five scaled responses that listed either descriptive attributes on a continuous scale, or were presented in a Likert format. Ryder and Southey employed across all 144 items of their questionnaire a consistent seven point anchored scale format. Again between two and seven items were used to produce each of 35 composite climate variables. They reported that the instrument, so presented, required less time to complete than did the original Jones and James version.
2.6 Dimensions of organisational climate
The definitions and theoretical positions on climate have varied considerably between the individual theorists. This has also been the case for the dimensions of climate and its measurement. Denison (1996) argues that developing a universal set of dimensions was often the central issue of the climate researchers so that comparative studies could be made possible in different organisational settings. He compared this approach to that of the culture research that used a post-modern perspective which examined the qualitative aspects of individual social contexts where each culture that was examined was seen as unique and was not expected to have generalisable qualities which had become central to the climate research.
It is possible that the dependence on the use of climate surveys as the research method of choice led those working in the climate area to seek generalisable qualities across settings. Jones and James (1979) argued that one of the assumptions of the
27 climate literature is that a relatively limited number of dimensions could characterise a wide cross-section of social settings.
Jones and James (1979) initially administered their 145 item instrument to a large sample of 4315 US Navy personnel. An exploratory Principal Components Analysis (PCA) produced a six factor (eigenvalues greater than unity) solution. Jones and James labelled their factors as follows:
‘Conflict and ambiguity’, which ‘reflected perceived conflict in organisational goals and objectives, combined with ambiguity of organisational structure and roles, a lack of interdepartmental cooperation, and poor communication from management. Also included were poor planning, inefficient job design, a lack of awareness of employee needs and problems, and a lack of fairness and objectivity in the reward process.’
‘Job challenge, importance and variety’, which ‘reflected a job perceived as challenging, important to the Navy, whic involved a variety of duties, including dealing with other people. The job was seen as providing autonomy and feedback, and demanding high standards of quality and performance.’
‘Leader facilitation and support’, which ‘reflected perceived leader behaviors such as the extent to which the leader was seen as helping to accomplish work goals by means of scheduling activities, planning, etc., as well as the extent to which he was perceived as facilitating interpersonal relationships and providing personal support.’
‘Workgroup cooperation, friendliness, and warmth’, which ‘generally described relationships among group members and their pride in the workgroup.’
‘Professional and organisational esprit’, which ‘reflected perceived external image and desirable growth potential offered by the job and by the Navy. Also
28 included were perceptions of an open atmosphere to express one’s feelings and thoughts, confidence in the leader, and consistently applied organisational policies, combined with nonconflicting roles expectations and reduced job pressure.’
‘Job standards’, which ‘reflected the degree to which the job was seen as having rigid standards of quality and accuracy, combined with inadequate time, manpower, training, and resources to complete the task.’
Jones and James applied their instrument to two other samples of health managers and firemen. PCA analysis in both of these cases extracted 6 factors with eigenvalues greater than unity. Analysis of the items on each factor, however, revealed only 5 factors to be common across the three samples (Conflict and ambiguity, Job challenge, importance and variety, Leader facilitation and support, Workgroup cooperation, friendliness, and warmth, and Professional and organisational esprit).
Jones and James reviewed the comparability of the results found in their US Navy sample and the findings of other similar studies. A number of the dimensions that had been used in other studies could be related to their own findings as shown in Table 2.2.
The identification of dimensions was also the subject of a study by Campbell, Dunnette, Lawler, and Weick (1970) when they reviewed the work of Litwin and Stringer (1966), Schneider and Bartlett (1968), Taguiri (1966), and Kahn, Wolfe, Quinn, Snoek, and Rosenthal (1964). Campbell et al. found four factors common to each of these studies: (a) individual autonomy, (b) degree of structure imposed on the position, (c) reward orientation, and (d) consideration, warmth and support. Whilst there is no definitive agreement on climate dimensions there does appear to be some
29 Table 2.2 Comparison of Climate Dimensions across Studies
WORKGROUP CO-OPERATION JONES AND JAMES (1979) FRIENDLINESS AND WARMTH Meyer (1968) Team Spirit Thornton (1969) Distant vs Close Working Relationship Friedlander & Margulis (1969) Intimacy Pritchard & Karasick (1973) Social relations Lawler, Hall & Oldham. (1974) Friendly-Unfriendly
JONES AND JAMES (1979) CONFLICT AND AMBIGUITY Litwin & Stringer (1968) Conflict Schneider & Barlett (1968) Conflict Pritchard & Karasick (1973) Conflict Meyer (1968) Organisational Clarity Payne, Pheysey & Pugh (1971) Normative Control Waters, Roach & Batlis (1974) Effective Organisational Structure Thornton (1969) Efficiency and Clarity of Purpose Campbell, Dunnette, Lawler & Weick(1970) Litwin & Stringer (1968) Structure Pritchard & Karasick (1973) Structure Schneider & Barlett (1968) Structure Structure JONES AND JAMES (1979) LEADERSHIP, FACILITATION and SUPPORT Schneider and Bartlett (1968) Managerial Support Campbell et al., (1970) Consideration, Warmth and Support Waters, Roach & Batlis (1974) Close, Impersonal supervision, and Employee centered Orientation Friedlander & Margulis (1969) Aloofness, Production Emphasis, Trust and Consideration (4 separate factors)
ADAPTED FROM JONES AND JAMES (1979) commonality of organisational dimensions that can be measured by a number of theorists and the debate continues over the narrowness of range used to describe different work environments (Pritchard and Karasick, 1973; James and James, 1989; James, James and Ashe, 1990; Schneider, 1975).
30 Ryder and Southey (1990) applied an exploratory PCA to the data they gathered from their Australian sample using their modified version of the Jones and James (1979) instrument. This procedure resulted in a 10 factor solution (using the criterion of the corresponding eigenvalue being greater than unity). The authors report that of those 10 factors, only 6 were interpretable. The dimensions they so identified were:
‘Leader Facilitation and Support’, with the leader providing support and facilitating the accomplishment of work goal’s, facilitating interpersonal relationships, being aware of employee needs and providing job feedback. It also encompasses openness of expression and allows for upward interaction.
‘Job Variety, Challenge and Esprit’, deals with not only job variety, challenge and autonomy but professional, work group and organisational esprit de corps. It also encompasses opportunities for growth and advancement, role ambiguity and efficiency of job design.
‘Conflict and Pressure’, deals with conflict in a role and between organisational goals and objectives, job pressure, planning and co-ordination, and opportunities to deal with others.
‘Organisational Planning Openness’, describes planning and effectiveness, and ambiguity of organisational structure. It also deals with job standards and importance, the consistent application of organisational policies, and confidence and trust down.
‘Workgroup Reputation, Co-operation, Friendliness and Warmth’, encompasses precisely the concepts named in its title.
‘Perceived Equity’, looks at interdepartmental co-operation, organisational communication down, and the fairness and objectiveness of the reward process.
Ryder and Southey noted that the major dimensions of psychological climate are
31 stable and would provide a framework for future research. In their study they modified the Jones and James (1979) questionnaire and reported improved measures of reliabilities.
2.7 A critique of climate theory
Schneider (1975) criticised the whole idea of an omnibus theory of climate and in particular the indiscriminate use of the term organisational climate. He proposed that the term organisational climate be used only to refer to an area of research rather than to a construct with a limited number of dimensions. From a review of early climate studies Schneider concluded that some dependent variable had implicitly driven the research on the climate construct. Many of the studies have looked at climate as a particular facet of organisational life, rather than a general omnibus measure. These studies included theorists such as, Lewin et al., (1939) who examined leadership style and social climate. Fleishman (1953) whose investigation looked at the climate for leadership whereas Argyris (1958) was concerned about the right type of climate, and McGregor (1960) looked at climate from the leadership perspective. Litwin and Stringer (1968) were studying a climate for motivation, Schneider and Bartlett (1968) were exploring the climate for new employees and Taylor and Bowers (1973) dealt with creativity. Schneider (1973) was concerned about psychological success whilst Renwick (1975) looked at conflict resolution. Additionally, several subsequent studies could also have fallen into this categorisation: Delbecq & Mills (1985) addressed innovation and Schneider (1990) studied service.
Schneider (1975) argues that these theorists could have investigated the same set of organisations because each of those climates could have existed side by side. It was a theme repeated in later work;
Organisations may have many climates, including a climate for creativity, for leadership, for safety, for achievement, and or for service. Any one research
32 effort probably can not focus on all of these but the effort should be clear about its focus. (Schneider, Parkington and Buxton, 1980, p. 255)
Schneider believed that the salience of a particular dimension could only be found in the context of a particular criterion of interest (1975). Schneider and Reichers (1983) further reinforced this view by strongly advocating that examining organisational climate without attaching a referent is meaningless.
Jones and James (1979) responded to Schneider’s criticism, by arguing that the call for criterion based climate studies did not rule out the possibility that a relatively small set of dimensions may still describe a wide range of environments. They postulated that a particular dimension may be related to the same criteria under consideration but be negatively related to another criterion and not related at all to others. James and James (1989) argued for the concept of a generalisable psychological climate (PCg), first developed by Lazarus (1982; 1984), as a general higher order factor integrating the meanings behind the psychological climate of an organisation.
Stated simply people respond to work environments in terms of how they perceive these environments, and a key substantive concern in perception is the degree to which individuals perceive themselves as being personally benefited as opposed to being personally harmed (hindered) by their environment. (James and James, 1989, p. 748)
They found strong support for this notion in their research and demonstrated the theorised relationship between the dimensions of climate as a generic concept and the underlying factors or PCg that make up the individual dimensions.
2.8 Measurement issues of the multilevel climate construct
Psychologists explain the behaviour of people through the use of both nomothetic (group) and idiographic (individual) means (Mullins, 1996). A fundamental
33 question inherent with organisational climate research is ‘What is the appropriate level of analysis; the organisation, the department or subunit, the workgroup or the individual?’. Many researchers have conceptualised climate as an individual and psychological variable, however, the difficulty has been justifying the extrapolation of results from one level of analysis, i.e. (the individual), to the broader context of the workgroup, the department or to the total organisation (Guion, 1973). The cross level interference problems together with the unit of analysis issue have been addressed by a number of researchers (Glick, 1980, 1985; Glick and Roberts, 1984; Mossholder and Bedeian, 1983). When Cameron (1983) discussed organisational effectiveness he also confirmed that a major problem for these types of studies is the primary level of analysis.
Guzzo (1982), Cummings (1983), Noord (1983) and Keely (1980) all use single indicators that are extrapolated to assess the whole organisation. The extrapolation of results from the individual level to the group level allows climate researchers to analyse and draw conclusions about the running of the total organisation and for groups of people within the organisation in terms of whatever effectiveness parameter is being investigated. Generally researchers have sought to do this by calculating the average (usually a mean) of results for a particular climate survey and then sought to discover the extent to which the results mapped into the structure and effectiveness of the organisation. There has been considerable discussion in the literature concerning the extent to which this practice is justified and in what context (Patterson, Payne & West, 1996; Glick, 1988, James, Joyce & Slocum, 1988; Denison, 1996).
In Argyris (1958), Forehand and Gilmer (1964) and Litwin and Stringer (1968) the unit of theory was focussed upon the organisation as the natural unit for climate research. Another group of these earlier theorists concentrated upon group or sub-unit, notably Hellriegal and Slocum (1974), Powell and Butterfield (1978) and Howe (1977).
More recently, James and Jones (1974) used the term psychological climate to embrace both individual and, when aggregated, organisational level units of analysis, although
34 later they (e.g., James et al., 1988) tended to use the term organisational climate to refer to these aggregated individual psychological climate scores.
Glick stated that organisational climate ‘is an organizational attribute that may be estimated with a central tendency, but the central tendency is not the organization itself’ (1988, p. 135). Whilst there are inherent difficulties with the aggregating of data sets and disagreements as to the dimensionality of organisational climate, i.e., dimensions of psychological climate may not be appropriate to organisational climate, however, it can still be estimated by aggregating individual psychological climate scores. Glick proposed that organisational climate as defined by James et. al. (1988) be renamed ‘aggregate psychological climate’. His overall conception of the construct regards climate as a broad class of organisational variables that are used to describe the context for individual members within an organisation’s formal and informal policy and procedures. The dimensions are as yet still not fully resolved and whilst climate is an emergent organisational level process it cannot entirely be decomposed to individual level cognition. Glick’s (1988) summation draws upon and supports the work of other theorists such as Schneider & Reichers (1983), Powell & Butterfield (1978), Campbell, Dunette, Lawler & Weick (1970) and Glick (1985).
The multiple level of units of theory is important because they may differ in their empirical approach. Whereas the term organisational climate connotes an organisational unit level of analysis, it does not refer to the individual, department or workgroup. The debate on the unit of theory as being the organisation is strengthened by the common practice of many researchers of using aggregation of psychological climate (Gavin and Howe, 1975; James, 1982; Jones and James, 1979; Schneider, 1975). In discussing the units of theory, Glick (1985) makes the point that psychological climate is very much linked to the organisational climate and that care needs to be taken and that separate cross-level analyses should be used.
James and Jones (1976) discussed the difficulties inherent in using individuals’
35 perceptions of organisational situations as the basis for higher level analysis in some depth. The concern that emerged from their work was that perceptually based data carried the risk of reflecting individual characteristics rather than differences in the situations being studied. When, for instance, an organisation hired certain kinds of persons into a particular group, the results of the study could be skewed. The process of aggregation, they argued, rested on a number of implicit assumptions:
The argument for aggregating perceptually based climate scores (i.e., psychological climate scores) appears to rest heavily on three basic assumptions: first, that psychological climate scores describe perceived situations; second, that individuals exposed to the same set of situational conditions will describe these conditions in similar ways; and third, that aggregation will emphasize perceptual similarities and minimize individual differences. (Jones and James, 1979, p. 206)
Mossholder and Bedeian (1982) defended the use of aggregated psychological climate in assessing how individuals perceive an organisation. They postulated that while it appears to require an organisational unit of analysis, the actual units of analyses are both organisational because psychological climate represents individuals, in general, and the results may also be aggregated.
Schneider and Reichers (1983) discussed how climates form and why aggregation is a legitimate technique. They considered three approaches to the formation of climate: the structural perspective; selection, attraction and attribution; and social interactionism. The structural perspective sees as arising from the structural characteristics of the organisation. The selection, attraction and attrition approach at which individuals (based on the work of Bowers, 1973) create homogeneous organisational membership and where there are similar climate perceptions among individuals. Thirdly, social interactionism approach is where individuals check, suspend, regroup and transform their own perceptions in the light of their interactions. This approach seeks to explain differences in climate across workgroups in the same 36 organisation that are not explained by the other approaches.
In the debate between Glick (1985) and James, Joyce and Slocum (1988) there is fundamental disagreement about the conceptualisation and measuring of organisational climate using psychological climate. James et al., argue that psychological climate, with its parsimonious set of dimensions and the scores obtained, does represent shared meaning and perceptual agreement which can be aggregated to give an overall indicator of organisational climate. They further point out that the basic unit of theory for organisational climate (aggregated psychological climate) must be the individual because ‘it is individuals, and not organizations, that cognize’ (1988, p. 130). The aggregation of climate is appropriate because of the shared assignment of meaning that allows a higher order of analysis for groups, sub-systems and organisations. It provides a mechanism for relating the construct of psychological climate at individual level of analysis to another form of the construct at the group, subsystem or organisational level yet the basic unit is psychological analysis. This is a crucial point for organisational research as it allows researchers the possibility of using aggregated psychological climate to describe organisations in psychological terms (James, 1982; Joyce & Slocum, 1979, 1984).
In the sampling process, within any organisation in order to use aggregated psychological climate to predict organisational climate there is a need to ensure that all members of the organisation, or a random stratified sub-sample of individuals covering all positions, are represented. Without such sampling procedures in place, James et al., (1984) conclude that the use of aggregation is unjustified.
Patterson, Payne and West (1996) discuss the problems that Schneider and Reichers (1983) faced where they could not account for differences that were found to exist across workgroups within the same organisation. This follows similar results found by James and Jones (1979) in their US Navy study and the variation Pritchard and Karasick (1973) found across regions. Schneider and Reichers (1983) addressed this
37 problem using social interactionism, drawing on the work of Mead and Bulmer (1969) in the area of symbolic interactionism, and suggested that climate perceptions were a function of social interactions. As discussed above these social interactions can be examined by looking at how people interpret meaning in the social context.
meaning (which includes perceptions, descriptions and evaluations) does not reside in any particular thing in itself, nor does it reside in the individual perceiver. Rather the meanings of things arise from the interactions among people. The actions of others act to define an event or procedure for the focal person. This is not meant to suggest that people simply apply the meanings given to them by others. Rather, individuals check, suspend, regroup and transform their own perceptions of events in light of the interactions they have with others in the setting. (Schneider and Reichers, 1983, p. 30)
Ashforth (1985) discusses the interactionist perspective and highlights the susceptibility of newcomers to influence outcomes in their desire to fit into a new setting. Social comparison theory explains that individuals compare their beliefs to others whom they perceive to be similar to them (for example, people in the same job). Normative social influence and the stake that group members have in maintaining the frame of reference of the prescribed behaviours, beliefs, and attitudes affect the development of organisational cultures. Patterson, Payne and West (1996) argue that these approaches should be seen as complementary rather than competing and that each may be useful for examining the various stages of development of a climate. Patterson et. al. (1996) rely upon the depiction by Ashforth of the aetiology (cause) for climates in the explication of the results, which were inconclusive from the social interactionist model perspective.
Another conceptual difficulty identified by Cameron (1983), is the reverse to that described above, where an organisation’s effectiveness is measured by single indicators of performance such as return on investment, overall performance rating and turnover. When organisational climate is represented by the aggregation of scores from
38 individuals within the organisation, a score would (obviously) exist for each individual and may be included in multivariate statistical analyses relating climate to other characteristics such as employee demographic variables. Within such analyses, should the researcher wish to also examine the relationship between these variables and a single indicator of performance, then the researcher is necessarily limited to either dealing with aggregate scores across individuals, or must assign, for each individual, a score representing that single performance indicator. Both approaches have advantages and disadvantages.
2.9 Organisational climate as a variable in theory and research
According to Schneider (1975), the basis of the climate function can be traced to two different schools of psychology: Gestalt and Functionalism. The Gestalt school argues that the perceiver has no choice but is actually driven to find order in the world. Nature has order, and the perceiver has to find that order through the process of closure.
The closure principle suggests that given some limited amount of information to which people ascribe order, the totality they may create represents more than the simple sum of the limited information perceived ... Given a set of cues about the world with some perceived relationship, i.e. there is sufficient information for order to be perceived, a whole or total concept is formed. (Schneider, 1975, p. 448).
Mullins (1996) discusses Gestalt theory in terms of its instant and spontaneous assumptions that we cannot stop ourselves making about our environment. Gestalt theory also stresses the drive to behave on the basis of this apprehended order and in a manner that suits the environment in which the perceivers finds themselves (Schneider, 1975; Kozlowski & Doherty, 1989). The earliest reported incident of the phenomenon was detailed in the work of Lewin et. al. (1939). In their experimentally created social climates they found that the behaviour of the boys in the study varied according to the social climate created by their leaders; authoritarian, democratic or laissez faire. 39 Functionalism provides a framework in which individuals can seek order in their environment. This allows them to function adaptively: they have a fundamental need to seek information about the status of their behaviour in terms of the environment within which they operate, ‘they seek information so that they can adapt to, or be in homeostatic balance, with their environment’ (Schneider, 1975, p. 450). Theorists such as Frederickson, Jenson and Beaton (1972), Fleishman (1953), Litwin and Stringer (1968) and Argyris (1957) support this view of Functionalism. And Schneider (1990) refined his view of climate to include ‘a sense of imperative’ for individuals.
Ashforth (1985) argues that a strong culture informs the climate of the organisation in two ways: directly by telling the individual what is important in the environment, and indirectly through its influence on the environment. Whereas climate influences factors in the workgroup, the process of newcomer socialisation, symbolic management and to a lesser extent the physical setting. The point for Ashforth is that culture underpins these factors so that the assumptions and values of the organisation (the culture) are behind the perceptions and inferences of the organisation (the climate) and the behaviour of the members of the organisation. Ashforth’s conceptualisation of the formation of climates and how it is based upon and is affected by an organisation’s culture is displayed in Figure 2.1
40 CLIMATE FORMATION
STRONG CULTURE Assumptions and values underpin Perceptions and inferences Informs climate in two ways
Directly by telling the Indirectly by its impact on individual what is the environment important INFLUENCED BY
WORKGROUP Festinger (1954): Social comparison theory Hamner and Organ (1978): norms and expectations, frame of reference, prescribed behaviours, sanctions
AFFECT Newcomer socialisation: desire for integration, desire to reduce anxiety
SYMBOLIC MANAGEMENT
PHYSICAL SETTING
CLIMATE ENACTED as a joint property of both the individual and the organisation both macro and micro ADAPTED FROM ASHFORTH (1985)
Figure 2.1 Climate Formation (from Ashforth, 1985)
When Moran and Volkwein (1992) examined the relationship between culture and climate they saw an organisation’s climate as a specific portion of the overall construct. They viewed climate as embedded into the overall construct of culture, which
41 was seen as larger and more abstract. As far as individual behaviour in the formation of climate is concerned, both Moran and Volkwein (1992) and Ashforth (1985) saw the contextualising of the psychological principles contained in the Gestalt and Functionalist approaches to behaviour. Figure 2.2 depicts how Moran and Volkwein conceptualised the relationship between climate and culture. They viewed culture as being the invisible construct which guides and inform individual behaviour, in effect setting an agenda from which climate can develop and where in their view it can have some enduring quality.
BRIDGING CULTURE AND CLIMATE
CULTURE: INVISIBLE
(Exists quite apart from individual variation)
Interacting Individuals (informed and constrained by common culture)
Contingencies in the internal and external environment
CLIMATE: VISIBLE
Collective and Individual properties
OPERATES AT THE LEVEL OF ATTITUDES AND VALUES. FORMS MORE QUICKLY, CHANGES MORE RAPIDLY: (changes in key staff, budgetary cuts)
RELATIVELY ENDURING
CULTURE STOPS CLIMATE BEING ENTIRELY TRANSITORY OPERATES AT THE PRECONSCOUS, SUBCONSCIOUS LEVEL
‘Culture….is the source of purposeful action and continuity from which the more routine adaptive behaviour exhibited in the organisation's climate derive their impetus.’
ADAPTED FROM MORAN AND VOLKWEIN (1992)
Figure 2.2 Moran and Volkwein (1992) depiction of culture and climate
42 An alternative perspective on the nature of the climate construct in theory and research is presented by Schneider (1975) describing climate to have been conceptualised across studies in one of three ways - as a dependent, independent, and intervening variable, which he considered were merely different vantage points. Those theorists that took climate as a dependent variable, George & Bishop (1971), Payne, Pheysey & Pugh (1971), Dietertly & Schneider (1974) and Lawler, Hall & Oldham (1974) used the construct to analyse varying situations and procedures in a macro sense. Secondly, the group that used climate as an independent variable, Lewin, Lippitt & White (1939), Argyris (1957), Andrews (1967), Frederickson et. al. (1972) and Pritchard & Karasick (1973) were concerned with interpreting of practices which produce varying organisational climates. Thirdly, the final group used the construct as an intervening variable, where climate was a pre-determined way of specifying types of procedures that will lead members to view climate in a particular way (McGregor, 1960; Likert, 1967; Hall & Schneider, 1973).
The view emerging from some theorists is that climate should be viewed as an intervening variable that is psychological by nature and represents an individual’s social interaction which is underpinned by the culture of the organisation (Ashforth, 1985; Moran & Volkwein, 1992). Moran and Volkwein (1992) have examined the constructs of climate and culture, tracing the theoretical antecedents, arguments and positions in an attempt to demonstrate differences and also provide a link between the two constructs. They have drawn upon work by Forehand and Gilmer (1964) and Pritchard and Karasick (1973) in forming their definition of organisational climate presented in section 2.1.
2.10 Organisational climate and models of organisational functioning
Although the debate over what organisational climate does and does not describe has been ongoing from the time Lewin, Lippitt and White (1939) first utilised the construct, an adequate and comprehensive theory of climate has been elusive.
43 James and Jones have provided a conceptualisation of ‘Organisational Functioning’ (Figure 2.3) that displays the role of organisational climate in relation to the resultant job behaviours and ultimately the end result criteria in an integrated model (1976, p. 96). Organisational climate is depicted as a situational variable along with more objective factors such as organisational structure, systems and norms and processes. These themselves are further broken down into a number of sub-systems. It is the action of these situational variables that in turn produce the perceived psychological climate and the perceived physical environment. There are a number of other causal influences but the prime relationship of the perceived climate and physical environment is with a range of individual characteristics such as attitudes, motivation, job satisfaction, expectancy instrumentality and reward reference. Other individual characteristics become moderating variables but the relationship with job behaviours and performance and end result outcomes for the organisation is clearly shown.
44 END RESULT CRITERIA CRITERIA INDIVIDUAL BEHAVIOURS AND BEHAVIOURS JOB AND BEHAVIOURS PERFORMANCE INDIVIDUAL RESOURCES INTELLIGENCE ABILITIES PERSONALITY RACE SOCIOECONOMIC STATUS INDIVIDUAL CHARACTERISTICS REFERENCE
ORGANISATIONALLY RELATED ATTITUDES MOTIVATIONAND JOB SATISFACTION EXPECTANCY INSTRUMENTALITY REWARD ) VARIABLES
INTERVENING PSYCHOLOGICAL CLIMATE (PC) PERCEIVED PHYSICAL ENVIRONMENT (HABITABILITY NORMS GROUP PROCESS SUBSYSTEM AND SUBSYSTEM AND SUBSYSTEM AND SUBSYSTEM GROUP STRUCTURE GROUP SYSTEMS AND SUBSYSTEM SUBSYSTEM AND GROUP AND SUBSYSTEM ENVIRONMENT SUBSYSTEM AND SUBSYSTEM GROUP PHYSICAL AND GROUPAND CONTEXT ORGANISATION SITUATION PROCESS ORGANISATIONAL CLIMATE STRUCTURE ORGANISATIONAL ORGANISATIONAL ORGANISATIONAL SYSTEMS ANDNORMS CONTEXT ORGANISATIONAL ORGANISATIONAL EXTERNAL ENVIRONMENT PHYSICAL ENVIRONMENT Source: Jones and James 1976 PHYSICAL EXTERNAL Figure 2.3Figure 1976 Model of Organisational Functioning Jones and James ENVIRONMENT ENVIRONMENT SOCIOCULTURAL
45 Kopelman, Brief, and Guzzo (1990) also provide a linear model of organisational functioning (Figure 2.4) that demonstrates the role of the culture and climate as they are ultimately linked to organisational productivity. Kopelman et. al.’s model starts with societal and organisational culture as setting the parameters of the human resource practices. It is the HRM practices that in turn engender the organisational climate, which produce the cognitive and affective states of individuals (work motivation and job satisfaction). The aggregation of individual perceptions determines the salient features of organisational behaviour and in sum make up the organisational productivity. Although the criterion of interest in this case is productivity, the model has utility for explanatory purposes with climate being depicted as an intervening variable. This model uses the role of HRM practices of the organisation as a situational variable that will ultimately affect the productivity of the organisation. Kopelman et. al.’s (1990) description of organisational climate reflects both individual and organisational characteristics. Similarly, salient organisational behaviours such as attachment, performance and citizenship are seen as intervening between the climate of the organisation and the ultimate outcomes. Attachment will affect such factors as absenteeism and turnover, leading to an increase in training separation and replacement costs. In a service industry the quality of the service provision is also likely be affected.
Performance relates to the manner in which the formal requirements of the job are attended to, and it is here that the citizenship or pre-social organisational behaviours have an important role. These refer to ‘constructive or co-operative gestures that are not mandatory’ without which attachment performance and ultimately productivity will slowly deteriorate (Brief and Motowidlo, cited in Kopelman et. al., 1990, p. 301). Schneider, Gunnarson and Niles-Jolly (1994) claim that organisational citizenship behaviour is essential in creating a climate that allows for organisational success. Perceptions of fairness and trust, norms of helpfulness and co-operation and fair reward systems based on a broad range of contributions are seen as essential in creating a good climate.
46 A MODEL OF CLIMATE, CULTURE AND PRODUCTIVITY
Human Resource Management Practices Organisational Climate Cognitive and Affective Salient Organisational SOCIETAL CULTURE Hiring Goal Emphasis States Behaviours Organisational Placing Means Emphasis Productivity Organisational Rewarding Reward Orientation Work Attachment Physical Motivation Culture Monitoring Task Support Performance Output Developing Socioemotional Support Job Citizenship Total labour costs Promoting Satisfaction
ADAPTED FROM KOPELMAN, BRIEF AND GUZZO 1990
Figure 2.4 A Model of Climate, Culture and Productivity (Adapted From Kopelman, Brief And Guzzo 1990) Modelling the way climate affects the outcomes of the organisation through the behaviour of the employees has its antecedents in the work of Likert (1961) who discussed climate in terms of an intervening variable. The role of climate in the provision of high quality service draws on the models provided by Likert (1961), James and Jones (1976), Kopelman et al., (1990) and others. Likert’s model used causal variables which included only those that were under direct management control; intervening variables that reflected the organisational climate such as performance goals, loyalties, attitudes, perceptions and motivation; and end result variables that are dependent variables that include productivity measures, costs, service and quality.
2.11 Climate, service quality and organisational performance
From the earliest studies the climate of an organisation has been shown to exert a powerful influence on the attitudes and behaviour of the people in the organisation. Many aspects and factors have been shown to have a relationship with organisational climate such as, work methods (Frederiksen, 1968, cited in James and Jones, 1974); satisfaction (Pritchard and Karasick, 1973); alienation (Witt, 1993); trust (Strutton, Toma and Pelton, 1993); productivity (Frederiksen, 1968, cited in James and Jones, 1974); turnover intentions (Parkington and Schneider, 1979); agency success (Schneider cited in Pritchard and Karasick, 1973); organisational income (Scheflen cited in Pritchard and Karasick, 1973); service quality (Schneider and Bowen, 1985); innovation (Scott and Bruce, 1994) and many other factors. It is therefore reasonable to conclude that organisational climate is of major importance in the understanding of how organisations work and the success they achieve.
When discussing the role of climate and its links to the provision of high quality service it is first necessary to understand the operational environment of the hospitality industry. The provision of high quality service has become essential to survival, and many hospitality organisations are attempting to implement various quality management schemes (Harrington & Akehurst, 1996). Higgins and Vincze (1993) argue that firms
48 wishing to be successful in the 1990’s must have a quality management programme in place and that quality has become a strategic imperative.
According to Partlow, the search for a sustainable competitive advantage in the hotel industry ‘has become focussed to a large degree on product and service quality’ (1994, p. 16). The performance and credibility of hospitality organisations like the Ritz Carlton, Hyatt, Sheraton, Pan Pacific Hotels, Southern Pacific Hotel Corporation and others which have already successfully implemented a quality management systems is a compelling justification for all other hospitality organisations also adopting similar strategies. Hoque (2000) suggests that it is the linking of good HRM practices to the business strategy that enhances performance.
Yet there is no guarantee that the introduction of quality programmes will lead to success. Harari (1993) points to a success rate of only 20-30 %. Similarly, Eskildon (1994) reported that 63 % of those surveyed with TQM programmes had failed to reduce internal defects by 10 % or more. Only one-fifth of British firms believed that their quality programmes had made a significant impact, and only one-third of US manufacturing and service firms believed their TQM efforts had made them more competitive. Morton (1994) put it even more succinctly when discussing why British firms could not match their Japanese counterparts, quoting a Japanese executive when asked, ‘why are you so open about your processes, the techniques and commercial decisions?’ The response was both realistic and depressing, ‘because we know you won’t do it anyway. You will not change’ (1994, p. XV).
Napier (1997) found that many North American organisations which start a formal quality initiative lose their way or give up within two years, in the process wasting a lot of time, effort and money. Most of the TQM programmes also fail to address the issues of psychological/behavioural aspects that are essential prerequisites for such change. Napier argues that they are focussed on the pure mechanics of implementation so that without the ‘...supporting behaviours quality systems either get
49 bastardised to the stage that they are rendered useless or are simply doomed to failure’ (1997 p. 7). Eskildon (1994) argues that companies such as Harley-Davidson, Hewlett- Packard, Xerox and Compaq have all achieved success by managing their TQM programs by creating clear goals, whilst many other companies which implement TQM concentrate on creating a culture, without creating clear goals for improving customer- value outcomes.
Goals have an important function in understanding the link between the daily activities of the organisation and the deeper psychological issues. It is here that climate has an important explanatory role to play. Goals and how an organisation goes about its business are key components by which the members of an organisation infer the climate of an organisation (Schneider, Brief and Guzzo, 1996). The influence of an organisations climate on employee behaviour extends beyond the implementation of proposed change, and has been demonstrated by numerous studies on all aspects of employee behaviour (Drory, 1993; Witt, 1993; Strutton, Toma & Pelton, 1993). An organisation needs to be aware of three separate kinds of climate in order to ensure the success of service focussed quality improvement efforts: (1) a climate for service, (2) a climate for innovation, and (3) a climate for human resources or employee welfare (Schneider, Gunnarson and Niles-Jolly, 1994).
The climate of the workplace is particularly relevant to all service industries where, like hospitality, the vast majority of its output is characterised by intangibility, heterogeneity, and simultaneous production and consumption. This is perhaps best described by Carlzon’s ‘moment of truth’ (1987) in the service encounter recognising that it is impossible to directly control the outcome of the service process. The management of service industries is different and according to Schneider, Gunnarson and Niles-Jolly (1994, p. 23) ‘...in the absence of direct control of the service encounter, it is the climate and culture that determines high quality service’. In many ways climate becomes a substitute for leadership, and understanding how it works is vital to those in
50 the hospitality industry (Kerr and Jermier, 1978).
2.12 Utilisation of the climate construct within a service quality perspective
The integral role of people in the development of a TQM plan (Price and Chen, 1993) is crucial to its implementation and success. Crom and France (1996) detail the consequences of ‘a climate of fear’ in relation to employee risk taking and how a variety of techniques including teamwork and the redesign of job processes can address this situation. Ryan (1995) discusses the need for the development of a climate for innovation in the context of a continuous improvement effort. Silcox, Cacioppe and Soutar (1996) concentrated on the way that various sub-cultures may be managed during any intervention. They recognise that cultural change for a large group has considerable difficulties, whereas the efficacy of small group cultural change is an alternative and more productive approach stating,
The commitment and self esteem of workers, the culture and climate of the organisation, together with the quality of the organisational communication and leadership have a direct effect on the quality of products and services and the overall productivity of the organisation and need to be examined and understood by managers considering a quality intervention. (Silcox, Cacioppe and Soutar, 1996, p. 26)
Tice (1993) supports this view claiming that ‘all too often the human, or behavioural side of TQM is either ignored altogether or given cursory attention’ (1993, p. 22). Libotte discusses the difficulty of ‘embedding the will for continuous improvement’ (1995, p. 48) and promotes the prescription of measurable results as well as new roles, and responsibilities in place of the use of new written procedures only. Easton (1992) comments upon the continued emphasis on financial and cost factors in the decision making of American industry to the neglect of other indicators concerning individuals. Heymann (1992) extends the discussion of the need to establish a quality culture to include the day-to-day behaviour that is evident in the organisation, and
51 Saraph and Sebastian (1993) discuss the need for quality goal setting. Partlow (1993) gives an extensive account of the practices and procedures that are seen to be central to the quality improvement process at the Ritz-Carlton group. These processes cover climate-related issues and give a clear indication of the importance of the role organisational climate plays in the quality management initiative.
Vallen (1993) provided clear evidence of the link between organisational climate and the burnout of service staff. The study used Likert’s ‘Profile of Organisational Characteristics (POC)’, an 18 item questionnaire divided into six categories, leadership; communication; interaction and influence; decision making, goal setting and control. This is based upon Likert’s (1961) four Systems of Management, ranging from System 1 (exploitative-authoritative), System 2 (benevolent – authoritative), System 3 (consultative) through to System 4 (participative). Apart from the strong correlation between burnout and a poor organisational climate, Vallen also noted that the hospitality firms surveyed in terms of their climate rarely used a consultative style. His research showed that service jobs with a high degree of customer interaction have a higher level of burnout. This burnout has been defined by Riggar as being displayed by the characteristics of ‘turnover, absenteeism, lowered productivity, psychological problems, etc.’ (Riggar, 1985 in Vallen, 1993, p. 55). Vallen’s study concluded that high emotional exhaustion and depersonalisation scores (high burnout) correlate with low POC scores in highly autocratic organisations. He recommended hospitality managers seeking to reduce their staff turnover should look to their organisational climate as it undoubtedly affects the ability of a hospitality organisation to deliver service quality. This is supported by the findings of Kordupleski, Rust and Zahorik (1993) who found that of the overall quality processes used in service industries was responsible for 70 % of the variation in an organisation’s output. The processes such as quality programmes can only be successful when there is genuine staff and management commitment. Meudell and Gadd (1994) also reported similar results, finding that 6% of individuals in an organisation felt the process of managing people was good and only 4% thought that management had displayed a positive attitude towards them.
52 The issue of management in organisations taking insufficient account of the individual behavioural perceptions and actions is a constant theme within the service quality literature. Building a responsive service climate was the focus of a study by Francese (1993), ‘previous research has shown that both customers and managers agree that a responsive service climate is the key to service quality and customer satisfaction’ (p. 55). Drawing on the work of Schneider and Bowen (1985) and Shoorman and Schneider (1988), Francese proposed a model for service organisations using a support dimension, a managerial dimension and an adaptive dimension correlating to a relationship of service responsiveness and service quality. She found a clear link between teamwork, entrepreneurial management behaviour and adaptive marketing policies and activities. As such, her results reveal the link between the areas service quality and responsiveness and organisational climate.
The above studies support theorists such as Schneider (1973) who explored the relationship between climate and service related issues. Initially, Schneider examined the relationship between service climate (and other more tangible factors) and customer intentions to switch their accounts to another bank. He found that none of the objective indexes of customer participation (size of bank balance, length of time as a bank customer) was related to switching intentions but found that switching behaviour was strongly related to climate perceptions. This study revealed that the measure of the atmosphere in the bank ‘warm and friendly’ was most strongly correlated with switching intentions. As such this supports the underlying assumption ‘that the climate bank employees create for customers is an extension of the climate bank management creates for employees’ (Schneider, 1973 p. 255). Customer retention has been clearly linked to the climate created for the employees of the organisation.
Ross (1995) in a study of the hotel industry in North Queensland found that there were major divergences between management and employee service quality ideals. Following Parkington and Schneider’s (1979) reasoning, this would appear to have negative consequences for the quality of service offerings of the hotels concerned.
53 These issues are highly relevant for service managers in their the day to day operations. Parkington and Schneider (1979) argue that it is possible to get operational staff enthusiastic using the usual management tools.
Through alterations of policies and procedures and goals it may be possible for management to effect changes in the degree to which there is emphasis on an enthusiastic service orientation more similar to that of boundary personnel. This should reduce the levels of role stress and the levels of negative employee outcomes. (1979, p. 279).
It is imperative that the quality of the service offerings should improve in service organisations. Organisational dynamics have a direct impact on the people the organisation serves, as well as on employee performance and attitudes. Schneider et. al. (1980) were explicit as to the implication this had for service management, arguing that consumers are better served if the policies, practices and procedures of an organisation meet the needs of, and satisfy, employees which results in a directly positive outcome in terms of service quality for the consumer. The creation of a climate for service is an example of organisational effectiveness of an organisation being responsive to its environment, in this case its customers. The relationship between a climate for service and the service quality perceptions of the customers is clearly supported by
the ways in which branch employees describe some facets of the service orientation of their branch and the support received from some systems outside the branch are related to what customers say about the quality of the service they receive in the branch. (Schneider et. al. 1980, p. 262)
In a study of the banking industry Schneider and Bowen (1985) isolated 10 dimensions of a climate for service, many of which showed a significant correlation with customer perceptions of service quality in the organisation. They are part of the overall climate framework and link with the climate dimensions identified by James and Jones (1979) and others. The service dimensions are shown in Table 2.3 together with the employee perceptions of each dimension.
54 Table 2.3 Climate for service (in banks)
Service Climate Dimensions Employee Perceptions
Bureaucratic orientation to service Following all rules & procedures Doing the job in a routine fashion
Enthusiastic orientation to service Keeping a sense of family Designing new ways to serve the customer
Managerial behaviour Planning and goal setting for service delivery
Service rewards Incentives and other rewards for service excellence
Customer retention Active attempts to retain customers Not giving special treatment to major customers
Personnel support Staffing and training permit good service
Easy access to customer records Operations support Error free records
Understanding of customers Marketing support Care in introducing new products and services
Equipment/supply and support Equipment is available and up operating Necessary supplies available
SOURCE: ADAPTED FROM SCHNEIDER (1990)
Whilst this study was completed in the banking industry it none-the-less has application across all service organisations and is of particular relevance for the hospitality industry because the nature of the service interaction in banks with its immediacy is replicated in hospitality.
Schneider and Bowen (1985) also derived five human resource dimensions of the relationship between climate and service quality with several items loading onto each dimension. The five dimensions were, work facilitation (10 items), supervision (14 items), organisational career facilitation (6 items), organisational status (4 items) and new employee socialisation (6 items). They found a consistent correlation between these
55 human resource dimensions and customer perceptions of employee morale, branch administration and most significantly overall quality. It is clear that the implications of these findings are;
Customer perceptions, attitudes and intentions seem to be affected by what the employees experience, both in their specific role as service employees and in their more general role as organizational employees. That is, organizational practices (both service related and human resource related) are apparently the source of cues visible to customers and are used by them to evaluate service quality … In other words, because services themselves yield little tangible evidence as a useful basis for evaluation, it is how they are delivered, and the context in which they are delivered that is important. (Schneider & Bowen, 1985, p. 430 - 431)
The climate of the organisation is an important factor in the creation of quality services as defined by the customer. Within the wider quality movement there is a call for the incorporation of the concept of employee satisfaction as well as the more widely used customer satisfaction into the overall focus of the business. This is because the evidence shows that without an environment which supports the employee it will be difficult to enlist the employee’s support for the objectives of management (Cole, Bacayan & White, 1993).
In comparing research, it becomes increasingly clear that when the climate for human resources dimensions are compared to the more generic climate dimensions that there is a significant overlap. Table 2.4 shows the degree of congruity between the human resource dimensions of Schneider and Bowen (1985; 1993) and the more generic one enunciated by Jones and James (1979) and James and James (1989).
56 Table 2.4 Comparison of HRM climate dimensions
SCHNEIDER AND BOWEN JONES AND JAMES (1979) (1985; 1993) JAMES AND JAMES (1989)
WORK FACILITATION WORK FACILITATION Supervisor helps achieve goal attainment ‘Conditions on my job do not permit people through such activities as scheduling, to reach their work goals.’ coordinating, planning and providing resources
GOAL EMPHASIS SUPERVISION Supervisor stimulates personal involvement Supervisors I work with use the rewards in meeting group goals. they have (praise, performance appraisals) JOB FEEDBACK to let people know when they have done a The extent to which an individual is aware of fine job. how well he is performing on his job.
OPPORTUNITIES FOR GROWTH AND ORGANISATIONAL CAREER ADVANCEMENT FACILITATION The degree to which an individual feels that The organisation provides information and the organisation provides a vehicle for counselling about my career. development of desired personal skills, goals and rewards. PROFESSIONAL ESPRIT DE CORPS ORGANISATIONAL STATUS The degree to which an individual believes People outside (the organisation) think the his profession has a good image to outsiders people who work here are high calibre and provides opportunities for growth and people. advancement.
NEW EMPLOYEE SOCIALISATION No directly comparable measure (but job People coming on the job get special pressure mentions training. See also training that helps them get started. Friedlander and Greenberg (1971))
James and James (1989) research into psychological climate concentrated on the extent to which a particular environment (as described by its climate) was of benefit or otherwise to the individuals exposed to it. Psychological climate was discussed in terms of being the underlying factor of the usual generic dimensions of climate that they had found to be invariant over a number of environments. Figure 2.5 displays four dimensions of psychological climate as identified by James and James (1989) together with the factors that load upon each dimension. There is considerable evidence, as
57 operation - Workgroup co Responsibility and effectiveness Workgroup to warmth friendliness warmth and friendliness warmth Workgroup Cooperation, autonomy Job autonomy Job importance Job challenge and variety REPRODUCED FROM JAMES (1989) AND JAMES Job challenge and General factorGeneral of Psychological Climate Psychological Role abiguity Role Role conflict Role Role overload Subunit conflict Organisation identification Management concern and awareness harmony Role stress and lack of ce n ue Hierarchical infl Psychological influence and trust Leader support Leader goal emphasis and facilitation Leader interaction facilitation Leader support and support Leader Figure 2.5Figure Factor General Climate of Psychological (Reproduced from James and James (1989)) 58 outlined above, to conclude the research on climate which Schneider and Bowen called a climate for human relations, and the generic climate dimensions of the many other theorists, including James and James, have examined are in fact the same construct but with slightly differing nomenclature. It also follows that the appropriate climate of the organisation is a prerequisite in the facilitation of service quality. The measurement of climate in an organisation may provide insights as to the issues that need to be addressed in order for the organisation to achieve its quality service goals.
The Schneider and Bowen (1993) study replicated previous research using the same climate dimensions for human resource management and the results were largely confirmed.
This research points out that managers, in their pursuit of service quality, need to create two related, but different, climates: a climate for service and a climate for employee well-being. Our research indicates that a climate for employee well being serves as a foundation for a climate for service. Employees need to feel that their own needs have been met within the organization before they can become enthusiastic about meeting the needs of customers. (Schneider and Bowen, 1993, p. 43)
Schneider and Bowen argue that companies like Four Seasons Hotels and others that have implemented employee-centred human resource management practices have a strategic advantage over other less successful competitors. This reinforces the validity and profound implications for those companies that are seeking to use organisational climate as a measure of the effectiveness of the organisation in providing quality service.
Schneider and Bowen (1993), however, make the point that a generic set of human resource management practices will not necessarily lead to customer perceptions of service quality. These practices should be designed to suit the particular
59 organisational setting and the consequent customer definition of service quality. In some settings, procedure driven human resource management practices may be appropriate and the empowerment of employees may be deemed less important in achieving the required goals in customer service.
It is also important to note that the measure of service quality that Schneider and Bowen (1985; 1993) used was developed for the measurement of service in banks. Whilst Schneider (1990) points to the fact that Zeithaml, Parasuraman and Berry (1990) have developed a generic service measurement tool (SERVQUAL), no correlation of the organisational climate and the measurement of its service quality was undertaken. However, Dean (1997) demonstrated a methodology for making such a comparison in a study of the applicability of the SERVQUAL model to the health care industry.
2.13 Customer and employee perceptions of customer satisfaction
It has been reported above that a number of studies have claimed there exists a positive relationship between organisational climate and customer satisfaction. A related, but different, question is the extent to which employees’ perceptions of customer satisfaction and customers own reports of satisfaction match.
This is an important issue, particularly for service industries such as the hotel industry, as customer feedback may be difficult to gather, and particularly difficult to gather in an unbiased form. Should a good correspondence exist between employee perceptions of customer satisfaction and reports of satisfaction directly provided by customers, then in many situations employee perception of customer satisfaction may be used as a more easily measured index of feedback to management. A small number of studies has addressed this issue.
Schneider et al., (1980) in a study gathering data from both customers and employees of 23 bank branches found a strong correspondence between branch customer attitudes about service quality and branch employee perceptions of the quality 60 of the service customers received (r = .67). Schneider and Bowen (1985) replicated the earlier study gathering data from 142 employees and 968 customers of 28 bank branches. This study also found a strong relationship between employee perception of customer satisfaction and that reported directly by customers (r = .63).
The results of these two studies indicate that direct reports of customer satisfaction are closely mirrored by employee perceptions of customer satisfaction. Consequently, it may be expected that in many instances employee perceptions of customer satisfaction will provide management with useful feedback. This would be particularly so in service environments where production and consumption are instantaneous and direct assessment of the customer perceptions, at the time, would negatively impact upon the product’s quality.
2.14 Climate and innovation.
Organisational analysts are increasingly reporting that the failure of innovations such as TQM, that are more often than not a failure in the implementation stage. It is here that climate has an important role to play, in both informing management of the likelihood of success and subsequently during the implementation stage (Klein & Sorra, 1996; Eskildon, 1994). Organisational members’ readiness to implement an innovation strategy is a function of, the climate for innovation; the fit between the values of the end users; and how well the planned innovation matches those values. According to Klein and Sorra (1996) ‘an organisation’s climate for the implementation of a given innovation refers to targeted employees’ summary perceptions of the extent to which their use of a specific innovation is rewarded, supported, and expected within their organization’ (p. 1060). Additionally, if there is a poor fit with the values of the targeted group of employees then the implementation will be less successful. Both climate and culture are important in the successful implementation of innovations such as quality improvement programmes (Klein and Sorra, 1996).
Scott and Bruce (1994) in discussing the same issues are less concerned with the 61 effect of individual values on innovation. They argue that people in the workplace respond to the signals and expectancies they receive, regulating their own behaviour in order to ‘realize positive self evaluative consequences such as self satisfaction and self pride’ (1994, p. 582). For them the effect of climate on innovation is unmediated by individual values and only two (performance reward dependency and flexibility) of 10 generic climate dimensions studied were consistently related to innovative performance.
Harrington and Akehurst (1996) found there appeared to be a high level of awareness of quality related issues in the cross section of the British hospitality industry. Few managers at an operational level had systems, such as human resource practices, in place to implement service quality initiatives.
Schneider, Brief and Guzzo (1996) examined the linkage between climate and culture and the concept of total organisational change that has been achieved by such firms as Ritz Carlton, AT&T and others. They suggest that these firms are more successful because they are more effective in managing three aspects of climate (nature of interpersonal relationships, the nature of the hierarchy and the focus on support and rewards) simultaneously. The need is to establish a climate that fosters innovation, customer service, and citizenship behaviour concurrently which in turn allows both high quality service and enhances organisational performance and success (Schneider, Gunnarson & Niles-Jolly, 1994).
2.15 Organisational climate and implications for the hotel industry
The hospitality industry in general and hotel companies in particular are becoming more aware of the need to understand their employee perceptions and the climate generated by their organisation because of its links to the perceptions and levels of customer service. While there are obvious advantages in understanding the forces that are involved in the creation of the organisational climate, it is the linking of that understanding to day to day activities that holds major significance for management.
62 The effect of managerial actions and leadership factors on the climate of the organisation has been known since the studies of Litwin and Stringer (1968), McGregor (1987) Kozlowski and Dougherty (1989) and others. Brown and Leigh (1996) argued for supportive management where subordinates may try and fail without any fear of reprisals. This is where employees are allowed to experiment with new methods bringing creativity to workplace problems. The level of control and freedom and sense of security that this supportive style of management engenders is more likely to produce a high level of job commitment and motivation (Argyris, 1964; Kahn, 1990). The Brown and Leigh (1996) study clearly demonstrates the positive relationship between supportive management together with clear work goals as being crucial in producing greater job effort, commitment, and performance. They conclude that the study,
demonstrated an important series of linkages to work relating psychological climate and job involvement to work performance and indicated that organizational environment is perceived as psychologically safe and meaningful is related directly to job involvement and indirectly to effort and work performance. (Brown and Leigh, 1996, p. 365)
It is established that the psychological climate of individual employees of an organisation can have pronounced positive or negative effects on the organisation and its performance. Therefore, it becomes essential that management on a continual basis should monitor employee attitudes such as the climate of the organisation. The importance of employee attitude has been recognised by British Petroleum Exploration who regularly surveys employees. It is especially useful when any organisational changes are being contemplated (Standing, Martin and Moravec, 1991). Blenkhorn & Gaber (1995) and Shea (1996) point out that the basis of every customer driven organisation is the staff who must be respected and valued by their organisation. Shea (1996) observed that climate surveys are being used by service organisations like Avis, Amex, St George Bank and the Ritz Carlton Hotel Group to monitor how their employees perceive the organisation.
63 Organisations pursuing a high quality service strategy appear to be more cognisant of the role that the climate of the organisation has in the provision of that service. Organisations like the Ritz Carlton ensure there are no mixed messages in the routines they follow and try to ensure that every employee feels valued by the organisation (Schneider, Brief and Guzzo, 1996). Their practices and procedures are enunciated in a document called the Ritz Carlton ‘Gold Standards’ which includes the ‘Credo’, the ‘Motto’, ‘Three Steps to Service’ and the ‘Basics’. It is this document that forms not only the core of the induction process for new staff, but also the operational mantra for all employees (Partlow, 1993). Another major international hotel chain, the Marriott Corporation, has for some years adopted an employee survey that is conducted on a twice-yearly basis to monitor employees’ attitudes to the company and management. This process is firmly embedded into the Marriott management procedures and is now used as one of the benchmarks of management performance. It ensures that employee opinions are taken into account and allows for a monitoring of individual departments by senior regional management. Each hotel general manager and departmental manager is required to discuss the results at employee meetings (personal communication Wallace, 1997).
The ramifications for the hospitality industry are plain: the climate of the workplace is a fundamental factor in the provision of high quality services. Without an understanding of the function of the climate (psychological climate) of the organisation, any attempt to improve the quality of the service provision will be in doubt. But, as Schneider et al., (1994) point out, there are in fact three climates that need to be created by senior managers, in their terms, a climate for innovation, a climate for service excellence and a climate for citizenship behaviour. These climates can coexistent in a successful organisation especially where there is implementation of quality management initiatives. Organisations need to recognise the climate for human resources or employee well being (called citizenship by Schneider et al., 1996) in their organisations
64 as the basis for the development of a climate for innovation and a climate for service. They should not be seen as separate entities so much as elements of a greater whole with the climate for service and the climate for innovation embedded in the larger concept of a climate for employee well being, which together represent the member perceptions of the total organisation. Pfeffer (1998) stresses that the successful management of people will have multiple dimensions that are shown by certain organisational characteristics,
! Employment security
! Selective hiring of new personnel
! Self-managed teams and decentralisation of decision making as the basic principles of organisational design
! Comparatively high compensation contingent on organisational performance
! Extensive training
! Reduced status distinctions and barriers, including dress, language, office arrangements, and wage differences across levels
! Extensive sharing of financial and performance information throughout the organisation
For Pfeffer these characteristics must be viewed in a holistic way because if firms try to implement these initiatives on a piecemeal basis they can actually be counter-productive. Whilst it clearly takes time to implement such an agenda for change, a time horizon needs to be set for implementation. Pfeffer (1998) has distilled these seven practices of successful organisations from various studies, related literature, observation and experience. In the hospitality industry context, very few organisations
65 would be able to claim the successful implementation of such practices, which was a view supported by Vallen (1993).
2.16 Summary and conclusion
In this chapter, a review of the literature related to the concept of organisational climate was presented. It was reported that there have been, and continues to be, some dispute as to the precise definition of this concept. For the purposes of the current study, organisational climate was described as an enduring characteristic of an organisation comprising of member collective perceptions about their organisation across a range of dimensions. It is a characteristic that is produced by interactions between employees, reflects prevalent norms, and acts as a source of influence for shaping behaviour.
Organisational climate was described as being distinct from organisational culture. Culture was described as being deeply rooted in the structure of an organisation, being based upon values, beliefs and assumptions held by its members. Climate was described to present social environments in relatively static terms measured by a broad set of dimensions and is considered as temporary. The above discussion showed that indices or organisational climate maybe determined by the aggregation of psychological climate values for individual members of the organisation.
A number of instruments have been presented that purport to provide measures of organisational climate within organisations. Two important (for this thesis) instruments were the Psychological Climate Questionnaire (PCQ) of Jones and James (1979) and the modified version of the same instrument presented by Ryder and Southey (1990). The original instrument provided measures on 5 or 6 dimensions which were aggregated across individuals to provide organisational climate measures for organisations and subunits of organisations on each of the dimensions. The version presented by Ryder and Southey (1990) contained modifications which rectified some scaling and presentation problems and reworded questions to both remove sexist language and make it more applicable to a non-military organisation. 66 Implications of the relationship between organisational climate and the delivery of high quality service within the hospitality industry were discussed. Sinclair (1996), for example, discussed the need to develop a climate of trust for the successful implementation of TQM. Other researchers, such as Partlow (1993) provide accounts of practices and procedures that relate to climate issues, which are seen by their organisations as central to the quality improvement process. A number of researchers reported the link between climate and burnout of employees.
A significant link was shown between the organisational climate of employees and the climate created by employees for their customers. Schneider (1973) reported that it was this customer climate that was most strongly linked with customers choosing to switch from one service deliverer to another.
In a related issue, Parkington and Schneider (1979) found a close correspondence between employee perception of service quality and the perceptions of the customer. Schneider and Bowen (1985 & 1993) also found this strong link between employee perception of customer satisfaction and that reported by customers.
The hospitality industry relies heavily upon the direct interaction between employees and customers to ensure the perception of the delivery of high quality service from the organisation. The literature presented in this chapter indicates that organisational climate is multidimensional and may be represented by aggregated scores on psychological dimensions gathered from individual employees. Within the hospitality industry, organisational climate is likely to be a key factor (or set of factors) in the delivery of high quality service to their customers. In Chapter 3 theoretical models will be presented proposing relationships between the dimensions of organisational climate, perceived customer satisfaction, and outcomes for hotels. Specific aims and hypotheses generated from these models will also be presented.
67 3.0 Theoretical Models and Hypotheses
3.1 The research question
This study will apply a modified version of the Jones and James (1979)
Psychological Climate Questionnaire to form aggregate scores representing the organisational climate within the 14 hotels under study. Relationships between the dimensions of organisational climate, so produced, and (a) employee demographic variables, (b) employee perceptions of customer satisfaction and (c) hotel performance
(as indexed by REVPAR) will be examined.
These examinations will be conducted in order to satisfy the overall aim of this study to answer the following research question:
What is the nature and degree of influence that organisational climate has upon the level of performance of organisations within the Australian hotel industry?
3.2 The dimensions of organisational climate within the hotels
The instrument used to investigate organisational climate in this study represents a modification of the instrument presented by Ryder and Southey (1990), which itself represented a modification of the Psychological Climate Questionnaire presented by
Jones and James (1979). The first specific aim of this study is:
Aim 1: To identify the underlying dimensions of organisational climate within Australian hotels.
This aim will be satisfied by applying the modified organisational climate
68 questionnaire to employees of the 14 hotels under study, and by appling Principal
Components Analysis (PCA) to the employee responses. Given, firstly, that the instrument which will be used is not identical to that used in earlier study, and secondly, that factor structures reflect not only the instrument, but also the sample under study, it is difficult to state highly specific hypotheses regarding the precise nature of the dimensions that will be extracted. Although the study of Ryder and Southey produced a set of organisational climate dimensions broadly consistent with those presented earlier by Jones and James, they extracted 10, rather than 6 factors, and provided only 2 of their 6 interpretable factors with nomenclature identical to that used in the earlier study.
Figure 3.1 illustrates the dimensions of organisational climate that might be expected on the basis of Jones and James (1979).
69 Figure 3.1 Organisational Climate Model A: The dimensions of organisational climate from the study of Jones and James (1979).
It might, however, be possible that part of the difference in factor structure found by Ryder and Southey, relates to differences between Australian culture and that of the U.S. If this is the case, the underlying dimensions of organisational climate may be more similar to those shown in Figure 3.2.
70 Figure 3.2 Organisational Climate Model B: The dimensions of organisational climate from the study of Ryder and Southey (1990).
On the basis of the factor structures of organisational climate presented by Jones and James (1979) and Ryder and Southey (1990) the following hypothesis is proposed:
Hypothesis 1: A limited number of factors will be identified as being able to determine the organisational climate across the hotels in this study.
71 It should be understood that neither Organisational Climate Model A, or
Organisational Climate Model B, are causal (or ‘structural’) models. What is illustrated in Figures 3.1 and 3.2 are possible dimensions which would underlie climate within an organisation.
3.3 The relationships between employee demographic variables, organisational climate, and employee perceptions of customer satisfaction
In Chapter 2 it was argued that organisational climate may be represented by the sum of the perceptions of psychological climate of the individual employees within an organisation. It was also argued that the environment within the service organisation would strongly affect the climate within which the customer operated (i.e., be strongly related to customer satisfaction). Further, a number of studies were presented which showed strong correspondence between the employee’s perception of customer satisfaction and customer reports of satisfaction. Structural Model A, presented in
Figure 3.3, proposes that employee demographic variables will affect organisational climate, and organisational climate affects employee perceptions of customer satisfaction.
72 Figure 3.3 Structural Model A
The second aim of this study is:
Aim 2: To evaluate the notion that employee perception of customer satisfaction is affected by organisational climate, which is, in turn, affected by demographic variables of the employees.
Or, in other words, the second aim of this project is to test Structural Model A.
Specific hypotheses generated from Structural Model A comprise:
Hypothesis 2: There is a relationship between an aggregate measure of organisational climate and employee perception of customer satisfaction.
Hypothesis 3: Employee demographic variables, taken as a multivariate
73 variable, are a predictor of an aggregate measure of organisational climate.
3.4 The relationships between the dimensions of organisational climate, employee perceptions of customer satisfaction, and performance of hotels.
As outlined in Chapter 2, the TQM literature clearly provides an expectation that a good organisational climate is associated with high levels of customer satisfaction, and that customer satisfaction is the key to financial success of an organisation.
Structural Model B, presented in Figure 3.4, describes such a relationship where the set of dimensions of organisational climate (taken for the moment as represented by the dimensions described by Jones and James, 1979) predict employee perception of customer satisfaction, which in turn predicts hotel performance (as indexed by
REVPAR).
The third aim of this study is:
Aim 3: To evaluate the notion that REVPAR is affected by the level of customer satisfaction (as indexed by employee perception of customer satisfaction) which, in turn, is affected by the set of dimensions of organisational climate.
Or, in other words, the second aim of this project is to test Structural Model B.
Specific hypotheses generated from Structural Model B comprise;
Hypothesis 4: There will be a relationship between employee perception of customer satisfaction and REVPAR.
Hypothesis 5: The dimensions of organisational climate, taken as a multivariate variable, are a predictor of an aggregate measure of employee perception of customer satisfaction.
74 Figure 3.4 Structural Model B
75 4.0 Method
4.1 Introduction
This chapter will provide details of the method used in this study to test the models and hypotheses presented in Chapter 3. The approach of this study, whilst primarily quantitative in nature, also includes some qualitative analysis and some qualitative interpretation of the data.
Justification for the particular research paradigm and the method of this study will be provided. Further, the use of the organisational climate survey method will be detailed together with the formulation and subsequent modification of the selected instrument. Details of the sample selection process will be explained and the parameters of the sample will be described. The selection process of the demographic details that were deemed to be of importance to the study and the organisational climate questionnaire will be justified.
To see what, if any, the relationship is between organisational climate and organisational performance required the collection of data on dependent variables that measured hotel performance. The two principal measures used for the study were employee perceptions of customer satisfaction, and the average daily room rate multiplied by the occupancy percentages for a hotel (REVPAR). This latter measure is a widely used industry method of comparing and assessing hotel performance. Morey and
Dittman (1995) used REVPAR as a benchmark in the evaluating of hotel general manager’s performance. Vallen and Vallen (1991) described REVPAR as an important statistic in hotel appraisals with it being the basis used by Lodging Magazine to define success in their annual study of hotels. Both of these dependent variables will be discussed in detail providing the rationale for their selection.
76 The pilot study and pre-test procedures are described, as is the affect. This feedback had regarding some major decisions on the final instruments, the approaches to the hotels, the process of dealing with the hotels' management and staff, and data collection methods. A description will also be given of the process required to get co- operation from the 14 hotels that participated in the study.
The data collection methods themselves will also be analysed, providing a detailed understanding of the various processes that were required and followed during the study. This analysis will detail the sensitivities that became apparent from both middle level and senior managers.
4.2 Justification for the paradigm and method
Justification for the research method is firmly rooted in the human relations’ school of management research. The psychological forces that impact upon individual employees’ behaviours and perceptions have been part of this research stream almost from the beginning of the school of thought. Human relations management theories that take the perspective of concern for individual employees, really only began to emerge in the last 75 years of this century. Prior to that time, the impact of management actions on the individual was taken very much for granted. Or, as in the hotel industry, there was often a paternalistic view taken of staff, if they were valued at all by the employer
(Davidson and De Marco, 1999). Of course if they were not valued, their job tenure would be very short indeed. Labour protection laws were only just being considered in the first quarter of the century in most western democratic countries. In many respects
Australia was ahead of many countries in protecting workers’ rights with its Harvester judgement of 1914 (Gardner & Palmer, 1997). Whilst the Harvester judgement, delivered from the industrial court, was about the living wage for a family, it showed
77 that the state held employers to be responsible for treating their workers reasonably. Of course the existence of a powerful trade union movement was crucial in obtaining fairness of treatment. Traditionally, the worldwide hotel industry has been an industry where the union movement has never really been strong in membership. This is particularly so in Australia. A reason put forward by Timo (1993) is the difficulty in covering a relatively small number of workers on one site.
Many human relations theorists and especially those who write about the hospitality industry continually stress that understanding employees is fundamental to achieving good service and customer satisfaction (Meudell & Gadd, 1994; Francese,
1993; Borchgrevink & Susskind, 1999). Organisational climate studies, as discussed in chapter 2 have been used as a management tool in the understanding and measuring of employee perceptions about their organisation (Jones & James, 1979; James & James
1989; Schneider & Bowen, 1985; and Ryder & Southey, 1990, among others). These perceptions can be expressed through a parsimonious set of dimensions that can be aggregated to the level of analysis of the organisation but can also be used to analyse the relationships of the individual to various dependent variables such as customer satisfaction and REVPAR.
Therefore, this particular research method was chosen because it has a significant research history and tradition of trying to explain and understand individuals within organisational settings. What is surprising is that there has been no empirical academic study completed within the hotel industry. Climate studies, as described in
Chapter 2, have been completed in a range of industries and the application of this research method to service industries has been a focus of Schneider and Bowen (1985).
Yet the construct’s use to date in the hotel industry literature has been confined to merely descriptive references which combine culture and climate (Fancese, 1999 and
78 Vallen, 1993). The only real use of organisational climate instruments has been through individual hotel companies doing one-off surveys and only one major chain, Marriott
Hotels and Resorts, has adopted it as standard management tool in the understanding of employee perceptions.
Much of the current leading edge management literature examines change management in all its many guises and it embraces the concept of the learning organisation. Senge (1990) enthuses about empowerment and quality products, processes and services. This is predicated upon a relatively stable work force. Yet in an industry, such as the four and five star hotel industry, where it is the norm for the labour costs to take 30 – 40% or more of turnover (Howarth, 1995), virtually no research has been done on this cost element and the interaction with management change.
4.3 Identification and rationale for the sample
The current study follows the methods of James and Jones (1976); Jones and James (1979); Schnieder (1973); Schneider and Bowen (1985); Furnham and Drakely (1993); Patterson et al., (1996); Ryder and Southey (1990) and many other organisational climate researchers in selecting an organisation as the unit of analysis. The focus of the data collected for the study was the hotel industry and in particular the four and five star hotels. Considerable thought was given to how the hotels would be selected for this Australian survey.
The survey is divided in 2 major parts: (1) assessing the organisational climate; and (2) ascertaining the demographic and performance data. Whilst the study focused upon the organisation as one unit of analysis it also uses the individual employee as the other unit of analysis, making it a multilevel approach. The research models presented in chapter 3 clearly show that to test the hypotheses posited, there is a need to undertake multilevel research. 79 Because there are many levels and styles of hotels and to ensure that the individual hotels returned enough questionnaires for meaningful statistical analysis it was decided to concentrate upon hotels that were at least four star and with in excess of
150 rooms. These parameters would ensure that the total staffing for each hotel was in excess of 100 people and the sample size would meet the requirements for the proposed quantitative statistical analysis.
The next issue to resolve was if this should be an Australia wide sample or concentrated upon, or within, one region. Whilst an Australia wide sample would have provided some results that could be interpreted more broadly, the sheer scale and management of the process would present certain logistical problems. Another consideration was whether to concentrate upon one company with hotels across the country. This was rejected because of the possible sensitivities one company may have with the results and the limitations this may place upon publication. Also, there would be a possible bias of any of the results obtained because of a particular company culture and procedures.
It was, therefore, considered that a broad regional study that encompassed a number of sub-regions representing an appropriate range of hotels would be optimal.
This necessitated an examination of the concentration of hotels in the four and five star category in Australia based upon the regional areas used by the Tourism Forecasting
Council (1998, p. 24-25). These areas were Sydney and NSW; Melbourne and Victoria;
Perth and Western Australia; Brisbane, Gold Coast, and Cairns; Adelaide and South
Australia; Hobart and Tasmania; Canberra; and Darwin and the Northern Territory.
Because of the requirement for a concentration of four and five star hotels with 150 rooms, only three areas geographically matched the profile, viz., Sydney, Melbourne, and Southeast Queensland (Brisbane, Gold Coast and the Sunshine Coast). Both Sydney
80 and Melbourne were ruled out, because, whilst they had the number of four and five star hotels, they were heavily concentrated within the cities and did not have the range of sub-regions. This left the Brisbane, Gold Coast and Cairns regions. As Queensland is so diverse, it was decided that Southeast Queensland encompassed such a wide variety of sub-regions and met the requirements in numbers of four and five star hotels that it was appropriate to concentrate the study upon this area.
What is a four and five star hotel? According to the Royal Automobile Club of
Queensland (RACQ, 1997) the definitions are:
Five star hotels -
International style establishments offering a superior standard of
appointments, furnishings and décor with an extensive range of first class
guest services. A number and variety of room styles and/or suites
available. Choice of dining facilities, 24 hour room service and additional
shopping or recreational facilities available.
Four and half star hotels -
Establishments offering all the comfort of a four star establishment but
with a greater range of facilities, higher levels of presentation and
individual guest services.
Four star hotels -
Exceptionally well appointed establishments with high quality
furnishings and offering a high degree of comfort. Fully air conditioned.
High standard of presentation and guest services provided. Restaurant
and meals available on premises.
81 Three star hotels -
Well appointed establishments offering a comfortable standard of
accommodation, with above average floor coverings, furnishings,
lighting and ample heating/cooling facilities.
(Source, RACQ, 1997, p.22)
At the time of the survey, and within the parameters set of four to five star hotels with 150 rooms plus, Brisbane had 13 hotels, the Gold Coast 15 hotels, and the
Sunshine/Fraser Coast four hotels making a total of 31 eligible hotels in the study area.
Those that actually took part were, five from Brisbane, six from the Gold Coast and three from the Sunshine/Fraser Coast. This represents 44% of the hotels.
4.4 Gaining co-operation of the hotels
When this research study was initially undertaken it was always realised that without the full co-operation of sufficient hotels it would not be viable. Two immediate issues needed to be addressed, firstly, how were hotels to be approached and selected, and secondly, on what basis could they be persuaded to co-operate. For initial contacts, the researcher used his personal network of hotel managers, where it became clear after numerous discussions there was a definite reluctance to take part. The reasons put forward included the total distrust of academic research. As is made clear by the comment of a general manager of a Queensland five star hotel when he expressed his opinion by stating what he thought of such studies as ‘being of absolutely no relevance to running an hotel’ (name withheld). At the other end of the negative comment spectrum was a comment by a senior HRM that ‘it was all very well doing research but the time it took of staff and management was just too much’ (name withheld).
82 It was at this stage that a reference group (expert panel) of 6 senior hotel executives was established. Their advice ranged from the formulation of the instruments as previously discussed to the lobbying of industry colleagues to encourage them to take part. Each member of the expert panel was visited and the overall project of ascertaining organisational climate and comparing it to organisational performance was explained in detail. Had such a survey been commissioned from private consultants it would cost several thousands of dollars. As a senior executive of Marriott in Australia was a member of the reference group, and as Marriott already used an organisational climate survey, the management benefits were easily made clear to all the executives.
In particular, Mr Grant Bowie, Chairman of the South East Queensland
Accommodation Division of the Queensland Hotel Association agreed to support the project and call for volunteers from all eligible members within the designated area.
This was done via a regular ‘Monthly Chairman’s Newsletter’. There is little doubt that without this support and the various networks of the expert panel it would have proved extremely difficult to proceed with the level of participation achieved. The researcher attended several formal and informal meetings with executives in the hotel industry in order to firstly talk about organisational climate and to inform them that a major study was to be undertaken.
A comment made by several of the expert panel was that this type of survey, asking staff their perceptions, was seen as a very threatening process to the management of a property. These comments were supported by 11 of 14 HR managers of the properties that eventually took part.
The response to the chairman’s newsletter brought forth 17 hotels that expressed an interest in taking part. All of these hotels were visited and detailed discussions took
83 place with the general managers and the HRM departments. This entailed a full explanation of the background of the instrument and its major dimensions. It also allowed the managers to ask how the results would be processed and how they might be interpreted. These discussions ended with the offer to each hotel that they would receive:
! full confidentially and anonymity
! a full descriptive statistical report on their own organisational climate with the
overall means for each dimension broken down into departments
! an interpretative report accompanying the statistical report on of their organisational
climate
! a pre-briefing with both senior management and departmental heads if required
! a post briefing with both senior management and departmental heads if required
In return the hotels would agree to facilitate the distribution of the questionnaire by:
! the general manager including a short note to encourage staff to complete the
questionnaire
! HRM would ensure that the questionnaires were distributed through the
departmental heads
! HRM would ensure that each departmental head was fully briefed on how the
questionnaire should be completed
! HRM would ensure that departmental heads provided a briefing to their staff
! HRM would distribute the management questionnaire
! HRM would act as a collection point
! the general manager or financial controller would complete the profile document
that gave key financial performance indicators
84 As can been seen from the above points, the process from both the hotels’ and researcher’s perspective was fairly complex and involved. Of the original 17, 2 hotels withdrew from participation when the full detail of the study was explained. The reasons given were, in one case, they were not prepared to spend the time required and in the second case, the manager was being transferred. A fact he was not aware of when he responded to the initial request.
One other hotel withdrew from participation at the last minute because they said they were going to undertake a major re-structure of their organisation. This left the 14 hotels that actually participated.
There was a fairly constant need to keep in weekly touch with the properties because the actual dates of the survey were being left to them to select in order to fit in with business requirements. It is noteworthy that the distribution and collection process was without any real problems. The promised reports on each hotel’s organisational climate were returned within two months of the data being collected. Whilst the detail of these findings are fully discussed in Chapters 5, 6 and 7 it is interesting to note that only two hotels took advantage of the offer to conduct a post report briefing. Four of the other hotels made contact, by phone, after the reports were sent to discuss the contents. The eight hotels that did not respond were all contacted. Several said they would get back to the researcher when they had time to digest the contents, but none has done so. The other hotels simply expressed their thanks, saying that, their management team would discuss the report.
The reports were by necessity quite detailed and from the experience of the 2 hotels who wanted detailed de-briefing for the senior management the main concern was to ensure that they were reading the results correctly. Contact with the HRM departments
85 of the hotels that required no explanation sometime after has revealed that the survey confirmed what senior managers already suspected about their organisational climate and thus were not keen to pursue the issue. A comment was made that ‘it confirms what we know but within the operational constraints there is little we can do to improve’
(name withheld).
4.5 Formulation of the survey instruments
The main questionnaire was the organisational climate, a modified version of
Jones and James (1979), plus demographic details for individual employees. Another instrument was developed to ascertain certain financial and statistical information. This information was required for the analysis of organisational climate in each hotel to test the hypothesis that performance is correlated with organisational climate. Also a third instrument was used to collect data only from managers within each property that focussed upon demographics and performance measures at the hotel level. This instrument was primarily used as a check on operational and performance indicators.
4.5.1 The organisational climate questionnaire
The instrument is capable of producing an empirical assessment of the influences exerted by both situational and individual factors in the development of work-related perceptions. As discussed in the Chapter 2 section 2.15, it was originally based upon 35 a priori scales drawn from the literature that are assessed by 145 items.
Ryder and Southey (1990) used the Jones and James instrument to survey a large public service building, construction and maintenance authority in Western Australia.
They reported that is was necessary to modify the instrument in several ways. Some of the items were reworded because of culturally specific meanings and the eradication of
86 sexist language. Further modification was required to remove wording that referred specifically to a military setting (the original Jones and James instrument was used on
US Navy personnel). The original questionnaire used a variety of response methods across items and so the response categories were also modified to a consistent seven point Likert type scale as this would reduce the time taken by each respondent to complete the questionnaire. Ryder and Southey also reported that the 145 item revised questionnaire took approximately 30 minutes to complete.
The instrument used in this study, followed Ryder and Southey’s modifications but additionally needed to adjust the language from that suitable for the public service to that of the hotel context. This required only some fine-tuning of certain item wordings.
However, there was a far more contentious issue of the formulating of the current instrument. A 30-minute completion time for the questionnaire (Ryder & Southey,
1990) was going to be a major impediment that threatened the feasibility of the whole study. The author’s 20 years of experience within the hotel industry and discussions with hotel managers confirmed that such a long and complicated survey instrument would simply not get the required response rate. Sekaran (1992) noted that there is an inverse relationship between the length of the questionnaire and response rate. Indeed many hotel managers indicated that participation would depend upon what they saw as being a reasonable length for the instrument.
After consultation and email discussions with one of the original co-authors of the questionnaire Professor Lawrence James, Pilot Oil Professor of Management at the
University of Tennessee, Knoxville, it was agreed that a shortened version of the instrument would still capture the construct. Because the original instrument had between two and seven items loading upon each factor and taking into account the analysis of Ryder and Southey’s which showed improved reliabilities it was decided
87 that the first 2 items loading onto the 35 a priori scales would be used. The approach of using a shortened version of the instrument was endorsed by Professor James (via e- mail, on the 24th February 1997) and also by Professor Ryder, Professor of
Management, Griffith University, Gold Coast (February 1997).
The shortened version of the organisational climate qustionnaire now consisted of 70 items with a rooted seven point Likert type scale: 1 - strongly disagree to 7 - strongly agree. All items were grouped onto five pages with between 13 and 15 items per page. The 70 items for the 35 a priori scales were randomly distributed thought the questionnaire. Instructions were given in the short preamble and the rooted seven point
Likert scale and instructions were repeated on every page. The 70 items and the 35 a priori scales used to develop them are presented in Table 4.1. The full questionnaire is presented in Appendix A.
88 ’ scales used by Jones and James (1979). James ’ scales used by Jones and a priori
20 Your supervisor is attentive to what you say. 21 time. of ahead work your schedule to need you help the provides supervisor Your 33 or herself. himself hard by working an example sets supervisor Your 45 a team. as to work or her him for work people the who encourages supervisor Your 38objectives. its meeting is group work your well how of aware are You 70 Your immediate supervisor is successful in dealing with higher levels of management. 44 Communication is hindered by following chain of command rules. 32 job. your in things different of do a number to opportunity the have You 43 job. in your and skills knowledge of your use to full make opportunities have You Trust Up problems and needs 15 49 Staff members generally trust their managers. employees. of problems and the needs about informed keep well Managers ’ Scale Item # Item Item # Item Scale ’ a priori 2. Facilitation Work Emphasis3. Goal 9 Facilitation4. Interaction Your supervisor ideas offers new jobfor related and problems. Feedback5. Job 30 14 performance. of standards high emphasises superior Your or to ideas exchange and her supervisor the Your encourages people opinions. him for work 6.who Confidence and 7. Upward Interaction 29 Employees evaluated. 4 of is performance 8. Awareness how your and stand you on where information good have You 51 trust Staff theirmembers generally supervisors. 37 of Expression9. Openness Your manager is successful in dealing with higher levels of management. groups. their work on in is going what know generally Supervisors Job Variety10. 31 to. attention are paid members staff of suggestions and The ideas 11.Job Challenge 25 2 job. in your variety is There of Your level job skill and a high requires training. Table 4.1‘ 35 the ‘ used in this study and of the 70 items PCQ version ofmodified the 1. Support 8 approach. to easy and friendly is supervisor Your 89
12 Responsibility is assigned so that individuals have authority their within own area. 24 they need. training on-the-job get members staff New 65 rules interfere able and job. to Excessive I am dohow regulations with my well 67produce. to pressure less much under is group work my groups, work other with Compared 66 job. a good doing is supervisor immediate my I think Overall de Corps Developmentand de Corps 48 56 development. and growth personal emphasises hotel The the local community. assisting concerned is hotel with This Design 62 jobs. their in pride take group work my of members Most 19 efficiently. are used resources that so are designed Procedures de Corps 60 member. staff to a prospective this hotel recommend I would circumstances Under most ’ Scale Item # Item Item # Item Scale ’ a priori 14. Esprit Organisational Growthfor 15. Opportunities 42 23 Esprit16. Workgroup in this Working is beneficialhotel to career. your job. in your knowledge and skills to learn worthwhile opportunities have You 17. Role Ambiguity 61 Job of 18. Efficiency department. another to change to want not would department our in personnel the of Most 11 Conflict19. Role job Your responsibilities defined. are clearly 720. Org. of Conflict Objectives and Goals well. to do its work needs group work etc. equipment, your supplies, the money, are able to get You 21.Job Pressure 18 59 57 start. the to complete you work opportunities have You 22.Planing & Coordination way. each others in is getting that everyone so are planned things hotel this In this Things in seemhotel to happen contrary andto rules regulations. 55 26 The way your work group is organised hinders the efficient conduct of work. Your hours of work are irregular. Table 4.1 Continued ‘ EspritProfessional 12. Job Autonomy13. 41 outsiders. to image a good has The hotel 1 job. in your exists action and thought independent for Opportunity
90
47 The to do strives hotel a better of hotels same other job type. the than 39 precision. demands job Your 54 important. is work Your 22 workgroup. in your friction There is StructureDown 52 defined. are clearly the hotel of The objectives Policy Org. of 36 People act as though everyone must be watched or they will slacken off. 50Effectiveness Discipline this in is hotel maintained consistently. Warmthand 69 productive. most be the would group work my hotel, this in groups work similar other all to Compared 40Cooperation other. each trust group work of your Members 63 ofhotel. the departments different the between relationships and co-operative are friendly there Generally with Otherswith 68 people are limited. to know to get opportunities job the In your ’ Scale Item # Item Item # Item Scale ’ a priori 25. Ambiguity of Org. of 25. Ambiguity 26. Trust Confidence & 46Job Standards27. hotel. this of and objectives the policies on information accurate to get possible It is 2728. Consistent Application in Everything this is checked;hotel individual judgement is trusted. not ImportanceJob 29. 17 3 The policies hotel’s are consistently applied to all staff members. in work. your quality of standards for rigid meet to 30. Reputation are required You 6 Friendliness31. Workgroup or unnecessary. unimportant relatively consider you job which on your tasks perform to are required You 34 Cooperation32. Workgroup 64 workgroup. of your the members of among most prevails atmosphere friendly A productive. most the of be one would departments other all to compared department, My 1033. Interdepartmental in workgroup. your exists of cooperation spirit A 53 departments of other department between and the There hotel. is conflict your Table 4.1 Continued ‘ 23. to Deal Opportunities Effectiveness and Planning 24. 13 5 people other Dealing job.with is part your of date. to up are kept work of your The methods 91
Down Rewardsof 58 35 the hotel this only information source of In important on is the grapevine. matters Hotel politics in count a promotion.getting ’ Scale Item # Item Item # Item Scale ’ a priori Table 4.1 Continued ‘ Communication34. Org. 35. Objectiveness & Fairness 16 28 affect you. changes which might about information advanced are given You liked Being is important in getting a promotion.
92 As discussed in Chapter 2 there has been some considerable debate over the validity of using a multilevel research approach with 2 units of analysis, namely the organisation and the individual employee, most notably from Glick (1985). This criticism was rebutted by James, Joyce and Slocum (1988) arguing it is the individuals that cognise and not the organisation. This study is conceptually based upon the use of multilevel research, using both the hotels (organisations) and their individual staff members (employees). Whilst it is both academically interesting and of potential value in a management sense to study organisational climate in its own right, the position taken here is that the explanatory power is greatly enhanced when it can be used to predict and interpret organisational performance. Therefore, the selection of the hotels was crucially important not only for sampling reasons but also to allow access to a large number of employees that are required for any organisational climate study.
4.5.2 Hotel profile and hotel managers questionnaire
The main purpose of these instruments was to gain performance data from the various hotels that would enable the researcher to establish some key performance indicator that could be related to organisational climate. The instruments sought several categories of information that included operational, financial and marketing statistics plus organisational structure and external factors. In order to design the instruments, input from the expert panel of 6 hotel executives was used.
The ‘Hotel Profile’ - After initial discussions it became clear that to access the last audited accounts of each property would be extremely problematic because in the view of the expert panel there would an unwillingness of hotel general managers to release this information. Secondly, there would be difficulty getting corporate approval for properties that belonged to large chains.
93 It was agreed by the expert panel that the information needed to be of a generally less sensitive nature and the sort of statistical information that general managers were giving out on a more routine basis to various federal, state and local authorities. In essence the information was restricted to room occupancy, average daily room rate (these 2 statistics are required to calculate REVPAR), standard room rates, business and revenue mix, staffing statistics and organisational structures. The general manager or financial controller should be asked to fill in the information requested. The hotel profile instrument is presented in Appendix B.
The ‘Hotel Managers Questionnaire’ - This replicated the demographic information that was to be gathered in the main organisational climate instrument to be given to the employees. None of this information was of concern to the expert panel.
The second section requested some financial performance indicators. It was felt that this information should be requested on the basis of budget performance on a five point
Likert type scale ranging from 1 - under budget to 5 - well above budget. This would give detailed indicators on how each hotel was operating but without giving the actual figures. The format was also followed for operational and customer satisfaction. The full instrument is presented in Appendix C. Of the expert panel of six executives from four and five star hotels only three took part in the study.
In addition to the usual demographics of gender and age it was of particular interest to see what effect education level, length of service within the hotel, length of time in the job, gross salary, employment status, hours worked, training interval, and functional area of individuals had upon organisational climate. The above demographic categories are used in the structural model A as exogenous variables that affect aggregate organisational climate .
94 4.6 Perceptions of customer satisfaction measure
The issue of how to measure the dependent variable customer satisfaction posed a particular problem of considerable concern. Academic analysis of the literature on customer satisfaction within the hotel industry brought forth an amazing lack of empirical data. Most data were based upon qualitative approaches or extremely small sample sizes. The approach to this issue, was the use of an expert panel as advocated by
Archer (1987), Elkin & Roberts (1987) and Moeller & Shafer (1987) as a method of assessing how this might be measured. The question of how to measure customer satisfaction was put to the expert panel all of whom said that they scrutinised the guest comment cards but they also readily agreed that these represented approximately only
4% of their customers. None of the expert panel used, in their hotels, any other means of systematically testing their customers’ satisfaction. All the hotel managers agreed that the most reliable method was the feedback obtained by their staff that was then fed back to the management team.
Lewis and Nightingale (1991) commented that hotel companies have difficulty in measuring customer satisfaction and, in spite of the inefficiency of comment cards, many still rely upon them. However, they also make the point that Marriott regularly surveys its customers randomly and chains like Sheraton are always looking at how the room comment cards can be improved. Francese (1993) highlighted the fact that hotels have built up an entrenched bureaucracy and bottom line thinking that often stifles the employees intuitively providing responsive customer service which, as Parasuraman,
Zeithaml and Berry (1985) have shown, is the key to service quality customer satisfaction.
95 As described in Chapter 2, Schneider and Bowen (1985) empirically demonstrated that customers and employees share perceptions and attitudes. It is therefore appropriate to use the employee perceptions of customer satisfaction as a reasonable measure of organisational performance in regard to customer satisfaction.
Additional to the above, cited links between employee perceptions, customer service and satisfaction, the recent work by Testra, Skaruppa & Pietrzak (1998) used the
Bagozzi (1992) framework in a study of the phenomenon in cruise line staff. This study used self-reported perceptions of job satisfaction and service intentions that were compared to customer satisfaction measured through structural equation modelling.
They found that there was an empirically verifiable relationship between the constructs and that job satisfaction directly related to customer satisfaction.
The above approach of employees reporting of customer satisfaction has been adopted as one of the research methodologies used in the current study.
4.7 Organisational performance – Revenue per available room (REVPAR)
Revenue per available room (REVPAR) has been selected as the prime indicator of a hotel’s performance because it combines 2 of the key performance indicators, that of occupancy percentage and average daily room rate. Figures on occupancy and room rates are collected and published by the Australian Bureau of Statistics (ABS) and are also disseminated by the major tourism bodies such as Tourism Queensland and the
TFC. It is indicative of their importance to the industry in monitoring its performance that both these measures are collected and published by the ABS and then further published by main tourism bodies throughout Australia.
96 4.7.1 Occupancy percentage
The occupancy percentage is the percentage of the total rooms let for any given period. The ABS figures are the aggregate performance of all hotels in a particular category – licensed hotels with facilities, within the region. This category covers all the major residential 3 to 5 star hotels and immediately provides a benchmark for hotel operators and managers as to how they are performing against the average for their particular area. It also can be used in conjunction with previous data to indicate the market conditions that have changed within the defined area and identify any specific trends. Many researchers such as Morey & Ditman (1995), Morey (1998) and Vallen &
Vallen (1991) have all shown the relationship between occupancy and profitability; the higher the occupancy, the higher the hotel’s profitability. It is the ‘short-hand’ used by hotel managers to judge how they are performing. Logically, if the hotel rooms are full then the hotel is gaining high revenue and the potential for high profits exists. During the data collection period for the survey, the 3 areas of Brisbane, Gold Coast and
Sunshine Coast recorded occupancy percentages of 63%, 64% and 57%, respectively, for the December 1997 quarter, and 64%, 61% and 59.6%, respectively, for the March
1998 quarter (QTTC, 1998).
4.7.2 Average daily room rate
Average daily room rate (ADRR) is the amount of money a hotel receives for the letting of the rooms for a particular period divided by the number of rooms let. The
ABS figures are the aggregate performance of all hotels in a particular category – licensed hotels with facilities within the region. This category covers all the major residential three to five star hotels and provides a benchmark for hotel operators and managers on performance. It also gives an indication of the prevailing market
97 conditions within the defined area. The amount of money a hotel can achieve for each of its rooms has a direct effect upon its profitability. Apart from the logic of such a statement it has also been the subject of study by many theorists such as Morey &
Ditman (1995), Morey (1998) and Vallen & Vallen (1991).
However, the measure of a hotel’s performance is a more complicated issue than that of occupancy percentage and the ADRR. A hotel’s performance affected by a range of other critical operational decisions and market mix. Hotels have published rates for differing types of rooms, e.g., suites are more expensive than standard twin rooms. They also have different rates for various types of business or market mix. The corporate business executive with an account at the hotel would normally receive a low rate because of the frequency of visits, whereas a casual visitor just travelling through the area who arrives without a reservation is likely to pay considerably more for the equivalent room. Multiple permutations are used in the setting of a room rate and a full explanation is not appropriate for this study. However, it can been seen when business and market mix is overlaid onto room types and the standard of amenities offered by particular properties, how complex the issue of average daily room rates is.
During the period of the data collection for this survey, Brisbane, Gold Coast and the Sunshine Coast recorded an average daily room rate of $97, $110 and $120 respectively for the December 1997 quarter and $113.60, $127.80 and $135.30 respectively for the March 1998 quarter (QTTC, 1998). It should be noted that the upward movement of all rates in the March 1998 quarter reflects the peak holiday season trading when all rates are high, and any annual increase would have occurred from January 1998. These are yet 2 more factors that need to be taken into account when assessing ADRR.
98 4.7.3 Revenue per Available Room (REVPAR)
The use of average daily room rate (ADDR) multiplied with the occupancy percentage of the rooms produces figure that is called REVPAR. REVPAR represents a complex figure combining both elements and is commonly used as one of the main performance indicators for the worldwide hotel industry. Whilst it is possible to use either ADDR or Occupancy singularly as points of reference for operating and financial performance, the combined figure gives a more refined measure. The hotel bedroom is a very perishable product in terms of its letting ability. Once a hotel bedroom is not let for a night that potential revenue can never be recovered, which is why occupancy is so critical to performance. However, most hotel managers worry about letting rooms too cheaply just to fill capacity, a point was confirmed by the expert panel. Once a hotel is known for discounting, this creates continual downward pressure on their rates. The general travel industry, such as, inbound and other travel agents and business groups all expect to be able to negotiate for low rates. The impact can be very severe on the
ADRR.
Therefore, the combination of occupancy and ADRR in REVPAR does provide a powerful performance indicator as far as rooms are concerned. A key element of
REVPAR is that it is able to truly represent a hotel’s performance, whether the marketing strategy is following the occupancy at any cost (profitless volume approach) or whether the hotel is holding out for a high room rate at the expense of occupancy.
4.8 Pilot and pre-testing procedure
Three instruments were to be used in the research study. As previously examined the main survey instrument for organisational climate was derived from 2 previous major applications of the instrument from its originators Jones and James 99 (1979) and one of its subsequent applications by Ryder and Southey (1990). The main hotel performance instrument had been designed in conjunction with the expert panel.
The final instrument, aimed at managers only, that required basic demographic, operational and budget performance data, was also designed in conjunction with the expert panel.
A range of pre-testing procedures were completed with the main organisational climate instrument because of the shortening of its length and the minor modification of wording to make it specific to the hotel context. Initially the organisational climate instrument was given to small tutorial groups of 15 to 20 third year undergraduate, hotel management students to complete. The main purpose was to ascertain the time scale required for completion and assess whether the modification to the wording was fully understood. Third year hotel management undergraduates were used because all students had gained work experience within the industry and they were working currently in a range of casual and part-time positions. This, therefore, provided a very suitable setting for some pre-testing. Additionally, approximately 30 % of the groups comprised overseas students, whose first language was not English, that provided an opportunity to test whether any of the item wording was ambiguous.
The students were not pre-briefed except to ask for their co-operation, and were asked to assume they were filling in the questionnaire at the request of their supervisor at work. Students who were not currently working were excluded. They were asked to follow the directions for the completion of the instrument as given by the introduction and take whatever time they need to complete it. The students took between 15 and 20 minutes to complete the questionnaire. After all students had finished, they were asked if there were any difficulties in completion or understanding the questions. Student comments were recorded. This process was repeated for six tutorial groups.
100 An analysis of comments was undertaken and it showed that the instructions needed slight modification. Although individual comments were made about certain item wording there was no discernible pattern on any particular item that might indicate it needed re-wording.
After the re-wording of the instructions, two hotels were approached to undertake the pre-testing on hotel employees of the main organisational climate instrument. This was done by using groups of staff varying between four and six from the food and beverage, rooms, and back of house departments. As with the student pre- testing, all the staff were thanked for their co-operation and asked to follow the instructions and take as much time as they required to complete the questionnaire. The groups all completed the questionnaire within 22 minutes with the fastest time being 14 minutes. All groups were asked for feedback and a number of comments were made about particular items and their wording; comments such as ‘a little confusing’ or ‘I wasn’t quite sure what it was asking’. The process of evaluating all the comments to see if any pattern emerged that required individual items to be re-worded was followed.
There was no discernible pattern to the staff comments, indicating that the items were generally well understood. It should be noted that each group was randomly selected, on the day, by the department head.
The whole pre-testing process in the hotels from greeting to getting feedback took no more than 40 minutes to complete. It was established that the instrument generally took under 20 minutes to complete and it was only a very small number of hotel staff that took the extra 2 minutes. All the questionnaires were checked to see whether answers showed the expected spread of responses, not all low, middle or high which might indicate the respondents were not discriminating in their answers.
101 It can, therefore, be said that the pre-test process enabled improvements to be made in the instructions and, when this was completed and used on the hotel staff sample, the instrument presented no problems for the staff to complete within a fairly small time frame. The time the questionnaire took individual employees to complete was critical, as the expert panel were of the view that a industry would not co-operate if the instrument was too long.
4.9 Administration of climate questionnaire
The 70-item organisational climate questionnaire formed part one of the omnibus survey. Part 2 consisted of demographic details and background of the respondents and their perceptions of operational and customer satisfaction. Attention was given to the readability and layout, with the typeface being ‘Times New Roman’ in a minimum font of 12-point. All answers either required a number to be circled, on a
Likert type scale, or a box to be ticked. One minor exception was included on the last question that asked about which department they worked, where it allowed the respondents to write in a department if theirs was not listed.
Each hotel was assigned a code number that was incorporated into the survey instrument for ease of identification and tracking purposes. All questionnaires were delivered by hand to each hotel at least one week prior the date of the study.
Additionally, sealed boxes with slots to take the returned questionnaires were provided to each HRM department to facilitate collection. An initial step was to ascertain the numbers of staff in each hotel, the human resource departments supplied the information. It had been agreed that each HRM department would take the responsibility of distributing the climate questionnaire within their property to individual heads of departments. HRM departments were also responsible for briefing
102 the department heads on how it should be distributed to employees. Department heads were aware the survey had the support of senior management as it had been discussed at the previous week’s departmental heads meeting. The department heads would actually distribute the questionnaire at the time the weekly wages slips were given out and simply ask their staff for co-operation in its completion. The staff where told to return it at their earliest convenience within 3 days.
Each questionnaire was given out with a short introductory letter together with concise instructions on how it should be completed, and a return addressed envelope.
Employees were told that they had the option of returning the sealed envelope to HRM or directly to the university. It should be noted that only the employees present on the day or the next day were given the questionnaires. Therefore employees that were away for the two days because of roster days off, holidays, part-time employment or casual employment were not included. Whilst this did reduce the overall numbers it was seen as a much more manageable process by the hotels that took part.
Less than 10 % of the total response from the employees were returned directly to the university. Arrangements were made to collect all the returned questionnaires within seven working days of distribution. With the exception of one hotel with a large number of employees that returned 61 completed questionnaires after the collection period there were a very small number forwarded on by the hotels that had been handed in late. All of the 14 hotels participating selected which week it was appropriate for them to distribute the surveys because of their individual business commitments. This process was completed within a three-month time frame.
103 4.10 Data collection and sorting procedures
These procedures have, in the main, been previously discussed in the above headings but it is relevant to list the chronology of events that were followed.
The questionnaires were arranged with the main organisational climate 70 item instrument being completed first and this was then followed by both the demographic and performance indicator questions. Oppenheim (1986) and Sekaran (1992) suggested this format as when the respondent reaches the end of the questionnaire they are likely to be convinced of the genuineness of the survey.
All the questionnaires were personally collected when they were completed.
This provided an opportunity to discuss with the HRM departments how the process of distribution had gone. Additionally, informal feedback was also obtained on how the staff had reacted. No significant problems were reported and the most common reaction was that the HRM departments thought that the response rate was very good for such a complicated instrument.
The total number of questionnaires delivered to each hotel was checked with the completed number of returns and the returned non-distributed ones. These were deducted from the delivered total so that the completion rate could be computed. All hotels were given a code and thus each batch was kept completely separate and the data was entered for each hotel batch by batch for processing on an SPSS computer package.
A standardisation procedure was required on one question from the questionnaires. As each hotel was allowed to use its own department names in order to facilitate the reporting back of individual departmental organisational climate for the hotels, the number of departments varied from a minimum of 8 to a maximum of 20. It
104 was decided that these should be collapsed into consolidated 6-department structure for the main analysis after the individual hotel reports had been produced.
All data were scanned to ensure that no questionnaire returns showed any patterns of regularity that might indicate the respondents were not discriminating in their answers to the 70 item organisational climate instrument. Only six were discarded because of this possible effect. The questionnaire was encoded to facilitate the entering and processing the data through the SPSS computer software package.
Chapter 5 reports descriptive and simple statistical analyses of the hotel general operating statistics and employee demographic data.
105 5.0 Hotel General Operating Statistics and Staff Demographic Data
5.1 Introduction
This chapter will present the initial analysis of the data collected in this study.
Whilst the prime focus in the chapter will be on a descriptive analysis of both the operating statistics of the 14 hotels and the demographic profile of their workforce, it will be supported by some simple inferential statistical analyses. The combination of a descriptive analysis with inferential statistics is a common procedure used by researchers when they are trying to interpret individual functions and behaviours
(Ghauri, Grenhaug and Kristianslund, 1995).
5.2 Analytical procedures
The data gathered in this study fall into 2 broad categories. The first, uses data for which each data point represents a single value for each of the 14 hotels (hereinafter referred to as ‘Hotel Level Data’, e.g., the Rack Rate). With a sample size of only 14, the analysis of data is limited to descriptive and simple inferential statistical analyses.
The second category uses data for which each data point represents a value for an individual staff member of a particular hotel (hereinafter referred to as ‘Staff Level
Data’, e.g., an individual employee’s age). Data may of course be aggregated to produce, for example, the mean value for a variable of employees within each of the hotels and so provide new aggregate variables that would then belong to the ‘Hotel
Level Data’ category (e.g., mean age of employees within each hotel). Given the large sample size of individual employees participating in this study, variables representing data gathered in the Staff Level Data category may also be subjected to complex multivariate inferential statistical analysis and structural equation modelling procedures that will be addressed in subsequent chapters.
106 The first part of this chapter reports Hotel Level Data for 14 hotels in South East
Queensland. This section will concentrate upon demographic data, general operational statistics and ranking of hotels by their REVPAR performance. Whilst REVPAR is a key performance indicator it must be interpreted in the context of other market information such as the business mix which is principally concerned with the types of markets the hotel attracts. As important as the business and revenue mix are, various other factors need to be used in an interpretative analysis of a hotel’s operation. In this research, data were also collected on employment statistics such as employee turnover and the wages-to-revenue ratio to provide a more inclusive set of data from which to analyses for each hotel’s operation and REVPAR.
In the second part of this chapter, Staff Level Data are reported. For both employees and managers, the results of key individual demographic data are given including gender, age profile, educational level, organisational tenure, job tenure, gross salary, mode of employment, hours worked, training frequency and training needs. In later chapters causal models will be evaluated that incorporate demographic variables presented in this chapter. For these demographic variables to be useful as predictors of outcomes such as the variation in REVPAR between hotels, the staff demographic variables must not only vary between individuals across the whole sample, but must also (when used to produce aggregate variables) vary between the hotels.
For example, if each hotel had roughly the same mix of staff, then, across the whole sample, variables such as age, gender, years of education, etc., would vary from individual to individual, but aggregate scores on these variables would not vary from hotel to hotel. If these aggregate scores did not vary between hotels, then these variables would not be useful in providing an explanation of the variation in REVPAR or
Organisational Climate between hotels. For this reason, in the second part of this
107 chapter, aggregate scores on Staff Level Data are presented and simple inferential statistical comparisons are made to examine whether these demographic variables vary across the hotels in our sample.
The data have been encoded and analysed using SPSS software (SPSS Inc.,
1998). In chapters 6 and 7 more complex multivariate inferential and structural equation modelling analyses will be presented.
5.3 Hotel level data
Firstly, within this section it is appropriate to provide some detail on the individual hotels that took part in the survey. Whilst some preliminary analysis of the hotels was undertaken in Chapter 3, principally to justify their selection for the sample, it did not provide any detail of their operational characteristics. In Tables 5.3.1 to 5.3.4 a summary is provided of the major characteristics and operational data of the hotels in the sample. These data provide a background for each hotel’s climate and is necessary information in order to be able to interpret each hotel’s climate score within its own business context. REVPAR has been selected as the main measure of a hotel’s performance in order to test (in later chapters) the hypothesis that a good organisational climate can predict improved organisational performance. Table 5.3.1 sets out the overall profile of the 14 participating hotels that were divided into four size groups:
100-199, 200-299, 300-399 and 400+ rooms, and that the RACQ rating was between four and five stars.
108 Table 5.3.1 Hotel Operational Statistics
REVPAR Hotel Room RACQ Rack ADRR Occup. REVPAR Rank Code Nos. Stars Rate $ $ % $
1 SC – 2 100-199 4.5 344.00 216.25 70.10 151.59
2 B - 9 100-199 5 270.00 166.21 74.70 124.16
3 GC – 1 400+ 4.5 170.00 129.50 74.73 96.77
4 GC – 3 300-399 5 280.00 132.01 68.00 89.76
5 B - 6 200-299 4 190.00 107.00 80.50 86.13
6 GC – 11 300-399 5 215.00 125.50 62.30 78.19
7 GC – 4 400+ 4.5 215.00 110.01 71.00 78.11
8 SC – 14 300-399 4 265.00 120.50 64.20 77.36
9 B - 8 100-199 4.5 180.00 102.86 72.60 74.68
10 B - 13 400+ 4.5 279.00 109.81 66.70 73.24
11 GC – 5 300-399 4.5 300.00 120.50 60.01 72.42
12 SC – 10 200-299 4 220.00 108.86 59.80 65.10
13 B - 7 300-399 4.5 200.00 121.86 52.23 63.29
14 GC – 12 200-299 4 225.00 88.80 70.10 62.25
Means 239.50 125.69 67.64 85.22 GC = Gold Coast, B = Brisbane, SC = Sunshine Coast
The standard published tariff (Rack Rate) when compared to the average daily room rate (ADRR) shows that there is little relationship except that ADRR is always substantially less, with a mean across the sample showing it at $125.90 or 52.48% of the quoted rack rate.
Table 5.3.1 showed quite large differences between the published Rack Rates and ADRR for each of the hotels in this sample. The question must be asked ‘why publish such a rate’? It is obviously an ambit claim but it does provide a starting point that hotel marketing executives can use to negotiate the real rates that they are likely to
109 achieve. Occasionally some guests will be charged this rate if demand exceeds supply at particular times. The rack rate also serves other purposes such as positioning the hotel in the market place and to some more naïve guests it can be used as the basis to offer discounts which attracts their business.
For the purposes of this research, how the hotel performs with respect to its rooms will be judged by its ADRR and occupancy percentage which when combined produces REVPAR (Table 5.3.1). There are 2 hotels that stand out in terms of ADRR
(hotels 2 and 9) and when their ADRR is combined with their occupancy it produces the
2 best REVPAR figures of $151.59 and $124.16 respectively. The third place in terms of REVPAR is $96.77 (hotel 1) with 2 other hotels (3 and 6) in the mid-to-high $80 range. The remaining 9 hotels REVPAR was less than $80, with the poorest performance at $62.25 (hotel 12).
Occupancy percentages for all properties are regularly collected then aggregated for regions or States and widely disseminated through government and state based tourism bodies. Occupancy percentage information is the most widely used statistic upon which to judge the health of the hotel industry. It is certainly true that a good level of occupancy is required before a hotel can perform well in financial terms, but there is also the situation were occupancy at any cost is not appropriate. The 2 lowest REVPAR ranked hotels (hotels 7 and 12) have vastly differing occupancy percentages, 52.23% and 70.10% respectively. Hotel 7 has retained a very respectable ADRR but at the cost of occupancy, whereas hotel 12 has done the opposite by achieving good occupancy but slashing its room rate, with it being the lowest of the sample by a considerable margin.
Table 5.3.2 details another element that feeds into the ADRR, that is, the mix of market segments of the total business. These market segments are broken into a number
110 of categories. The term FIT (free independent traveller) refers to a guest who does not pre book but turns up at the hotel seeking accommodation and thus is most likely to pay the full rack rate. Conference delegates are staying in the hotel and attending a conference either in-house or in the area. Whilst the rate for the conference business is generally one of the lowest, it has the advantage that the guests normally stay for reasonable periods of time, viz., 5.6 nights for international delegates, 4.3 nights for interstate delegates and 2.6 nights for Queensland delegates (QTTC, 1997).
Tour group business is normally the lowest rate (with the possible exception of aircrew). It is based upon volume and repeatability, and normally subject to yearly negotiation. These groups can be both domestic and inbound tourists but within the sample hotels in this study the predominance is mainly inbound Asian groups.
Corporate business is very much dependent upon the location of the hotel. It can be very high yielding because executives tend to want superior rooms. The hotels in this sample are all in the price range that would suggest senior executives rather than the lower level corporate employees. The one exception is the aircrew market whose business is normally confined to major cities with airports - Brisbane and Coolangatta in this sample. Although there is an airport at Maroochydore on the Sunshine Coast the level of airline business generated is negligible. For aircrew contracts it is only the major hotels that are considered by the airlines and they require the hotel manager to trade off the daily room rate for a substantial number of guaranteed bed-nights on a daily basis for the length of the contracted period. In general, this is the best room rate offered by any major hotel and has the capacity to negatively affect the ADRR quite considerably. Two hotels in the Brisbane sample (hotels 6 & 13) and one on the Gold
Coast (hotel 5) both have large aircrew contracts.
111 Table 5.3.2 Rooms Business Mix
Hotel REVPAR F.I.T. Conference Tour Corpor- Govern- Leisure Code Rank Delegates Groups Ate Ment % % % % % %
SC – 2 1 80.00 12.00 2.00 5.00 1.00
B - 9 2 13.50 1.50 55.50 9.50 20.00
GC - 1 3 44.00 4.60 38.40 1.80 11.20
GC - 3 4 17.50 36.70 25.20 3.20 17.40
B - 6 5 8.00 5.00 8.00 54.00 7.00 18.00
GC - 11 6 8.00 12.00 51.00 6.00 5.00 18.00
GC - 4 7 25.00 4.00 71.00
SC – 14 8 67.70 25.90 1.90 4.50
B - 8 9 8.00 10.00 20.00 44.00 8.00 10.00
B - 13 10 11.70 8.30 14.30 50.90 10.50 4.30
GC - 5 11 3.00 10.00 45.00 5.00 1.00 36.00
SC – 10 12 60.80 8.20 23.50 7.50
B - 7 13 5.00 16.00 59.00 11.00 9.00
GC - 12 14 81.60 3.20 15.20
GC = Gold Coast, B = Brisbane, SC = Sunshine Coast
The last 2 categories are government and leisure. Generally the government rate will be somewhere between the conference and corporate rate, whilst the leisure rate will be the second highest level of normal rates to the rack rate. These are normally pre- booked and are often accommodation, holiday or short break packages put together by the hotel companies.
By examining Table 5.3.2 it is evident that not all the sample hotels are in all market sectors. Despite detailed discussions with the management of the hotels in the pre-testing processes to ensure the common usage and understanding of the terminology
112 for the market sectors, it is clear that not all hotels use the market segments descriptions provided above. In particular, hotel 2 and hotel 10 have used the FIT category to account for both FIT and the leisure segments. As they are both high yielding market segments it has no major effect on the analysis and just reflects these hotels’ individual reporting system. Hotels 1, 4 and 14 also had similar difficulties with splitting their market share of FIT and leisure.
It is of interest to note that a high level of FIT business does not guarantee good
REVPAR. Hotels 2 and 10 are at different ends of the REVPAR ranking but both have a high level of FIT. The hotel that was most reliant upon tour group business also returned the poorest REVPAR ranking (hotel 14). Unless the hotel was able to negotiate an extremely advantageous contract in terms of room rate such a high percentage of tour group business will always lead to a poor performance, judged by REVPAR, despite achieving a reasonable occupancy 70.10%.
The corporate level of business shown was concentrated, as would be expected, in the Brisbane hotels (6, 7, 8, 9 and 13). This pattern, to a much lesser extent, was also repeated for the business generated from government organisations.
Table 5.3.3 displays additional information covering revenue generated by rooms, food and beverage (F&B) and other sources. It also gives 2 key employment statistics of employee turnover percentage and wage cost as a percentage of revenue.
The percentage of revenue gained from rooms and F&B reflect the type and style of the operation. The highest figure for room revenue is hotel 12 with 77.7% and the highest for F&B is hotel 1 with 60.00%. There is no discernible pattern linking the percentage of room or F&B revenues with the REVPAR ranking.
113 It is worth noting that the hotels which reported significant other revenue, 3, 10 and 14 are all in locations that are either sport or eco-tourism oriented and not in a main coastal resort or city locations. Their attraction to the customers lies in the very range of other services in addition to rooms and F&B. To an extent, they tend to offer a more self-contained package with customers not needing to leave the resort during their stay.
These hotels were also evenly spread in terms of their REVPAR returns.
Table 5.3.3 Hotel Revenue Mix Percentages and Key Employment Percentages
Hotel REVPAR Rooms F&B Other Employee Wages to Revenue Code Rank % % % turnover% %
SC - 2 1 57.00 36.00 7.00 33.00 29.00
B - 9 2 70.00 26.00 4.00 5.00 32.50
GC - 1 3 26.00 60.00 14.00 not given not given
GC - 3 4 31.40 35.60 33.00 32.30 49.70
B - 6 5 55.00 39.00 6.00 38.00 30.00
GC - 11 6 60.00 34.00 6.00 30.00 34.00
GC - 4 7 75.00 25.00 9.40 39.44
SC - 14 8 45.60 26.20 28.20 38.00 36.60
B - 8 9 59.00 39.00 2.00 not given 34.00
B - 13 10 55.70 41.10 3.20 49.90 37.90
GC - 5 11 49.00 46.00 5.00 not given 38.00
SC - 10 12 29.70 31.40 38.90 103.00 38.30
B - 7 13 71.00 23.00 7.00 30.00 35.00
GC - 12 14 77.70 15.10 7.20 not given 37.07
Means 54.44 34.10 12.42 36.86 36.27 GC = Gold Coast, B = Brisbane, SC = Sunshine Coast
In examining the key employment percentages (Table 5.3.3) there are some
114 obvious gaps where individual hotels would not release these figures although having previously agreed to do so. It was always known that these are very sensitive figures and thus closely guarded (comments from the expert panel). The hotels were asked on 2 separate occasions to provide the figures but declined. A range of reasons given were ‘we have had a change in management personnel which has adversely affected this year’s figure’ (anon); ‘the executive committee has changed its mind’ (anon); and ‘we have had a policy change’ (anon). Three of the hotels that did not supply the employment turnover percentages were ranked poorly in their REVPAR performance (hotels 5, 8 & 13). Only hotel 1 of these non-returns was ranked highly in the REVPAR performance indicator.
In addition to the non-responses, hotels 4 and 9 returned figures of less than 10% for employee turnover. Hotel 9 has a relatively small full time staff of 55 casual staff running a city centre property that is quite new and features an all suite accommodation layout. Upon questioning, the hotel said the figure was based upon their full time staff and did not include the management and that 5% was correct. It can be seen that this particular hotel (9) is deriving a high proportion of its revenue from rooms. They have a policy of trying to maximise the use of casuals in both the housekeeping and restaurant areas. When hotel 4 was approached about its low staff turnover figure (9.4% - they employ 130 full time and 88 casual) they also responded by saying it excluded casual (all hotels excluded casuals in turnover statistics). Judged in the light of the other returns from similar hotels the figure for these two hotels is very low and some level of doubt must exist on its accuracy.
The general industry standard levels for employee turnover discussed by the expert panel was that 20-30% per year would be very good but that 30-40% was more likely to be the norm. It was noted, however, that this often rose very substantially in isolated and remote locations. As can be seen in Table 5.3.3, 6 hotels reported their employee turnover in the range of 30% to 40%. Two hotels were, however, higher by a large margin, with hotel 10 with 103% and hotel 13 with 49.9%. Hotel 10 is situated in
115 an isolated offshore location and its turnover figure is similar to other isolated hotels but, of course, that does not diminish the problem posed by such a turnover level. It also performed poorly in the REVPAR rating. Hotel 13 is in a city centre location and a figure of almost 50% must give rise to concern in terms of operational efficiency and management effectiveness. This hotel was also poorly rated by REVPAR.
The last column in Table 5.3.3 provides another critical employment statistic, that of wages cost to total revenue. In any service industry, such as hotels, and especially the four to five star hotel category, this percentage is often indicative of the level of profitability. The expected range for such hotels is 30% to 40% (Howarth,
1995). Of course this figure is dependent upon the revenue generated as much as it is on staff pay levels. There has been a significant shift in the pattern of employment over the last decade toward higher levels of casual employment in the hotel industry (Timo,
1993). Because of the huge variability in demand, hotel companies are increasingly seeking to use labour as a variable cost rather than a fixed cost. As stated above, hotel 9 employs 50% of its workforce on a casual basis and produced one of the best wages to revenue percentages.
Most of the percentages are grouped in the mid-to-high 30’s that indicates an acceptable range for the categories of hotels. There is one exception, however, with a return of a 49% wages cost to revenue (hotel 3). This must indicate a major imbalance in wages cost. In terms of its REVPAR performance it was fourth and it also uses a 50% casual staffing complement. The other data from this hotel suggests that it is not performing reasonable well but such a high percentage of wage cost must make the hotel unprofitable. Subsequent to the collection of the data in this particular hotel several senior management changes have taken place with some 30 staff redundancies and a major organisational restructure has occurred which would tend to support the assumption that they were not trading profitability. 116 Table 5.3.4 Comments by hotels on what affected trading conditions
Hotel Code Comments
01 Undertaking room refurbishment 02 Undertaking room refurbishment 03 Very variable international visitor numbers 04 Increase in room supply but no increase in demand 05 Slow down in Japanese and Asian markets and opening new 110 room block 06 New hotel and location on South Bank 07 Rate drop by major city centre competitors 08 Oversupply of Brisbane city rooms and weak commercial growth 09 Lack of confidence in marketplace and rate dumping by five star Brisbane competitors 10 Changing market trends and internal organisational cultural change and restructuring process 11 Asian downturn, QTTC ineffectiveness and rate cutting by new hotels 12 Downturn in Gold Coast visitor numbers which causes the four and five star hotels reducing rates which impacts on whole market 13 Asia market downturn 14 Payroll tax and worker’s compensation payments far too high. Six retrenchments because of wage increase through enterprise bargain
Table 5.3.4 shows the individual comments made by the managements of the participating hotels when they were asked what internal/external factors affected their operations during the year. By far the largest group of comments is related to markets and marketing - 9 in total. Such concern with the markets and marketing is supported by leading services marketing theorist’s (Ziethaml, Berry & Parasuraman, 1988) and many operational management theorists that see the integration of marketing with operations as the very key to financial success (Slack, Chambers, Harland, Harrison and Johnston,
1995). In simple terms, without attracting a sufficient number of customers, no operation, especially a hotel can survive.
The second most frequent group of comments were those that related to competition in rates or rate dumping (the practice of dropping room rates substantially
117 to ensure business). This is one of the first management strategies that some hotels seek to utilise when there are difficult trading conditions. Such a strategy impacts upon employees and employers because it has the consequence of reducing the wage cost, by making people redundant, and increasing the use of casuals who are only used when the business can support their use.
If the larger hotels cut rates they will draw business from the smaller hotels unless they also match or go lower. Whilst it can be argued that this is ‘the market’ working at its most efficient, when a large number of jobs and percentage of capital investment is involved this is often a less than satisfactory mechanism. It is not for this thesis to discuss the merits or otherwise of open market economic systems but even in this small sample it is apparent how dramatic the consequences are for employees and investors when such circumstances occur.
Four of the hotels mentioned upgrades to their accommodation or being ‘new’ in the market place. Only 2, however, were concerned about new hotels opening and leading to an over supply situation in hotel rooms. One comment was directed toward the employment regulations and one to a management and cultural change process. The hotel that mentioned the management and cultural issues is the hotel with the largest employee turnover percentage.
Staff level data
Table 5.4.1 shows the response rate for the survey of the 14 hotels. Several points need to be made with regard to its interpretation. Firstly, the majority of the questionnaires were distributed to all staff in each hotel regardless of their employment status on the one day. Only a very small number of hotels used 2 days. The distributed number of questionnaires does not equate to the total number of employees of all 118 employment modes for each hotel but merely the employees that were present at the time of distribution. A full description of the procedure was provided in chapter 3.
Table 5.4.1 Response rate for the survey of the 14 hotels
Returned Percentage Hotel Code Distributed Response % 01 1400 565 40 02 180 132 73 03 590 278 47 04 155 56 36 05 250 91 36 06 160 47 29 07 90 47 52 08 95 45 47 09 72 37 51 10 250 87 34 11 200 88 44 12 70 32 45 13 282 198 70 14 240 75 31 Total 4034 1778 44
The average response rate achieved was 44% from 4034 questionnaires distributed over 14 hotels in the survey area. The range of returns saw a spread of response rates from 29% for hotel 6 through to 73% for hotel 2.
As shown in Table 5.4.2 there is a relatively even split between the genders of the employees with females at four percentage points ahead of males. However the picture dramatically changes when the managers’ profile is examined, with males exceeding female managers by 31 %. Whilst this gender spilt may well reflect most industries, it again reinforces the fact that women are under represented at the senior levels of four and five star hotels.
119 Table 5.4.2 Gender – employees and managers
Employees Frequency %* Male 835 48.0 Female 906 52.0 Total 1792 100.0
Managers Males 95 65.5 Females 50 34.5 Total 143 100.0 *Percentages were calculated after missing or invalid responses (in this case 2.8% of employees and 0.0% of managers) were excluded.
Table 5.4.3 shows the gender makeup of employees for each of the 14 hotels in the sample. To examine whether gender mix varied significantly between the hotels a contingency table analysis was conducted on the data presented in Appendix D.
An important consideration in the calculation of the Chi-square statistic for contingency table analysis is the magnitude of the expected frequency for each of the cells (Mason, Lind and Marchal, 1998). The expected frequencies are in the denominator for the calculation of chi-square. If the expected frequency for any cell is quite low, the value contributed to the final chi-square value may be disproportionately large and possibly result in a type I error (inappropriate rejection of the null hypothesis).
When the expected frequency is too low in one or more cells, Mason et. al. (1998) propose combining several adjacent cells.
Howell, (1997) states that it is still open to question precisely how small is ‘too small’ for an expected frequency. He reports that the most common convention to deal with this problem is to require that all expected frequencies should be at least 5. This is a conservative position that he occasionally violates. Howell reported a computer simulation conducted by Bradley et al., (1979, cited in Howell, 1997) using tables
120 ranging in size from 2 x 2 to 4 x 4. It found that for the sorts of problems that would actually arise in practice, the actual proportion of type I errors rarely exceeds .06.
Bearing in mind, first, that the number of cells in the tables presented in the following sections are greater than those used in the simulation, the possibility of a type I error should be even lower than reported by Bradley et al., (1979, cited in Howell, 1977).
This occurs in an individual cell that will have a smaller impact on the final chi-square value when there is a larger total number of cells in the analysis. Secondly, when collapsing adjacent cells for the analysis, this must be conducted in a manner that will produce a comparison that is meaningful. The following convention was applied. A contingency table analysis was conducted to establish the number of cells with expected values less than 5. In analyses where the expected value of one or more cells was less than 5, adjacent cells were collapsed in a meaningful way and the analysis re-run. If in this analysis there were still cells with an expected frequency less than 5, adjacent cells were again collapsed – provided this could be done in a meaningful way – and the analysis was re-run. This process continued until either all the cells had expected frequencies that were less than 5, or further collapsing of cells would result in an inappropriate aggregation of categories.
121 Table 5.4.3 Gender of employees for each of the 14 hotels
Male Female Row Total Hotel 1 290 260 550 52.7% 47.3% 31.6% 255 66 121 45.5% 54.5% 7.0% 3 128 146 274 46.7% 53.3% 15.7% 429 25 54 53.7% 46.3% 3.1% 545 42 87 51.7% 48.3% 5.0% 620 27 47 42.6% 57.4% 2.7% 720 29 49 40.8% 59.2% 2.8% 89 37 46 19.6% 80.4% 2.6% 915 24 39 38.5% 61.5% 2.2% 10 45 41 86 52.3% 47.7% 4.9% 11 39 51 90 43.3% 56.7% 5.2% 12 14 18 32 43.8% 56.3% 1.8% 13 89 103 192 46.4% 53.6% 11.0% 14 34 37 74 50.0% 50.0% 4.3% Column 835 906 1741 Total 48.0% 52.0% 100.%
Table 5.4.3 shows a great range in the gender balance between the hotels in the sample. This ranges from equal number of each gender in hotel 14, to 80.4% female in hotel 8. The level for females employed are not reflected by males in any hotel. The greatest proportion of male employees occurred for hotel 4 with 53.7% of the employees being male. In the contingency table analysis of gender across the 14 hotels, no cell had an expected frequency less than 5. The minimum expected frequency was
15.35 (Appendix D). The analysis found that gender mix varied significantly across the
2 14 hotels (χ (13) = 26.49, p < .05).
122 Table 5.4.4 shows the age profile of employees and managers. Whilst there are very few real insights to be gleaned from the age profile data, these figures reinforce the fact that the majority of employees are in the categories 15 - 24 and 25 - 34 years of age and that the split is fairly even between the 2 age groups. It may well be that the industry should consider recruiting from the older segments of the working population that seem relatively under represented. An advantage of employing older employees is that they have a lesser propensity to change jobs frequently.
The management age profiles show that most managers are in the 25 – 34 years of age. To an extent the large numbers of managers in that bracket does provide encouragement for young employees who are seeking to move up in the organisation, as achieving the level of a manager becomes a very attainable career goal. It also reflects the fact that there is also a very high turnover of managers.
Table 5.4.5 shows the age profile of employees at each of the hotels. An initial contingency table analysis found 28 cells (33.3%) to have expected frequencies less than 5, with a minimum expected count of .06 (Appendix D). The age categories were collapsed from 6 down to 4 (15-24, 25-34, 35-44, and over 45 years). When the analysis was re-run, one cell (1.8%) had an expected frequency less than 5, and the minimum expected frequency for this cell was 4.81.
123 Table 5.4.4 Age profile of employees and managers
Employees Frequency %* 15 – 24 years 528 30.5 25 – 34 603 34.8 35 – 44 340 19.6 45 – 54 212 12.2 55 –64 45 2.6 65 + 3 .2 Total 1792 100.0 Managers 15 – 24 years 3 2.1 25 – 34 58 40.0 35 – 44 55 37.9 45 – 54 22 15.2 55 – 64 6 4.1 65 + 1 .7 Total 145 100.0
*Percentages were calculated after missing or invalid responses (in this case 3.4% of employees and 0.0% of managers) were excluded.
The pattern of a young employee profile exists across all of the hotels in this sample. The largest proportion of employees in the over 45 years category occurs for hotel 3 with 21.1%. Although a number of other hotels (11 and 12) approach this figure, the proportion of employees in this category represents only 2.6% of the employees of hotel 9. This contingency table analysis found the pattern of distribution of employee
2 ages to significantly vary between hotels (χ (39) = 87.94, p < .001).
124 Table 5.4.5 Age profile of employees for each of the 14 hotels
Row 15-24yrs 25-34yrs 35-44yrs 45+yrs Total HOTEL 1 176 174 105 90 545 32.3% 31.9% 19.3% 16.5% 31.5% 243461810117 36.8% 39.3% 15.4% 8.5% 6.8% 364965758275 23.3% 34.9% 20.7% 21.1% 15.9% 41326105 54 24.1% 48.1% 18.5% 9.3% 3.1% 53927129 87 44.8% 31.0% 13.8% 10.3% 5.0% 61614133 46 34.8% 30.4% 28.3% 6.5% 2.7% 720194 5 48 41.7% 39.6% 8.3% 10.4% 2.8% 814197 7 47 29.8% 40.4% 14.9% 14.9% 2.7% 9 8 19 11 1 39 20.5% 48.7% 28.2% 2.6% 2.3% 10 29 29 16 13 87 33.3% 33.3% 18.4% 14.9% 5.0% 11 17 28 28 17 90 18.9% 31.1% 31.1% 18.9% 5.2% 12 11 11 4 6 32 34.4% 34.4% 12.5% 18.8% 1.8% 13 66 71 28 25 190 34.7% 37.4% 14.7% 13.2% 11.0% 14 12 24 27 11 74 16.2% 32.4% 36.5% 14.9% 4.3% Column 528 603 340 260 1731 Total 30.5% 34.8% 19.6% 15.0% 100.0%
Table 5.4.6 shows the educational level of employees and managers. The general pattern of qualifications for the employees follows a predictable pattern of falling numbers with increasing qualification level. Over 60% of employees have qualifications at the post-secondary level and above. This figure certainly supports the view that the industry workforce is relatively well qualified.
125 Table 5.4.6 Educational level of employees and managers
Employees Frequency %* Secondary 601 35.1 Post-secondary 261 15.3 Apprenticeship 233 13.6 Assoc. Dip 279 16.3 Degree 284 16.6 Post Grad. 53 3.1 Total 1792
Managers
Secondary 29 20.3 Post-secondary 14 9.8 Apprent’ship 23 16.1 Assoc. Dip 36 25.2 Degree 33 23.1 Post grad. 8 5.6 Total 145 *Percentages were calculated after missing or invalid responses (in this case 4.5% of employees and 1.4% of managers) were excluded.
The management returns show an encouraging pattern with the largest percentage of respondents having associate diploma level qualifications, at 24.8%, and that figure being closely followed by degree qualifications at 22.8%. Only 20% of this sample had no post secondary qualifications. It was not the aim of this study to examine qualification patterns in the industry but it would be most interesting to analyse this area further to see how many managers, and at what levels, have hospitality or related degrees.
Table 5.4.7 shows the educational level of employees for each of the 14 hotels in the sample. An initial contingency table analysis found 13 cells (15.5%) to have an expected frequency less than 5, with the minimum expected frequency to be 0.96
(Appendix D). The education level categories were collapsed from 6 to 5 (secondary, post-secondary, apprenticeship, associate diploma, degree and postgraduate) and the
126 analysis re-run. Two cells were found to have expected frequencies of less than 5. The minimum expected frequency was 4.20.
This contingency table analysis found Education Level to vary significantly
2 across the hotels in the sample (χ (52) = 74.17, p < .05).
Table 5.4.7 Educational level of employees for each of the 14 hotels
Secondary Post Apprent- Ass. Degree & Row Secondary iceship Diploma Post- Total Hotel 1 200 82 81 75 100 538 37.2% 15.2% 15.1% 13.9% 18.6% 31.4% 2 51 11 22 19 15 118 43.2% 9.3% 18.6% 16.1% 12.7% 6.9% 3 106 39 36 40 50 271 39.1% 14.4% 13.3% 14.8% 18.5% 15.8% 4159 9 127 52 28.8% 17.3% 17.3% 23.1% 13.5% 3.0% 5 34 11 8 15 20 88 38.6% 12.5% 9.1% 17.0% 22.7% 5.1% 6104 8 101345 22.2% 8.9% 17.8% 22.2% 28.9% 2.6% 7 17 6 5 9 12 49 34.7% 12.2% 10.2% 18.4% 24.5% 2.9% 8219 2 5 8 45 46.7% 20.0% 4.4% 11.1% 17.8% 2.6% 9 17 5 3 4 10 39 43.6% 12.8% 7.7% 10.3% 25.6% 2.3% 10 24 17 8 16 21 86 27.9% 19.8% 9.3% 18.6% 24.4% 5.0% 11 26 16 16 17 13 88 29.5% 18.2% 18.2% 19.3% 14.8% 5.1% 12 6 9 3 7 6 31 19.4% 29.0% 9.7% 22.6% 19.4% 1.8% 13 49 30 17 40 50 186 26.3% 16.1% 9.1% 21.5% 26.9% 10.9% 14 25 13 15 10 12 75 33.3% 17.3% 20.0% 13.3% 16.0% 4.4% Column 601 261 233 279 337 1711 Total 35.1% 15.3% 13.6% 16.3% 19.7% 100.0%
Table 5.4.8 gives details of the organisational tenure for employees and managers. Timo (1993) in discussing employee turnover suggested that there were some benefits that offset the many negatives of high employee turnover. One benefit was the
127 introduction of employees with fresh attitudes and approach. There is, however, an overwhelming preponderance of evidence that employee turnover must be a considerable cost to hotels, with loss of quality and efficiency in operations, involving continual re-training and management time. Having 58.5% of the staff being with the organisation for 2 years or less very much reinforces the relatively high employee turnover that has become endemic within the Australian hotel industry.
The management figures also display a similar pattern in the first 2 categories, but there is a notable change in the 6 to 8 years with a much larger number staying on for that period. It is company policy in many of the major national and international hotel chains that managers should be rotated quite frequently. Black (personal communication, 8th April 1999) supports this, he recounted that Sheraton see a 3-year tenure of senior management being the normal maximum. He further stated that senior management changes always had a considerable impact upon the staff within a hotel and they were by no means always beneficial.
Table 5.4.8 Organisational tenure for employees and managers
Employees Frequency %* 0 - 2 years 1017 56.5 3 – 5 403 23.2 6 – 8 186 10.7 9 – 11 95 5.5 12 – 14 27 1.6 15 – 17 11 .6 Total 1792 100.0 Managers 0 – 2 70 48.3 3 – 5 36 24.8 6 – 8 26 17.9 9 – 11 9 6.2 12 – 14 4 2.8 Total 145 100.0 *Percentages were calculated after missing or invalid responses (in this case 3.0% of employees and 0.0% of managers) were excluded.
Table 5.4.9 gives details of the organisational tenure for employees for each of
128 the 14 hotels in the sample. An initial contingency table analysis found 40 cells (47.6%) to have an expected frequency less than 5, with the minimum expected frequency to be
.20 (Appendix D). The organisational tenure categories were collapsed from 6 to 3 (0-2,
3-5, and over 6 years) and the analysis re-run. No cells were found to have expected frequencies of less than 5. The minimum expected frequency was 5.87.
Although the hotels display large numbers of employees with an organisational tenure of 2 years or less (with the smallest number falling into this category being hotel
14 with 20% of employees). Four hotels had 20% or more of their employees having been with the hotel for 6 or more years (hotels 1, 11, 12 and 14, with 27.9%, 23.3%,
25.0% and 20.0%, respectively). This contingency table analysis found organisational
2 tenure to vary significantly across the hotels in the sample (χ (26) = 158.88, p < .001).
129 Table 5.4.9 Organisational tenure for employees for each of the 14 hotels
Row 0-2yrs 3-5yrs 6+yrs Total Hotel 1 268 124 152 544 49.3% 22.8% 27.9% 31.3% 2 79 23 19 121 65.3% 19.0% 15.7% 7.0% 3 152 78 44 274 55.5% 28.5% 16.1% 15.8% 427208 55 49.1% 36.4% 14.5% 3.2% 558228 88 65.9% 25.0% 9.1% 5.1% 6405247 85.1% 10.6% 4.3% 2.7% 7461249 93.9% 2.0% 4.1% 2.8% 8329546 69.6% 19.6% 10.9% 2.6% 9380139 97.4% 0.0% 2.6% 2.2% 10 59 28 0 87 67.8% 32.2% 0.0% 5.0% 11 47 22 21 90 52.2% 24.4% 23.3% 5.2% 12 19 5 8 32 59.4% 15.6% 25.0% 1.8% 13 122 36 34 192 63.5% 18.8% 17.7% 11.0% 14 30 30 15 75 40.0% 40.0% 20.0% 4.3% Column 1017 403 319 1739 Total 58.5% 23.2% 18.3% 100.0%
Table 5.4.10 focuses upon the length of time employees and managers have been in particular jobs. The data for the employees, of course, shows that when compared with the results for organisational tenure, a higher percentage have held their jobs for 2 years or less (65.7%). The percentage of those in the 3 – 5 year category was 20.7% giving a cumulative percentage for these two-year bands of 86.4%, almost 5% up on the equivalent organisational tenure figure. This is even stronger confirmation of the shortness in time that employees are spending in their jobs and the comments made for organisational tenure above are reinforced. For the hotel industry in this sample these figures indicate a significant operational impediment. 130 When examining the managers’ data an even more dramatic picture emerges with 64.8% and 22.1% for the first 2 categories. To some extent this may be seen as positive because it opens up career path opportunities for ambitious staff but the quality and depth of their understanding and experience in dealing with people, both customers and employees, is very limited. In an industry based upon quality service this presents problems in consistency of the service offered.
Table 5.4.10 Job tenure for employees and managers
Employees Frequency %* 0 - 2 years 1134 65.7 3 – 5 358 20.7 6 – 8 122 7.1 9 – 11 79 4.6 12 – 14 19 1.1 15 – 17 14 .8 Total 1792 100.0 Managers 0 - 2 years 94 64.8 3 – 5 32 22.1 6 – 8 12 8.3 9 – 11 2 1.4 12 – 14 5 3.4 Total 145 100.0 *Percentages were calculated after missing or invalid responses (in this case 3.7% of employees and 0.0% of managers) were excluded.
Table 5.4.11 gives details of the job tenure for employees for each of the 14 hotels in the sample. An initial contingency table analysis found 43 cells (51.2%) to have an expected frequency less than 5, with the minimum expected frequency to be .25
(Appendix D). The job tenure categories were collapsed from 6 to 3 (0-2, 3-5, and over
6 years) and the analysis re-run. One cell was found to have an expected frequency of less than 5, and the minimum expected frequency for this cell was 4.20.
131 Table 5.4.11 Job tenure for employees for each of the 14 hotels
Row Tenure 0-2yrs Tenure 3-5yrs Tenure 6+yrs Total Hotel 1 312 118 109 539 57.9% 21.9% 20.2% 31.2% 2 85 23 12 120 70.8% 19.2% 10.0% 7.0% 3 173 70 29 272 63.6% 25.7% 10.7% 15.8% 429177 53 54.7% 32.1% 13.2% 3.1% 560225 87 69.0% 25.3% 5.7% 5.0% 6404347 85.1% 8.5% 6.4% 2.7% 7460349 93.9% 0.0% 6.1% 2.8% 8374546 80.4% 8.7% 10.9% 2.7% 9351238 92.1% 2.6% 5.3% 2.2% 10 66 20 1 87 75.9% 23.0% 1.1% 5.0% 11 56 19 15 90 62.2% 21.1% 16.7% 5.2% 12 21 3 7 31 67.7% 9.7% 22.6% 1.8% 13 137 32 23 192 71.4% 16.7% 12.0% 11.1% 14 37 25 13 75 49.3% 33.3% 17.3% 4.3% Column 1134 358 234 1726 Total 65.7% 20.7% 13.6% 100.0%
The four hotels with the largest proportion of employees falling into the 6 years and over tenure category (hotels 1, 11, 12, 14) are the same four hotels with the largest proportion of their employees falling into the 6 years and over job tenure category
(20.2%, 16.7%, 22.6%, and 17.3%). What is interesting is the range in size of these 4 hotels which range in size from 31 employees (hotel 12) to 539 (hotel 1). This contingency table analysis found job tenure to vary significantly across the hotels in the
2 sample (χ (26) = 110.37, p < .001).
132 In table 5.4.12 the reported gross salary for employees and managers is given. At first examination it confirms that the hotel industry is indeed a low paying employer with 85.8% of employees earning less than $30,000 per year. These figures, however, should be read in conjunction the mode of employment (Table 5.4.7) and hours worked data (Table 4.4.8) to assess how many employees are full-time and how many are part- time or casual. Notwithstanding the other data it is very notable that an extremely small percentage of staff earn in excess of $36,000 (5.5%). It is an inescapable conclusion that the hotels in this study are not paying what would be considered an attractive salary, especially to a single wage family.
The majority of managers earn less than $50,000, although a few earn $100,000 or more.
Table 5.4.12 Gross salary for employees and managers
Employees $ Frequency %* 0 – 5,000 80 4.8 6 – 10,000 94 5.6 11 – 15,000 156 9.3 16 – 20,000 232 13.9 21 – 25,000 531 31.8 26 – 30,000 339 20.3 31 – 35,000 145 8.7 36 – 40,000 53 3.2 41 – 45,000 22 1.3 46 – 50,000 2 .1 50,000 + 15 .9 Total 1792 100.0 Managers 30 – 39,000 42 30.0 40 – 49,000 42 30.0 50 – 59,000 26 18.6 60 – 69,000 13 9.3 70 – 79,000 5 3.6 80 – 89,000 3 2.1 90 – 99,000 1 .7 100,000 + 8 5.7 Total 145 100.0 *Percentages were calculated after missing or invalid responses (in this case 3.7% of employees and 3.4% of managers) were excluded.
Table 5.4.13 gives details of the current gross salary for employees for each of the 14 hotels in the sample. An initial contingency table analysis found 85 cells (55.2%) 133 to have an expected frequency less than 5, with the minimum expected frequency to be
.04 (Appendix D). The salary categories were collapsed from 11 to 3 ($0-20,000,
$21,000-30,000, and over $30,000) and the analysis re-run. One cell was found to have an expected frequency of less than 5, and the minimum expected frequency for that cell was 4.40. Although displaying an across the board trend of relatively low salaries for employees, there exists a considerable variation between hotels. For example, within the
$31,000 and greater category, the proportion of staff falling into this category ranges from 3.2% for hotel 12 to 28.0% for hotel 4. Both of these hotels had relatively small numbers of employees with 31 and 50 respectively.
Table 5.4.13 Gross salary for employees of the 14 hotels
$0- $21,000- over Row 20,000 30,000 30,000 Total Hotel 1 173 272 77 522 33.1% 52.1% 14.8% 31.3% 2 26 70 21 117 22.2% 59.8% 17.9% 7.0% 3 104 128 29 261 39.8% 49.0% 11.1% 15.6% 411251450 22.0% 50.0% 28.0% 3.0% 529457 81 35.8% 55.6% 8.6% 4.9% 615247 46 32.6% 52.2% 15.2% 2.8% 714266 46 30.4% 56.5% 13.0% 2.8% 822202 44 50.0% 45.5% 4.5% 2.6% 99 252 36 25.0% 69.4% 5.6% 2.2% 10 33 46 7 86 38.4% 53.5% 8.1% 5.2% 11 18 49 21 88 20.5% 55.7% 23.9% 5.3% 12 5 25 1 31 16.1% 80.6% 3.2% 1.9% 13 73 84 32 189 38.6% 44.4% 16.9% 11.3% 14 30 31 11 72 41.7% 43.1% 15.3% 4.3% Column 562 870 237 1669 Total 33.7% 52.1% 14.2% 100.0%
134 This contingency table analysis found the pattern of gross salary of employees to
2 vary significantly across the hotels in the sample (χ (26) = 65.89, p < .001).
Table 5.4.14 shows the data on mode of employment for the hotel employees, with 60.6% being in full time employment. A relatively low percentage of 10.6% are part time with casual employment being at 28.8% overall. If these figures are read in conjunction with the employee salary it certainly confirms that the industry is not paying its workers highly. This must contribute to the generally high levels of staff turnover with the associated ramifications for any hotel’s operation and service quality.
All managers were employed on a full time basis.
Table 5.4.14 Mode of employment for employees
Employees Frequency %*
Full time 1041 60.6
Part time 183 10.6
Casual 495 28.8
Total 1792 100.0
*Percentages were calculated after missing or invalid responses (in this case 4.1%) were excluded.
Table 5.4.15 gives details of the mode of employment for employees for each of the 14 hotels in the sample. An initial contingency table analysis found 4 cells (9.5%) to have an expected frequency less than 5, with the minimum expected frequency to be
3.30 (Appendix D). The mode of employment categories was collapsed from 3 to 2
(full-time vs part-time and casual) and the analysis re-run. No cells were found to have expected frequencies of less than 5. The minimum expected frequency was 12.23. A considerable variation exists between the hotels regarding the proportion of employees employed full-time, ranging from 50.6% (hotel 10) to 90.3% (Hotel 12). This
135 contingency table analysis found the pattern of mode of employment to vary
2 significantly across the hotels in the sample (χ (13) = 67.942, p < .001).
Table 5.4.15 Mode of employment for employees for the 14 hotels
Part-time & Row Full- time Casual Total Hotel 1 304 238 542 56.1% 43.9% 31.5% 2 71 48 119 59.7% 40.3% 6.9% 3 151 117 268 56.3% 43.7% 15.6% 444852 84.6% 15.4% 3.0% 5483886 55.8% 44.2% 5.0% 637946 80.4% 19.6% 2.7% 7341549 69.4% 30.6% 2.9% 8281846 60.9% 39.1% 2.7% 9271037 73.0% 27.0% 2.2% 10 44 43 87 50.6% 49.4% 5.1% 11 74 16 90 82.2% 17.8% 5.2% 12 28 3 31 90.3% 9.7% 1.8% 13 104 88 192 54.2% 45.8% 11.2% 14 47 27 74 63.5% 36.5% 4.3% Column 1041 678 1719 Total 60.6% 39.4% 100.0%
Table 5.4.16 shows the hours worked by employees and when this is linked to both current gross salary and mode of employment it yet again demonstrates the relatively low pay for the majority of employees. The single largest category is the 36 –
40 hours per week full time employment with 44.3% of the sample. Hours worked in excess of 40 hours per week were relatively low percentages. No survey was conducted on hours worked by managers.
136 Table 5.4.16 Hours worked by employees
Employees Frequency %* 0 – 5 hours 8 .5 6 – 10 33 1.9 11 – 15 38 2.2 16 – 20 98 5.7 21 – 25 115 6.7 26 – 30 166 9.6 31 – 35 134 7.8 36 – 40 765 44.3 41 – 45 200 11.6 46 – 50 88 5.1 50 + 82 4.7 Total 1792 100.0 *Percentages were calculated after missing or invalid responses (in this case 3.6%) were excluded.
Table 5.4.17 gives details of the hours worked for employees for each of the 14 hotels in the sample. An initial contingency table analysis found 87 cells (56.5%) to have an expected frequency less than 5, with the minimum expected frequency to be
0.15 (Appendix D). The hours worked categories were collapsed from 11 to 4 (0-25, 26-
35, 36-40, and over 40 hours) and the analysis re-run. No cells were found to have expected frequencies of less than 5. The minimum expected frequency was 5.41.
137 Table 5.4.17 Hours worked by employees for the 14 hotels
0-25 26-35 36-40 Over 40 Row hrs hrs hrs hrs Total Hotel 1 81 112 271 77 541 15.0% 20.7% 50.1% 14.2% 31.3% 212 35 40 32 119 10.1% 39.4% 33.6% 26.9% 6.9% 369 38 103 63 273 25.3% 13.9% 37.7% 23.1% 15.8% 47 3 26 19 54 13.0% 5.6% 48.1% 33.3% 3.1% 517 13 41 16 87 19.5% 14.9% 47.1% 18.4% 5.0% 65 5 17 19 46 10.9% 10.9% 37.0% 41.3% 2.7% 78 7 19 15 49 16.3% 14.3% 38.8% 30.6% 2.8% 89 9 20 8 46 19.6% 19.6% 43.5% 17.4% 2.7% 97 3 19 8 37 18.9% 8.1% 51.4% 21.6% 2.1% 10 10 12 38 27 87 11.5% 13.8% 43.7% 31.0% 5.0% 11 11 8 52 19 90 12.2% 8.9% 57.8% 21.1% 5.2% 12 3 2 22 5 32 9.4% 6.3% 68.8% 15.6% 1.9% 13 41 39 66 45 191 21.5% 20.4% 34.6% 23.6% 11.1% 14 12 14 31 18 75 16.0% 18.7% 41.3% 24.0% 4.3% Column 292 300 765 370 1727 Total 16.9% 17.4% 44.3% 21.4% 100.0%
This contingency table analysis found the pattern of hours worked to vary
2 significantly across the hotels in the sample (χ (39) = 113.45, p < .001).
138 Table 5.4.18 gives information on training frequency (expressed as time since last training session) for both employees and managers.
Table 5.4.18 Time since last training session for employees and managers
Employees Frequency %* 0 – 1 years 1199 72.1 1 – 2 238 14.3 2 – 3 99 6.0 3 – 4 40 2.4 4 – 5 29 1.7 5 – 6 11 .7 6– 7 47 2.8 Total 1792 100.0 Managers 0 – 1 years 105 74.5 1 – 2 21 14.9 2 – 3 6 4.3 3 – 4 2 1.4 4 – 5 1 .7 5 – 6 2 1.4 6 – 7 4 2.8 Total 145 100.0 *Percentages were calculated after missing or invalid responses (in this case 7.2% of employees and 2.8% of managers) were excluded.
It is of note that 72.1% of employees have attended a training session within a 12-month period. If this figure is considered at face value, it presents a picture of a very proactive training regime being implemented by the hotels. This view is reinforced when one sees that within the last 3 years 92.4% have attended training. There is no doubt that the training of staff in the hotel industry now receives a very high profile. In Australian hospitality and tourism training under the auspices of TTA, which is a government appointed ITAB (Industry Training Advisory Board), all training in this industry sector has received a very high profile and been recognised as world’s best practice.
These training frequency figures, however, must be set in the context of employee turnover and job tenure figures which both indicated the high turnover levels of employees. It is, therefore, a necessity to have this constant training program established because of the sheer volume turnover of staff numbers. Obviously such a
139 high level of training also indicates that considerable resources are being expended not for improvement but to remain competitive in service levels.
The data for the managers also demonstrate that it is the norm to attend training. This confirms that there may well be a link to the relatively short organisational and job tenure figures.
Table 5.4.19 gives details of the time since last training session for employees for each of the 14 hotels in the sample. An initial contingency table analysis found 59 cells (60.2%) to have an expected frequency less than 5, with the minimum expected frequency to be 0.21 (Appendix D). The time since last training categories were collapsed from 7 to 3 and the analysis re-run. 3 cells were found to have expected frequencies of less than 5. The minimum expected frequency was 4.35.
140 Table 5.4.19 Time since last training session for employees for the 14 hotels
0 - 1 yr 1 - 2 yr Over 2 Row years Total Hotel 1 369 73 75 517 71.4% 14.1% 14.5% 31.1% 282 16 16 117 71.9% 14.0% 14.0% 6.9% 3 167 51 40 258 64.7% 19.8% 15.5% 15.5% 444 7 3 54 81.5% 13.0% 5.6% 3.2% 566 7 12 85 77.6% 8.2% 14.1% 5.1% 638 6 1 45 84.4% 13.3% 2.2% 2.7% 731 10 6 47 66.0% 21.3% 12.8% 2.8% 836 6 4 45 78.3% 13.0% 8.7% 2.8% 930 3 2 35 85.7% 8.6% 5.7% 2.1% 10 49 10 20 79 62.0% 12.7% 25.3% 4.8% 11 73 6 9 88 83.0% 6.8% 10.2% 5.3% 12 29 1 2 32 90.6% 3.1% 6.3% 1.9% 13 135 28 25 188 71.8% 14.9% 13.3% 11.3% 14 50 14 11 75 66.7% 18.7% 14.7% 4.5% Column 1199 238 226 1663 Total 72.1% 14.3% 13.6% 100.0%
Although all the hotels demonstrate a high proportion of staff who have attended a training session within the past 12 months (the lowest being hotel 10, with 62.% of employees), a considerable range in patterns exist with hotel 6 reporting only 2.2% of employees to have had their last training session over 2 years ago, to hotel 10 where the figure was 25.3% of employees. This contingency table analysis found the pattern of time since last training session to vary significantly across the hotels in the sample
2 (χ (26) = 47.69, p < .01).
Table 5.4.20 shows the responses to the question of whether more training is needed for both employees and managers. The result shows an interesting contrast with
141 the employees saying a very firm ‘No’ to more training (61.6%), whereas the managers had an almost overwhelming response the other way, with 72.5% wanting more training.
The managers seem to be looking to training as a tool that will assist them in their ever increasingly complex task of running a hotel.
Table 5.4.20 Employees and managers were asked, do you need training?
Employees Frequency %*
Yes 659 38.4
No 1056 61.6
Total 1792 100.0
Managers
Yes 103 72.5
No 39 27.5
Total 145 100.0
*Percentages were calculated after missing or invalid responses (in this case 4.3% of employees and 2.1% of managers) were excluded.
5.5 Summary and Discussion
In this chapter, first the general operating statistics were reported for the 14 hotels in our sample, and second a comprehensive presentation of the demographic data from the managers and employees of our sample was reported.
The former data were important in providing a description of the organisations under study, and reported important indices of hotel performance such as ADRR and
REVPAR. These data were relevant to this study as they provided the context and
142 framework within which the collection of the organisational climate information took place. In order to interpret the organisational climate data with the more sophisticated statistical techniques this context and framework of the study is important.
Specifically, the Hotel Level Data show hotels 2 and 9 to record the best
REVPAR figures, and that some hotels would appear to concentrate too much on
ADRR at the expense of occupancy e.g. hotel 7. Conversely, others concentrated upon occupancy at the expense of ADRR, e.g. hotel 6. The lowest REVPARs were recorded by hotel 7 and 12, with the latter hotel being very reliant upon tour groups in the room revenue mix. Again, hotels 2 and 9 did well in the high yielding room revenue sectors of
FIT and corporate customers. The employee statistics of turnover and wages to sales again reinforced that hotel 2 and 9 were performing well.
When managers were asked to make comments on any issues that affected their performance the majority of comments were directed toward the marketing function. In an increasingly competitive market, looking for the extra market share is vital in any hotel’s performance.
In addition to the Hotel Level Data, demographic data were reported that were gathered from individual staff members within the hotels (Staff Level Data). These data are important for the current study as, firstly, when aggregated they provide a profile of the employees across the 14 hotels, secondly, also when aggregated they provide a basis for comparison of the different hotels, and thirdly, these variables are incorporated into
Structural Model A.
The overall response rate from staff members varied from 29% to 73 % with average rate of 44% equating to 1778 respondents for the demographic information.
143 This has allowed a reasonable level of confidence in the results and their subsequent analysis in conjunction with Organisational Climate data in later chapters.
As far as gender is concerned, the results show that whilst management is still heavily biased toward males the actual workforce is much more evenly split with the bias in favour of females. The workforce is concentrated in the under 35 years of age range (65.3%) with the managers showing a slightly older profile with 67.9% in the 25
– 44 age bracket.
The educational qualification’s data show 65% of the employees with post- secondary qualification and 28.7% of managers with undergraduate or post-graduate qualifications. The training intervals show that the vast majority of both managers and employees had attended training during the year (74.5% and 72.1% respectively). This certainly presents a picture of an educated and trained workforce.
However, these figures need to be set against those for organisational tenure, with employees at 56.5% and managers at 48.3% for less than 2 years service. This data are compounded by the job tenure figures of less than 2 years (employees 65.7% and managers 64.8%). It is the major problem for the industry with employee turnover norm being in the 30-40% range. When the salary levels are examined, with 85.8% of staff under $30,000 and 94.5% under $35,000, it provides an insight why the industry has high employee turnover. This is further supported by the mode of employment figures showing that full-time positions account for 60.6%, casuals 28.8% and part-time 10.6%.
The results in the need for training may well indicate that employees are getting to the stage that further training could in fact be counter productive to the operation; it is possible employees are attending as a requirement without any real motivation to learn or improve. As hotels are spending large sums of money on training a more in depth 144 analysis of the type and nature of the training for employees may be warranted. The completion of the training session feedback form may not be providing any real insights as to the value of the training.
In addition to qualitative descriptions of the staff member demographic data, quantitative inferential statistical comparisons were conducted to compare the 14 hotels in this study on each of the demographic variables recorded from employees. These were important comparisons for this study. A structural model had been proposed
(Structural Model A) which proposes that these demographic variables affect
Organisational Climate. For these variables to explain variation in Organisational
Climate between the hotels, it is a necessary condition for a variation in these demographic variables between hotels. In each case it was found that staff across the 14 hotels significantly differed on each of these variables.
The dimensions of organisational climate from the sample will be investigated in
Chapter 6. Chapter 7 will investigate the relationships between REVPAR, employee demographic variables, and the organisational climate.
145 6.0 The dimensions of Organisational Climate in 14 Australian
Hotels
6.1 Introduction
This chapter, firstly, will present a reliability analysis on the participants’ responses to the 70 items of the modified version of the Psychological Climate
Questionnaire (PCQ) used in this study. Secondly, an analysis of the underlying dimensions of Organisational Climate within the sample will be conducted by applying a type of Factor Analysis, Principal Components Analysis (PCA), to the participant responses to the modified PCQ.
6.2 Reliability analysis
6.2.1 Approaches to the estimation of reliability of a test instrument
The term reliability, when referring to a psychological test instrument such as a questionnaire, has been described as ‘the attribute of consistency in measurement’
(Gregory, 1996, p. 84). Gregory describes reliability as ‘best viewed as a continuum ranging from minimal consistency of measurement (e.g., simple reaction time) to near perfect reliability of results (e.g., weight)’ (p. 84). The simplest method of determining the reliability of test scores is the administration of the same test on 2 occasions to the same set of respondents. In this situation, a perfectly reliable test would provide identical responses for all respondents on both test occasions. In such a situation, the correlating scores from the first administration with those of the second administration would find a perfect correlation (r = 1.00). Should the instrument be ‘perfectly unreliable’ respondents would have different scores on the first administration with
146 respect to the second administration, and there would be no correlation between test scores (r = 0.00).
The administration of this instrument on 2 occasions to the respondents was not a practical approach given the constraints of the current study.
An approach at estimation of the reliability of an instrument that is presented to respondents only once is ‘split-half reliability’. In this approach the test is split into 2 equivalent halves, and the scores for respondents on one half are correlated with those scores on the second half of the test. The difficulty in this approach is determining whether the 2 halves are equivalent. Chronbach (1951, cited in Gregory, 1996) proposed the coefficient alpha (commonly referred to as ‘Chronbach’s Alpha’) which ‘may be thought of as the mean of all possible split-half coefficients. A test with ‘robust’ reliability would be expected to display a Chronbach Alpha in excess of 0.90.
The reliability of individual items within an instrument may also be examined.
The scores for each individual item within the instrument may be correlated with scores on the total test (Gregory, 1996). An instrument with a high level of internal consistency would consist of items that are reasonably homogeneous and which display high item- total correlations.
6.2.2 Reliability analysis of responses to the 70 item version of the psychological climate questionnaire
Table 6.1 provides the results of the reliability analysis of the 70 items of the modified version of the PCQ for the 1401 participants who provided a complete set of responses. The analysis indicated a particularly high level of reliability with a
147 Chronbach’s Alpha of .959. The individual item-total correlation coefficients ranged in magnitude from .09 to .72.
148 e Alpha if Deleted Corrected Item-Total Corr. Scale Variance if Item Deleted Scale Mean if Item Deleted 4.624.47 1.65 1.70 341.73 341.88 2830.545.08 2815.12 .295.17 1.51 .37 341.274.53 1.57 .96 2790.67 341.18 .96 1.68 2775.31 .57 341.825.05 2773.66 .64 .96 1.41 .61 341.30 .96 2786.42 .96 .64 .96 Mean S. Dev relatively unimportant or unnecessary. needs to do its work well. own area. exchange ideas and opinions. you. of time. 1 Opportunity for independent thought and action exists in your job. 2 Your job requires a high level of skill and training. 3 You are required to meet rigid standards of quality in your work. 4 Staffmembers generally trust their supervisors. 5 Themethods of your work are kept up to date. 6 You are required to perform tasks on your job which consideryou 4.95 7 You are able to get the money, supplies, equipment, etc. your work group 5.87 1.53 8 Your supervisor is friendly and easy to approach. 9 Your supervisor offers ideas new for job and related problems.10 341.40 A spirit of cooperation exists in your workgroup. 1.1511 Your job responsibilities are clearly defined. 5.0512 2783.70 Responsibility is assigned so that individuals have authority within their 340.4813 Dealing otherwith people is part of your job. 5.04 2829.86 1.5714 Your supervisor .61 encourages the people who work for him or her to 5.08 5.2315 341.30 Staffmembers generally trust their managers. 1.50 .4316 You are given advanced information about changes which might affect 1.46 1.31 2821.82 .96 341.31 5.6917 The hotel’s policies are consistently applied to all staff members.18 341.27 You have opportunitiescompleteto 341.12 thestart. work you 19 2776.08 .96 Procedures are designed so that resources are used efficiently. .36 5.3420 1.36 Your supervisor is attentive to 2777.32 what say.you 2801.2321 Your supervisor provides the help you need to schedule your work ahead 5.43 .67 340.66 1.4522 4.64 There is friction in your workgroup. .68 .58 .96 2792.29 6.41 341.01 1.40 4.92 1.73 .96 2783.89 4.76 340.92 .62 .96 5.44 .96 341.71 .95 1.50 2802.17 .64 1.62 2775.12 339.94 341.43 1.20 .96 341.59 .54 2861.79 2779.41 340.91 .58 5.27 .96 2762.40 2818.99 .21 .65 .96 1.42 .70 .96 .50 341.08 .96 .96 4.37 2777.48 .96 .96 1.85 .70 341.98 2792.97 .96 .45 .96 Table 6.1Table 70 items of Statisticstheofforeach the modified version of the Psychological Climate Questionnaire entered into th Item reliability analysis. reliability
149 Alpha if Deleted Corrected Item-Total Corr. Scale Variance if Item Deleted Scale Mean if Item Deleted 4.82 1.67 341.53 2774.414.59 .61 1.57 341.765.68 .96 2778.84 1.25 .62 340.67 2810.15 .96 4.97 .545.78 1.76 .96 5.23 341.38 1.25 2762.47 1.38 340.77 341.12 2797.66 .64 2802.82 .64 .96 .54 .96 .96 Mean S. Dev job. performance is evaluated. workgroup. your job. team. this hotel. 23 You have opportunitiesto learn worthwhileskills andknowledge in your 24 New staffmembers get on-the-job training they need.25 There is variety in job.your 26 Your hours of work are irregular.27 Everything in this hotel is checked, individual judgement is not trusted.28 Being liked is important in getting a promotion.29 You have good information on where standyou and how your 4.0030 Your superior emphasises high standards of performance.31 The ideas and suggestions of staffmembers are paid attention to.32 You have the opportunity to do a number of different things in job.your 33 1.59 5.06 Your supervisor sets an example by working hard himself or herself.34 A friendly atmosphere prevails among most of the members of your 342.35 5.0235 Hotel politics count in getting a promotion. 1.59 4.7936 People act as though everyone must be watched 2845.33or they will slacken off. 5.0837 Supervisors generally know what is going on in 5.53 their work groups. 341.29 1.5938aware You are ofhow wellyour workgroupmeeting is objectives. its 1.47 2.9139 4.03 Your job demands precision. .21 2796.06 1.6940 341.33 Members of workyour group trust each other. 1.26 341.5741 The hotel has a good image to outsiders.42 341.27 1.64 1.58 2797.23 3.53 Working in this hotel is beneficial 5.05 to your career. 5.10 340.82 .5143 2769.79 4.86 You have opportunities to makefull use of your knowledge and skills in .96 2764.76 343.44 342.32 2806.6944 .50 Communication is hindered by following 2.16 the chain of command rules. 1.32 1.3845 .72 Your supervisor encourages the people who 1.76 work for them to work 2824.39 as a 2809.47 .96 .66 342.82 341.30 341.2546 .57 It is possible to get accurate information on the policies and objectives of 341.49 3.85 .96 .33 3.14 .43 2858.96 2799.42 2791.42 .96 2795.00 .96 .96 1.49 1.58 .09 .59 5.11 .62 .96 .96 .46 5.34 342.50 343.21 5.65 1.38 2814.85 .96 2827.54 .96 .96 1.59 .96 341.24 5.51 1.24 341.01 .42 .32 2801.89 340.70 1.27 2785.75 2816.21 .55 340.84 .96 .96 .57 2825.70 .51 .96 .96 .42 .96 .96 Table 6.1 (continued) Item
150 Alpha if Deleted Corrected Item-Total Corr. Scale Variance if Item Deleted Scale Mean if Item Deleted 4.535.04 1.62 341.824.35 1.43 2761.33 341.314.41 1.72 2789.46 .71 342.00 1.744.59 2816.61 .61 341.94 .96 5.22 1.65 2817.10 .355.37 .96 1.32 341.764.61 .35 1.35 341.13 2790.67 .96 5.16 1.60 340.98 2814.47 .52 .96 5.15 341.74 2800.02 1.25 .484.60 2823.46 1.38 341.19 .58 .96 1.50 341.20 2815.91 .34 .96 341.75 2847.17 .96 .50 2818.95 .96 .24 .39 .96 .96 .96 Mean S. Dev employees. management. hotel. work. grapevine. way. staff member. another department. different departments of the hotel. most productive. job. 47 The hotel strives to do a better job than other hotels of the same type.48 The hotel emphasises personal growth and development.49 Managers keep well informed about the needs and problems of 50 5.77 Discipline in this hotel is maintained consistently.51 Your manager is successful in his dealing with higher levels of 52 1.21 The objectives of the hotel are clearly defined.53 There is conflict between your department and other departments of the 340.59 5.0554 Your work is important.55 The way your work group is organised hinders the efficient conduct of 2812.87 1.5356 This hotel is concerned with assisting the local community.57 Things in this hotel seem to happen contrary to rules and regulations. .5458 341.30 In this hotel the only source of information on important matters is the 4.7859 2777.39 In this hotel things are planned so that everyone is getting in each others 3.97 .96 60 1.49 Under most circumstances I would recommend this hotel to a prospective 5.47 .6561 Most of 4.77 the personnel 1.55 in my department 341.57would not want to change to 62 1.22 Most members ofmy work group take pride in their jobs. 342.38 2794.0263 Generally there are friendly and co-operative relationships 1.45 between the .96 340.88 2812.1564 My department, compared to all other departments would be one of the .56 341.58 2810.6665 Excessive rules and regulations interfere with how well I am able to do my .42 2823.7666 Overall I thinkmy immediate supervisor is doing a good job. .56 .96 6.13 .38 5.21 .96 .96 1.08 1.35 .96 340.22 341.15 5.51 2833.20 2799.01 1.37 .43 .58 340.84 2789.32 .96 .96 .64 .96 Table 6.1 (continued) Item
151 Alpha if Deleted Corrected Item-Total Corr. Scale Variance if Item Deleted Scale Mean if Item Deleted 5.064.75 1.565.08 341.29 1.36 2861.54 1.42 341.64 341.27 2862.79 .12 2790.44 .13 .96 .61 .96 .96 Mean S. Dev pressure to produce. would be the most productive. management. 67 Compared with other work groups, my work group is under much less 68 In my job the opportunities to get to know people are limited.69 Compared to all other similar work groups in this hotel, my work group 70 Your immediate supervisor is successful in dealing with higher levels of 4.95 1.66 341.40 2836.24 .26 .96 Table 6.1 (continued) Item N = 1401 Scale Mean = 346.35 Scale Variance = 2883.93 Alpha.9594 =
152 6.3 Statistical techniques to identify underlying dimensions in a data matrix of participant responses
6.3.1 Factor analysis
Factor analysis is a generic name for one of the multivariate techniques that is used to ascertain the underlying structure in a data matrix (Hair et al., 1995). It analyses a large number of variables by identifying common and unique sets of variance that are referred to as dimensions, factors or components. These techniques allow the researcher to summarise and reduce the data. The process of summary and reduction allows the data to be described by a much smaller number of variables than the original. Factor analysis is a technique that considers all the variables simultaneously. It is an interdependence technique where the variates (factors) are formed to explain the whole variable set and thus each variate is predicted by all of the others. Factor analysis may be either exploratory where the data are searched for the underlying structure or confirmatory. In confirmatory factor analysis the researcher is seeking to confirm a structure that has already been identified from previous research. There are 2 main factor analytic methods, Principal Components Analysis (PCA), sometime called just
‘component analysis’ and Common Factor Analysis.
6.3.2 Principal components analysis
PCA relies upon the total variance to derive the factors with small proportions of unique variance. This technique is appropriate when the main concern is to predict the minimum number of factors that are required to account for the maximum proportion of the variance and when there is an a priori set of variables (Ghauri et al., 1995).
153 Whilst PCA provides a parsimonious description of a dataset, like all methods of factor analysis it suffers from the problem of factor indeterminacy. That is, for any data set the factor solution is not unique.
6.4 Principal components analyses of organisational climate data
The major thrust of this research was to collect data that provided the basis for determining organisational climate and its effect upon hotel performance. Jones and
James (1979) conducted a literature search and identified 35 a priori scales, which could relate to organisational climate. They produced a 145-item questionnaire, which attempted to measure these 35 scales. Each scale was represented by between 2 and 7 items in their questionnaire. Thirty-five composite variables were produced representing these theoretical scales and these 35 variables were entered into an exploratory PCA which produced 6 factors with eigenvalues greater than 1. This questionnaire was presented to 2 other samples and 5 of the 6 factors were reproduced. On the basis of the
Jones and James study, organisational climate may be seen as composed of 6 dimensions, as illustrated in Figure 6.1
154 Figure 6.1 Organisational Climate Model A: The dimensions of Organisational
Climate from the study of Jones and James (1979).
Ryder and Southey (1990), in their study of a public service organisation in
Western Australia, modified the questionnaire of Jones and James. The original instrument had between 3 and 5 scaled responses. Ryder and Southey used a consistent
7 point anchored scale. Again between 2 and 7 items were used to represent the original
35 a priori scales. An exploratory PCA was again applied to the 35 component variables with their sample size of only 147. Ten factors were extracted, although only 6 were identified as being interpretable. On the basis of the Ryder and Southey study
155 organisational climate may be seen as composed of 6 dimensions, as illustrated in
Figure 6.2.
Figure 6.2 Organisational Climate Model B: The dimensions of Organisational
Climate from the study of Ryder and Southey (1990).
156 The current study modifies the questionnaire used by Ryder and Southey which was based upon the original Jones and James instrument, such that only 2 items are used to represent each of the 35 original ‘a priori’ components. This reduced the questionnaire length to 70 items.
6.5 Variables entered into the PCA
In this study a consistent 7 point anchored scale is used across all items of a modified version of the PCQ. In this respect, the instrument used here is similar to that used by Ryder and Southey (1990). Ryder and Southey (1990) present an exploratory
PCA of responses of 147 participants to a modified version of the PCQ. In that study between 2 and 7 items were used to represented the Jones and James original 35 ‘a priori’ scales and these 35 composite items were entered into their PCA. Given the present study used only two items to represent each of these 35 ‘a priori’ scales (and so a precise replication of the either the Jones and James, or the Ryder and Southey PCA is not possible). Additionally, the substantial sample size of the present study (1,401), it was decided to conduct the PCA presented here on the 70 individual items of the modified PCQ used in this study. Further, given the differences in the factors described by the James and Jones, and the Ryder and Southey studies and the use of individual items in the current analysis, it was decided it would be more appropriate to conduct an exploratory, rather than confirmatory, PCA. The PCA, when completed, was followed by a Varimax rotation.
6.6 Proportion of variance explained by principal components
The PCA followed by a Varimax Rotation extracted 13 components with eigenvalues greater than 1 (Appendix E). The 13 components accounted for 57.6% of the total variance (Table 6.2). Given the large number of items entered into this analysis
157 it represents a good solution. The number of components expected to be extracted generally lies in the range of K/3 and K/5, where K represents the number of variables entered into the analysis (Tabachnick & Fidell, 1996, p. 672).
Table 6.2 Percentage of variance explained by Principal Components with
Eigenvalues greater than 1
Factor % of Cumulative Variance %
1 29.0 29.0 2 4.5 33.5 3 3.6 37.1 4 3.3 40.4 5 2.7 43.1 6 2.5 45.6 7 2.1 47.7 8 2.0 49.6 9 1.7 51.4 10 1.7 53.1 11 1.6 54.6 12 1.5 56.1 13 1.5 57.6
6.7 Rotated principal component loadings
The rotated factor component loadings are presented for items of the modified
PCQ in Table 6.3. For each item, only the ‘primary’ loading is presented (that is the greatest loading for that item across the factors), and only items with primary loadings on factors 1 through 7 are included (Appendix E presents both primary and minor loadings for all items). For comparison purposes, Table 6.3 also includes, for both the
Jones and James, and Ryder and Southey studies, the factor upon which each individual
158 item would be assigned on the basis of the loadings of the composite variables used in the earlier studies.
159 are Ryder & Ryder Southey James Jones & .63 3 1 Loading Factor 2 Professional and organisational esprit opinions. Item Factor 1 Leader facilitation and support 45 431 Your supervisor encourages the people who work for them to work as a team.37 Staffmembers generally trust their supervisors.30 The ideas and suggestions of staffmembers are paid attention to.51 Supervisors generally know what is going on in their work groups.15 Your superior emphasises high standards of performance.29 Your manager is successful in his dealing with higher levels of management.38 Staffmembers generally trust their managers. You have good information on where standyou and how your performance is evaluated.aware You are ofhow wellyour workgroupmeeting is objectives. its .60 .41 .49 .54 3 .53 2 .38 .53 3 .55 5 1 5 1 .48 2 3 5 1 1 1 5 1 1 1 1 Table 6.3Table Rotated of Primary Component loadings for the items version modified of the Also PCQ. included for comparison purposes Item # 66 820 Overall I thinkmy immediate supervisor is doing a good job. 9 Your supervisor is friendly and easy to approach.33 Your supervisor is attentive to what say.you 70 Your supervisor offers ideas new for job and related problems.21 Your supervisor sets an example by working hard himself or herself.14 Your immediate supervisor is successful in dealing with higher levels ofmanagement Your supervisor provides the help you need to schedule your work ahead of time. Your supervisor encourages the people who work for him or her to exchange ideas and .64 .64 .7947 .6848 .7452 3 The hotel strives to do a better job than other hotels of the same type.41 The hotel emphasises personal growth and development.46 3 The objectives of the hotel are clearly defined. .7660 The hotel has a good image to outsiders.56 3 It is possible to get accurate information on the policies and objectives of this hotel. 3 .75 Under most circumstances I would recommend this hotel to a prospective staffmember. This hotel is concerned with assisting the local community. 3 1 1 3 .55 3 3 .61 1 .69 1 1 5 .66 1 1 1 .50 .65 2 5 4 .62 4 5 1 2 5 2 4 2 the factors upon which those items loaded in the earlier studies of Jones and James (1979) loaded in the earlier studies and Ryder Southey (1990). the factors of James upon which those items Jones and
160 Ryder & Ryder Southey James Jones & .54 1 2 .77 2 2 Loading Factor 4 Regulations, organisation and pressure Factor 3 Conflict and ambiguity to do its work well. Factor 5 Job variety, challenge and autonomy Item Factor 2 Professional and organisational esprit (continued) 1617 5 You are given advanced information about changes which might affect you.24 The hotel’s policies are consistently applied to all staff members.12 Themethods of your work are kept up to date. New staffmembers get on-the-job training they need. Responsibility is assigned so that individuals have authority within their own area. .54 .45 .51 1 2 .46 1 .49 6 2 1 4 1 2 4 3223 You have the opportunity to do a number of different things in job.your You have opportunitiesto learn worthwhileskills andknowledge in job.your .54 .76 5 2 2 2 Table 6.3 (continued) Item # 424950 Working in this hotel is beneficial to your career. Managers keep well informed about the needs and problems of employees. Discipline in this hotel is maintained consistently.191811 Procedures are designed so that resources are used efficiently. 7 You have opportunitiescompleteto thestart. work you Your job responsibilities are clearly defined. You are able to get the money, supplies, equipment, etc. your work group needs .48655559 Excessive rules and regulations interfere with how well I am able to do my job.57 The way your work group is organised hinders the efficient conduct of work.44 5 In this hotel things are planned so that everyone is getting in each others’ way.58 Things in this hotel seem to happen contrary to rules and regulations. .6067 Communication is hindered by following chain of command rules. .4836 In this hotel the only source of information on important matters is the grapevine. .46 Compared with other work groups, my work group is under much less pressure to produce. People act as though everyone must be watched or they will slacken off. .5725 1 .66 1 There is variety in job. your .46 5 .65 .66 .56 1 .55 5 .59 5 2 5 2 1 .56 3 .39 1 4 1 3 1 3 3 5 6 3 3 2 6 3 1 4
161 Ryder & Ryder Southey James Jones & .66.46 6 2 4 4 Loading FactorWorkgroup 6 co-operation, friendliness and warmth Factor 7 Job standards Item Factor 5 Job variety, challenge and autonomy (continued) 54 Your work is important. Item # 4361 1 You have opportunities to makefull use of your knowledge and skills in job.your Most of the personnel in my department would not want to change to another department. Opportunity for independent thought and action exists in 40your job.3422 Members of workyour group trust each other.10 A friendly atmosphere prevails among most of the members of your workgroup.62 .44 There is friction in your workgroup. A spirit of cooperation exists in your workgroup. Most members ofmy work group take pride in their jobs. .46 2 339 Your job requires a high level of skill and training. You are required to meet rigid standards 4 of quality in your work. Your job demands precision. .64 2 .37 2 4 2 2 .50 .69 .69 .54 5 2 -.55 .71 4 4 6 4 4 2 2 5 4 5 5 2
162 6.8 Interpretation of meaning of the principal components
Of the 13 components extracted, 7 were found to be interpretable and related to those previously described by either Jones and James (1979) or Ryder and Southey
(1990). Table 6.4 shows the 7 factors obtained from the PCA for this study with corresponding factors from both the Jones and James, and the Ryder and Southey studies.
Factor 1 accounted for 29% of the variance and included items such as ‘Overall I think my immediate supervisor is doing a good job’, and ‘Your supervisor is attentive to what you say’. This component was labelled ‘Leader facilitation and support’ and was judged to be consistent with factor 3 from Jones and James ‘Leader facilitation and support’ (with 11 of the 17 items loading here on factor 1 representing sub-components of composite variables loading on this factor in the earlier study). It was also consistent with factor 1 ‘Leader facilitation and support’ from Ryder and Southey (16 of the 17 items loading here on factor 1 representing sub-components of composite variables loading on this factor in the earlier study).
Factor 2 accounts for 4.5% of the variance and includes items such as ‘The hotel strives to do a better job than other hotels of the same type’, and ‘The hotel has a good image to outsiders’. This factor was labelled ‘Professional and organisational esprit’ and was seen to be consistent with factor 5 from Jones and James ‘Professional and organisational esprit’ (6 of 10 items loading here on factor 2 representing sub- components of composite variables loading on this factor in the earlier study). This is a component that was not identified by Ryder and Southey.
163 is esprit.pressure. autonomy. esprit. and warmth.friendliness and warmth. friendliness and variety and warmth. friendliness F3-ambiguity. and Conflict F4- and organisation Regulations, - F1-F5-ambiguity and Conflict and challenge variety, Job F6- co-operation, group Work F2- importance Job challenge, 7/9 Job standardsF7- - F4- cooperation Workgroup 4/6 esprit and challenge Job variety, F2- 5/5 6/6 Job standards F6- F5- co-operation reputation, Workgroup F3-pressure and Conflict 4/5 5/8 2/4 F4- openness planning Organisational 3/4 Table 6.4 Table found by those Jones James and and study, this found in (Factors) components principal between Relationship falling on the corresponding items (1979) and Ryder Southey (1990). The proportion offactor in each of the earlier studies FactorF1-support and facilitation Leader F2- organisational and Professional F3-support and facilitation Leader F5- organisational and Professional 11/17 6/10 (1979) Jones and James F1-support and facilitation Leader - 16/17 Ppn(1990) Ryder and Southey Ppn also indicated.
164 Factor 3 accounted for 3.6% of the variance and included items such as
‘Procedures are designed so that resources are used efficiently’, ‘Your job responsibilities are clearly defined’, and ‘The methods of your work are kept up to date’. This component was labelled ‘Conflict and ambiguity’ and was seen to be consistent with factor 1 from Jones and James ‘Conflict and ambiguity’ (7 of the 9 items loading here on factor 3 representing sub-components of composite variables loading on that factor in the earlier study).
Factor 4 accounted for 3.3% of the variance, and included such items as
‘Excessive rules and regulations interfere with how well I am able to do my job’,
‘Things in this hotel seem to happen contrary to rules and regulations’, and ‘Compared with other work groups, my work group is under much less pressure to produce’. This component was labelled ‘Regulations, organisation and pressure’ and appeared to overlap with that identified by Ryder and Southey as ‘Conflict and Pressure’ (5 of the 8 items loading on factor 4 representing sub-components of composite variables loading on that factor in the earlier study).
Factor 5 accounted for 2.7% of the variance and included items such as ‘There is variety in your job’, and ‘You have opportunities to learn worthwhile skills and knowledge in your job’. This component was labelled ‘Job variety, challenge and autonomy and was consistent with factor 2 identified by Ryder and Southey ‘Job variety, challenge and esprit’ (6 of the 6 items loading on factor 5 representing sub- components of composite variables loading on that factor in the earlier study).
Factor 6 accounted for 2.5% of the variance and included items such as
‘Members of your work group trust each other.’ and ‘A friendly atmosphere prevails among most of the members of your workgroup’. This component was labelled
165 ‘Workgroup co-operation, friendliness and warmth’ and was consistent with both Jones and James factor 4 ‘Workgroup cooperation friendliness and warmth’ (5 out of 5 items), and factor 5 from Ryder and Southey ‘Workgroup reputation, co-operation, friendliness and warmth’ (4 out of 5 items).
Factor 7 accounted for 2.1% of the variance and included items such as ‘Your job requires a high level of skill and training’, ‘You are required to meet rigid standards of quality in your work’, and ‘Your job demands precision’. This component was labelled ‘Job standards’. Despite 3 of the 4 items presented here falling on factor 4 of
Ryder and Southey (‘Organisational planning openness’), and although only 2 of the 4 items loading on factor 6 from Jones and James (‘Job standards’), the same label was applied as all of the 4 items falling on this factor in the present study are consistent with the spirit of the factor definition provided by Jones and James.
6.9 Variation in climate dimensions between hotels
6.9.1 Generating climate dimension sores
The preceding sections of this chapter describe an examination of the instrument used here to measure organisational climate and its underlying dimensions across the 14 hotels participating in this study. Whilst such investigations are of interest in their own right, the principal purpose of this study is to examine the relationship between
Organisational Climate and Hotel Performance. To make such comparisons possible, it is necessary to generate new variables to act as indices of each of the underlying dimensions, and to provide an overall index of Organisational Climate.
Following a PCA as conducted here, there are a variety of methods that may be followed to generate new variables. One would be to use the factor scores for each
166 participant, and thereby have a score on each factor for each participant. These scores could then be averaged across employees within each hotel, and thereby provide an
Organisational Climate score for each hotel on each of the underlying dimensions. Such an approach, however, has the disadvantage that it provides no basis for comparison in future investigations should the same instrument be applied to other samples (as equivalent factor scores would not exist).
The approach taken here was as follows. For each of the 7 factors, for each participant, the arithmetic mean was calculated across all of the PCQ items with their primary loading on that factor. This created 7 new variables, with each participant having a score on each variable. An eighth variable was then produced which consisted of the arithmetic mean of these 7 variables to produce a single Composite Measure of
Organisational Climate. The means were then calculated for each of these new variables to provide a score for each hotel on each of the 7 underlying Organisational Climate dimensions, and on the Composite Measure of Organisational Climate. These values are presented in Table 6.5.
167 Table 6.5 Mean scores on climate dimensions and for the Composite Measure of Organisational Climate across the 14 Hotels in the study.
HotelF1F2F3F4F5F6F7Composite
1 4.79 5.04 4.83 3.89 4.58 4.72 5.37 4.75 2 5.36 5.56 5.29 3.31 5.10 5.21 6.00 5.12 3 5.40 5.55 5.19 3.36 5.08 5.15 5.83 5.08 4 5.15 4.96 4.84 3.70 5.08 5.01 5.78 4.93 5 5.17 5.33 4.96 3.63 4.96 4.98 5.63 4.95 6 5.17 5.13 4.87 3.53 4.92 5.05 5.65 4.90 7 5.55 5.54 5.13 3.10 4.83 5.27 5.59 5.00 8 5.36 5.16 5.07 3.05 4.83 5.11 5.74 4.90 9 5.19 5.44 5.10 3.26 4.68 4.89 5.53 4.87 10 5.35 5.13 4.79 3.48 5.25 5.20 5.76 4.99 11 5.50 5.29 5.18 3.25 5.05 5.15 5.76 5.02 12 6.01 5.91 5.86 2.73 5.71 5.32 6.19 5.39 13 5.08 4.74 4.80 3.71 4.71 4.91 5.61 4.80 14 4.90 4.77 4.59 3.61 4.90 4.99 5.68 4.78
6.9.2 Comparison of Climate Dimensions between the 14 Hotels in the Study.
Although more complicated multivariate and structural equation modelling techniques will be applied in Chapter 7 to the Organisational Climate data, a necessary condition for an explanation of variation in hotel performance as a function of
Organisational Climate is a variation in Organisational Climate between the hotels. To establish whether this necessary condition exists, a set of simple univariate statistics was calculated.
The mean score for each of the underlying dimensions of organisational climate, and for the Composite Measure of Organisational Climate (Table 6.5), were compared across the 14 hotels in the study. Given these new variables represented
168 orthogonal underlying dimensions, a multivariate analysis of variance was considered inappropriate due to its assumptions of underlying relationships between the dependent variables. Consequently, the mean score for each of the underlying climate dimensions were compared across the 14 hotels using 7 separate one-way Analysis of Variance
(ANOVA). To control for Type I error, as a consequence of the number of comparisons, using a Bonferroni adjustment, the alpha level was adjusted from .05 to .006. The results of these analyses are summarized in Table 6.6.
Table 6.6
Summary of results of 7 oneway ANOVA’s. Each ANOVA compared the 14 means of each of the hotels on one of the 7 dimensions of organizational climate.
D.F. D.F. Between Within Total F Prob. Bet. within SS SS SS
Leader facilitation and 12 1566 133.42 1578.96 1712.38 11.03 .0000 support Professional and 12 1612 130.89 1377.07 1507.96 12.77 .0000 organisational esprit
Conflict and 12 1626 75.60 1604.27 1679.87 6.39 .0000 ambiguity
Regulations, 12 1596 133.47 1491.89 1625.35 11.90 .0000 organisation and pressure Job variety, challenge 12 1632 111.41 2183.47 2294.88 6.94 .0000 and autonomy
Workgroup 12 1641 67.59 872.56 940.15 10.59 .0000 co-operation, friendliness and warmth
Job standards 12 1650 79.78 1441.19 1520.97 7.61 .0000 Composite 12 1367 33.05 496.00 529.00 7.59 .0000
These analyses compared separately for each of the 8 variables (each of the dimensions of Organisational Climate and the Composite Measure of Organisational
Climate), the mean scores on that variable for each of the 14 hotels in the study. These
169 analyses established that significant differences existed between hotels on each of the 7 dimensions of Organisational Climate, and on the Composite Measure or Organisational
Climate (in all cases p < .0001). Consequently it is possible that Organisational Climate might provide some degree of explanation for differences between hotel performance.
This issue will be addressed in Chapter 7.
6.10 Summary and Discussion
In this chapter, employee responses to an instrument designed to provide measures of organisational climate in 14 hotels were presented. The instrument used was a version of the Psychological Climate Questionnaire first presented by Jones and
James (1979). The instrument was modified in 2 important ways, first, following the study of Ryder and Southey using a consistent 7 point anchored scale for participant responses. Second, whereas both of the earlier studies had used between 2 and 7 items to represent 35 ‘a priori’ scales, this study consistently used only 2 items.
Consequently, the instrument used here comprised of only 70 items, rather than the 145 of the original.
A reliability analysis was conducted on the participants’ responses. A coefficient alpha (Chronbach’s) of 0.96 was found. This represents an excellent result and indicates a high level of internal consistency for the instrument as applied in this study.
Homogeneous tests exhibit high values of the coefficient alpha (Gregory, 1996, p. 96), whereas heterogeneous tests which ‘measure more than one trait invariably produce low values of coefficient alpha’. Such an outcome provides support for the notion of calculating a single overall index of organisational climate from the version of the PCQ used in this study.
170 An analysis of the item-total correlations for each item in the instrument found some items to correlate as high as .72 with the total test score. Some items, however, displayed correlations as low as .09. Such information would be useful in the future should an even shorter version of the PCQ be developed. It might be decided to remove items with low item-total correlations, particularly where items do not have significant loadings on any of the principal components described below. Such an exercise is, however, beyond the scope of the current investigation.
Applying PCA to the responses of hotel employees to the modified and simplified form of the PCQ used in this study found 7 underlying dimensions of
Organisational Climate that were interpretable. These 7 dimensions accounted for 48% of the variance.
Jones and James’ analysis of responses from U.S. naval personnel produced a 6 factor solution accounting for 59% of the variance. They labelled their factors ‘Conflict and Ambiguity’, ‘Job Challenge, Importance, and Variety’, ‘Leader Facilitation and
Support’, ‘Workgroup Cooperation, Friendliness, and Warmth’, ‘Professional and
Organisational Esprit’, and ‘Job Standards’. Jones and James repeated their study using samples of firemen and health managers to assess the generalisability of their factor structure in representing Organisational Climate. They replicated the first 5 of their 6 dimensions across their samples.
Ryder and Southey (1990) modified the Jones and James questionnaire to provide a consistent method of response by participants to each question. These authors produced a solution accounting for 57% of the variance which extracted 10 factors, of which 6 were deemed to be interpretable. They labelled their factors as ‘Leader
Facilitation and Support’, ‘Job Variety, Challenge and Autonomy’, ‘Conflict and
171 Pressure’, ‘Organisational Planning Openness’, ‘Workgroup Reputation, Cooperation,
Friendliness and Warmth’, and ‘Perceived Equity’.
Although Ryder and Southey concluded ‘that the major dimensions of the PCQ are stable and may provide a comparative framework in the study of organisational climates’ (page 49), they provided only one of their dimensions with the same label as provided by Jones and James.
In the current study, a simplified version of the Ryder and Southey version of the PCQ was used. The version used a consistent number of items (2) representing each of the 35 ‘a priori’ scales originally proposed by Jones and James. This contrasts with both of the earlier studies that used up to 7 items to represent one of these sub-scales.
The PCA presented here produced 7 factors for which labels were proposed; ‘Leader facilitation and support’, ‘Professional and organisational esprit’, ‘Conflict and ambiguity’, ‘Regulations, organisation and pressure’, ‘Job variety, challenge and autonomy’, ‘Workgroup co-operation, friendliness and warmth’, and ‘Job standards’.
An interesting mix of factors emerged when contrasted with the earlier 2 studies.
Five of the components presented here were interpreted as essentially the same, and given the same labels, as those presented by Jones and James. ‘Leader facilitation and support’ (which was also found by Ryder and Southey), ‘Professional and organisational esprit’, ‘Conflict and ambiguity’, ‘Workgroup co-operation, friendliness and warmth’ and ‘Job standards’. Of the remaining 2 factors extracted here, ‘Job variety, challenge and autonomy’ was interpreted as essentially the same, and given the same label, as factor 2 of Ryder and Southey. The component labeled here as
‘Regulations, organisation and pressure’ was found to have significant overlap with
‘Conflict and Pressure’ of Ryder and Southey.
172 Although it was identified in all three studies, the factor Leader Facilitation and
Support explained the largest amount of variance in both the current study, and that of
Ryder and Southey. In the original study by Jones and James, this factor accounted for the third largest amount of variance. This pattern across studies may simply reflect the existence of a greater variation in leadership within civilian organisations (both private and public) than would be found within the military sample used by Jones and James.
On the basis of the analysis presented here Organisational Climate within the 14
Australian hotels used in this sample may be considered to be composed of 7 underlying dimensions (Aggregate Organisational Climate Model C). Each of which has been described within either of the earlier studies of Jones and James, and Ryder and
Southey. This is illustrated in Figure 6.3.
173 Autonomy
Figure 6.3 Organisational Climate Model C: The dimensions of Organisational
Climate of the 14 Hotels participating in the current study.
The PCA presented here produced a lower proportion of variance explained by the 7 factors for which labels were proposed, than was the case for the original version of the PCQ presented by Jones and James (1979). Here the 7 dimensions accounted for
48% of the variance whereas Jones and James presented factors accounting for 59% of the variance. A number of reasons could explain the differences between the 2 studies.
174 First, in their original study Jones and James used enlisted U.S. Navy personnel. In the present study, both full-time (permanent), and casual employees completed the study. It must be remembered that a PCA is not simply an analysis of the instrument, but also an analysis of the population responding to the instrument. It is entirely possible that permanent and casual staff would represent essentially different sub-populations. If this were true, the presence of the two sub-populations would serve to increase the variance to be explained, and thereby reduce the proportion of variance explained by the principal components. Second, this instrument is a simplified version of that originally presented by Jones and James in which only 70 items were presented compared with
144. Both the original version of the PCQ and the modified version used by Ryder and
Southey, used up to seven items, and never less than two, to represent each of the 35 ‘a priori’ scales. In this study two items were used to represent each of these ‘a priori’ scales. The averaging procedure of the earlier study would serve to minimise noise in the data matrix. Third, given the relatively large size of the sample, the analysis conducted here was performed on the individual items and not aggregate constructs.
Analysing scores on the individual items (as was done here) would serve to increase the level of unique variance for each item, and thereby reduce the proportion of variance that may be explained by common factors.
A similar comparison with the results of Ryder and Southey is difficult. They report ‘the solution accounted for 57.4% of variance’ (page 48). However, it is not clear whether they are reporting the variance accounted for by the 10 factors they extracted with eigenvalues greater than unity (which would be the usual interpretation of their precise wording), or by the 6 factors they deemed to be interpretable (which is implied, but not explicitly stated in their results). If it is the former, then the 13 factors extracted by the analysis reported here accounted for a comparable proportion of variance
(57.6%). 175 On the basis of the loadings of items of the PCQ on different principal components, new variables were produced to provide each participant with a score on each of the underlying dimensions of Organisational Climate and provide an overall index of Organisational Climate termed here ‘Composite Measure of Organisational
Climate’. Simple univariate comparisons were made by calculating the mean scores on these new variables across employees within each hotel and applying ANOVA to compare these means across the 14 hotels. Such variation in Organisational Climate between the hotels leaves open the possibility that Organisational Climate may provide some explanation of variation between hotels in their financial performance. This issue will be examined in Chapter 7.
In summary, in this study the employees of 14 hotels completed a version of the original Jones and James (1979) PCQ. The items were both modified following the procedures described by Ryder and Southy (1990) so that each item required a consistent mode of response, and reduced in length from 144 to 70 items. A reliability analysis demonstrated a high level of internal consistency with a coefficient Alpha
(Chronbach’s) of 0.96. A PCA extracted 7 interpretable components that accounted for
48% of the variance. A comparison with the factors presented by Jones and James, and
Ryder and Southey found 5 of the 7 to be consistent with factors extracted in the former study, and the remaining 2 to be consistent with factors extracted in the latter study.
Univariate analyses demonstrated that each of the dimensions of Organisational Climate varied significantly across the 14 hotels participating in this study.
176 7.0 Analyses of the relationships between: Employee Demographic
Variables, Organisational Climate, Customer Satisfaction, and
REVPAR
7.1 Overview
In this chapter the relationships between the dimensions of organisational climate, customer satisfaction and REVPAR will be examined. As described in chapter
4, data presented in this study fall into 2 categories. The first represents data on variables for which a single value exists for each of the 14 hotels (Hotel Level Data). A particular variable may fall into this category of data either as a consequence of the fact that it is inherently only a property of the hotel and not the individual staff member (e.g.
REVPAR), or as a result of the aggregation of scores of staff members within each hotel to produce a score which is an ‘average’ value for staff members of that hotel. The second category of data represents variables where there is a score for each individual staff member who participated in the study (Staff Level Data).
It is important to understand the different outcomes that may come about by analysing data from these different categories. For example, there may be 2 variables for which there are scores for each staff member in the sample. This then allows the calculation of the aggregate scores for each hotel consisting of the mean score of the staff members’ responses for that hotel. This would yield 2 new variables, each consisting of 14 cases. It is mathematically possible to, first, analyse the relationship between these 2 variables using the scores from all of the individual staff members participating in the study and find a significant relationship between the 2 variables, and second, analyse the relationship between these 2 variables using the aggregate scores and find no variation between hotels on either variable and find a non-significant relationship between the 2 variables. 177 Different analyses within this chapter will be undertaken at different levels of the data from the responses of staff members across 14 hotels. Consequently, on some occasions it is necessary to perform analyses in which an individual ‘case’ represents an individual employee and in other analyses an individual ‘case’ may represent an Hotel.
The models outlined in Chapter 3 guide the overall analyses presented in this chapter. First, an explanation of the statistical and modelling procedures is presented.
Second, a method to define an aggregate (composite) measure of Organisational
Climate is presented. Third, a series of analyses are presented to examine the viability of
Structural Model A. Fourth, a series of analyses are presented to examine the viability of Structural Model B.
7.2 Statistical analyses and modeling techniques used in this chapter
7.2.1 Multiple linear regression
This statistical technique relies upon two or more predictors that are jointly regressed against the criterion variable, and is known as multiple linear regression. The correlation coefficient r indicates the strength of the relationship between 2 variables but does not give the magnitude of the variance in that dependent variable that will be explained when several independent variables are theorised to simultaneously influence it. Where the dependent variable is, for example, organisational climate it may be explained by a range of independent demographic and other variables (predictors).
Multiple linear regression is a technique that provides the calculation of the multiple correlation coefficient R which is an index of correlation between a set of independent variables and the dependent variable. More importantly, the technique provides an explanation of how much of the dependent variable is explained by a set of
178 predictors by providing an index of the proportion of variance explained in the dependent variable by the set of independent variables (R2).
7.2.2 Structural equation modelling
Ullman (1996) describes structural equation modeling as ‘a collection of statistical techniques that allow examination of a set of relationships between one or more IVs [independent variables], either continuous or discrete, and one or more DVs
[dependent variables], either continuous or discrete’ (p. 709). Structural equation modeling is used to examine the efficacy of proposed cause and effect relationships between a set of variables. In its most simple form, structural equation modeling represents the application of correlation and multiple linear regression to evaluate the strength of the relationships between variables. Within the framework of this type of analytical approach, for an hypothesised model to be a viable explanation of the relationships between variables, it is necessary, but not sufficient, for correlation coefficients or regression coefficients to be significant between variables for which cause and effect relations are proposed.
A number of indices of the goodness of fit of the hypothesised model in providing a parsimonious explanation of the relationships between variables exist. A good fit is sometimes indicated by a non-significant χ2 value (Ullman, 1996). Ullman, however, lists a number of problems associated with the use of χ2 as a goodness of fit index when conducting structural equation modeling. First, with small samples the computed χ2 need not have a χ2 distribution. Second, with large samples trivial differences between an estimated population values may be significant. Third, when assumptions underlying the χ2 test statistic are violated, the associated probability levels
179 are unreliable. As a consequence of these problems, Ullman (1996) reports that numerous measures of goodness of fit have been proposed.
The Goodness of fit index (GFI) calculates a weighted proportion of variance in the sample covariance matrix accounted for by the estimated population covariance matrix (Tanaka and Huba, 1989, as cited in Ullman, 1996). Tanaka and Huba propose the GFI to be analogous to R2 in multiple regression. A GFI of 1 represents a perfect fit between the hypothesised model and the observed data. A GFI value in excess of .9 is usually accepted as indicating a good fit of the model to the empirical data.
The Adjusted Fit Index (AGFI) represents a modification of the GFI which incorporates both the number of parameter estimates in the model and the number of data-points in the sample. The greater the number of estimated parameters, the smaller the AGFI value is relative to the GFI. Conversely, the smaller the number of data- points, the smaller the AGFI will be with respect to the GFI.
One goal of all modeling, including structural equation modeling, is parsimony.
As a general rule, increasing the number of parameters in a model will serve to increase the fit of the model to observed data. A good model, therefore, contains as few parameters as possible. A number of Parsimony fit indices exist. These include;
The PGFI represents a modification of the GFI to take into account parsimony of the model (Mulaik, et. al., 1989, as cited in Ullman, 1996). The PGFI may be described by
PGFI = [1 – (No. of estimated parameters/No. of data-points)] * GFI
180 The closer this fit index is to a value of 1.00, the better the fit of the model. This index provides a heavy penalty for the estimation of a large number of parameters within a model and is usually much smaller than other indices of fit (Ullman, 1996).
The Akaike Information Criterion (AIC) and the Consistent Akaike Information
Criterion (CAIC) are 2 different measures of parsimony of fit that each use χ2 and the degrees of freedom:
2 AIC = χ model – 2dfmodel
2 CAIC = χ model – (lnN + 1) dfmodel
For these 2 indices small values indicate a parsimonious fit. Ullman reports, however, there to be no consensus regarding precisely how small is small enough.
7.3 Structural Model A: Relationship between demographic variables, Organisational Climate and Customer Satisfaction.
In Structural Model A (Figure 7.1) it is proposed, firstly, that a number of employee demographic variables (Gender, Age, Education, Organisational Tenure, Job
Tenure, Income, Hours Worked, Employment Status and Training Interval) will affect
Organisational Climate (as represented by our Composite Measure of Organisational
Climate). Secondly, that Organisational Climate will affect customer satisfaction (as represented by our measure of Employee Perceptions of Customer Satisfaction).
181 Figure 7.1 Structural Model A
This model was examined, firstly, by applying multiple regression using the demographic variables listed above to predict our Composite Measure of Organisational
Climate. Secondly, Pearson’s correlation coefficient (r) was used to establish whether there is a link between Organisational Climate and Employees’ Perceptions of Customer
Satisfaction, and third, structural equation modeling techniques were used to devise an overall index of goodness of fit of the model.
7.3.1. Multiple linear regression analysis examining the relationship between employee demographic variables and organisational climate proposed by structural model A.
Scores for each employee on 9 demographic variables (Gender, Age, Education,
Organisational Tenure, Job Tenure, Income, Hours Worked, Employment Status, and
Training Interval) were entered into a Multiple Linear Regression analysis as predictor
182 variables (Independent Variables) of our Composite Measure of Organisational Climate
(Dependent Variable).
Significant correlation’s (p< .001) were found between our Composite Measure of Organisational Climate and the predictor variables Income (r = .107), Hours Worked
(r = .148), Employment Status (r = -.090), and Training Interval (r = -.120). The
Multiple Linear Regression indicated a significant link between the set of predictor variables and Organisational Climate (F(10,1359) = 6.58, p < .001). Overall, however, this link was relatively modest with a multiple correlation coefficient of R = .21. This result means that, while the relationship between the set of predictor variables is statistically significant, only 4.5% (R2 = .045) of the variance in our Composite Measure of
Organisational Climate may be explained by these 9 demographic variables.
An examination of the relative contribution of different predictor variables as indexed by their regression coefficients (Table 7.1), shows significant individual contribution for hours worked per week (t(1359) = 3.092, p < .01) and time since last training session (t(1359) = -3.84, p < .001).
183 Table 7.1 Regression coefficients and associated probabilities for Multiple
Linear Regression using demographic variables to predict Composite Measure of
Organisational Climate.
Variable B S.E. B Beta t Sig t
Constant 4.605 .156 29.427 .000
Gender .048 .034 .039 1.381 .168 Age -.007 .019 -.012 - .375 .708 Education -.017 .011 -.046 -1.615 .107 Length of Service -.014 .024 -.023 - .566 .571 Length in Job -.008 .025 -.013 - .323 .747 Gross salary .021 .014 .0614 1.536 .125 Hours/week .042 .013 .133 3.092 .002 Mode of Employment -.001 .028 -.001 - .032 .974 Time since Training -.051 .013 -.109 -3.837 .000
7.3.2 An examination of the relationship between organisational climate and employee perception of customer satisfaction as proposed by Structural Model A.
A second stage of analysis for Structural Model A was conducted which examined the relationship predicted by the model to exist between the Composite
Measure of Organisational Climate and Employees’ Perception of Customer
Satisfaction. The individual responses of each employee in the sample were entered into the analysis which found a significant correlation between the 2 measures (r= .425, p<.001). This analysis indicated that 18.1% of the variance in employee perceptions of customer satisfaction could be accounted for by the Composite Measure of
Organisational Climate.
184 When this analysis was conducted for each hotel individually, the relationship was shown to hold for each of the hotels (Table 7.2).
Table 7.2 Pearson r correlation coefficients examining the relationship between the Composite Measure of Organisational Climate and Employee
Perceptions of Customer Satisfaction for each of the Hotels participating in the study.
Hotel Pearson r Sample size
1 .439** 428
2 .372** 95
3 .274** 230
4 .346* 49
5 .254* 64
6 .435** 35
7 .388* 41
8 .316* 40
9 .515** 29
10 .287* 71
11 .318** 74
12 .479* 22
13 .561** 144
14 .449** 60
*significant at .05 level **significant at .01 level
The analyses above show significant relationships between the Composite
Measure of Organisational Climate and Employee Perceptions of Customer Satisfaction when analyses are carried out on data from individuals within the organisations studied.
It is, however, possible that these results need not reflect the differences between the 14 hotels studied here. To examine this, the Mean Composite Measure of Organisational 185 Climate score, and Mean Customer Satisfaction score was calculated for each Hotel
(Table 7.3).
Table 7.3 Mean Composite Measure of Organisational Climate, Mean
Employee Perception of Customer Satisfaction and REVPAR for each of the 14
Hotels.
Hotel Organisation Customer REVPAR
1 4.7384 3.77 96.77 2 5.1061 4.15 151.59 3 5.0809 4.25 89.76 4 4.9139 3.55 78.11 5 4.9166 3.99 72.42 6 4.9362 3.91 86.13 7 4.9553 3.96 63.29 8 4.8533 3.91 74.68 9 4.9184 4.5 124.16 10 4.9736 3.95 65.10 11 5.0399 3.96 78.19 12 5.3833 4.07 62.25 13 4.8178 3.67 73.24 14 4.8139 3.58 77.36
The correlation between these 2 variables was then calculated to show a correlation of r = .469. This analysis shows that 22% of the variance in Mean Customer
Satisfaction between the Hotels could be explained by differences in Mean
Organisational Climate between the Hotels.
7.3.3 Structural equation modeling
The data were entered into the AMOS program (Arbuckle, 1997) and the analysis calculated the goodness of fit of the empirically derived data with the theoretical model (Appendix F). Maximum likelihood estimation was employed to test
186 all models. The independence model that tests that the hypothesised variables showed
2 that they are uncorrelated and therefore were easily rejected (χ (66) = 3332, p < .001).
2 Despite the analysis finding a significant chi-square (χ (10) = 59.462, p < .001), the goodness of fit indices demonstrated support for the model (Table 7.4).
Table 7.4 Goodness of fit and parsimony of fit indices for structural equation analysis.
Index Value
Goodness of fit indices NFI 0.982 GFI 0.992 AGFI 0.938 IFI 0.985 CFI 0.985 RMR 0.040
Parsimony of fit indices PGFI 0.127 AIC 195.8 CAIC 610.3
The fit indices ‘goodness of fit index’ (GFI) and the ‘adjusted fit index’ (AGFI) are both in excess of .9 and indicate a good fit of the model to the data (Tabachnick and
Fidell, 1996, p. 750). The degree of parsimony fit indices, the PGFI, the Akaike
Information Criterion (AIC) and the Consistent Akaike Information Critereon (CAIC), however, with values of 0.135, 173.5 and 519.9 respectively are relatively low in the former case, and high in the latter 2 cases (Tabachnick and Fidell, 1996, p. 750). These indices reflect the fact that a relatively large numbers of variables were used in the model to produce the fit achieved.
187 7.3.4 Summary of analysis of Structural Model A
Although Structural Equation Modeling Analysis yielded a respectable
Goodness of Fit index (.991), this index could be viewed as indicating that the large sample size used in this analysis provided strong evidence of a weak relationship. The nine demographic variables were shown in the regression analysis to have a significant predictive relationship with the Composite Measure of Organisational Climate. And given all 10 predictor variables combined served only to explain 4.5% of the variance, it is doubtful that in ‘real world’ applications such a relationship would be useful in examining financial outcomes for hotels.
Although the only fair interpretation is that poor support was found for the global model presented here. The relatively strong relationship between mean employee perceptions of customer satisfaction and mean Composite Measure of Organisational
Climate in which 22% of the variation in one was explained by the other may have ‘real world’ application. This relationship is relevant to the following analysis of Structural
Model B.
7.4 Structural Model B: The relationship between Organisational Climate, Customer satisfaction, and REVPAR.
In Structural Model B relationships were proposed between the underlying dimensions of Organisational Climate, Employee Perceptions of Customer Satisfaction, and REVPAR. The PCA presented in Chapter 5 did not provide a perfect replication of the factor structure described by either Jones and James (1979) or by Ryder and Southey
(1990), rather a factor structure that essentially reflected a mix of factors consistent with factors described in both of the earlier studies. On the basis of the results presented in
188 Chapter 6, a slightly modified version of Structural Model B is now presented in Figure
7.2.
Autonomy
Figure 7.2 Structural Model B
This model was tested by first, applying multiple regression using the 7 dimensions of Organisational Climate to predict Employee Perception of Customer
Satisfaction. Second, a Pearson r correlation was used to establish whether there is a link between Employee Perception of Customer Satisfaction and REVPAR, and third, structural equation modeling techniques were used to devise an overall index of goodness of fit of the model.
189 7.4.1 Multiple linear regression analysis examining the relationship between organisational climate dimensions and employee perception of customer satisfaction proposed by Structural Model B.
For each of the employees, the factor scores on each of the 7 dimensions of
Organisational Climate were entered as predictor (Independent) variables of Employee
Perception of Customer Satisfaction (Dependent Variable) in a Multiple Linear
Regression.
Employee Perception of Customer Satisfaction was shown to be significantly correlated with each of the 7 factors (Table 7.5).
Table 7.5 Correlations between Employee Perception of Customer Satisfaction and each of the 7 dimensions of Organisational Climate.
Factor Pearson r 1. Leader Facilitation and Support .386* 2. Professional and Organisational Esprit .534* 3. Conflict and Ambiguity .412* 4. Regulations, Organisation and pressure -.309* 5. Job variety, challenge and autonomy .293* 6. Workgroup cooperation, friendliness and warmth .294* 7. Job standards .247*
* correlation is significant at the 0.001 level
The Multiple Linear Regression indicated a significant link between the set of predictor variables and Employee Perception of Customer Satisfaction (F(7,1381) =
83.953, p < .001). This link was reflected by a relatively strong multiple correlation coefficient of R = 0.547. This result means that 30% (R2 = 0.30) of the variance in
190 Employee Perception of Customer Satisfaction may be explained by these 7 dimensions of Organisational Climate.
An examination of the relative contribution of different predictor variables as indexed by their regression coefficients (Table 7.6), shows significant individual contribution for Professional and Organisational Esprit (t(1374) = 13.713, p < .001),
Regulations, Organisation and Pressure (t(1374) = -2.626, p < .005), and Job Variety,
Challenge and Autonomy (t(1374) = -2.946, p < .005).
Table 7.6 Regression coefficients and associated probabilities for Multiple
Linear Regression using Organisational Climate Dimensions to predict Employee
Perception of Customer Satisfaction.
BStd.Beta T Sig. Err. Constant 2.024 .177 11.44 .000 1. Leader Facilitation and -9.8-03 .027 -.015 -.364 .719 Support 2. Professional and .351 .026 .490 13.71 .000 Organisational Esprit 3. Conflict and Ambiguity 4.6E-02 .025 .067 1.841 .066 4. Regulations, Organisation and -5.0E-02 .019 -.070 -2.63 .009 pressure 5. Job variety, challenge and -5.7E-02 .019 -.098 -2.95 .003 autonomy 6. Workgroup cooperation, 4.5E-02 .028 .047 1.612 .107 friendliness and warmth 7. Job Standards 2.2E-02 .021 .030 1.070 .285
191 7.4.2 An examination of the relationship between REVPAR and employee perception of customer satisfaction as proposed by Structural Model B.
Structural Model B predicts a relationship to exist between REVPAR and
Employee Perception of Customer Satisfaction. Seemingly the question as to whether this relationship exists between 2 continuous variables may simply be answered by conducting a simple correlation between the 2 variables. These 2 variables, however, each fall into the 2 different categories of data (Hotel Level Data, and Staff Level Data) and so 2 different analyses were conducted, one at each level of data.
In the first analysis, the 2 variables were examined at the Staff Data Level. Each employee had a score for Employee Perception of Customer Satisfaction. A score for each employee was assigned for REVPAR, simply by assigning the REVPAR score for the hotel in which the employee worked. A simple Pearson r correlation was then conducted which found a significant correlation between REVPAR and Employee
Perception of Customer Satisfaction (r = 0.112, p < .001).
In the second analysis (conducted at the Hotel Level), for each hotel an aggregate score for Employee Perception of Customer Satisfaction was calculated by simply taking the mean score of this variable across all employees in the hotel.
REVPAR and Mean Employee Perception of Customer Satisfaction were then entered into a simple Pearson r correlation. If one accepts the a priori notion that should customer satisfaction affect REVPAR, then the effect should be positive, then the analysis finds a significant effect for Average Customer Satisfaction as a predictor of
REVPAR (r = 0.479, P < .05, one-tailed). Regardless of whether this a priori assumption is made, the analysis found 23% of the variance (r2) in REVPAR to be explained by variation in Mean Employee Perception of Customer Satisfaction.
192 7.4.3 Structural equation modeling
The Staff Level Data were entered into the AMOS program (Arbuckle, 1997) and the analysis calculated the goodness of fit of the empirically derived data with the theoretical model (Appendix F). Maximum likelihood estimation was employed to estimate all models. The independence model that tests the hypothesis that all variables
2 are uncorrelated was easily rejected (χ (36) = 5284, p < .001). Despite the analysis
2 finding a significant chi-square (χ (7) = 49.004, p < .001) the goodness of fit indices demonstrated a good fit for the model (Table 7.7).
Table 7.7 Goodness of fit and parsimony of fit indices for structural equation analysis.
Index Value Goodness of fit indices NFI 0.991 GFI 0.993 AGFI 0.953 IFI 0.992 CFI 0.992 RMR 0.354 Parsimony of fit indices PGFI 0.154 AIC 125.0 CAIC 363.4
The fit indices ‘goodness of fit index’ (GFI) and the ‘adjusted fit index’ (AGFI) are both in excess of .9 and indicate a good fit of the model to the data (Tabachnick and
Fidell, 1996, p. 750).
193 7.4.4 Summary of analysis of Structural Model B
The structural equation modeling analysis provided good support for the model with a goodness of fit index of .993. Further, the magnitude of the relationships between variables, within the model are likely to provide useful insights in ‘real world’ applications. Using Multiple Linear Regression, it was shown that the seven dimensions of Organisational Climate explained 30% of the variation, in employee perception of customer satisfaction. A correlation analysis found that 23% of the variation in
REVPAR could be explained by the variation in mean employee perception of customer satisfaction.
7.5 Summary and discussion
In this chapter, the relationships between a set of employee demographic variables (described in Chapter 5), the seven dimensions of Organisational Climate
(extracted in Chapter 6), a Composite Measure of Organisational Climate (described in
Chapter 6), employee perception of customer satisfaction, and a measure of hotel financial performance (REVPAR) were investigated.
Two structural models were presented which proposed a priori relationships between these variables. In the first model (Structural Model A), it was proposed that variation within staff demographic variables would cause variation in hotel
Organisational Climate (as indexed by the Composite Measure of Organisational
Climate). And that variation in hotel Organisational Climate would cause variation in customer satisfaction (as measured by Employee Perception of Customer Satisfaction).
A significant relationship between the Composite Measure of Organisational
Climate and Employee Perception of Customer Satisfaction was found. This was true
194 for the Staff Level Data, where correlation was performed on scores from each individual employee. In this analysis, 18.1% of the variation in customer satisfaction was explained by variation in the Organisational Climate. More importantly, this relationship also held for data at the Hotel Level, when aggregate variables were produced by averaging scores for employees within each hotel, and thereby producing a single score for Organisational Climate and a single score for Customer Satisfaction for each of the 14 hotels. The correlation of these 2 new variables found 22% of the variance between hotels in Employee Perception of Customer Satisfaction could be explained by variation between hotels in Organisational Climate. These results provided strong support for part of Structural Model A.
Analysis of relationships within other parts of Model A did not produce such strong support. Nine employee demographic variables were proposed to affect
Organisational Climate within the hotels. Regression analysis found a significant relationship to exist. Although this relationship was statistically significant, only 4.5% of the variation in the composite measure of Organisational Climate was found to be explained in terms of the 10 demographic variables. These results serve to describe a small, but significant, effect on Organisational Climate of employee demographic variables, which served to produce a respectable overall goodness of fit index for this model. Overall it should be interpreted that the large number of cases entered into the structural equation modeling analysis provided strong support for a large number of weak relationships. Taken as a whole the model is not likely to have relevance in ‘real world’ applications. The particular link within the model between the Composite
Measure of Organisational Climate and employee perception of customer satisfaction would, however, be relevant and also relates to the analysis of Structural Model B.
195 In the second structural model (Structural Model B), it was proposed that variation in the 7 dimensions of Organisational Climate (described in Chapter 6) would produce variation in customer satisfaction (as indexed by Employee Perception of
Customer Satisfaction), which in turn would lead to variation in REVPAR. Good support was found for this model both in terms of statistical significance and in terms of magnitude of effects.
First, all of the dimensions of Organisational Climate are significantly correlated
(at the .001 level) with Employee Perception of Customer Satisfaction. When used as predictors in a multiple linear regression, variation of this set of 7 Organisational
Climate dimensions was found to explain 30.0% of the variation in Employee
Perception of Customer Satisfaction.
The second part of Structural Model B predicted a relationship between customer satisfaction and a measure of hotel financial performance (REVPAR). A significant correlation was found between these 2 variables when conducted using Staff
Level Data (r = .112). More importantly, when an aggregate variable was produced by calculating the mean score for Employee Perception of Customer Satisfaction for each of the hotels, and this new variable was correlated with REVPAR (and thereby conducting an analysis at the Hotel Level), 23% of the variation in REVPAR between the hotels could be explained by variation in Employee Perception of Customer
Satisfaction.
The results of this analysis provide strong support for Structural Model B. The magnitude of the relationships between the variables indicates that these relationships may be viewed as having a commercial, as well as a statistical and theoretical, significance.
196 In conclusion, the major finding of this chapter is that Organisational Climate, whether treated as a multidimensional construct or as an aggregate measure, can be demonstrated to have both a statistically significant, and a commercially relevant, effect on customer satisfaction (as indexed by employee perceptions). Further, customer satisfaction has also been found to have both a statistically significant, and commercially relevant, effect on REVPAR.
197 8.0 General Discussion and Conclusions
8.1 Overview of study
This study gathered data from 14 four to five-star hotels in South-East
Queensland, Australia, in an attempt to examine the nature and degree of influence organisational climate has upon the level of performance of organisations within the
Australian hotel industry. Employee Perception of Customer Satisfaction was studied both as an index of hotel performance and as an intervening variable between organisational climate and REVPAR (average daily room rate multiplied by occupancy rate) - an index of hotel financial performance.
In addition to measures attempting to represent organisational climate and customer satisfaction, a number of descriptive statistics were gathered relating to both the operating characteristics of hotels, and the employees working within these organisations. The gathering of this information represented an attempt to satisfy the first 2 aims of this study;
To provide a profile of the hotels participating in the study in terms of their key operating characteristics.
To provide a profile, in terms of key demographic variables, of staff employed within the hotels in our sample.
8.2 Hotel operating statistics
Despite the fact that all hotels in the study were in the range four to five-star, as expected, the hotels nevertheless varied considerably across their key operating statistics. The size of the hotels, for example, varied from less than 200 rooms to over
198 400 and occupancy rates varied from 52% to 80%. The rack rate for hotels varied between $170 and $344. Interesting as these variations between hotels may be, the principal aim of this study was to examine variation in organisational climate and its effect on hotel financial performance. A necessary condition for such a comparison to be meaningful is the existence of significant variation in financial performance, as indexed here by the measure REVPAR. This important variable demonstrated a considerable degree of variation between hotels, from a figure of $62 to $124.
8.3 Staff demographic data
Overall the staff profile indicated a young, educated and trained, relatively gender-balanced group which received comparatively low levels of remuneration and displayed a high level of turnover.
The data demonstrated roughly equal numbers of males and female employees across the sample. When this analysis was confined to management, however, females were found to be under represented. In general the workforce is a young one, with the majority of employees being under 35 years of age. The pattern of young employees was also reflected in management, where most managers were in the 25-34 years category.
The workforce was found to be well qualified with over 60% of staff to have qualifications at the post-secondary level and above. Employees also reported a high frequency of job training with 72% of employees having attended a training session within the past 12 months. Despite employees demonstrating relatively high levels of education and training, remuneration was relatively poor. This was true, even when the mode of employment of many employees was accounted for. Only 5% of employees received in excess of $36,000 p.a., despite the fact that 61% of employees are in full- 199 time employment and 65% of employees worked 36 hours or more per week. This compares with a current average full-time wage in Australia of $38,615.20 and average total earnings (which includes overtime) of $40,768 (Martin, 1999).
Perhaps understandably, in the light of the remuneration data, there was a very high turnover of staff, with 58.5% of employees having been with their organisation for less than 2 years. These figures, of course, were reflected in the job tenure data with
66% of employees in their current job for less than 2 years.
Although it is beyond the analyses and aims of the current project, the data presented here leave open the question as to whether the high turnover in staff is in part a consequence of the relatively poor remuneration given to a relatively well educated workforce. It would seem that the workforce is generally capable of finding what they perceive as more rewarding employment elsewhere. If this is indeed the case, the opportunity costs of such a situation need to be considered and investigated. The costs would certainly involve the ongoing direct costs of continually training large numbers of new employees. Further, there is naturally a direct cost to the employer arising from new staff performing less efficiently in their positions, whilst undergoing orientation, training and learning their job, than would an experienced employee.
The high turnover of staff may, of course, not be strongly related to level of remuneration. Vallen (1993) reported that service jobs with a high degree of customer interactions have a higher level of burnout. Within Vallen’s survey, hospitality firms rarely used a consultative style. He concluded that high burnout was correlated with low organisational climate scores in highly autocratic organisations. In the context of the current study, a workforce which is not motivated to remain with its employer, is a workforce that would be expected to generate a less than optimal organisational climate.
200 As will be described below, such a climate may well result in lower levels of customer satisfaction and poorer financial outcomes for hotels.
8.4 Variation in staff demographic variables between hotels
One of the models tested in this study (Structural Model A) predicted staff demographic variables would affect organisational climate. For such variables to explain differences in organisational climate between hotels, the variables need also demonstrate variation between hotels. The third aim of this study was to establish whether the staff demographic variables vary significantly between the hotels in the sample.
All of the key staff demographic variables were found to significantly vary between hotels. Gender-balance varied between hotels from close to 50/50 in one hotel to 80% female to 20% male in another. Although across all of the hotels, the workforce was young, the percentage of employees in the ‘over 45 years of age’ category ranged from 3% to 21%. The level of education varied from 53% with post-secondary qualifications to 81%. Organisational tenure varied from a figure as low as 40% of staff having been with the organisation less than 2 years to figures in excess of 85% for 3 other hotels. Job tenure was reported to range between hotels from 49% to 94% of employees having been in their current job for less than 2 years. Hotels varied between
3% and 24% of employees earning in excess of $31,000 p.a. In one hotel, only 51% of employees were in full-time employment whereas this ran as high as 90% in another. In terms of hours worked per week, one hotel had only 58% of employees work 36 hrs or more per week, whereas this figure was as high as 84% in another. Although all hotels displayed high proportions of staff having received training in the last 12 months, the figure varied from as low as 62% of employees to 91%.
201 Like the hotel operating statistics, these data indicate that despite the fact that all of the hotels were four to five-star, there was considerable variation in employee characteristics across the hotels. This situation leaves open the possibility that these employee variables will serve to explain some of the variation in organisational climate between the hotels.
8.5 The Measurement of organisational climate within the hotels of the sample
The principal thrust of this thesis was the investigation of organisational climate and its effect on hotel performance. Individual employees in this study were presented with an instrument which aimed to measure their perceptions of the psychological climate in which they worked. Organisational climate within each hotel was estimated by averaging these responses.
The instrument used was a modified version of one presented in an earlier study by Ryder and Southey (1990), which itself was a modification of the Psychological
Climate Questionnaire presented by Jones and James, 1979). The instrument was modified for this study principally to reduce its length to something that was practicable for use within the hospitality industry. To this end, the instrument was reduced from its original 145 items, to 70 items.
The fourth specific aim of the study was to identify the underlying dimensions of organisational climate within Australian hotels. This was addressed by a Principal
Components Analysis (PCA) that was applied to the employee responses to the organisational climate Questionnaire.
H1 A limited number of factors will be identified as being able to determine the organisational climate across the hotels in this study.
202 The first hypothesis was supported in so far as the PCA extracted 7 interpretable underlying dimensions. These dimensions were given the following labels.
! Leader facilitation and support
! Professional and organisational esprit
! Conflict and ambiguity
! Regulations, organisation and pressure
! Job variety, challenge and autonomy
! Workgroup co-operation, friendliness and warmth
! Job standards
Although the instrument used in this study used only half the items, and on many items required a different style of response, these factors were consistent with those originally described by Jones and James for a sample of U.S. military personnel. Their study produced the following factors;
! Conflict and Ambiguity
! Job Challenge, Importance, and Variety
! Leader Facilitation and Support
! Workgroup Cooperation, Friendliness, and Warmth
203 ! Professional and Organisational Esprit
! Job Standards
As can be seen above, 5 of the components extracted in this study were interpreted as essentially the same and given the same title as components described in the Jones and James study. Of the remaining 2 components extracted here, the first, ‘Job variety, challenge and autonomy’ was interpreted as essentially the same, as the second factor of
Ryder and Southey. The second, ‘Regulations, organisation and pressure’ was found to have significant overlap with Ryder and Southey’s component ‘Conflict and Pressure’.
Although it might be argued that the present study did not extract precisely six underlying dimensions as was reported in the two earlier studies of Jones and James, and Ryder and Southey. When the results are analysed, in terms of the item loadings and the associated meaning of these dimensions, the results are broadly consistent with those of Jones and James. When they are not consistent with that study, they would appear to be consistent with the results of Ryder and Southey.
By the application of PCA to the data, it was possible to describe the underlying dimensions of organisational climate within the sample of 14 hotels. Further, this analysis provided a method by which each of the employees in the sample could be assigned a value for each of these 7 psychological dimensions. An overall composite value of psychological climate, for each employee could also be produced by averaging these seven dimensions. By aggregating over these scores, it was also possible to assign a value for each of the hotels on each of the underlying dimensions of organisational climate and on the composite measure of organisational climate. These procedures then allowed the possibility of further analyses to examine the relationship of these dimensions to variables of hotel performance. 204 8.6 Testing Structural Model A
In Chapter 3 a structural model (Structural Model A) was presented which proposed that organisational climate would be affected by the demographic characteristics of employees within the hotels, and that organisational climate would affect employee perception of customer satisfaction. The fifth aim of this project was effectively to test Structural Model A.
Figure 8.1 Structural Model A
This model directly produced hypotheses 2 and 3;
H2 There will be a significant correlation between an aggregate measure of organisational climate and employee perception of customer satisfaction.
205 In a strict statistical sense hypothesis 2 may be interpreted as being supported; a multiple linear regression using the demographic variables produced a statistically significant effect (p < .001). However, only 4.5% of the variance in organisational climate was explained by the 9 predictor variables. Consequently, 95.5% of variation in organisational climate was not predicted by the relationship. Although a real but weak link might be argued between these variables, its utility in any practical real world application would be extremely limited.
H3 Employee demographic variables, taken as a multivariate variable, will be a significant predictor of an aggregate measure of organisational climate.
Hypothesis 3 received strong support. When analyses were conducted at the hotel level, a correlation (r) of .469 was found between the composite measure of organisational climate and Employee Perception of Customer Satisfaction. This analysis shows that 22% of the variance in Mean Customer Satisfaction between the Hotels could be explained by differences in Mean organisational climate between the Hotels.
When examined overall, Structural Model A provides a poor explanation of the overall relationship between the variables. The link between the employee variables and that of global organisational climate provides very little explanation of variation in organisational climate, despite the fact that 9 variables are used to provide the explanation. Given that other parts of the model display such poor links, the fact that a relatively strong relationship is found between the Composite Measure of organisational climate and Employee Perception of Customer Satisfaction does not provide strong support for the model. Of itself, however, the result is very interesting. The result is consistent with the notion that organisational climate has a major impact on customer satisfaction. This relationship was investigated further in Structural Model B.
206 8.7 Testing Structural Model B
In Chapter 3, a structural model (Structural Model B) was presented which proposed that the financial performance of the hotels (as indexed by REVPAR) would be affected by customer satisfaction (as indexed by Employee Perception of Customer
Satisfaction). Further, the seven dimensions of organisational climate would affect that customer satisfaction. The sixth aim of this project was effectively to test Structural
Model B.
Autonomy
Figure 8.2 Structural Model B.
This model directly produced hypotheses 4 and 5;
H4 There will be a significant correlation between employee perception of
207 customer satisfaction and REVPAR.
H5 The dimensions of organisational climate, taken as a multivariate variable, will be a significant predictor of an aggregate measure of employee perception of customer satisfaction.
H4 was supported when analyses were conducted on both employee level and hotel level data. The correlation (r) between Average Customer Satisfaction (hotel level) and REVPAR was 0.479. This indicated that 23% of the variance in REVPAR could be explained by variation in Mean Employee Perception of Customer Satisfaction. This result provided strong evidence of a link between customer satisfaction and hotel financial performance.
H5 was also supported. Using multiple linear regression, it was found that the set of 7 dimensions of organisational climate accounted for 30% of the variation in customer satisfaction (as indexed by Employee Perceptions of Customer Satisfaction).
This result provides strong evidence of a link between organisational climate and customer satisfaction.
8.8 Implications of the result that Structural Equation Model B is supported
Taken together, in conjunction with the structural equation modelling analysis
(in which GFI = .993), these two results provide strong evidence in support of
Structural Equation Model B. Further, this evidence is not simply of a link that might have some theoretical significance, the magnitude of the relationships between organisational climate, Employee Perception of Customer Satisfaction, and REVPAR, are of an order which has significant practical implications for hotel management. This outcome leads to predictions that programs which increase the positive aspects of
208 organisational climate, would serve to produce an increase in customer satisfaction which in turn would result in an increase in REVPAR.
These results serve to underline the fact that the major resource component in service delivery is the hotel employee (the deliverer of the service). This is important in the context of Bandura’s statement that ‘people act on their judgments of what they can do, as well as their beliefs about the likely effects of various actions’ (as cited in
Kopelman, Brief, and Guzzo, 1990, p. 294). Schneider (1989, p. 748) stated,
‘employees observe what happens to them (and around them) and then draw conclusions about the organisational priorities. They then set their own priorities accordingly’. In an industry in which the quality of service delivered to a customer is directly dependent upon individual employees, the environment, or climate, in which that employee works will directly modulate the quality of service delivered. This conclusion is supported elsewhere by the results of Schneider and Bowen (1985) and
Cole, Bacayan and White (1993) who provide evidence that a good organisational climate has a positive effect on service outcomes. Schneider (1973) similarly found that it was the atmosphere in a bank, whether it was ‘warm and friendly’ that best predicted customer-switching intentions.
The hotel industry, like most service industries, is one in which the vast majority of its output is intangible and represents a coincidence of production and consumption.
Within such a framework our results serve to reinforce Schneider, Gunnarson and Niles-
Jolly in their claim that ‘in the absence of direct control of the service counter, it is the climate and culture that determines high quality service’ (1994, p. 23). As stated by
Schneider and Bowen ‘because services themselves yield little tangible evidence as a useful basis for evaluation, it is how they are delivered, and the context in which they are delivered that is important’ (1985, p. 431).
209 A prescription that emerges from this project would be for all hotels in the industry to follow the lead of the Marriott group, the only hotel to be named in Fortune
Magazine’s top one hundred American companies (Branch, 1999), to instigate a regular measurement of organisational climate. And further, that such measurement of organisational climate not be an end in itself, but a tool to guide and evaluate programs to improve organisational climate (and therefore customer satisfaction and financial returns) on an ongoing basis.
8.9 The validity of measures used in this study
As in all research, this research is only as valid as the variables are valid in measuring concepts they purport to measure.
8.9.1 The index of financial performance REVPAR
In the case of financial performance, the variable used here to index this performance was REVPAR. This measure is a standard measure used within the hotel industry as a yardstick by which hotel performance may be compared. It is a relatively
‘concrete’ variable in which there may be little dispute as to its interpretation.
8.9.2 Organisational climate
In the case of organisational climate. The instrument used in the current study was, in one sense, new. In its current form, this is the first time it has been applied to a large sample. Having said that, although the instrument may be described as ‘new’, the items within the instrument are not. Although it contained a set of only 70 items, these items were previously used in a much longer instrument used by Ryder and Southey, and represented modifications of items of an instrument presented by Jones and James
210 (1979). PCA have been reported on both of these earlier versions of the instrument.
Despite this being a new instrument, the PCA conducted on the responses of the employees in this study were consistent with those of the earlier 2 studies. In this study,
5 of the factors extracted were given the same nomenclature as factors presented by
Jones and James. A sixth factor was given the same label as a factor described by Ryder and Southey and a seventh factor was judged to have significant overlap with another of
Ryder and Southey’s factors.
The order of the factors differ from those of Jones and James. This is not unusual in PCA studies as the order reflects the proportion of variance accounted for in each individual sample. In this case the fact that Leadership facilitation and support came out as the first factor (i.e. accounting for the largest proportion of the variance) is understandable given one might expect greater variation in leadership style within less bureaucratic private organisations than one might expect in the military (the sample used by Jones and James) and so one might expect it to be able to explain a greater proportion of the variance in the sample. In this respect, this study was also consistent with that of Ryder and Southey, who also used a civilian sample and also found
Leadership facilitation and support to account for the largest proportion of variance explained.
The consistency of the PCA results with those reported elsewhere provides some degree of confidence in the instrument measuring organisational climate – or at least measuring organisational climate within the terms of the concept as it is currently dealt with by many researchers within the literature.
The question of the dimensions that are extracted when the data are factor analysed is an important one. Is it certain that the underlying dimensions described in 211 this study are the true underlying dimensions of organisational climate within the hotels studied? Possibly not. Within the context of the various factors that have been proposed in the literature, is it reasonable that the underlying dimensions described in this study are valid descriptions of organisational climate within the hotels studied? Probably yes.
The answer to the first question was in the negative due to the following problem. A factor analysis (in this case PCA) produces underlying orthogonal dimensions that sum linearly, and which are produced from a matrix of numbers that represent the responses of a particular group of people to a particular set of questions. If an instrument contained no questions related to leadership, for example, then no underlying dimension related to leadership would be extracted. Further, if an instrument were produced with varying numbers of questions related to leadership, then the leadership dimension, if extracted, would account for varying proportions of variance depending on the proportion of items in the instrument that were related to leadership.
So in an absolute sense, it is impossible to know whether the instrument included the perfect set of items to identify the true underlying dimensions.
The answer to the second question was in the positive. Although the instrument used here had not been used before in its current form, it represented a development of one presented earlier by Jones and James (1979). The set of questions developed in the original version was collated following interviews, observations, and literature reviews.
This procedure identified 35 a priori scales. Each one of these ‘scales’ was included in the original instrument with each being represented by between 2 and 7 items. These same a priori scales were included in the instrument used here where each scale was represented by 2 items. In these terms, the instrument used here can be considered to have included a broad range of concepts that have been associated with organisational climate within the literature. Within this context, the dimensions of organisational
212 climate identified here would be interpreted as a reasonable description of the dimensions present within the hotels of this study.
8.9.3 Customer Satisfaction
Following advice given directly by a panel of industry experts, it was decided to use customer satisfaction, as measured by employee Perception of Customer
Satisfaction. Industry advice indicated that within the hotel industry, poor levels of customer satisfaction are first noticed by staff prior to the formal feedback on the quality of service.
Given that this measure correlated with measures of organisational climate, the argument might be presented that this correlation merely reflects the fact that employees working in a better organisational climate are more likely to perceive customers as more satisfied than will employees working in a poorer organisational climate. Regardless of the level of satisfaction actually felt, or reported, by the customers themselves.
A number of arguments may be presented to regarding this proposal. First, a number of studies have shown a close correspondence between employee estimates of customer satisfaction and that reported by customers (Parkington and Schneider, 1979;
Schneider and Bowen, 1985; 1993).
Second, and more importantly, had the outcome of this study been merely the demonstration of a correlation between organisational climate and Employee Perception of Customer Satisfaction, then the outcome might well be open to major criticism and concern regarding confounding variables. This study, however, shows more than this.
This study found, firstly, organisational climate (both in terms of an overall measure of organisational climate, and in terms of multiple correlation of the dimensions of
213 organisational climate) and Employee Perception of Customer Satisfaction to be correlated. The 7 dimensions of organisational climate were found to account for 30% of the variation in employee perception of customer satisfaction. Secondly, and importantly, in addition Employee Perception of Customer Satisfaction was significantly correlated with financial performance as indexed by REVPAR.
In a ‘worst case’ the results presented here would necessarily be interpreted as indicating that 23% of the variation in REPAR between the hotels may be explained as a direct consequence of variation in Organisational Climate. On the other hand, the interpretation presented here, of Organisational Climate affecting customer satisfaction and customer satisfaction affecting REVPAR, provides a parsimonious explanation of the process by which Organisational Climate would produce an effect on hotel financial performance. That is the process by which variation in Organisational Climate may result in variation in REVPAR between hotels is by its effect on customer satisfaction which in turn produces an effect on REVPAR.
8.10 The issue of multilevel variables and the interpretation of relationships
When conducting a study that examines organisational climate and an organisation’s performance, issues related to the level of measurement arise. Within this thesis, the terms ‘Staff Level Data’ and ‘Hotel Level Data’ have been used to differentiate 2 levels of measurement. The term ‘Staff Level Data’ referred to scores for which a single score existed for each employee, whereas the term ‘Hotel Level Data’ referred to scores for which a single score existed only for each hotel.
In one sense, organisational climate is a property of the organisation and not the individual. Performance indicators of the organisation are also almost exclusively expressed as properties of the organisation, and not of the individual within the 214 organisation.
Although organisational climate is a property of the organisation, the way in which a value for organisational climate is usually assigned to an organisation (or organisational sub-unit) is to obtain measures of Psychological Climate for individuals within the organisation and to then average the scores across individuals within the organisation. This procedure was carried out in this study, the result of which was that for each employee there existed a score on each of the 7 dimensions of organisational climate and on the Composite Measure of organisational climate. For each hotel, 8 new variables were calculated which represented the mean score across employees within each hotel for each dimension and for the composite measure. Similarly, in this case, the index of customer satisfaction was Employee Perception of Customer Satisfaction, and so again the value on this variable was obtained for each of the employees. A new variable was then produced which represented the mean Employee Perception of
Customer Satisfaction across the employees within each of the hotels. By creating these mean scores for each of the hotels the procedure created Hotel Level Data from Staff
Level Data.
This procedure is an intuitively obvious method to provide scores on variables for each of the 14 hotels. Variables that represent data on the Hotel Level scale are critically important to examine whether the relationships between the variables explain differences between the hotels. For example, it is quite possible for there to be a strong and highly significant relationship between the Composite Measure of Organisational
Climate and Employee Perceptions of Customer Satisfaction when analysing Staff
Level Data. At the same time, it is also possible that the relationship between the Hotel
Level Data (that is the Mean Composite Measure of Organisational Climate, and the
Mean Employee Perceptions of Customer Satisfaction) to be null and the statistical
215 significance zero. So demonstration of a relationship between variables using Staff
Level data is insufficient evidence to provide an explanation of variation between the hotels.
Consequently, in the results presented here, comparisons were presented at both the Staff Level and Hotel Level. The critical question for each of the comparisons presented was whether, when using Hotel Level Data the relatively large proportion of the variance in one variable was explained by another variable. Such comparisons were critical in evaluating the efficacy of proposed relationships between variables in the study.
For the application of structural equation modeling techniques, one needs a large number of scores for each of the parameters in the structural model. To apply such techniques to Structural Model B, necessitated the use of Staff Level data. In this situation, it was necessary to generate Staff Level Data from Hotel Level Data, rather than the reverse that had been done. This arose as the variable REVPAR only exists as a single value for each of the hotels. To generate a value for each employee, each employee was simply assigned the REVPAR value for the hotel within which they worked. This enabled structural modeling techniques to be applied to provide an overall index of the fit of the model to complement to comparisons that were presented using correlational and regression techniques.
It would be difficult to interpret this step as in any way leading to a false conclusion of a relationship existing between Employee Perception of Customer
Satisfaction and REVPAR, as proposed by Structural Model B. The correlation when using Staff Level Data (r=.112, i.e. the correlation used within the structural equation modeling procedure) actually served to underestimate the magnitude of the relationship
216 between the variables. The Hotel Level (r=.479) in which 23% of the variation in
REVPAR could be accounted for by the variation in Mean Employee Perception of
Customer Satisfaction.
In all cases presented here, relationships between variables described when analysing Staff Level Data, were only judged to be meaningful when those relationships between variables were also represented by significant proportions of variance when using Hotel Level Data.
8.11 Generalising results
This study is interested in generating statements that are relevant to the hotel industry and, in particular, the Australian hotel industry. There are 2 limitations regarding such generalisation of the results presented here. First, the study was limited to hotels within Queensland, and second, the study was limited to four to five-star hotels.
With regard to the first limitation, it is unlikely that this geographic limitation will to a large extent limit the generalisation of these results to four to five-star hotels in the rest of Australia. Within Australia, employees are highly mobile and move from resort to city and back very easily (Timo, 1993). This characteristic alone means that the sample of employees is representative of a group beyond the geographical limits implied by the location of the hotels in the sample. With regards to the hotels themselves, with the exception of two properties that are actually owned and operated by the same company, hotel management companies ran the other hotels. In some cases it may be argued that one would expect greater variation between different hotel chains, than between hotels within the same chain in different states of Australia.
217 With regard to the second limitation, it is not unlikely that the results reported here will better describe the important relationships between the variables of organisational climate, customer satisfaction, and REVPAR for hotels with four to five star ratings than for hotels with less than four stars. Hotels with different star ratings, by their very nature, will lead to both different expectations of the style and degree of service and interaction between hotel staff and customers. Having said this, it awaits further study to determine how these variables might interact to predict hotel financial performance for hotels with different star ratings.
8.12 Future research
The outcomes of this project strongly suggest that future studies which incorporate measures of organisational climate, hotel performance and direct measures of customer satisfaction could provide further evidence of the importance of organisational climate to financial performance of hotels within the Australian industry.
Further research could examine how to increase the quality of the organisational climate, and thereby affect changes in customer satisfaction and REVPAR. There are a number of features of the hotels that are already very positive with regard to the generation of a good organisational climate. The staff tended to be young with a reasonable gender mix, and are relatively well educated. One particular feature should be examined, is the very high turnover rate of staff evidenced within the hotels. Changes that serve to reduce the turnover rate may well serve to increase the quality of the organisational climate.
8.13 Summary and conclusion
This study gathered data from 14 four to five star hotels in South-East
218 Queensland, Australia, in an attempt to examine the nature and degree of influence organisational climate has upon the performance of hotels. Employee Perception of
Customer Satisfaction was studied both as an index of performance and as an intervening variable between organisational climate and financial performance as indexed by REVPAR. The data provided a description of a, young, relatively gender balanced, well educated and trained workforce which received relatively low levels of financial remuneration and displayed very high levels of turnover. A new instrument was used to measure the dimensions of organisational climate across the hotels. PCA produced results consistent with earlier (and longer) versions of the instrument which had been reported elsewhere. This analysis described organisational climate within the sample to be composed of 7 underlying dimensions. The most important finding of the study was that variation in these 7 dimensions of organisational climate accounted for
30% of the variation in Employee Perception of Customer Satisfaction. Furthermore, that Employee Perception of Customer Satisfaction accounted for 23% of the variation in REVPAR between the hotels. For the hotel industry represented in this sample, 30% variation in their customer satisfaction and 23% variation in their REVPAR was directly accounted for by their organisational climate. Organisational climate is a constant that can be measured and improved by the application of good management practices.
219 TABLE OF APPENDICES
Appendix A Organisational climate questionnaire, employee demographics, and employee perception of operations and customer satisfaction
Appendix B Hotel Profile Instrument
Appendix C Hotel managers demographics, operation performance and perception of customer satisfaction
Appendix D Staff Demographic Data and Contingency Table Analyses
Appendix E Reliability Analyses and Principal Components Analysis of Employee Organisational Climate Data
Appendix F Amos Printouts of Structural Equation Modelling analyses
220 Appendix A
Organisational climate questionnaire, employee demographics, and
employee perception of operations and customer satisfaction
221 CENTRE FOR TOURISM AND HOTEL MANAGEMENT RESEARCH
GRIFFITH UNIVERSITY GOLD COAST CAMPUS TELEPHONE: (07) 5594 8771
PART 2 – EMPLOYEES DEMOGRAPHIC DETAILS
Please tick one box only for each question
1. Gender Male ❑ Female ❑
2. Age 15-20 yrs ❑ 21-30 ‘ ❑ 31-40 ‘ ❑ 41-50 ‘ ❑ over 50 ❑
3. Education - Highest level attempted:
Secondary ❑ Post-Secondary Certificate ❑ Apprenticeship ❑ Associate/Diploma ❑ Degree ❑ Post Graduate Diploma/Degree ❑
4. Total length of service with the hotel:
Less than 6 months ❑ 6 months to 1 year ❑ 1-2 years ❑ 3- 4 years ❑ 5 -7 years ❑ 7-10 years ❑ over 10 years ❑
222 5. Length of time in present job:
Less than 6 months ❑ 6 months to 1 year ❑ 1- 2 years ❑ 3 - 4 years ❑ 5 - 7 years ❑ 7 -10 years ❑ over 10 years ❑
6. Current gross salary:
$10-15,000 ❑ $16-20,000 ❑ $21-25,000 ❑ $26-30,000 ❑ $31-35,000 ❑ $36-40,000 ❑ $41-45,000 ❑ $46-50,000 ❑ Over $50,000 ❑
7. How would you rate the hotel’s operational performance in the following areas? Please rate even if you do not directly work in the areas. (Tick one box on each line)
OPERATIONAL PERFORMANCE:
Area Very Under Acceptable Good Outstanding Marginal Performing Performance Performance Performance
A. Food & ❑❑ ❑ ❑ ❑ Beverage B. Rooms ❑❑ ❑ ❑ ❑
C. Overall ❑❑ ❑ ❑ ❑
223 8. How would you rate the hotel’s performance in satisfying customers in the following areas? Please rate even if you do not directly work in the areas. (Tick one box on each line)
CUSTOMER SATISFACTION:
Very Extremely Area Low Average High Low High
A. Food & ❑❑ ❑ ❑ ❑ Beverage B. Rooms ❑❑ ❑ ❑ ❑
C. Overall ❑❑ ❑ ❑ ❑
9. Overall Performance Rating. Please take into account all factors that affected
performance both internally and externally. (Tick the appropriate response)
OVERALL PERFORMANCE:
1-Poor to 5-Outstanding
1❑ 2❑ 3❑ 4❑ 5❑
10. Is your position? Full time ❑ Part time ❑ Casual ❑
11. Average hours worked per week: > 10 ❑ 11 – 15 ❑ 16 – 20 ❑ 21 – 30 ❑ 31 – 35 ❑ 36 – 40 ❑ 41 – 45 ❑ 46 – 50 ❑ 50 + ❑
224 12. When did you last take part in a formal education or training session (in-house or external)?
In the last 3 months ❑ 3 to 6 months ❑ 6 to 12 months ❑ 1 to 2 years ❑ 2 to 3 years ❑ 4 to 5 years ❑ 5 years or more ❑
13. Do you feel that you need to undertake additional education or training for your present position? Yes ❑ No ❑
14. Please indicate the department in which you work:
Food and Beverage Service ❑ Kitchen & Stewarding ❑
Front Office & Reservation5s ❑ Housekeeping & Linen Room ❑
Purchasing, Stores & Accounts ❑ Marketing, Sales & Public Relations ❑
Administration ❑ Engineering, Maintenance & Security ❑
Conference & Convention ❑ Concierge & Porters ❑
Casino Operations ❑ Casino Administration ❑ ❑❑ ❑❑ ❑❑
Other please state: ______
Are there any other departmental areas that you would like included?: ______
Many thanks for your time and trouble. Please seal questionnaire in the envelope provided and place it in the box provided at the human resources office. Alternatively you may mail it directly to the university.
225 CENTRE FOR TOURISM AND HOTEL MANAGEMENT RESEARCH
GRIFFITH UNIVERSITY GOLD COAST CAMPUS TELEPHONE: (07) 5594 8771
PART 1 – EMPLOYEE OPINION SURVEY
QUESTION 1
1234567
Strongly Disagree Tend to Unsure Tend to Agree Strongly Disagree Disagree Agree Agree
Please circle how strongly you agree or disagree according to the above scale with each of the following statements:
Circle the most appropriate response: SDDTDU TAASA a. Opportunities for independent thought and action exist in your job. 1 2 3 4 5 6 7 b. Your job requires a high level of skill and training. 1 2 3 4 5 6 7 c. You are required to meet rigid standards of quality in your work. 1 2 3 4 5 6 7 d. Staff members generally trust their supervisors. 1 2 3 4 5 6 7 e. The methods of your work are kept up-to-date. 1 2 3 4 5 6 7 f. You are required to perform tasks on your job which you consider relatively unimportant or unnecessary. 1 2 3 4 5 6 7 g. You are able to get the money, supplies, equipment, etc., your work group needs to do its work well. 1 2 3 4 5 6 7 h. Your supervisor is friendly and easy to approach. 1 2 3 4 5 6 7 i. Your supervisor offers new ideas for job-related problems. 1 2 3 4 5 6 7 j. A spirit of co-operation exists in your work group. 1 2 3 4 5 6 7 k. Your job responsibilities are clearly defined. 1 2 3 4 5 6 7 l. Responsibility is assigned so that individuals have authority within their own area. 1 2 3 4 5 6 7 m. Dealing with other people is part of your job. 1 2 3 4 5 6 7 n. Your supervisor encourages the people who work for him or her to exchange ideas and opinions. 1 2 3 4 5 6 7
226 QUESTION 2
1234567
Strongly Disagree Tend to Unsure Tend to AgreeStrongly Disagree Disagree Agree Agree
Please circle how strongly you agree or disagree according to the above scale with each of the following statements:
Circle the most appropriate response: SD D TD U TA A SA a. Staff members generally trust their managers. 1 2 3 4 5 6 7 b. You are given advance information about changes (policies, procedures, etc.) which might affect you. 1 2 3 4 5 6 7 c. The hotel’s policies are consistently applied to all staff members. 1 2 3 4 5 6 7 d. You have opportunities to complete the work you start. 1 2 3 4 5 6 7 e. Procedures are designed so that resources (equipment, people, time, etc.) are used efficiently. 1 2 3 4 5 6 7 f. Your supervisor is attentive to what you say. 1 2 3 4 5 6 7 g. Your supervisor provides the help you need to schedule your work ahead of time. 1 2 3 4 5 6 7 h. There is friction in your work group. 1 2 3 4 5 6 7 i. You have opportunities to learn worth while new skills and knowledge in your job. 1 2 3 4 5 6 7 j. New staff members get the on-the-job training they need. 1 2 3 4 5 6 7 k. There is variety in your job. 1 2 3 4 5 6 7 l. Your hours of work are irregular. 1 2 3 4 5 6 7 m. Everything in this hotel is checked, individual judgement is not trusted. 1 2 3 4 5 6 7 n. Being liked is important in getting a promotion. 1 2 3 4 5 6 7
227 QUESTION 3
1234567
Strongly Disagree Tend to Unsure Tend to Agree Strongly Disagree Disagree Agree Agree
Please circle how strongly you agree or disagree according to the above scale with each of the following statements:
Circle the most appropriate response: SD D TD U TA A SA a. You have good information on where you stand and how your performance is evaluated. 1 2 3 4 5 6 7 b. Your supervisor emphasises high standards of performance. 1 2 3 4 5 6 7 c. The ideas and suggestions of staff members are paid attention to. 1 2 3 4 5 6 7 d. You have the opportunity to do a number of different things in your job. 1 2 3 4 5 6 7 e. Your supervisor sets an example by working hard himself/herself. 1 2 3 4 5 6 7 f. A friendly atmosphere prevails among most of the members of your work group. 1 2 3 4 5 6 7 g. Hotel ‘politics’ count in getting a promotion. 1 2 3 4 5 6 7 h. People act as though everyone must be watched or they will slack off. 1 2 3 4 5 6 7 i. Supervisors generally know what is going on in their work groups. 1 2 3 4 5 6 7 j. You are aware of how well your workgroup is meeting its objectives. 1 2 3 4 5 6 7 k. Your job demands precision. 1 2 3 4 5 6 7 l. Members of your work group trust each other. 1 2 3 4 5 6 7 m. The hotel has a good image to outsiders. 1 2 3 4 5 6 7 n. Working in this hotel is beneficial to your career. 1 2 3 4 5 6 7 o. You have opportunities to make full use of your knowledge and skills in your job. 1 2 3 4 5 6 7
228 QUESTION 4
1234567
Strongly Disagree Tend to Unsure Tend to Agree Strongly Disagree Disagree Agree Agree
Please circle how strongly you agree or disagree according to the above scale with each of the following statements:
Circle the most appropriate response: SD D TD U TA A SA a. Communication is hindered by following chain of command rules. 1 2 3 4 5 6 7 b. Your supervisor encourages the people who work for them to work as a team. 1 2 3 4 5 6 7 c. It is possible to get accurate information on the policies and objectives of this hotel. 1 2 3 4 5 6 7 d. The hotel strives to do a better job than other hotels of the same type. 1 2 3 4 5 6 7 e. The hotel emphasises personal growth and development. 1 2 3 4 5 6 7 f. Managers keep well informed about the needs and problems of employees. 1 2 3 4567 g. Discipline in this hotel is maintained consistently. 1 2 3 4 5 6 7 h. Your manager is successful in his dealings with higher levels of management. 1 2 3 4 5 6 7 i. The objectives of the hotel are clearly defined. 1 2 3 4 5 6 7 j. There is conflict (rivalry and hostility) between your department and other departments of the hotel. 1 2 3 4 5 6 7 k. Your work is important. 1 2 3 4 5 6 7 l. The way your work group is organised hinders the efficient conduct of work. 1 2 3 4 5 6 7 m. This hotel is concerned with assisting the local community. 1 2 3 4 5 6 7 n. Things in this hotel seem to happen contrary to rules and regulations. 1 2 3 4 5 6 7
229 QUESTION 5
1234567
Strongly Disagree Tend to Unsure Tend to Agree Strongly Disagree Disagree Agree Agree
Please circle how strongly you agree or disagree according to the above scale with each of the following statements:
Circle the most appropriate response: SD D TD U TA A SA a. In this hotel about the only source of information on important matters is the grapevine (rumour). 1 2 3 4 5 6 7 b. In this hotel, things are planned so that everyone is getting in each other’s way. 1 2 3 4 5 6 7 c. Under most circumstances I would recommend this hotel to a prospective staff member. 1 2 3 4 5 6 7 d. Most of the personnel in my department would not want to change to another department. 1 2 3 4 5 6 7 e. Most members of my work group take pride in their jobs. 1 2 3 4 5 6 7 f. Generally there are friendly and cooperative relationships between the different department of the hotel. 1 2 3 4 5 6 7 g. My department, compared to all other department in the hotel would be one of the most productive. 1 2 3 4 5 6 7 h. Excessive rules and regulations interfere with how well I am able to do my job. 1 2 3 4 5 6 7 i. Overall, I think my immediate supervisor is doing a good job. 1 2 3 4 5 6 7 j. Compared with other works groups, my work group is under much less pressure to produce. 1 2 3 4 5 6 7 k. In my job, the opportunities to get to know people are limited. 1 2 3 4 5 6 7 l. Compared to all other similar works groups in the hotel, my group would be the most productive. 1 2 3 4 5 6 7 m. Your immediate supervisor is successful in dealing with higher levels of management. 1 2 3 4 5 6 7
230 Appendix B
Hotel Profile Instrument
231 CENTRE FOR TOURISM AND HOTEL MANAGEMENT RESEARCH
GRIFFITH UNIVERSITY GOLD COAST CAMPUS TELEPHONE: (07) 5594 8771
HOTEL PROFILE
Please use the 1996/1997 financial year as the basis for your answers:
1. What is your annual room occupancy level in percentage terms? ______%
2. What is your annual average daily room rate? $______
3. What is your rack rate for a standard room? $______
4. What is your accommodation business mix?
F.I.T % Conferences % Group Tour % Corporate % Government % Leisure % 100%
5. What is your revenue mix? Rooms % F & B % Other % 100%
6. What is your staffing level? (please include management)
Peak Low Full Time Part Time Casual
232 04 7. What is your annual staff turnover percentage? ______%
8. What is your wage cost to revenue percentage? ______%
9. Which of the following organisational structures and levels best represents your hotel? (Please tick)
General Manager General Manager General Manager Other (please state) Executive Committee Executive Committee Executive Committee ______Department Heads Senior Department Heads Department Heads ______Supervisors Other Department Heads Operational Staff ______Operational Staff Assistant Dept Heads ______Supervisors ______Assistant Supervisors ______Operational Staff ______
A ❑ B ❑ C ❑ D ❑
10. What external factors (beyond your control) affected your operational performance last year?
______
______
PLEASE ATTACH ADDITIONAL NOTES IF REQUIRED
11. Were there any special internal circumstances that affected your operational performance last year?
______
______
PLEASE ATTACH ADDITIONAL NOTES IF REQUIRED
233 Appendix C
Hotel managers demographics, operation performance and perception of
customer satisfaction
234 CENTRE FOR TOURISM AND HOTEL MANAGEMENT RESEARCH
GRIFFITH UNIVERSITY GOLD COAST CAMPUS TELEPHONE: (07) 5594 8771 GENERAL MANAGER, SENIOR EXECUTIVES AND DEPARTMENT HEADS ONLY
PART 1 - DEMOGRAPHIC DETAILS
Please tick one box only for each question
1. Gender: Male ❑ Female ❑
2. Age: 15-24yrs ❑ 25-34 ‘ ❑ 35-44 ❑ 45-54 ‘ ❑ 55-64 ❑ 65+ ❑
3. Education - Highest level attempted (not necessarily completed):
Primary ❑ Secondary ❑ Post-Secondary Certificate ❑ Apprenticeship ❑ Associate/Diploma ❑ Degree ❑ Post Graduate Diploma/Degree ❑
235 4. Total length of service with the hotel:
0-2 yrs ❑ 3-5 yrs ❑ 6-8 ‘ ❑ 9-11 ‘ ❑ 12-14 ‘ ❑ 15-17 ‘ ❑
5. Length of time in present job:
0-2yrs ❑ 3-5 yrs ❑ 6-8 ‘ ❑ 9-11 ‘ ❑ 12-14 ‘ ❑ 15-17 ‘ ❑
6. Current gross salary: $30 – 39000 ❑ $40 – 49000 ❑ $50 – 59000 ❑ $60 – 69000 ❑ $70 – 79000 ❑ $80 – 89000 ❑ $90 – 99000 ❑ over $100,000 ❑
7. Do you receive additional benefits?
Yes ❑ No ❑
8. Please give a dollar value of the benefits: ______
236 9. When did you last undertake a formal training or education program (in house or external?
0 months to 1 year ❑ 1 to 2 years ❑ 2 to 3 years ❑ 3 to 4 years ❑ 4 to 5 years ❑ 5 to 6 years ❑ 6 to 7 years ❑
10. Do you feel that you need to undertake additional education or training for your present position? Yes ❑ No ❑
If yes please indicate what education or training would be most appropriate:
______
______
237 PART 2 – PERFORMANCE INDICATORS
Please rate the hotels performance in the following areas (tick the appropriate box):
It is important that you use your judgement alone in this rating.
11. FINANCIAL PERFORMANCE
Marginally Marginally Under Well Above Revenue Under On Budget Above Budget Budget Budget Budget A. Food & ❑❑ ❑ ❑ ❑ Beverage B. Rooms ❑❑ ❑ ❑ ❑
C. Overall ❑❑ ❑ ❑ ❑
12. FINANCIAL PERFORMANCE
Marginally Gross Under Marginally Well Above Under On Budget Operating Budget Above Budget Budget Profit Budget A. Food & ❑❑ ❑ ❑ ❑ Beverage B. Rooms ❑❑ ❑ ❑ ❑
C. Overall ❑❑ ❑ ❑ ❑
13. OPERATIONAL PERFORMANCE
Area Very Under Acceptable Good Outstanding Marginal Performing Performance Performance Performance
A. Food & ❑❑ ❑ ❑ ❑ Beverage B. Rooms ❑❑ ❑ ❑ ❑
C. Overall ❑❑ ❑ ❑ ❑
238
14. CUSTOMER SATISFACTION
Very Extremely Area Low Average High Low High
A. Food & ❑❑ ❑ ❑ ❑ Beverage B. Rooms ❑❑ ❑ ❑ ❑
C. Overall ❑❑ ❑ ❑ ❑
15. Please indicate any specific techniques used to ascertain customer satisfaction:
In room comment cards ❑ Restaurant comment cards ❑ Staff incident/compliment reports ❑ Own telephone survey of guests ❑ Market research survey ❑ Focus group ❑
Other (please state) ______
16. Overall Performance Rating
Please take into account all factors that affected performance both internally and
externally (tick your response)
1-Poor to 5-Outstanding
1 ❑ 2 ❑ 3 ❑ 4 ❑ 5 ❑
17. Who do you consider to be your main competitors?
Please place in the collection box in the human resources office at your earliest convenience. 239 Appendix D
Staff Demographic Data and Contingency Table Analyses
240 HOTEL hotel code by GENDER gender
GENDER Page 1 of 1 Count | Col Pct |male female | Row | 1 | 2 | Total HOTEL ------+------+------+ 1 | 290 | 260 | 550 | 34.7 | 28.7 | 31.6 +------+------+ 3 | 55 | 66 | 121 | 6.6 | 7.3 | 7.0 +------+------+ 4 | 128 | 146 | 274 | 15.3 | 16.1 | 15.7 +------+------+ 5 | 29 | 25 | 54 | 3.5 | 2.8 | 3.1 +------+------+ 6 | 45 | 42 | 87 | 5.4 | 4.6 | 5.0 +------+------+ 7 | 20 | 27 | 47 | 2.4 | 3.0 | 2.7 +------+------+ 8 | 20 | 29 | 49 | 2.4 | 3.2 | 2.8 +------+------+ 9 | 9 | 37 | 46 | 1.1 | 4.1 | 2.6 +------+------+ 10 | 15 | 24 | 39 | 1.8 | 2.6 | 2.2 +------+------+ 11 | 45 | 41 | 86 | 5.4 | 4.5 | 4.9 +------+------+ 12 | 39 | 51 | 90 | 4.7 | 5.6 | 5.2 +------+------+ 13 | 14 | 18 | 32 | 1.7 | 2.0 | 1.8 +------+------+ 14 | 89 | 103 | 192 | 10.7 | 11.4 | 11.0 +------+------+ 15 | 37 | 37 | 74 | 4.4 | 4.1 | 4.3 +------+------+ Column 835 906 1741 Total 48.0 52.0 100.0
Chi-Square Value DF Significance ------
Pearson 26.49008 13 .01460 Likelihood Ratio 27.73679 13 .00985 Mantel-Haenszel test for 4.09789 1 .04294 linear association
Minimum Expected Frequency - 15.348
Number of Missing Observations: 51
241 HOTEL hotel code by AGE age
AGE Page 1 of 1 Count | Col Pct |15-24yrs 24-34yrs 35-44yrs 45-54yrs 55-64yrs 65+yrs | Row | 1 | 2 | 3 | 4 | 5 | 6 | Total HOTEL ------+------+------+------+------+------+------+ 1 | 176 | 174 | 105 | 71 | 18 | 1 | 545 | 33.3 | 28.9 | 30.9 | 33.5 | 40.0 | 33.3 | 31.5 +------+------+------+------+------+------+ 3 | 43 | 46 | 18 | 10 | | | 117 | 8.1 | 7.6 | 5.3 | 4.7 | | | 6.8 +------+------+------+------+------+------+ 4 | 64 | 96 | 57 | 48 | 10 | | 275 | 12.1 | 15.9 | 16.8 | 22.6 | 22.2 | | 15.9 +------+------+------+------+------+------+ 5 | 13 | 26 | 10 | 3 | 2 | | 54 | 2.5 | 4.3 | 2.9 | 1.4 | 4.4 | | 3.1 +------+------+------+------+------+------+ 6 | 39 | 27 | 12 | 8 | 1 | | 87 | 7.4 | 4.5 | 3.5 | 3.8 | 2.2 | | 5.0 +------+------+------+------+------+------+ 7 | 16 | 14 | 13 | 3 | | | 46 | 3.0 | 2.3 | 3.8 | 1.4 | | | 2.7 +------+------+------+------+------+------+ 8 | 20 | 19 | 4 | 4 | 1 | | 48 | 3.8 | 3.2 | 1.2 | 1.9 | 2.2 | | 2.8 +------+------+------+------+------+------+ 9 | 14 | 19 | 7 | 6 | | 1 | 47 | 2.7 | 3.2 | 2.1 | 2.8 | | 33.3 | 2.7 +------+------+------+------+------+------+ 10 | 8 | 19 | 11 | 1 | | | 39 | 1.5 | 3.2 | 3.2 | .5 | | | 2.3 +------+------+------+------+------+------+ 11 | 29 | 29 | 16 | 11 | 2 | | 87 | 5.5 | 4.8 | 4.7 | 5.2 | 4.4 | | 5.0 +------+------+------+------+------+------+ 12 | 17 | 28 | 28 | 13 | 4 | | 90 | 3.2 | 4.6 | 8.2 | 6.1 | 8.9 | | 5.2 +------+------+------+------+------+------+ 13 | 11 | 11 | 4 | 4 | 2 | | 32 | 2.1 | 1.8 | 1.2 | 1.9 | 4.4 | | 1.8 +------+------+------+------+------+------+ 14 | 66 | 71 | 28 | 20 | 4 | 1 | 190 | 12.5 | 11.8 | 8.2 | 9.4 | 8.9 | 33.3 | 11.0 +------+------+------+------+------+------+ 15 | 12 | 24 | 27 | 10 | 1 | | 74 | 2.3 | 4.0 | 7.9 | 4.7 | 2.2 | | 4.3 +------+------+------+------+------+------+ Column 528 603 340 212 45 3 1731 Total 30.5 34.8 19.6 12.2 2.6 .2 100.0
Chi-Square Value DF Significance ------
Pearson 108.65497 65 .00056 Likelihood Ratio 108.98016 65 .00052 Mantel-Haenszel test for .02426 1 .87622 linear association
Minimum Expected Frequency - .055 Cells with Expected Frequency < 5 - 28 OF 84 ( 33.3%)
Number of Missing Observations: 61
242 HOTEL hotel code by EDUCAT education level
EDUCAT Page 1 of 1 Count | Col Pct |secondar post sec apprenti assocait degree post gra |y ondary ceship e diplom d Row | 1 | 2 | 3 | 4 | 5 | 6 | Total HOTEL ------+------+------+------+------+------+------+ 1 | 200 | 82 | 81 | 75 | 82 | 18 | 538 | 33.3 | 31.4 | 34.8 | 26.9 | 28.9 | 34.0 | 31.4 +------+------+------+------+------+------+ 3 | 51 | 11 | 22 | 19 | 11 | 4 | 118 | 8.5 | 4.2 | 9.4 | 6.8 | 3.9 | 7.5 | 6.9 +------+------+------+------+------+------+ 4 | 106 | 39 | 36 | 40 | 46 | 4 | 271 | 17.6 | 14.9 | 15.5 | 14.3 | 16.2 | 7.5 | 15.8 +------+------+------+------+------+------+ 5 | 15 | 9 | 9 | 12 | 5 | 2 | 52 | 2.5 | 3.4 | 3.9 | 4.3 | 1.8 | 3.8 | 3.0 +------+------+------+------+------+------+ 6 | 34 | 11 | 8 | 15 | 19 | 1 | 88 | 5.7 | 4.2 | 3.4 | 5.4 | 6.7 | 1.9 | 5.1 +------+------+------+------+------+------+ 7 | 10 | 4 | 8 | 10 | 11 | 2 | 45 | 1.7 | 1.5 | 3.4 | 3.6 | 3.9 | 3.8 | 2.6 +------+------+------+------+------+------+ 8 | 17 | 6 | 5 | 9 | 10 | 2 | 49 | 2.8 | 2.3 | 2.1 | 3.2 | 3.5 | 3.8 | 2.9 +------+------+------+------+------+------+ 9 | 21 | 9 | 2 | 5 | 7 | 1 | 45 | 3.5 | 3.4 | .9 | 1.8 | 2.5 | 1.9 | 2.6 +------+------+------+------+------+------+ 10 | 17 | 5 | 3 | 4 | 7 | 3 | 39 | 2.8 | 1.9 | 1.3 | 1.4 | 2.5 | 5.7 | 2.3 +------+------+------+------+------+------+ 11 | 24 | 17 | 8 | 16 | 17 | 4 | 86 | 4.0 | 6.5 | 3.4 | 5.7 | 6.0 | 7.5 | 5.0 +------+------+------+------+------+------+ 12 | 26 | 16 | 16 | 17 | 11 | 2 | 88 | 4.3 | 6.1 | 6.9 | 6.1 | 3.9 | 3.8 | 5.1 +------+------+------+------+------+------+ 13 | 6 | 9 | 3 | 7 | 3 | 3 | 31 | 1.0 | 3.4 | 1.3 | 2.5 | 1.1 | 5.7 | 1.8 +------+------+------+------+------+------+ 14 | 49 | 30 | 17 | 40 | 44 | 6 | 186 | 8.2 | 11.5 | 7.3 | 14.3 | 15.5 | 11.3 | 10.9 +------+------+------+------+------+------+ 15 | 25 | 13 | 15 | 10 | 11 | 1 | 75 | 4.2 | 5.0 | 6.4 | 3.6 | 3.9 | 1.9 | 4.4 +------+------+------+------+------+------+ Column 601 261 233 279 284 53 1711 Total 35.1 15.3 13.6 16.3 16.6 3.1 100.0
Chi-Square Value DF Significance ------
Pearson 88.74589 65 .02685 Likelihood Ratio 89.06722 65 .02546 Mantel-Haenszel test for 9.25581 1 .00235 linear association
Minimum Expected Frequency - .960 Cells with Expected Frequency < 5 - 13 OF 84 ( 15.5%)
Number of Missing Observations: 81
243 HOTEL hotel code by LENGTH_S length of service
LENGTH_S Page 1 of 1 Count | Col Pct |0-2yrs 3-5yrs 6-8yrs 9-11yrs 12-14yrs 15-17yrs | Row | 1 | 2 | 3 | 4 | 5 | 6 | Total HOTEL ------+------+------+------+------+------+------+ 1 | 268 | 124 | 70 | 67 | 15 | | 544 | 26.4 | 30.8 | 37.6 | 70.5 | 55.6 | | 31.3 +------+------+------+------+------+------+ 3 | 79 | 23 | 19 | | | | 121 | 7.8 | 5.7 | 10.2 | | | | 7.0 +------+------+------+------+------+------+ 4 | 152 | 78 | 44 | | | | 274 | 14.9 | 19.4 | 23.7 | | | | 15.8 +------+------+------+------+------+------+ 5 | 27 | 20 | 5 | 2 | 1 | | 55 | 2.7 | 5.0 | 2.7 | 2.1 | 3.7 | | 3.2 +------+------+------+------+------+------+ 6 | 58 | 22 | 7 | 1 | | | 88 | 5.7 | 5.5 | 3.8 | 1.1 | | | 5.1 +------+------+------+------+------+------+ 7 | 40 | 5 | | 1 | | 1 | 47 | 3.9 | 1.2 | | 1.1 | | 9.1 | 2.7 +------+------+------+------+------+------+ 8 | 46 | 1 | 2 | | | | 49 | 4.5 | .2 | 1.1 | | | | 2.8 +------+------+------+------+------+------+ 9 | 32 | 9 | 5 | | | | 46 | 3.1 | 2.2 | 2.7 | | | | 2.6 +------+------+------+------+------+------+ 10 | 38 | | 1 | | | | 39 | 3.7 | | .5 | | | | 2.2 +------+------+------+------+------+------+ 11 | 59 | 28 | | | | | 87 | 5.8 | 6.9 | | | | | 5.0 +------+------+------+------+------+------+ 12 | 47 | 22 | 9 | 11 | | 1 | 90 | 4.6 | 5.5 | 4.8 | 11.6 | | 9.1 | 5.2 +------+------+------+------+------+------+ 13 | 19 | 5 | 1 | 2 | 1 | 4 | 32 | 1.9 | 1.2 | .5 | 2.1 | 3.7 | 36.4 | 1.8 +------+------+------+------+------+------+ 14 | 122 | 36 | 8 | 11 | 10 | 5 | 192 | 12.0 | 8.9 | 4.3 | 11.6 | 37.0 | 45.5 | 11.0 +------+------+------+------+------+------+ 15 | 30 | 30 | 15 | | | | 75 | 2.9 | 7.4 | 8.1 | | | | 4.3 +------+------+------+------+------+------+ Column 1017 403 186 95 27 11 1739 Total 58.5 23.2 10.7 5.5 1.6 .6 100.0
Chi-Square Value DF Significance ------
Pearson 364.57871 65 .00000 Likelihood Ratio 371.23288 65 .00000 Mantel-Haenszel test for 7.96189 1 .00478 linear association
Minimum Expected Frequency - .202 Cells with Expected Frequency < 5 - 40 OF 84 ( 47.6%)
Number of Missing Observations: 53
244 HOTEL hotel code by LENGTH_J length of job
LENGTH_J Page 1 of 1 Count | Col Pct |0-2yrs 3-5yrs 6-8yrs 9-11yrs 12-14yrs 15-17yrs | Row | 1 | 2 | 3 | 4 | 5 | 6 | Total HOTEL ------+------+------+------+------+------+------+ 1 | 312 | 118 | 49 | 49 | 11 | | 539 | 27.5 | 33.0 | 40.2 | 62.0 | 57.9 | | 31.2 +------+------+------+------+------+------+ 3 | 85 | 23 | 11 | | 1 | | 120 | 7.5 | 6.4 | 9.0 | | 5.3 | | 7.0 +------+------+------+------+------+------+ 4 | 173 | 70 | 23 | 3 | 1 | 2 | 272 | 15.3 | 19.6 | 18.9 | 3.8 | 5.3 | 14.3 | 15.8 +------+------+------+------+------+------+ 5 | 29 | 17 | 2 | 3 | 1 | 1 | 53 | 2.6 | 4.7 | 1.6 | 3.8 | 5.3 | 7.1 | 3.1 +------+------+------+------+------+------+ 6 | 60 | 22 | 4 | 1 | | | 87 | 5.3 | 6.1 | 3.3 | 1.3 | | | 5.0 +------+------+------+------+------+------+ 7 | 40 | 4 | 1 | 1 | | 1 | 47 | 3.5 | 1.1 | .8 | 1.3 | | 7.1 | 2.7 +------+------+------+------+------+------+ 8 | 46 | | 1 | 2 | | | 49 | 4.1 | | .8 | 2.5 | | | 2.8 +------+------+------+------+------+------+ 9 | 37 | 4 | 4 | 1 | | | 46 | 3.3 | 1.1 | 3.3 | 1.3 | | | 2.7 +------+------+------+------+------+------+ 10 | 35 | 1 | | 2 | | | 38 | 3.1 | .3 | | 2.5 | | | 2.2 +------+------+------+------+------+------+ 11 | 66 | 20 | 1 | | | | 87 | 5.8 | 5.6 | .8 | | | | 5.0 +------+------+------+------+------+------+ 12 | 56 | 19 | 8 | 7 | | | 90 | 4.9 | 5.3 | 6.6 | 8.9 | | | 5.2 +------+------+------+------+------+------+ 13 | 21 | 3 | | 3 | 1 | 3 | 31 | 1.9 | .8 | | 3.8 | 5.3 | 21.4 | 1.8 +------+------+------+------+------+------+ 14 | 137 | 32 | 7 | 7 | 4 | 5 | 192 | 12.1 | 8.9 | 5.7 | 8.9 | 21.1 | 35.7 | 11.1 +------+------+------+------+------+------+ 15 | 37 | 25 | 11 | | | 2 | 75 | 3.3 | 7.0 | 9.0 | | | 14.3 | 4.3 +------+------+------+------+------+------+ Column 1134 358 122 79 19 14 1726 Total 65.7 20.7 7.1 4.6 1.1 .8 100.0
Chi-Square Value DF Significance ------
Pearson 212.87162 65 .00000 Likelihood Ratio 233.03936 65 .00000 Mantel-Haenszel test for 5.29004 1 .02145 linear association
Minimum Expected Frequency - .251 Cells with Expected Frequency < 5 - 43 OF 84 ( 51.2%)
Number of Missing Observations: 66
245 HOTEL hotel code by GRS_SAL current gross salary Count |$0-5000 $6-10,00 $11-15,0 $16-20,0 $21-25,0 $26-30,0 $31-35,0 $36-40,0 $41-45,0 $46-50,0 over $50 Col Pct | 0 00 00 00 00 00 00 00 00 ,000 Row | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | Total HOTEL ------+------+------+------+------+------+------+------+------+------+------+------+ 1 | 16 | 33 | 48 | 76 | 166 | 106 | 41 | 24 | 10 | | 2 | 522 | 20.0 | 35.1 | 30.8 | 32.8 | 31.3 | 31.3 | 28.3 | 45.3 | 45.5 | | 13.3 | 31.3 +------+------+------+------+------+------+------+------+------+------+------+ 3 | | 4 | 6 | 16 | 46 | 24 | 16 | 3 | 1 | | 1 | 117 | | 4.3 | 3.8 | 6.9 | 8.7 | 7.1 | 11.0 | 5.7 | 4.5 | | 6.7 | 7.0 +------+------+------+------+------+------+------+------+------+------+------+ 4 | 18 | 20 | 36 | 30 | 72 | 56 | 17 | 6 | 2 | | 4 | 261 | 22.5 | 21.3 | 23.1 | 12.9 | 13.6 | 16.5 | 11.7 | 11.3 | 9.1 | | 26.7 | 15.6 +------+------+------+------+------+------+------+------+------+------+------+ 5 | 4 | 1 | 3 | 3 | 11 | 14 | 11 | 3 | | | | 50 | 5.0 | 1.1 | 1.9 | 1.3 | 2.1 | 4.1 | 7.6 | 5.7 | | | | 3.0 +------+------+------+------+------+------+------+------+------+------+------+ 6 | 9 | 4 | 5 | 11 | 32 | 13 | 6 | 1 | | | | 81 | 11.3 | 4.3 | 3.2 | 4.7 | 6.0 | 3.8 | 4.1 | 1.9 | | | | 4.9 +------+------+------+------+------+------+------+------+------+------+------+ 7 | 4 | 1 | 3 | 7 | 9 | 15 | 6 | | 1 | | | 46 | 5.0 | 1.1 | 1.9 | 3.0 | 1.7 | 4.4 | 4.1 | | 4.5 | | | 2.8 +------+------+------+------+------+------+------+------+------+------+------+ 8 | 3 | 6 | 2 | 3 | 13 | 13 | 6 | | | | | 46 | 3.8 | 6.4 | 1.3 | 1.3 | 2.4 | 3.8 | 4.1 | | | | | 2.8 +------+------+------+------+------+------+------+------+------+------+------+ 9 | 7 | 1 | 7 | 7 | 12 | 8 | | 1 | | | 1 | 44 | 8.8 | 1.1 | 4.5 | 3.0 | 2.3 | 2.4 | | 1.9 | | | 6.7 | 2.6 +------+------+------+------+------+------+------+------+------+------+------+ 10 | 2 | 2 | 2 | 3 | 19 | 6 | 1 | 1 | | | | 36 | 2.5 | 2.1 | 1.3 | 1.3 | 3.6 | 1.8 | .7 | 1.9 | | | | 2.2 +------+------+------+------+------+------+------+------+------+------+------+ 11 | 8 | 1 | 7 | 17 | 33 | 13 | 5 | 1 | | | 1 | 86 | 10.0 | 1.1 | 4.5 | 7.3 | 6.2 | 3.8 | 3.4 | 1.9 | | | 6.7 | 5.2 +------+------+------+------+------+------+------+------+------+------+------+ 12 | | 3 | 4 | 11 | 33 | 16 | 15 | 6 | | | | 88 | | 3.2 | 2.6 | 4.7 | 6.2 | 4.7 | 10.3 | 11.3 | | | | 5.3 +------+------+------+------+------+------+------+------+------+------+------+ 13 | | 1 | | 4 | 8 | 17 | 1 | | | | | 31 | | 1.1 | | 1.7 | 1.5 | 5.0 | .7 | | | | | 1.9 +------+------+------+------+------+------+------+------+------+------+------+ 14 | 6 | 14 | 24 | 29 | 54 | 30 | 13 | 4 | 8 | 1 | 6 | 189 | 7.5 | 14.9 | 15.4 | 12.5 | 10.2 | 8.8 | 9.0 | 7.5 | 36.4 | 50.0 | 40.0 | 11.3 +------+------+------+------+------+------+------+------+------+------+------+ 15 | 3 | 3 | 9 | 15 | 23 | 8 | 7 | 3 | | 1 | | 72 | 3.8 | 3.2 | 5.8 | 6.5 | 4.3 | 2.4 | 4.8 | 5.7 | | 50.0 | | 4.3 +------+------+------+------+------+------+------+------+------+------+------+ Column 80 94 156 232 531 339 145 53 22 2 15 1669 Total 4.8 5.6 9.3 13.9 31.8 20.3 8.7 3.2 1.3 .1 .9 100.0
246 Chi-Square Value DF Significance ------
Pearson 244.13788 130 .00000 Likelihood Ratio 245.77581 130 .00000 Mantel-Haenszel test for .00172 1 .96692 linear association
Minimum Expected Frequency - .037 Cells with Expected Frequency < 5 - 85 OF 154 ( 55.2%)
Number of Missing Observations: 123
247 HOTEL hotel code by HOURS hours worked per week Count | Col Pct |0-5hrs 6-10hrs 11-15hrs 16-20hrs 21-25hrs 26-30hrs 31-35hrs 36-40hrs 41-45hrs 46-50hrs over 50 | hrs Row | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | Total HOTEL ------+------+------+------+------+------+------+------+------+------+------+------+ 1 | 1 | 7 | 7 | 35 | 31 | 58 | 54 | 271 | 45 | 22 | 10 | 541 | 12.5 | 21.2 | 18.4 | 35.7 | 27.0 | 34.9 | 40.3 | 35.4 | 22.5 | 25.0 | 12.2 | 31.3 +------+------+------+------+------+------+------+------+------+------+------+ 3 | | 1 | 2 | 3 | 6 | 18 | 17 | 40 | 14 | 9 | 9 | 119 | | 3.0 | 5.3 | 3.1 | 5.2 | 10.8 | 12.7 | 5.2 | 7.0 | 10.2 | 11.0 | 6.9 +------+------+------+------+------+------+------+------+------+------+------+ 4 | 3 | 11 | 10 | 27 | 18 | 22 | 16 | 103 | 39 | 18 | 6 | 273 | 37.5 | 33.3 | 26.3 | 27.6 | 15.7 | 13.3 | 11.9 | 13.5 | 19.5 | 20.5 | 7.3 | 15.8 +------+------+------+------+------+------+------+------+------+------+------+ 5 | 1 | | 2 | 2 | 2 | 3 | | 26 | 12 | 3 | 3 | 54 | 12.5 | | 5.3 | 2.0 | 1.7 | 1.8 | | 3.4 | 6.0 | 3.4 | 3.7 | 3.1 +------+------+------+------+------+------+------+------+------+------+------+ 6 | | 2 | 1 | 3 | 11 | 6 | 7 | 41 | 11 | 3 | 2 | 87 | | 6.1 | 2.6 | 3.1 | 9.6 | 3.6 | 5.2 | 5.4 | 5.5 | 3.4 | 2.4 | 5.0 +------+------+------+------+------+------+------+------+------+------+------+ 7 | | 1 | 1 | 1 | 2 | 4 | 1 | 17 | 12 | 5 | 2 | 46 | | 3.0 | 2.6 | 1.0 | 1.7 | 2.4 | .7 | 2.2 | 6.0 | 5.7 | 2.4 | 2.7 +------+------+------+------+------+------+------+------+------+------+------+ 8 | | | 1 | 1 | 6 | 4 | 3 | 19 | 5 | 5 | 5 | 49 | | | 2.6 | 1.0 | 5.2 | 2.4 | 2.2 | 2.5 | 2.5 | 5.7 | 6.1 | 2.8 +------+------+------+------+------+------+------+------+------+------+------+ 9 | | 1 | 3 | 1 | 4 | 7 | 2 | 20 | 6 | | 2 | 46 | | 3.0 | 7.9 | 1.0 | 3.5 | 4.2 | 1.5 | 2.6 | 3.0 | | 2.4 | 2.7 +------+------+------+------+------+------+------+------+------+------+------+ 10 | 1 | 1 | 1 | 4 | | 2 | 1 | 19 | 3 | 1 | 4 | 37 | 12.5 | 3.0 | 2.6 | 4.1 | | 1.2 | .7 | 2.5 | 1.5 | 1.1 | 4.9 | 2.1 +------+------+------+------+------+------+------+------+------+------+------+ 11 | | 1 | 2 | 5 | 2 | 7 | 5 | 38 | 18 | 3 | 6 | 87 | | 3.0 | 5.3 | 5.1 | 1.7 | 4.2 | 3.7 | 5.0 | 9.0 | 3.4 | 7.3 | 5.0 +------+------+------+------+------+------+------+------+------+------+------+ 12 | | 2 | 1 | 1 | 7 | 3 | 5 | 52 | 9 | 8 | 2 | 90 | | 6.1 | 2.6 | 1.0 | 6.1 | 1.8 | 3.7 | 6.8 | 4.5 | 9.1 | 2.4 | 5.2 +------+------+------+------+------+------+------+------+------+------+------+ 13 | | 1 | 1 | 1 | | 1 | 1 | 22 | 5 | | | 32 | | 3.0 | 2.6 | 1.0 | | .6 | .7 | 2.9 | 2.5 | | | 1.9 +------+------+------+------+------+------+------+------+------+------+------+ 14 | 1 | 5 | 6 | 10 | 19 | 22 | 17 | 66 | 13 | 9 | 23 | 191 | 12.5 | 15.2 | 15.8 | 10.2 | 16.5 | 13.3 | 12.7 | 8.6 | 6.5 | 10.2 | 28.0 | 11.1 +------+------+------+------+------+------+------+------+------+------+------+ 15 | 1 | | | 4 | 7 | 9 | 5 | 31 | 8 | 2 | 8 | 75 | 12.5 | | | 4.1 | 6.1 | 5.4 | 3.7 | 4.1 | 4.0 | 2.3 | 9.8 | 4.3 +------+------+------+------+------+------+------+------+------+------+------+ Column 8 33 38 98 115 166 134 765 200 88 82 1727 Total .5 1.9 2.2 5.7 6.7 9.6 7.8 44.3 11.6 5.1 4.7 100.0
248 Chi-Square Value DF Significance ------
Pearson 244.36246 130 .00000 Likelihood Ratio 250.87489 130 .00000 Mantel-Haenszel test for 3.72831 1 .05350 linear association
Minimum Expected Frequency - .148 Cells with Expected Frequency < 5 - 87 OF 154 ( 56.5%)
Number of Missing Observations: 65
249 HOTEL hotel code by MODEMPL mode of employment
MODEMPL Page 1 of 1 Count | Col Pct |full tim part tim casual |e e Row | 1 | 2 | 3 | Total HOTEL ------+------+------+------+ 1 | 304 | 48 | 190 | 542 | 29.2 | 26.2 | 38.4 | 31.5 +------+------+------+ 3 | 71 | 46 | 2 | 119 | 6.8 | 25.1 | .4 | 6.9 +------+------+------+ 4 | 151 | 11 | 106 | 268 | 14.5 | 6.0 | 21.4 | 15.6 +------+------+------+ 5 | 44 | | 8 | 52 | 4.2 | | 1.6 | 3.0 +------+------+------+ 6 | 48 | 5 | 33 | 86 | 4.6 | 2.7 | 6.7 | 5.0 +------+------+------+ 7 | 37 | 4 | 5 | 46 | 3.6 | 2.2 | 1.0 | 2.7 +------+------+------+ 8 | 34 | | 15 | 49 | 3.3 | | 3.0 | 2.9 +------+------+------+ 9 | 28 | | 18 | 46 | 2.7 | | 3.6 | 2.7 +------+------+------+ 10 | 27 | 1 | 9 | 37 | 2.6 | .5 | 1.8 | 2.2 +------+------+------+ 11 | 44 | 42 | 1 | 87 | 4.2 | 23.0 | .2 | 5.1 +------+------+------+ 12 | 74 | 3 | 13 | 90 | 7.1 | 1.6 | 2.6 | 5.2 +------+------+------+ 13 | 28 | | 3 | 31 | 2.7 | | .6 | 1.8 +------+------+------+ 14 | 104 | 8 | 80 | 192 | 10.0 | 4.4 | 16.2 | 11.2 +------+------+------+ 15 | 47 | 15 | 12 | 74 | 4.5 | 8.2 | 2.4 | 4.3 +------+------+------+ Column 1041 183 495 1719 Total 60.6 10.6 28.8 100.0
Chi-Square Value DF Significance ------
Pearson 394.58509 26 .00000 Likelihood Ratio 372.78829 26 .00000 Mantel-Haenszel test for 6.83703 1 .00893 linear association
Minimum Expected Frequency - 3.300 Cells with Expected Frequency < 5 - 4 OF 42 ( 9.5%)
Number of Missing Observations: 73
250 HOTEL hotel code by TRAINSES last training session
TRAINSES Page 1 of 1 Count | Col Pct |0-1yr 1-2yrs 2-3yrs 3-4yrs 4-5yrs 5-6yrs 6-7yrs | Row | 1 | 2 | 3 | 4 | 5 | 6 | 7 | Total HOTEL ------+------+------+------+------+------+------+------+ 1 | 369 | 73 | 34 | 11 | 8 | 3 | 19 | 517 | 30.8 | 30.7 | 34.3 | 27.5 | 27.6 | 27.3 | 40.4 | 31.1 +------+------+------+------+------+------+------+ 3 | 82 | 16 | 10 | 2 | | | 4 | 114 | 6.8 | 6.7 | 10.1 | 5.0 | | | 8.5 | 6.9 +------+------+------+------+------+------+------+ 4 | 167 | 51 | 16 | 6 | 6 | 2 | 10 | 258 | 13.9 | 21.4 | 16.2 | 15.0 | 20.7 | 18.2 | 21.3 | 15.5 +------+------+------+------+------+------+------+ 5 | 44 | 7 | | 1 | 1 | | 1 | 54 | 3.7 | 2.9 | | 2.5 | 3.4 | | 2.1 | 3.2 +------+------+------+------+------+------+------+ 6 | 66 | 7 | 5 | 2 | 1 | 2 | 2 | 85 | 5.5 | 2.9 | 5.1 | 5.0 | 3.4 | 18.2 | 4.3 | 5.1 +------+------+------+------+------+------+------+ 7 | 38 | 6 | 1 | | | | | 45 | 3.2 | 2.5 | 1.0 | | | | | 2.7 +------+------+------+------+------+------+------+ 8 | 31 | 10 | 2 | 1 | 1 | | 2 | 47 | 2.6 | 4.2 | 2.0 | 2.5 | 3.4 | | 4.3 | 2.8 +------+------+------+------+------+------+------+ 9 | 36 | 6 | | 2 | 2 | | | 46 | 3.0 | 2.5 | | 5.0 | 6.9 | | | 2.8 +------+------+------+------+------+------+------+ 10 | 30 | 3 | | | 1 | | 1 | 35 | 2.5 | 1.3 | | | 3.4 | | 2.1 | 2.1 +------+------+------+------+------+------+------+ 11 | 49 | 10 | 7 | 7 | 2 | 1 | 3 | 79 | 4.1 | 4.2 | 7.1 | 17.5 | 6.9 | 9.1 | 6.4 | 4.8 +------+------+------+------+------+------+------+ 12 | 73 | 6 | 4 | 2 | 2 | | 1 | 88 | 6.1 | 2.5 | 4.0 | 5.0 | 6.9 | | 2.1 | 5.3 +------+------+------+------+------+------+------+ 13 | 29 | 1 | | | | | 2 | 32 | 2.4 | .4 | | | | | 4.3 | 1.9 +------+------+------+------+------+------+------+ 14 | 135 | 28 | 16 | 3 | 2 | 3 | 1 | 188 | 11.3 | 11.8 | 16.2 | 7.5 | 6.9 | 27.3 | 2.1 | 11.3 +------+------+------+------+------+------+------+ 15 | 50 | 14 | 4 | 3 | 3 | | 1 | 75 | 4.2 | 5.9 | 4.0 | 7.5 | 10.3 | | 2.1 | 4.5 +------+------+------+------+------+------+------+ 251 Column 1199 238 99 40 29 11 47 1663 Total 72.1 14.3 6.0 2.4 1.7 .7 2.8 100.0
Chi-Square Value DF Significance ------
Pearson 94.58367 78 .09742 Likelihood Ratio 110.43554 78 .00922 Mantel-Haenszel test for 1.72676 1 .18882 linear association
Minimum Expected Frequency - .212 Cells with Expected Frequency < 5 - 59 OF 98 ( 60.2%)
Number of Missing Observations: 129
252 Appendix E
Reliability Analysis and
Principal Components Analysis of Employee Organisational Climate
Data
253 1. Variables
1a. Opportunity for independent thought and action exists in your job. 1b. your job requires a high level of skill and training. 1c. You are required to meet rigid standards of quality in your work. 1d. Staff members generally trust their supervisors. 1e. The methods of your work are kept up to date. 1f. You are required to perform tasks on your job which you consider relatively unimportant or unnecessary. 1g. You are able to get the money, supplies, equipment, etc. your work group needs to do its work well. 1h. Your supervisor is friendly and easy to approach. 1i. Your supervisor offers new ideas for job and related problems. 1j. A spirit of cooperation exists in your workgroup. 1k. Your job responsibilities are clearly defined. 1l. Responsibility is assigned so that individuals have authority within their own area. 1m. Dealing with other people is part of your job. 1n. Your supervisor encourages the people who work for him or her to exchange ideas and opinions.
2a. Staff members generally trust their managers. 2b. You are given advanced information about changes which might affect you. 2c. The hotel’s policies are consistently applied to all staff members. 2d. You have opportunities to complete the work you start. 2e. Procedures are designed so that resources are used efficiently. 2f. Your supervisor is attentive to what you say. 2g. Your supervisor provides the help you need to schedule your work ahead of time. 2h. there is friction in your workgroup. 2i. You have opportunities to learn worthwhile skills and knowledge in your job. 2j. New staff members get on-the-job training they need. 2k. There is variety in your job. 2l. Your hours of work are irregular. 2m. Everything in this hotel is checked, individual judgement is not trusted. 2n. Being liked is important in getting a promotion.
3a. You have good information on where you stand and how your performance is evaluated. 3b. Your superior emphasises high standards of performance. 3c. The ideas and suggestions of staff members are paid attention to. 3d. you have the opportunity to do a number of different things in your job. 3e. Your supervisor sets an example by working hard himself or herself. 3f. A friendly atmosphere prevails among most of the members of your workgroup. 3g. Hotel politics count in getting a promotion. 3h. People act as though everyone must be watched or they will slacken off. 3i. Supervisors generally know what is going on in their work groups. 3j. You are aware of how well your work group is meeting its objectives. 3k. Your job demands precision. 3l. Members of your work group trust each other. 3m. The hotel has a good image to outsiders. 3n. Working in this hotel is beneficial to your career. 3o. You have opportunities to make full use of your knowledge and skills in your job.
4a. Communication is hindered by following chain of command rules. 4b. Your supervisor encourages the people who work for them to work as a team. 4c. It is possible to get accurate information on the policies and objectives of this hotel. 4d. The hotel strives to do a better job than other hotels of the same type. 4e. The hotel emphasises personal growth and development. 4f. Managers keep well informed about the needs and problems of employees. 4g. Discipline in this hotel is maintained consistently. 4h. Your manager is successful in his dealing with higher levels of management. 4i. The objectives of the hotel are clearly defined. 4j. There is conflict between your department and other departments of the hotel. 4k. Your work is important. 4l. The way your work group is organised hinders the efficient conduct of work.
254 4m. This hotel is concerned with assisting the local community. 4n. Things in this hotel seem to happen contrary to rules and regulations.
5a. In this hotel the only source of information on important matters is the grapevine. 5b. In this hotel things are planned so that everyone is getting in each others’ way. 5c. Under most circumstances I would recommend this hotel to a prospective staff member. 5d. Most of the personnel in my department would not want to change to another department. 5e. Most members of my work group take pride in their jobs. 5f. Generally there are friendly and co-operative relationships between the different departments of the hotel. 5g. My department, compared to all other departments would be one of the most productive. 5h. Excessive rules and regulations interfere with how well I am able to do my job. 5i. Overall I think my immediate supervisor is doing a good job. 5j. Compared with other work groups, my work group is under much less pressure to produce. 5k. In my job the opportunities to get to know people are limited. 5l. Compared to all other similar work groups in this hotel, my work group would be the most productive. 5m. Your immediate supervisor is successful in dealing with higher levels of management.
255 2. Reliability Analysis R E L I A B I L I T Y A N A L Y S I S - S C A L E (A L P H A)
Mean Std Dev Cases
1. I_1A 4.9550 1.5344 1401.0 2. I_1B 5.0457 1.5673 1401.0 3. I_1C 5.8687 1.1538 1401.0 4. I_1D 5.0435 1.4977 1401.0 5. I_1E 5.2277 1.3110 1401.0 6. I_1F 4.6160 1.6448 1401.0 7. I_1G 4.4668 1.6997 1401.0 8. I_1H 5.6931 1.3623 1401.0 9. I_1I 5.0821 1.4559 1401.0 10. I_1J 5.3419 1.4494 1401.0 11. I_1K 5.4318 1.3958 1401.0 12. I_1L 5.0842 1.5185 1401.0 13. I_1M 6.4097 .9520 1401.0 14. I_1N 5.1734 1.5700 1401.0 15. I_2A 4.7566 1.6195 1401.0 16. I_2B 4.5275 1.6817 1401.0 17. I_2C 4.6395 1.7323 1401.0 18. I_2D 5.4433 1.1987 1401.0 19. I_2E 4.9215 1.4970 1401.0 20. I_2F 5.2655 1.4221 1401.0 21. I_2G 5.0528 1.4112 1401.0 22. I_2H 4.3712 1.8457 1401.0 23. I_2I 4.8223 1.6731 1401.0 24. I_2J 5.0592 1.5848 1401.0 25. I_2K 4.8630 1.7590 1401.0 26. I_2L 3.5289 2.1599 1401.0 27. I_2M 3.9993 1.5886 1401.0 28. I_2N 2.9065 1.6364 1401.0 29. I_3A 4.5860 1.5716 1401.0 30. I_3B 5.5296 1.2573 1401.0 31. I_3C 4.7852 1.4741 1401.0 32. I_3D 5.0214 1.5918 1401.0 33. I_3E 5.0835 1.6855 1401.0 34. I_3F 5.6831 1.2539 1401.0 35. I_3G 3.1370 1.5838 1401.0 36. I_3H 4.0314 1.5813 1401.0 37. I_3I 5.1035 1.3758 1401.0 38. I_3J 5.0542 1.3196 1401.0 39. I_3K 5.5082 1.2683 1401.0 40. I_3L 5.1113 1.3796 1401.0 41. I_3M 5.6538 1.2349 1401.0 42. I_3N 5.3448 1.5925 1401.0 43. I_3O 4.9657 1.7632 1401.0 44. I_4A 3.8494 1.4917 1401.0 45. I_4B 5.5767 1.2490 1401.0 46. I_4C 5.2334 1.3827 1401.0 47. I_4D 5.7652 1.2124 1401.0 48. I_4E 5.0528 1.5311 1401.0
256 R E L I A B I L I T Y A N A L Y S I S - S C A L E (A L P H A)
Mean Std Dev Cases
49. I_4F 4.5303 1.6193 1401.0 50. I_4G 4.7823 1.4872 1401.0 51. I_4H 5.0400 1.4297 1401.0 52. I_4I 5.4711 1.2175 1401.0 53. I_4J 4.3505 1.7207 1401.0 54. I_4K 6.1313 1.0757 1401.0 55. I_4L 4.4140 1.7378 1401.0 56. I_4M 4.7730 1.4471 1401.0 57. I_4N 3.9693 1.5477 1401.0 58. I_5A 4.5903 1.6451 1401.0 59. I_5B 5.2156 1.3242 1401.0 60. I_5C 5.3747 1.3470 1401.0 61. I_5D 4.6146 1.5986 1401.0 62. I_5E 5.2049 1.3475 1401.0 63. I_5F 5.1599 1.2479 1401.0 64. I_5G 5.1485 1.3802 1401.0 65. I_5H 4.6017 1.5037 1401.0 66. I_5I 5.5125 1.3654 1401.0 67. I_5J 5.0578 1.5580 1401.0 68. I_5K 4.9536 1.6579 1401.0 69. I_5L 4.7052 1.3626 1401.0 70. I_5M 5.0757 1.4172 1401.0
N of Statistics for Mean Variance Std Dev Variables SCALE 346.3498 2883.9276 53.7022 70
257 R E L I A B I L I T Y A N A L Y S I S - S C A L E (A L P H A)
Item-total Statistics
Scale Scale Corrected Mean Variance Item- Alpha if Item if Item Total if Item Deleted Deleted Correlation Deleted
I_1A 341.3947 2783.7034 .6045 .9585 I_1B 341.3041 2821.8218 .3582 .9593 I_1C 340.4811 2829.8612 .4296 .9591 I_1D 341.3062 2776.0812 .6691 .9583 I_1E 341.1221 2801.2272 .5835 .9586 I_1F 341.7338 2830.5426 .2896 .9596 I_1G 341.8829 2815.1163 .3655 .9593 I_1H 340.6567 2792.2928 .6236 .9585 I_1I 341.2677 2777.3219 .6809 .9583 I_1J 341.0079 2783.8878 .6403 .9584 I_1K 340.9179 2802.1683 .5401 .9587 I_1L 341.2655 2790.6666 .5669 .9586 I_1M 339.9400 2861.7935 .2084 .9595 I_1N 341.1763 2775.3096 .6417 .9584 I_2A 341.5931 2762.4029 .6984 .9582 I_2B 341.8223 2773.6562 .6066 .9585 I_2C 341.7102 2775.1188 .5797 .9586 I_2D 340.9065 2818.9905 .4989 .9589 I_2E 341.4283 2779.4079 .6480 .9584 I_2F 341.0842 2777.4800 .6967 .9583 I_2G 341.2969 2786.4232 .6411 .9584 I_2H 341.9786 2792.9653 .4488 .9591 I_2I 341.5275 2774.4109 .6055 .9585 I_2J 341.2905 2796.0591 .5093 .9588 I_2K 341.4868 2795.0043 .4615 .9590 I_2L 342.8208 2858.9629 .0879 .9608 I_2M 342.3505 2845.3307 .2129 .9598 I_2N 343.4433 2824.3884 .3269 .9594 I_3A 341.7637 2778.8406 .6193 .9585 I_3B 340.8201 2806.6905 .5679 .9587 I_3C 341.5646 2769.7889 .7216 .9582 I_3D 341.3283 2797.2293 .4999 .9589 I_3E 341.2662 2764.7555 .6563 .9583 I_3F 340.6667 2810.1452 .5432 .9588 I_3G 343.2127 2827.5433 .3199 .9594 I_3H 342.3183 2809.4657 .4293 .9591 I_3I 341.2463 2791.4229 .6233 .9585 I_3J 341.2955 2799.4226 .5927 .9586 I_3K 340.8415 2825.6949 .4199 .9591 I_3L 341.2384 2801.8917 .5487 .9587 I_3M 340.6959 2816.2089 .5050 .9589
258 R E L I A B I L I T Y A N A L Y S I S - S C A L E (A L P H A)
Item-total Statistics
Scale Scale Corrected Mean Variance Item- Alpha if Item if Item Total if Item Deleted Deleted Correlation Deleted
I_3N 341.0050 2785.7478 .5690 .9586 I_3O 341.3840 2762.4739 .6385 .9584 I_4A 342.5004 2814.8487 .4224 .9591 I_4B 340.7730 2797.6556 .6411 .9585 I_4C 341.1163 2802.8186 .5409 .9587 I_4D 340.5846 2812.8744 .5411 .9588 I_4E 341.2969 2777.3875 .6456 .9584 I_4F 341.8194 2761.3324 .7050 .9582 I_4G 341.5675 2794.0213 .5578 .9587 I_4H 341.3098 2789.4611 .6120 .9585 I_4I 340.8787 2810.6581 .5561 .9587 I_4J 341.9993 2816.6093 .3524 .9594 I_4K 340.2184 2833.1994 .4329 .9591 I_4L 341.9358 2817.0987 .3459 .9594 I_4M 341.5767 2823.7600 .3776 .9592 I_4N 342.3804 2812.1473 .4227 .9591 I_5A 341.7595 2790.6657 .5210 .9588 I_5B 341.1342 2814.4677 .4819 .9589 I_5C 340.9750 2800.0172 .5759 .9587 I_5D 341.7352 2823.4634 .3409 .9594 I_5E 341.1449 2799.0068 .5828 .9586 I_5F 341.1899 2815.9096 .5018 .9589 I_5G 341.2013 2847.1652 .2366 .9596 I_5H 341.7480 2818.9486 .3928 .9592 I_5I 340.8373 2789.3178 .6431 .9585 I_5J 341.2919 2861.5411 .1197 .9601 I_5K 341.3961 2836.2437 .2545 .9597 I_5L 341.6445 2862.7878 .1322 .9599 I_5M 341.2741 2790.4420 .6109 .9585
Reliability Coefficients
N of Cases = 1401.0 N of Items = 70
Alpha = .9594
259 3. Principal Components Analysis
Analysis number 1 Pairwise deletion of cases with missing values
Mean Std Dev Cases Label
I_1A 4.91572 1.55387 1768 I_1B 5.04262 1.57541 1783 I_1C 5.85202 1.18007 1784 I_1D 5.02302 1.52508 1781 I_1E 5.22278 1.32199 1782 I_1F 3.38440 1.67313 1782 I_1G 4.45465 1.70186 1775 I_1H 5.66591 1.37622 1772 I_1I 5.09055 1.47296 1767 I_1J 5.28708 1.49579 1780 I_1K 5.40045 1.42785 1778 I_1L 5.07601 1.51588 1776 I_1M 6.37535 .98033 1785 I_1N 5.16367 1.57916 1778 I_2A 4.75518 1.62658 1785 I_2B 4.52664 1.68483 1783 I_2C 4.64362 1.74339 1779 I_2D 5.42817 1.21527 1782 I_2E 4.92853 1.50036 1777 I_2F 5.21163 1.46050 1772 I_2G 5.03180 1.43341 1761 I_2H 3.65127 1.84598 1775 I_2I 4.82479 1.67522 1775 I_2J 5.04730 1.58479 1776 I_2K 4.86213 1.75998 1777 I_2L 4.42785 2.17874 1774 I_2M 4.02201 1.59724 1772 I_2N 5.07372 1.65276 1777 I_3A 4.57015 1.58756 1782 I_3B 5.50703 1.27479 1777 I_3C 4.74719 1.49465 1780 I_3D 5.00898 1.59833 1782 I_3E 5.05697 1.71391 1773 I_3F 5.64587 1.27908 1779 I_3G 4.87040 1.59475 1767 I_3H 3.96901 1.60981 1775 I_3I 5.09837 1.39455 1779 I_3J 5.01805 1.34664 1773 I_3K 5.50929 1.27035 1777 I_3L 5.07926 1.41815 1779 I_3M 5.64759 1.23324 1782 I_3N 5.32697 1.60342 1780 I_3O 4.93659 1.77287 1782 I_4A 4.16629 1.50733 1756 I_4B 5.54972 1.26261 1770 I_4C 5.20372 1.39481 1772 I_4D 5.71896 1.23291 1772 I_4E 5.04084 1.52152 1763 I_4F 4.49831 1.63354 1770 I_4G 4.79153 1.48291 1770 I_4H 5.03333 1.44016 1770 I_4I 5.44855 1.23402 1759 I_4J 3.65705 1.71003 1767 I_4K 6.14813 1.06739 1762 I_4L 3.61736 1.74808 1751 I_4M 4.75085 1.46036 1762 I_4N 4.07548 1.56425 1762 I_5A 3.45495 1.67406 1776 I_5B 2.83851 1.37734 1771 I_5C 5.36266 1.34421 1773
260 Mean Std Dev Cases Label I_5D 4.57490 1.60181 1769 I_5E 5.19255 1.35991 1771 I_5F 5.16131 1.26327 1773 I_5G 5.10985 1.40793 1766 I_5H 3.45000 1.54846 1760 I_5I 5.47225 1.41264 1766 I_5J 2.97561 1.59844 1763 I_5K 3.11205 1.70035 1767 I_5L 4.69187 1.39375 1759 I_5M 5.06444 1.43499 1769
Correlation Matrix:
I_1A I_1B I_1C I_1D I_1E I_1F I_1G I_1A 1.00000 I_1B .36277 1.00000 I_1C .29384 .43687 1.00000 I_1D .45426 .24380 .31985 1.00000 I_1E .36536 .24670 .34006 .48785 1.00000 I_1F -.14695 -.03812 -.06707 -.15591 -.15114 1.00000 I_1G .23433 .07491 .11461 .23760 .30702 -.05158 1.00000 I_1H .42556 .19697 .22424 .57576 .36548 -.13966 .19932 I_1I .47173 .26467 .25186 .50856 .40662 -.16232 .25273 I_1J .43661 .23724 .27167 .51336 .38267 -.15528 .22858 I_1K .26406 .13624 .29414 .35100 .41165 -.18812 .22566 I_1L .45847 .26599 .24785 .38921 .34105 -.11332 .25274 I_1M .14355 .12299 .14953 .09055 .07850 -.04800 .09792 I_1N .47846 .25251 .28036 .46084 .36941 -.15368 .19161 I_2A .41224 .20422 .30059 .68438 .45637 -.19368 .28593 I_2B .34659 .17695 .21994 .42117 .40761 -.18951 .32802 I_2C .31188 .14674 .22571 .43086 .38059 -.19910 .30638 I_2D .29671 .18002 .21542 .33626 .37424 -.15766 .24349 I_2E .37696 .18990 .27981 .43874 .46632 -.23257 .34671 I_2F .46364 .21442 .28993 .59731 .41690 -.20764 .20904 I_2G .38834 .17834 .23835 .50817 .41955 -.15579 .22567 I_2H -.27040 -.11230 -.15968 -.35549 -.19373 .18657 -.10417 I_2I .40107 .31851 .24695 .34663 .36844 -.19400 .25875 I_2J .23101 .13392 .24699 .34035 .40597 -.15424 .26223 I_2K .40271 .37071 .19607 .26864 .24350 -.10794 .14783 I_2L -.03890 .02926 -.01293 -.01485 -.06522 .12174 -.06539 I_2M -.16895 -.05100 .02911 -.12267 -.05481 .14720 -.03515 I_2N -.16019 -.09018 -.07481 -.20623 -.13177 .10516 -.11878 I_3A .35467 .23052 .26932 .39587 .41264 -.14824 .22950 I_3B .30408 .19236 .39888 .38936 .36152 -.16250 .17990 I_3C .48487 .24819 .24556 .49509 .41073 -.18905 .28775 I_3D .46913 .34709 .21000 .30105 .27083 -.10212 .16997 I_3E .41136 .24045 .22944 .49824 .34524 -.21492 .14675 I_3F .31183 .16092 .25459 .40302 .31212 -.12424 .14496 I_3G -.16271 -.09018 -.07610 -.21652 -.15560 .12472 -.09638 I_3H -.30925 -.10952 -.07534 -.27853 -.19459 .20855 -.13444 I_3I .34404 .15438 .28161 .49946 .36749 -.17106 .19932 I_3J .34237 .20075 .26938 .37492 .34783 -.14387 .21451 I_3K .27311 .47329 .43556 .24116 .29891 -.09901 .10751 I_3L .33781 .23146 .25092 .42193 .32083 -.12490 .13146 I_3M .24996 .13589 .26470 .33641 .29424 -.16884 .20989 I_3N .39354 .31382 .24580 .35095 .31702 -.15675 .24929 I_3O .45316 .41896 .32482 .38011 .40611 -.12666 .26766 I_4A -.24797 -.06982 -.08958 -.28834 -.18513 .20846 -.08932 I_4B .35847 .17326 .27733 .42811 .37249 -.18261 .16405 I_4C .30943 .09495 .20858 .33044 .33869 -.15484 .23630 I_4D .29904 .15618 .31706 .32901 .32538 -.17050 .20394 I_4E .39369 .22059 .29842 .40690 .38842 -.20658 .28530 I_4F .41033 .23555 .26928 .46351 .38916 -.17108 .27527 I_1A I_1B I_1C I_1D I_1E I_1F I_1G I_4G .30032 .18748 .28388 .39405 .37244 -.16517 .25886 I_4H .36920 .23417 .26500 .38796 .36987 -.13826 .23772 I_4I .28160 .14547 .29026 .31373 .31717 -.18415 .24774 I_4J -.17195 -.02982 -.10526 -.22723 -.19658 .18180 -.17379 I_4K .28766 .34238 .29810 .27986 .28516 -.15313 .11133 I_4L -.19777 -.08266 -.14544 -.21479 -.17741 .20245 -.07751 261 I_4M .20803 .12726 .12639 .20469 .25719 -.12384 .22019 I_4N -.24573 -.12223 -.18456 -.27857 -.18764 .20932 -.11518 I_5A -.28926 -.09467 -.19631 -.32594 -.25396 .24837 -.13329 I_5B -.24995 -.08757 -.20853 -.28347 -.23233 .28478 -.11580 I_5C .35050 .11757 .21957 .37755 .31018 -.19960 .24105 I_5D .27254 .22977 .12425 .17396 .15889 -.04788 .09670 I_5E .36986 .28008 .31404 .37976 .33805 -.14015 .13851 I_5F .27044 .12163 .20383 .31712 .28889 -.14841 .20740 I_5G .13847 .19201 .21078 .17459 .16395 -.03843 .05213 I_5H -.24972 -.05659 -.12929 -.24751 -.20642 .27360 -.09619 I_5I .34738 .15026 .23551 .50805 .36768 -.19432 .19320 I_5J -.07254 -.10055 -.15176 -.06645 -.03240 .13846 .04366 I_5K -.18231 -.04947 -.05427 -.09537 -.08789 .14175 -.06758 I_5L .06829 .14989 .15878 .10334 .11902 .02756 .01074 I_5M .35954 .19701 .22457 .41667 .40335 -.15587 .19475
I_1H I_1I I_1J I_1K I_1L I_1M I_1N I_1H 1.00000 I_1I .66537 1.00000 I_1J .51196 .52169 1.00000 I_1K .31916 .35773 .40265 1.00000 I_1L .34906 .39677 .39279 .42227 1.00000 I_1M .16419 .15823 .17418 .11396 .15827 1.00000 I_1N .54148 .62645 .49822 .35332 .43502 .20501 1.00000 I_2A .49508 .49600 .48031 .39819 .41099 .12225 .46026 I_2B .35293 .41658 .37012 .39343 .36819 .12862 .41185 I_2C .30310 .33472 .31057 .38085 .37296 .09157 .33814 I_2D .30629 .29196 .31592 .40720 .31874 .11089 .30140 I_2E .35401 .41713 .42878 .44348 .43322 .12913 .39754 I_2F .68519 .62116 .56800 .39887 .38460 .17113 .57530 I_2G .55939 .55461 .47816 .42472 .39592 .13837 .49940 I_2H -.31931 -.30095 -.46849 -.24438 -.21269 -.03519 -.29247 I_2I .31350 .42039 .33549 .29675 .34018 .13484 .43162 I_2J .27374 .36331 .34041 .39547 .29969 .05035 .31885 I_2K .22227 .29743 .30123 .18016 .29583 .12397 .32714 I_2L -.06781 -.03385 -.01692 -.10005 -.06763 .09748 -.00930 I_2M -.15368 -.12378 -.14497 -.05471 -.07950 -.00539 -.10475 I_2N -.19035 -.20309 -.13472 -.20087 -.13242 .04176 -.16272 I_3A .37947 .42840 .35705 .43092 .36986 .09997 .45189 I_3B .40442 .45529 .37297 .31126 .27844 .18986 .45709 I_3C .47194 .53894 .46428 .33350 .41848 .15706 .57073 I_3D .26264 .32650 .31544 .17499 .32010 .13629 .34360 I_3E .54541 .55107 .47925 .27734 .34500 .15131 .48682 I_3F .39331 .36565 .55119 .27513 .32118 .16454 .37305 I_3G -.17532 -.16261 -.15151 -.18462 -.15760 .06638 -.14204 I_3H -.23743 -.22358 -.26255 -.19484 -.20375 -.08646 -.22681 I_3I .46026 .46556 .45635 .33966 .39180 .11128 .42460 I_3J .36531 .41320 .38279 .31521 .33541 .14339 .38038 I_3K .17352 .24854 .21826 .21977 .29112 .15619 .27313 I_3L .32390 .34864 .54338 .28086 .35345 .14062 .36861 I_3M .28304 .27249 .30094 .25008 .27953 .16072 .27344 I_3N .31048 .38244 .33873 .27027 .32357 .20153 .36349 I_3O .31516 .41231 .36830 .34845 .39776 .11153 .41710 I_4A -.26008 -.24198 -.26484 -.17124 -.18156 -.03146 -.21290 I_4B .48610 .50861 .42924 .32133 .32144 .19723 .55368 I_4C .27259 .32436 .29146 .28717 .29724 .18155 .31109 I_4D .28949 .32099 .31925 .28827 .30372 .18937 .31652 I_4E .31519 .36780 .36887 .35534 .37308 .13202 .39272 I_4F .42168 .48703 .40308 .37740 .39744 .13246 .45432 I_4G .27595 .33972 .31201 .33060 .34180 .08410 .32709 I_4H .36608 .45979 .35691 .31306 .35544 .12725 .38122 I_4I .24906 .31402 .30688 .33538 .27900 .15441 .30353 I_4J -.16018 -.16557 -.21359 -.18944 -.13842 -.00295 -.13484 I_4K .23535 .24864 .25593 .27217 .27066 .16044 .25176 I_4L -.21953 -.21327 -.28059 -.17243 -.16331 -.04408 -.21821 I_4M .16136 .19232 .16384 .22418 .25561 .06051 .19303 I_4N -.24037 -.23808 -.28046 -.18605 -.19627 -.09272 -.23694 I_5A -.28072 -.27342 -.28630 -.26058 -.23176 -.07559 -.26832 I_5B -.26933 -.24913 -.26096 -.22784 -.19848 -.10249 -.22775 I_5C .34074 .34560 .37615 .30685 .31307 .15475 .36302 I_5D .21172 .24614 .25652 .11855 .19305 .12568 .26388 262 I_5E .33630 .39297 .47517 .28790 .34097 .11171 .37700 I_5F .23563 .27406 .31550 .25487 .25048 .13653 .25508 I_5G .14624 .15128 .18604 .16133 .15391 .01429 .15930 I_5H -.25382 -.19703 -.21996 -.18830 -.15945 -.04854 -.18108 I_5I .63650 .58882 .46915 .34047 .29920 .14106 .51505 I_5J -.07781 -.08522 -.07093 -.04983 -.03862 -.04274 -.06024 I_5K -.11731 -.13950 -.14005 -.10462 -.10377 -.25932 -.15763 I_5L .09408 .12143 .13607 .12476 .09828 -.01710 .11862 I_5M .43677 .54095 .38035 .34219 .35039 .11202 .45306
I_2A I_2B I_2C I_2D I_2E I_2F I_2G I_2A 1.00000 I_2B .51781 1.00000 I_2C .49000 .52580 1.00000 I_2D .37578 .38909 .37340 1.00000 I_2E .49681 .46848 .48633 .47721 1.00000 I_2F .56180 .43236 .37260 .38273 .46758 1.00000 I_2G .50237 .44047 .40203 .42320 .49488 .68371 1.00000 I_2H -.31158 -.21261 -.20514 -.21188 -.23536 -.35216 -.28746 I_2I .40342 .39810 .35003 .29135 .38055 .37270 .34121 I_2J .38095 .38818 .35255 .30972 .45973 .37884 .39721 I_2K .28675 .27533 .23822 .21948 .29193 .30205 .28727 I_2L -.01884 .00726 -.02679 -.07012 -.08267 -.03278 -.06194 I_2M -.09469 -.08369 -.03750 -.04866 -.04387 -.10612 -.09425 I_2N -.25251 -.18651 -.22732 -.17648 -.15621 -.21094 -.18308 I_3A .48830 .46908 .39347 .34105 .43939 .46660 .46435 I_3B .41853 .35226 .32389 .31052 .40547 .48942 .45038 I_3C .54603 .46515 .43347 .33948 .49849 .58127 .51946 I_3D .33034 .29430 .24955 .20980 .27448 .33921 .28311 I_3E .46624 .36435 .32256 .28872 .38416 .60994 .52484 I_3F .40265 .28596 .27436 .28295 .30273 .42980 .35742 I_3G -.26460 -.18833 -.26325 -.15307 -.16576 -.19181 -.18512 I_3H -.29950 -.22270 -.18231 -.22073 -.22860 -.25991 -.24330 I_3I .49004 .37535 .39323 .27203 .43647 .52790 .48615 I_3J .42516 .41058 .34121 .29822 .37804 .44182 .41969 I_3K .26107 .21984 .22007 .23552 .25883 .24385 .21515 I_3L .42561 .29772 .28733 .26920 .29612 .39499 .33871 I_3M .37748 .28390 .31576 .23743 .32717 .30728 .27516 I_3N .39910 .31207 .34368 .24372 .34897 .33356 .31006 I_3O .42298 .34890 .36292 .33603 .41797 .39903 .36682 I_4A -.28252 -.22846 -.17692 -.16117 -.24318 -.28238 -.23664 I_4B .43314 .38894 .36037 .30735 .40974 .53709 .49448 I_4C .40008 .38335 .39252 .28264 .40241 .34057 .32766 I_4D .39287 .31690 .35310 .27132 .36510 .33171 .31099 I_4E .46489 .41821 .45725 .33946 .48811 .39089 .37879 I_4F .55802 .48255 .46077 .33998 .49388 .49371 .46373 I_4G .44467 .39837 .52165 .28812 .45830 .35287 .35083 I_4H .50663 .40301 .34087 .30823 .40532 .44563 .39634 I_4I .40266 .37543 .39984 .28726 .39006 .31418 .30222 I_4J -.22558 -.23191 -.29469 -.15250 -.26740 -.18153 -.15892 I_4K .27356 .20999 .23960 .25291 .26670 .27115 .24277 I_4L -.20158 -.17056 -.10136 -.17950 -.21735 -.26853 -.21728 I_4M .27147 .25503 .33385 .18174 .24668 .20249 .23966 I_4N -.28067 -.24909 -.24968 -.25961 -.26218 -.29402 -.22374 I_5A -.37556 -.37736 -.34793 -.25968 -.34262 -.33726 -.25979 I_5B -.29937 -.24148 -.23918 -.24186 -.33304 -.31407 -.23327 I_5C .42764 .30456 .34592 .26632 .37056 .36506 .31304 I_5D .21698 .20429 .09957 .18735 .14920 .26653 .22520 I_5E .40071 .29917 .24960 .28345 .31695 .41879 .35085 I_5F .36523 .27857 .37351 .21035 .33560 .29744 .26583 I_5G .14154 .08491 .10705 .12180 .16850 .15495 .14192 I_5H -.24346 -.21482 -.17182 -.19605 -.24522 -.26489 -.20358 I_5I .46289 .34370 .31546 .28724 .37409 .64330 .54225 I_5J -.03370 -.00911 .02249 -.01312 -.02779 -.08020 .04162 I_5K -.10970 -.12583 -.08546 -.06624 -.10437 -.13862 -.13448 I_5L .09491 .05128 .10206 .09062 .09811 .09977 .11425 I_5M .45032 .37571 .33273 .29908 .39966 .49985 .45171
I_2H I_2I I_2J I_2K I_2L I_2M I_2N I_2H 1.00000 I_2I -.14741 1.00000 263 I_2J -.19595 .36878 1.00000 I_2K -.11325 .48874 .22678 1.00000 I_2L .11250 -.02967 -.07046 .03812 1.00000 I_2M .16553 -.13288 -.00599 -.08322 .10977 1.00000 I_2N .19131 -.15084 -.16924 -.10416 .09733 .17859 1.00000 I_3A -.23326 .39298 .41777 .30067 -.04388 -.01687 -.20593 I_3B -.23799 .31320 .35366 .21933 -.01787 .01430 -.10870 I_3C -.29547 .48874 .39131 .34154 -.04872 -.11820 -.20002 I_3D -.13937 .48627 .18775 .71500 .03990 -.09248 -.11769 I_3E -.34824 .33186 .31152 .30052 -.02384 -.10317 -.19161 I_3F -.37941 .27077 .25349 .21749 .00004 -.10475 -.12624 I_3G .24166 -.15393 -.20737 -.11705 .07659 .15084 .43662 I_3H .31205 -.18551 -.13633 -.16863 .13096 .30542 .26652 I_3I -.31225 .35639 .36482 .25077 -.04883 -.05691 -.14826 I_3J -.23365 .34506 .37670 .30284 -.04297 -.03473 -.14246 I_3K -.13697 .28834 .18623 .31841 .02224 -.02335 -.04498 I_3L -.44152 .28792 .26389 .25362 -.03021 -.08972 -.15622 I_3M -.19617 .28452 .23274 .17398 -.00979 -.07913 -.13381 I_3N -.16434 .51931 .27484 .39186 -.01226 -.09850 -.10102 I_3O -.21221 .53604 .31763 .46903 -.03992 -.10086 -.22016 I_4A .29942 -.20139 -.19950 -.17600 .06642 .21586 .20089 I_4B -.27344 .36202 .34238 .27397 .00286 -.09904 -.14996 I_4C -.16652 .34181 .29355 .22305 .03274 -.08213 -.11976 I_4D -.19224 .30926 .31512 .21427 .00475 -.07636 -.12616 I_4E -.22324 .49440 .38847 .33141 -.03246 -.09941 -.20774 I_4F -.26116 .49225 .38529 .33757 -.05735 -.09730 -.24520 I_4G -.22888 .31211 .34785 .22025 -.06851 -.02062 -.17671 I_4H -.26150 .36718 .33039 .27813 .01507 -.11207 -.18237 I_4I -.18787 .34831 .35255 .23225 -.03352 -.07641 -.12126 I_4J .28293 -.12226 -.15698 -.09237 .12841 .17958 .17355 I_4K -.15343 .30592 .18523 .30217 -.04095 -.05794 -.13490 I_4L .29732 -.12249 -.18834 -.11015 .11139 .11349 .13290 I_4M -.12441 .24843 .22923 .16525 -.04139 -.07128 -.12449 I_4N .29294 -.19104 -.20758 -.14380 .02766 .16342 .22694 I_5A .27944 -.24260 -.27608 -.13985 .02913 .20432 .26193 I_5B .28847 -.21294 -.25144 -.13669 .07445 .19115 .15174 I_5C -.24825 .33976 .30556 .24218 -.05260 -.15373 -.17339 I_5D -.18078 .22154 .10689 .27884 -.01001 -.07316 -.06870 I_5E -.34391 .28483 .27014 .30804 -.06890 -.06531 -.16005 I_5F -.19361 .25491 .24195 .17549 -.05094 -.05463 -.15491 I_5G -.02400 .07992 .14644 .12630 .01870 -.02150 -.01958 I_5H .26615 -.14728 -.17843 -.08399 .10492 .23616 .14953 I_5I -.33618 .30522 .31546 .23597 -.04270 -.08862 -.17373 I_5J .08542 -.04684 -.03701 .00960 .00466 .06366 .01106 I_5K .08295 -.20400 -.08943 -.19678 .00641 .14622 .05297 I_5L .00864 .02494 .08338 .07209 .02302 .04445 -.02634 I_5M -.25547 .36199 .30675 .26252 -.02070 -.08852 -.16680
I_3A I_3B I_3C I_3D I_3E I_3F I_3G I_3A 1.00000 I_3B .44296 1.00000 I_3C .50788 .47019 1.00000 I_3D .32748 .27206 .40424 1.00000 I_3E .40406 .48493 .50431 .34374 1.00000 I_3F .32537 .32405 .39510 .27283 .40415 1.00000 I_3G -.22479 -.13043 -.16427 -.09097 -.17235 -.11877 1.00000 I_3H -.18773 -.13408 -.26523 -.18080 -.24214 -.23200 .32679 I_3I .42657 .40565 .47534 .27663 .50738 .42669 -.15651 I_3J .44065 .41107 .48425 .32710 .40732 .36196 -.11003 I_3K .28008 .29958 .25407 .31042 .26666 .21447 -.03699 I_3L .32358 .33113 .38229 .30817 .38457 .56180 -.13534 I_3M .29971 .31886 .37612 .19448 .27562 .31270 -.10415 I_3N .29916 .28969 .42877 .38427 .33687 .30045 -.11943 I_3O .42774 .33384 .46649 .47877 .37611 .33302 -.20670 I_4A -.18286 -.17755 -.26075 -.17642 -.26678 -.18840 .24883 I_4B .41497 .50911 .49472 .31022 .49757 .40815 -.13218 I_4C .35553 .34641 .38756 .25423 .30488 .30461 -.12847 I_4D .33370 .39184 .38602 .24694 .31771 .33129 -.11015 I_4E .43398 .39635 .47672 .34260 .35632 .31289 -.21103 I_4F .49438 .38752 .57197 .37289 .44138 .33918 -.24078 I_4G .37784 .38524 .40977 .25027 .33963 .30860 -.20891 264 I_4H .42646 .36783 .48741 .33886 .42523 .28206 -.21282 I_4I .36686 .36384 .39600 .27437 .29557 .30069 -.16382 I_4J -.18638 -.14251 -.19710 -.07817 -.16909 -.21428 .20429 I_4K .25776 .23234 .26582 .31409 .25016 .24640 -.10678 I_4L -.18185 -.20022 -.21701 -.12366 -.26308 -.23090 .13168 I_4M .24069 .18209 .25356 .17726 .17556 .12149 -.18010 I_4N -.24203 -.22556 -.28440 -.15864 -.26923 -.23789 .23801 I_5A -.31649 -.26659 -.33932 -.15518 -.31895 -.29412 .28247 I_5B -.22818 -.24031 -.29526 -.14085 -.31740 -.25134 .17584 I_5C .32054 .29669 .40729 .25240 .32451 .32083 -.19242 I_5D .19325 .16158 .26771 .30147 .27642 .24428 -.03356 I_5E .32196 .34297 .36545 .32928 .41158 .43396 -.14285 I_5F .29453 .24760 .35341 .20762 .28508 .32128 -.15770 I_5G .14226 .17096 .14007 .12051 .14636 .18037 -.02708 I_5H -.17987 -.15664 -.24173 -.07934 -.23363 -.21727 .19122 I_5I .37643 .45677 .48996 .26283 .61233 .38396 -.16324 I_5J -.02369 -.09794 -.02715 -.02201 -.10498 -.09078 .02044 I_5K -.10893 -.10631 -.16206 -.19598 -.11996 -.15110 .04121 I_5L .10781 .11059 .08035 .08867 .09907 .13729 -.00540 I_5M .41161 .40805 .46921 .30626 .48418 .29144 -.17671
I_3H I_3I I_3J I_3K I_3L I_3M I_3N I_3H 1.00000 I_3I -.19962 1.00000 I_3J -.14981 .45778 1.00000 I_3K -.09339 .23070 .32070 1.00000 I_3L -.25642 .38041 .38249 .32621 1.00000 I_3M -.17341 .30563 .29489 .20516 .31620 1.00000 I_3N -.18293 .27974 .31872 .29575 .31275 .42630 1.00000 I_3O -.23215 .37504 .38700 .37520 .34999 .32036 .56332 I_4A .34633 -.26610 -.18878 -.10808 -.19121 -.15290 -.15564 I_4B -.20153 .48800 .39581 .22933 .36217 .31856 .35113 I_4C -.21137 .31518 .35867 .19108 .23661 .38215 .37461 I_4D -.17554 .32067 .33006 .30433 .27999 .48798 .37190 I_4E -.25081 .39428 .36578 .23813 .29289 .41099 .47993 I_4F -.24452 .49935 .45262 .25751 .34092 .36813 .42557 I_4G -.18603 .36940 .33390 .26555 .29983 .33843 .32260 I_4H -.22366 .40667 .38617 .27569 .30206 .35055 .32846 I_4I -.18989 .35024 .37188 .25587 .27970 .40540 .34402 I_4J .25523 -.17927 -.19476 -.07269 -.20854 -.18656 -.16676 I_4K -.14642 .23107 .23831 .39299 .25799 .24881 .35371 I_4L .27665 -.22416 -.19344 -.10388 -.24434 -.12806 -.06467 I_4M -.16179 .22519 .19139 .17797 .15392 .27166 .28632 I_4N .32557 -.23896 -.21713 -.11738 -.25298 -.24844 -.16866 I_5A .36454 -.29782 -.25584 -.11890 -.22784 -.29691 -.27562 I_5B .30927 -.28574 -.23438 -.13519 -.20466 -.28905 -.23558 I_5C -.25525 .34467 .29919 .19075 .32452 .44009 .47652 I_5D -.12093 .18490 .24551 .22914 .29400 .14317 .22168 I_5E -.26435 .37510 .36905 .32035 .49267 .31498 .29239 I_5F -.19066 .30540 .28650 .17359 .30139 .31368 .30400 I_5G -.03648 .18680 .16517 .21743 .18389 .11031 .07662 I_5H .32381 -.21766 -.18848 -.10794 -.17489 -.23977 -.14021 I_5I -.23528 .49330 .38374 .18695 .36416 .27509 .29421 I_5J .07842 -.05520 -.04400 -.14922 -.07386 -.03976 -.02599 I_5K .16835 -.08750 -.14356 -.05362 -.12307 -.10308 -.21714 I_5L .01839 .13274 .11443 .16779 .14445 .03109 .01978 I_5M -.22795 .42458 .37474 .24222 .30600 .28024 .32265
I_3O I_4A I_4B I_4C I_4D I_4E I_4F I_3O 1.00000 I_4A -.19520 1.00000 I_4B .39448 -.20063 1.00000 I_4C .33151 -.22249 .40933 1.00000 I_4D .38549 -.17088 .38704 .48664 1.00000 I_4E .50422 -.22539 .42288 .52718 .56760 1.00000 I_4F .47271 -.26588 .45747 .46542 .44762 .59387 1.00000 I_4G .36277 -.18481 .39780 .38332 .42063 .48672 .50908 I_4H .40742 -.22561 .41192 .37233 .38975 .44581 .53125 I_4I .36532 -.20750 .38501 .51287 .52199 .51243 .47766 I_4J -.19055 .25025 -.13505 -.18924 -.14883 -.21915 -.21058 265 I_4K .35935 -.13601 .28258 .19261 .25386 .28736 .27996 I_4L -.13550 .39222 -.18793 -.12012 -.16022 -.15349 -.19445 I_4M .25467 -.11489 .20411 .27901 .28804 .40706 .33135 I_4N -.20510 .33320 -.22944 -.22317 -.23474 -.25987 -.27702 I_5A -.31675 .32450 -.29766 -.35468 -.33918 -.38346 -.37524 I_5B -.24942 .31808 -.26098 -.27107 -.33403 -.31322 -.29360 I_5C .35720 -.24954 .34341 .37263 .43034 .49318 .43494 I_5D .24806 -.14590 .20209 .16337 .14648 .16034 .20269 I_5E .37481 -.21946 .34554 .26540 .31024 .32253 .33389 I_5F .31162 -.15677 .29349 .35966 .33224 .37365 .37806 I_5G .15633 -.05985 .16201 .08564 .15405 .09952 .14123 I_5H -.16867 .35418 -.20656 -.22423 -.23210 -.22239 -.23835 I_5I .32655 -.22930 .54818 .28442 .31331 .34750 .43504 I_5J -.01724 .11675 -.05753 -.06052 -.07949 -.00589 -.00173 I_5K -.15863 .13617 -.13343 -.13692 -.14579 -.14693 -.11844 I_5L .10024 .00707 .08761 .02443 .06975 .04407 .07561 I_5M .36081 -.23859 .43838 .33589 .31924 .39860 .46148
I_4G I_4H I_4I I_4J I_4K I_4L I_4M I_4G 1.00000 I_4H .42495 1.00000 I_4I .43346 .43706 1.00000 I_4J -.21741 -.19094 -.17080 1.00000 I_4K .26575 .24305 .24613 -.12538 1.00000 I_4L -.14997 -.17688 -.17505 .23665 -.05244 1.00000 I_4M .29282 .29280 .31881 -.11894 .21611 .00761 1.00000 I_4N -.24984 -.22166 -.23344 .32541 -.10549 .38440 -.07120 I_5A -.31335 -.29697 -.31082 .32523 -.15250 .33115 -.16973 I_5B -.24672 -.26218 -.28933 .35345 -.16581 .38867 -.13741 I_5C .32712 .35370 .39709 -.21143 .25691 -.21200 .32778 I_5D .12111 .21615 .12786 -.08380 .18682 -.17287 .10573 I_5E .32570 .35651 .26709 -.17940 .33206 -.25882 .18055 I_5F .32736 .34328 .33752 -.45117 .20874 -.17322 .20144 I_5G .13130 .15809 .11051 .02571 .24539 -.02166 .11451 I_5H -.16493 -.22496 -.24305 .26594 -.11962 .38075 -.12928 I_5I .32353 .43645 .30047 -.14094 .25491 -.22238 .16462 I_5J -.05174 -.04898 -.07848 .03899 -.13970 .19437 .01205 I_5K -.06139 -.10357 -.09490 .15626 -.11399 .12734 -.08317 I_5L .08975 .07374 .05076 .07163 .19551 .05130 .08941 I_5M .33189 .64388 .34419 -.16545 .22866 -.20954 .25041
I_4N I_5A I_5B I_5C I_5D I_5E I_5F I_4N 1.00000 I_5A .42877 1.00000 I_5B .40739 .53734 1.00000 I_5C -.24627 -.33653 -.32756 1.00000 I_5D -.14093 -.15324 -.09981 .23499 1.00000 I_5E -.23159 -.27843 -.24840 .31303 .38408 1.00000 I_5F -.19747 -.27364 -.25806 .38653 .21011 .35320 1.00000 I_5G -.04708 .00376 -.05153 .12621 .13649 .27101 .10615 I_5H .35305 .39717 .49997 -.27971 -.07782 -.18378 -.17809 I_5I -.24183 -.26063 -.29618 .34607 .20997 .36908 .27325 I_5J .18130 .15952 .22211 -.03879 .01163 -.11275 .00258 I_5K .17619 .20972 .24350 -.15304 -.14269 -.08949 -.13989 I_5L .01136 .03591 .06042 .04787 .07561 .18607 .02511 I_5M -.19526 -.25513 -.25703 .33521 .22977 .32856 .32398
I_5G I_5H I_5I I_5J I_5K I_5L I_5M I_5G 1.00000 I_5H .02648 1.00000 I_5I .20412 -.23303 1.00000 I_5J -.14194 .22237 -.08653 1.00000 I_5K .00318 .19113 -.11632 .14192 1.00000 I_5L .64248 .07544 .13499 -.11540 .10187 1.00000 I_5M .20314 -.22987 .60255 -.05890 -.13410 .16243 1.00000
Extraction 1 for analysis 1, Principal Components Analysis (PC)
266 ------F A C T O R A N A L Y S I S ------
267 Initial Statistics:
Variable Communality * Factor Eigenvalue Pct of Var Cum Pct * I_1A 1.00000 * 1 20.28428 29.0 29.0 I_1B 1.00000 * 2 3.16968 4.5 33.5 I_1C 1.00000 * 3 2.53426 3.6 37.1 I_1D 1.00000 * 4 2.27544 3.3 40.4 I_1E 1.00000 * 5 1.90237 2.7 43.1 I_1F 1.00000 * 6 1.73948 2.5 45.6 I_1G 1.00000 * 7 1.45607 2.1 47.7 I_1H 1.00000 * 8 1.38525 2.0 49.6 I_1I 1.00000 * 9 1.22485 1.7 51.4 I_1J 1.00000 * 10 1.16759 1.7 53.1 I_1K 1.00000 * 11 1.10980 1.6 54.6 I_1L 1.00000 * 12 1.02730 1.5 56.1 I_1M 1.00000 * 13 1.01748 1.5 57.6
I_1N 1.00000 * 14 .98009 1.4 59.0 I_2A 1.00000 * 15 .90769 1.3 60.3 I_2B 1.00000 * 16 .88659 1.3 61.5 I_2C 1.00000 * 17 .85652 1.2 62.7 I_2D 1.00000 * 18 .82640 1.2 63.9 I_2E 1.00000 * 19 .80714 1.2 65.1 I_2F 1.00000 * 20 .79285 1.1 66.2 I_2G 1.00000 * 21 .77782 1.1 67.3 I_2H 1.00000 * 22 .75530 1.1 68.4 I_2I 1.00000 * 23 .73690 1.1 69.5 I_2J 1.00000 * 24 .72842 1.0 70.5 I_2K 1.00000 * 25 .70838 1.0 71.5 I_2L 1.00000 * 26 .68002 1.0 72.5 I_2M 1.00000 * 27 .66246 .9 73.4 I_2N 1.00000 * 28 .65611 .9 74.4 I_3A 1.00000 * 29 .62698 .9 75.3 I_3B 1.00000 * 30 .61318 .9 76.1 I_3C 1.00000 * 31 .60861 .9 77.0 I_3D 1.00000 * 32 .60485 .9 77.9 I_3E 1.00000 * 33 .58232 .8 78.7 I_3F 1.00000 * 34 .58119 .8 79.5 I_3G 1.00000 * 35 .57369 .8 80.4 I_3H 1.00000 * 36 .56269 .8 81.2 I_3I 1.00000 * 37 .54876 .8 81.9 I_3J 1.00000 * 38 .53654 .8 82.7 I_3K 1.00000 * 39 .53226 .8 83.5 I_3L 1.00000 * 40 .50707 .7 84.2 I_3M 1.00000 * 41 .50414 .7 84.9 I_3N 1.00000 * 42 .48984 .7 85.6 I_3O 1.00000 * 43 .47811 .7 86.3 I_4A 1.00000 * 44 .46842 .7 87.0 I_4B 1.00000 * 45 .46368 .7 87.6 I_4C 1.00000 * 46 .45928 .7 88.3 I_4D 1.00000 * 47 .45121 .6 88.9 I_4E 1.00000 * 48 .43999 .6 89.6 I_4F 1.00000 * 49 .43111 .6 90.2 I_4G 1.00000 * 50 .42662 .6 90.8 I_4H 1.00000 * 51 .41395 .6 91.4 I_4I 1.00000 * 52 .39888 .6 91.9 I_4J 1.00000 * 53 .39134 .6 92.5 I_4K 1.00000 * 54 .38656 .6 93.1 I_4L 1.00000 * 55 .37164 .5 93.6 I_4M 1.00000 * 56 .36512 .5 94.1 I_4N 1.00000 * 57 .35079 .5 94.6 I_5A 1.00000 * 58 .34896 .5 95.1 I_5B 1.00000 * 59 .34087 .5 95.6 Variable Communality * Factor Eigenvalue Pct of Var Cum Pct * I_5C 1.00000 * 60 .32509 .5 96.1 I_5D 1.00000 * 61 .32433 .5 96.5 I_5E 1.00000 * 62 .31249 .4 97.0 I_5F 1.00000 * 63 .30561 .4 97.4 I_5G 1.00000 * 64 .30072 .4 97.8 268 I_5H 1.00000 * 65 .28702 .4 98.2 I_5I 1.00000 * 66 .27311 .4 98.6 I_5J 1.00000 * 67 .25843 .4 99.0 I_5K 1.00000 * 68 .24295 .3 99.3 I_5L 1.00000 * 69 .23557 .3 99.7 I_5M 1.00000 * 70 .21953 .3 100.0
------F A C T O R A N A L Y S I S ------
PC extracted 13 factors.
VARIMAX rotation 1 for extraction 1 in analysis 1 - Kaiser Normalization.
VARIMAX converged in 10 iterations.
269 Rotated Factor Matrix:
Factor 1 Factor 2 Factor 3 Factor 4 Factor 5
I_5I .78513 I_1H .76147 I_2F .74750 I_1I .74048 I_3E .68318 I_5M .64066 I_2G .64029 .38232 I_1N .63083 I_4B .60019 .32269 I_1D .54827 .32482 I_3C .54264 .30491 I_3I .53171 I_3B .52722 I_4H .48633 .38638 I_2A .48012 .31882 .37099 I_3A .40572 .36489 I_3J .38169
I_4D .69007 I_4E .65698 I_4I .65156 I_3M .61499 I_4C .61013 I_5C .54845 I_4M .50018 I_3N .48021 .34605 I_4F .42562 .48015 I_4G .46372 .30484
I_2E .31405 .59566 I_2D .56499 I_1K .55832 I_1G .54268 I_2B .31935 .53658 I_2C .36634 .51323 I_1E .33491 .49122 I_2J .46440 I_1L .44849
I_5H .66098 I_4L .66025 I_5B .65429 I_4N .58890 I_4A .55544 I_5A .54966 I_5J .46421 I_3H .38905
I_2K .76994 I_3D .75628 I_2I .32013 .54086 I_3O .32755 .45948 I_5D .44143 I_1A .37311 .37497
I_3L I_3F .30155 I_2H I_1J .45882 I_5E Factor 1 Factor 2 Factor 3 Factor 4 Factor 5
I_1B .34856 I_1C I_3K 270 I_4K
I_3G I_2N
I_5G I_5L
I_4J .31232 I_5F .33482
I_1M I_5K
I_2M
I_2L
I_1F .34721
271 Factor 6 Factor 7 Factor 8 Factor 9 Factor 10
I_5I I_1H I_2F I_1I I_3E I_5M I_2G I_1N I_4B I_1D I_3C I_3I I_3B I_4H I_2A I_3A I_3J
I_4D I_4E I_4I I_3M I_4C I_5C I_4M I_3N I_4F I_4G
I_2E I_2D I_1K I_1G I_2B I_2C I_1E I_2J I_1L
I_5H I_4L
I_5B I_4N I_4A I_5A I_5J I_3H .34251 I_2K I_3D I_2I I_3O .32306 I_5D .31648 I_1A
I_3L .69406 I_3F .63847 I_2H -.54682 I_1J .54244 I_5E .49822
Factor 6 Factor 7 Factor 8 Factor 9 Factor 10
I_1B .70626 I_1C .69224 I_3K .65464 272 I_4K .46327
I_3G .75557 I_2N .74610
I_5G .85196 I_5L .84870
I_4J -.67927 I_5F .67340
I_1M I_5K
I_2M
I_2L I_1F
273 Factor 11 Factor 12 Factor 13
I_5I I_1H I_2F I_1I I_3E I_5M I_2G I_1N I_4B I_1D I_3C I_3I I_3B .31582 I_4H I_2A I_3A I_3J
I_4D I_4E I_4I I_3M I_4C I_5C I_4M I_3N I_4F I_4G
I_2E I_2D I_1K I_1G I_2B I_2C I_1E I_2J I_1L
I_5H I_4L I_5B I_4N I_4A I_5A I_5J I_3H .34559 I_2K I_3D I_2I I_3O I_5D I_1A -.30983
I_3L I_3F I_2H I_1J I_5E
I_1B I_1C Factor 11 Factor 12 Factor 13
I_3K I_4K
I_3G I_2N 274 I_5G I_5L
I_4J I_5F
I_1M .68779 I_5K -.67369
I_2M .53476
I_2L .77626 I_1F .44449
275 Factor Transformation Matrix:
Factor 1 Factor 2 Factor 3 Factor 4 Factor 5
Factor 1 .60134 .43884 .38733 -.26018 .27352 Factor 2 .12255 .02222 .06034 .73231 .30310 Factor 3 -.53483 .65106 .27332 .10909 .09796 Factor 4 .38622 .07990 .32272 .30506 - .51511 Factor 5 -.21974 .15054 .06232 -.18677 - .52261 Factor 6 .13955 .33848 -.34766 -.13860 - .19583 Factor 7 -.19429 .13769 -.07790 .33972 - .02891 Factor 8 -.17696 -.38416 .53507 -.09210 - .01163 Factor 9 -.07835 -.10380 .38318 .06014 - .03440 Factor 10 -.09604 -.10554 .06905 -.24997 .45203 Factor 11 -.08251 -.11893 .23775 .02387 - .16258 Factor 12 -.16120 .18406 .16941 -.16124 .08321 Factor 13 .05646 -.05431 .11638 -.14289 - .08411
Factor 6 Factor 7 Factor 8 Factor 9 Factor 10
Factor 1 .25227 .20571 -.13910 .06648 .12773 Factor 2 -.05606 .33069 .28276 .28183 - .16610 Factor 3 -.35255 .00690 -.09089 -.19387 .14023 Factor 4 -.17425 -.48044 -.02040 -.10691 - .01451 Factor 5 .16477 .32975 .04178 .61336 .04268 Factor 6 .04770 -.05194 .59987 -.19831 - .04271 Factor 7 .74795 -.23381 .00220 -.06436 .41142 Factor 8 .21128 .26905 .26236 -.44593 .06297 Factor 9 -.04275 -.26000 .33720 .32053 .01394 Factor 10 -.05833 -.50426 .14198 .36600 .21020 Factor 11 .04053 -.00675 -.35744 .10741 - .12324 Factor 12 .28522 -.17663 .19572 -.01832 - .74719 Factor 13 -.23842 .16081 .40208 .00536 .37614
Factor 11 Factor 12 Factor 13
Factor 1 .07185 -.02196 -.03992 Factor 2 -.01843 .16912 .15586 Factor 3 .02983 .05440 -.01326 Factor 4 -.23830 .22626 .02626 276 Factor 5 -.14529 .27393 -.07319 Factor 6 .45010 .21654 .17909 Factor 7 -.07518 -.14309 .08568 Factor 8 .03526 .36598 -.04824 Factor 9 .43916 -.42741 -.41213 Factor 10 -.07957 .44274 .22298 Factor 11 .50526 -.07917 .69122 Factor 12 -.33252 -.18696 .15333 Factor 13 -.37572 -.46957 .45308
277 Factor Loadings
Factor 1 Leader facilitation and support loading Item# Item 5i. Overall I think my immediate supervisor is doing a good job. .79 1h. Your supervisor is friendly and easy to approach. .76 2f. Your supervisor is attentive to what you say. .75 1i. Your supervisor offers new ideas for job and related problems. .74 3e. Your supervisor sets an example by working hard himself or herself. .68 5m. Your immediate supervisor is successful in dealing with higher levels of management .64 2g. Your supervisor provides the help you need to schedule your work ahead of time. .64 1n. Your supervisor encourages the people who work for him or her to exchange ideas and opinions. .63 4b. Your supervisor encourages the people who work for them to work as a team. .60 1d. Staff members generally trust their supervisors. .55 3c. The ideas and suggestions of staff members are paid attention to. .54 3i. Supervisors generally know what is going on in their work groups. .53 3b. Your superior emphasises high standards of performance. .53 4h. Your manager is successful in his dealing with higher levels of management. .49 2a. Staff members generally trust their managers. .48 3a. You have good information on where you stand and how your performance is evaluated. .41 3j. You are aware of how well your work group is meeting its objectives. .38
Factor 2 Professional and organisational esprit 4d. The hotel strives to do a better job than other hotels of the same type. .69 4e. The hotel emphasises personal growth and development. .66 4i. The objectives of the hotel are clearly defined. .65 3m. The hotel has a good image to outsiders. .62 4c. It is possible to get accurate information on the policies and objectives of this hotel. .61 5c. Under most circumstances I would recommend this hotel to a prospective staff member. .55 4m. This hotel is concerned with assisting the local community. .50 3n. Working in this hotel is beneficial to your career. .48 4f. Managers keep well informed about the needs and problems of employees. .48 4g. Discipline in this hotel is maintained consistently. .46
Factor 3 Conflict and ambiguity 2e. Procedures are designed so that resources are used efficiently. .60 2d. You have opportunities to complete the work you start. .57 1k. Your job responsibilities are clearly defined. .56 1g. You are able to get the money, supplies, equipment, etc. your work group needs to do its work well. .54 2b. You are given advanced information about changes which might affect you. .54 2c. The hotel’s policies are consistently applied to all staff members. .51 1e. The methods of your work are kept up to date. .49 2j. New staff members get on-the-job training they need. .46 1l. Responsibility is assigned so that individuals have authority within their own area. .45
278 Factor 4 Regulations, organisation and pressure 5h. Excessive rules and regulations interfere with how well I am able to do my job. .66 4l. The way your work group is organised hinders the efficient conduct of work. .66 5b. In this hotel things are planned so that everyone is getting in each others’ way. .65 4n. Things in this hotel seem to happen contrary to rules and regulations. .59 4a. Communication is hindered by following chain of command rules. .56 5a. In this hotel the only source of information on important matters is the grapevine. .55 5j. Compared with other work groups, my work group is under much less pressure to produce. .46 3h. People act as though everyone must be watched or they will slacken off. .39
Factor 5 Job variety, challenge and autonomy 2k. There is variety in your job. .77 3d. You have the opportunity to do a number of different things in your job. .76 2i. You have opportunities to learn worthwhile skills and knowledge in your job. .54 3o. You have opportunities to make full use of your knowledge and skills in your job. .46 5d. Most of the personnel in my department would not want to change to another department. .44 1a. Opportunity for independent thought and action exists in your job. .37
Factor 6 Workgroup co-operation, friendliness and warmth 3l. Members of your work group trust each other. .69 3f. A friendly atmosphere prevails among most of the members of your workgroup. .64 2h. There is friction in your workgroup. -.55 1j. A spirit of cooperation exists in your workgroup. .54 5e. Most members of my work group take pride in their jobs. .50
Factor 7 Job standards 1b. Your job requires a high level of skill and training. .71 1c. You are required to meet rigid standards of quality in your work. .69 3k. Your job demands precision. .66 4k. Your work is important. .46
279 4. Oneway ANOVAs of new Organisational Climate Dimensions and Composite Measure of Organisational Climate
- - - - - O N E W A Y - - - - -
Variable F1 By Variable HOTEL hotel code
Analysis of Variance
Sum of Mean F F Source D.F. Squares Squares Ratio Prob.
Between Groups 12 133.4169 11.1181 11.0268 .0000 Within Groups 1566 1578.9603 1.0083 Total 1578 1712.3772
Variable F2 By Variable HOTEL hotel code
Analysis of Variance
Sum of Mean F F Source D.F. Squares Squares Ratio Prob.
Between Groups 12 130.8890 10.9074 12.7682 .0000 Within Groups 1612 1377.0711 .8543 Total 1624 1507.9600
Variable F3 By Variable HOTEL hotel code
Analysis of Variance
Sum of Mean F F Source D.F. Squares Squares Ratio Prob.
Between Groups 12 75.6003 6.3000 6.3853 .0000 Within Groups 1626 1604.2743 .9866 Total 1638 1679.8745
Variable F4 By Variable HOTEL hotel code
Analysis of Variance
Sum of Mean F F Source D.F. Squares Squares Ratio Prob.
Between Groups 12 133.4651 11.1221 11.8983 .0000 Within Groups 1596 1491.8868 .9348 Total 1608 1625.3519
280 - - - - - O N E W A Y - - - - -
Variable F5 By Variable HOTEL hotel code
Analysis of Variance
Sum of Mean F F Source D.F. Squares Squares Ratio Prob.
Between Groups 12 111.4105 9.2842 6.9393 .0000 Within Groups 1632 2183.4695 1.3379 Total 1644 2294.8800
Variable F6 By Variable HOTEL hotel code
Analysis of Variance
Sum of Mean F F Source D.F. Squares Squares Ratio Prob.
Between Groups 12 67.5928 5.6327 10.5933 .0000 Within Groups 1641 872.5589 .5317 Total 1653 940.1517
- - - - - O N E W A Y - - - - -
Variable F7 By Variable HOTEL hotel code
Analysis of Variance
Sum of Mean F F Source D.F. Squares Squares Ratio Prob.
Between Groups 12 79.7824 6.6485 7.6118 .0000 Within Groups 1650 1441.1894 .8734 Total 1662 1520.9718
Variable CLIMATE By Variable HOTEL hotel code
Analysis of Variance
Sum of Mean F F Source D.F. Squares Squares Ratio Prob.
Between Groups 12 33.0528 2.7544 7.5921 .0000 Within Groups 1367 495.9426 .3628 Total 1379 528.9954
281 Appendix F
Model Testing
282 Part 1 Structural Equation Model A
283 Multiple Linear Regression
- - Correlation Coefficients - -
CLIMATE GENDER AGE EDUCAT LENGTH_S LENGTH_J GRS_SAL HOURS MODEMPL TRAINSES
CLIMATE 1.0000 .0207 -.0193 -.0399 -.0214 -.0338 .1074 .1475 -.0904 -.1198 ( 1443) ( 1418) ( 1410) ( 1397) ( 1417) ( 1408) ( 1367) ( 1405) ( 1398) ( 1360) P= . P= .436 P= .469 P= .136 P= .421 P= .204 P= .000 P= .000 P= .001 P= .000
GENDER .0207 1.0000 -.0439 -.0328 -.0725 -.0837 -.1829 -.1361 .1111 -.0915 ( 1418) ( 1741) ( 1723) ( 1701) ( 1730) ( 1714) ( 1656) ( 1708) ( 1706) ( 1647) P= .436 P= . P= .068 P= .176 P= .003 P= .001 P= .000 P= .000 P= .000 P= .000
AGE -.0193 -.0439 1.0000 -.2350 .4440 .4114 .2147 .0758 -.2307 .1537 ( 1410) ( 1723) ( 1731) ( 1695) ( 1724) ( 1708) ( 1649) ( 1701) ( 1695) ( 1640) P= .469 P= .068 P= . P= .000 P= .000 P= .000 P= .000 P= .002 P= .000 P= .000
EDUCAT -.0399 -.0328 -.2350 1.0000 -.1501 -.1367 .0513 -.0048 .0657 -.0629 ( 1397) ( 1701) ( 1695) ( 1711) ( 1704) ( 1687) ( 1635) ( 1684) ( 1677) ( 1623) P= .136 P= .176 P= .000 P= . P= .000 P= .000 P= .038 P= .844 P= .007 P= .011
LENGTH_S -.0214 -.0725 .4440 -.1501 1.0000 .7285 .2902 .1847 -.3123 .1194 ( 1417) ( 1730) ( 1724) ( 1704) ( 1739) ( 1720) ( 1657) ( 1711) ( 1706) ( 1648) P= .421 P= .003 P= .000 P= .000 P= . P= .000 P= .000 P= .000 P= .000 P= .000
LENGTH_J -.0338 -.0837 .4114 -.1367 .7285 1.0000 .2158 .0894 -.2423 .1610 ( 1408) ( 1714) ( 1708) ( 1687) ( 1720) ( 1726) ( 1658) ( 1706) ( 1701) ( 1644) P= .204 P= .001 P= .000 P= .000 P= .000 P= . P= .000 P= .000 P= .000 P= .000
GRS_SAL .1074 -.1829 .2147 .0513 .2902 .2158 1.0000 .6509 -.5637 .0399 ( 1367) ( 1656) ( 1649) ( 1635) ( 1657) ( 1658) ( 1669) ( 1651) ( 1639) ( 1595) P= .000 P= .000 P= .000 P= .038 P= .000 P= .000 P= . P= .000 P= .000 P= .111
HOURS .1475 -.1361 .0758 -.0048 .1847 .0894 .6509 1.0000 -.6566 .0029 ( 1405) ( 1708) ( 1701) ( 1684) ( 1711) ( 1706) ( 1651) ( 1727) ( 1710) ( 1645) P= .000 P= .000 P= .002 P= .844 P= .000 P= .000 P= .000 P= . P= .000 P= .907
MODEMPL -.0904 .1111 -.2307 .0657 -.3123 -.2423 -.5637 -.6566 1.0000 -.0619 ( 1398) ( 1706) ( 1695) ( 1677) ( 1706) ( 1701) ( 1639) ( 1710) ( 1719) ( 1638) P= .001 P= .000 P= .000 P= .007 P= .000 P= .000 P= .000 P= .000 P= . P= .012
TRAINSES -.1198 -.0915 .1537 -.0629 .1194 .1610 .0399 .0029 -.0619 1.0000 ( 1360) ( 1647) ( 1640) ( 1623) ( 1648) ( 1644) ( 1595) ( 1645) ( 1638) ( 1663) P= .000 P= .000 P= .000 P= .011 P= .000 P= .000 P= .111 P= .907 P= .012 P= .
(Coefficient / (Cases) / 2-tailed Significance) ‘ . ‘ is printed if a coefficient cannot be computed
284 * * * * M U L T I P L E R E G R E S S I O N * * * *
Listwise Deletion of Missing Data
Equation Number 1 Dependent Variable.. CLIMATE
Block Number 1. Method: Enter GENDER AGE EDUCAT LENGTH_S LENGTH_J GRS_SAL HOURS MODEMPL TRAINSES
Variable(s) Entered on Step Number 1.. TRAINSES last training session 2.. GRS_SAL current gross salary 3.. EDUCAT education level 4.. GENDER gender 5.. LENGTH_J length of job 6.. AGE age 7.. MODEMPL mode of employment 8.. LENGTH_S length of service 9.. HOURS hours worked per week
Multiple R .21227 Analysis of Variance R Square .04506 DF Sum of Squares Mean Square Adjusted R Square .03821 Regression 9 20.92228 2.32470 Standard Error .59464 Residual 1254 443.40578 .35359
F = 6.57450 Signif F = .0000
------Variables in the Equation ------
Variable B SE B Beta T Sig T
GENDER .047572 .034458 .039192 1.381 .1677 AGE -.006936 .018501 -.012163 -.375 .7078 EDUCAT -.017371 .010754 -.046347 -1.615 .1065 LENGTH_S -.013752 .024284 -.023310 -.566 .5713 LENGTH_J -.008069 .025006 -.012781 -.323 .7470 GRS_SAL .021161 .013775 .061422 1.536 .1248 HOURS .041617 .013458 .132591 3.092 .0020 MODEMPL -8.69224E-04 .026909 -.001276 -.032 .9742 TRAINSES -.050640 .013198 -.108827 -3.837 .0001 (Constant) 4.605277 .156497 29.427 .0000
End Block Number 1 All requested variables entered.
285 Thu Mar 16 15:53:40 2000
Amos Version 3.61 (w32)
by James L. Arbuckle
Copyright 1994-1997 SmallWaters Corporation 1507 E. 53rd Street - #452 Chicago, IL 60615 USA 773-667-8635 Fax: 773-955-6252 http://www.smallwaters.com
******************************************** * Structural Model A * *------* * * ********************************************
Serial number 55501773
Structural Model A Page 1
User-selected options
286 ------
Output:
Maximum Likelihood
Output format options:
Compressed output
Minimization options:
Technical output Machine-readable output file
Sample size: 1207
Your model contains the following variables
climate observed endogenous cs_over observed endogenous
gender observed exogenous age observed exogenous educat observed exogenous length_s observed exogenous length_j observed exogenous grs_sal observed exogenous hours observed exogenous modempl observed exogenous trainses observed exogenous
other2 unobserved exogenous other unobserved exogenous
Number of variables in your model: 13 Number of observed variables: 11 Number of unobserved variables: 2 Number of exogenous variables: 11 Number of endogenous variables: 2
Summary of Parameters
Weights Covariances Variances Means Intercepts Total ------Fixed: 2 0 0 0 0 2 Labeled: 0 0 0 0 0 0 Unlabeled: 10 36 11 0 0 57 ------Total: 12 36 11 0 0 59
The model is recursive.
Model: Your_model Computation of Degrees of Freedom
Number of distinct sample moments: 66 Number of distinct parameters to be estimated: 57 ------Degrees of freedom: 9
287 Minimization History
0e 8 0.0e+00 -4.1849e-01 1.00e+04 3.36432295221e+03 0 1.00e+04 1e 0 1.9e+01 0.0000e+00 8.71e-01 8.91386526368e+02 18 1.02e+00 2e 0 1.2e+02 0.0000e+00 4.47e-01 5.93916156150e+02 2 0.00e+00 3e 0 5.3e+01 0.0000e+00 4.47e-01 1.91502709126e+02 1 1.24e+00 4e 0 7.3e+01 0.0000e+00 3.75e-01 7.69821486886e+01 1 1.20e+00 5e 0 1.1e+02 0.0000e+00 2.25e-01 5.91740289912e+01 1 1.12e+00 6e 0 1.2e+02 0.0000e+00 6.10e-02 5.84641179855e+01 1 1.03e+00 7e 0 1.2e+02 0.0000e+00 3.53e-03 5.84623686521e+01 1 1.00e+00 8e 0 1.2e+02 0.0000e+00 1.10e-05 5.84623686370e+01 1 1.00e+00
Minimum was achieved
Chi-square = 58.462 Degrees of freedom = 9 Probability level = 0.000
Maximum Likelihood Estimates ------
Regression Weights: Estimate S.E. C.R. Label ------
climate <------gender 0.055 0.035 1.585 climate <------age -0.006 0.019 -0.336 climate <------educat -0.016 0.011 -1.448 climate <----- length_s -0.013 0.024 -0.545 climate <----- length_j -0.011 0.025 -0.416 climate <------grs_sal 0.021 0.014 1.477 climate <------hours 0.039 0.013 2.854 climate <------modempl -0.012 0.027 -0.433 climate <----- trainses -0.051 0.014 -3.778 cs_over <------climate 0.478 0.030 16.088
Covariances: Estimate S.E. C.R. Label ------
modempl <----> trainses -0.042 0.033 -1.275 hours <------> trainses -0.063 0.072 -0.866 grs_sal <----> trainses 0.023 0.065 0.353 length_j <---> trainses 0.229 0.035 6.549 length_s <---> trainses 0.174 0.037 4.681 educat <-----> trainses -0.165 0.060 -2.735 age <------> trainses 0.219 0.040 5.542 gender <-----> trainses -0.039 0.019 -2.129 hours <------> modempl -1.173 0.061 -19.393 grs_sal <-----> modempl -0.918 0.052 -17.492 length_j <----> modempl -0.184 0.024 -7.511 length_s <----> modempl -0.278 0.027 -10.357 educat <------> modempl 0.094 0.042 2.250 age <------> modempl -0.227 0.028 -8.129 gender <------> modempl 0.052 0.013 4.006 grs_sal <------> hours 2.322 0.119 19.458 length_j <------> hours 0.227 0.052 4.333 length_s <------> hours 0.396 0.057 6.952 educat <------> hours -0.015 0.091 -0.167 age <------> hours 0.226 0.059 3.800
288 gender <------> hours -0.144 0.028 -5.101 length_j <----> grs_sal 0.387 0.048 8.010 length_s <----> grs_sal 0.561 0.053 10.590 educat <------> grs_sal 0.129 0.082 1.572 age <------> grs_sal 0.418 0.055 7.644 gender <------> grs_sal -0.190 0.026 -7.320 length_s <---> length_j 0.625 0.032 19.449 educat <-----> length_j -0.167 0.044 -3.835 age <------> length_j 0.386 0.030 12.760 gender <-----> length_j -0.049 0.013 -3.641 educat <-----> length_s -0.225 0.047 -4.807 age <------> length_s 0.460 0.033 13.947 gender <-----> length_s -0.044 0.014 -3.096 age <------> educat -0.368 0.050 -7.320 gender <------> educat -0.010 0.023 -0.426 gender <------> age -0.038 0.015 -2.524
Variances: Estimate S.E. C.R. Label ------
gender 0.249 0.010 24.556 age 1.108 0.045 24.556 educat 2.626 0.107 24.556 length_s 0.991 0.040 24.556 length_j 0.861 0.035 24.556 grs_sal 3.102 0.126 24.556 hours 3.800 0.155 24.556 modempl 0.800 0.033 24.556 trainses 1.655 0.067 24.556 other2 0.345 0.014 24.556 other 0.385 0.016 24.556
Summary of models ------
Model NPAR CMIN DF P CMIN/DF ------Your_model 57 58.462 9 0.000 6.496 Saturated model 66 0.000 0 Independence model 11 3332.678 55 0.000 60.594
Model RMR GFI AGFI PGFI ------Your_model 0.039 0.991 0.937 0.135 Saturated model 0.000 1.000 Independence model 0.390 0.637 0.564 0.531
DELTA1 RHO1 DELTA2 RHO2 Model NFI RFI IFI TLI CFI ------Your_model 0.982 0.893 0.985 0.908 0.985 Saturated model 1.000 1.000 1.000 Independence model 0.000 0.000 0.000 0.000 0.000
Model PRATIO PNFI PCFI ------Your_model 0.164 0.161 0.161
289 Saturated model 0.000 0.000 0.000 Independence model 1.000 0.000 0.000
Model NCP LO 90 HI 90 ------Your_model 49.462 28.937 77.483 Saturated model 0.000 0.000 0.000 Independence model 3277.678 3092.130 3470.521
Model FMIN F0 LO 90 HI 90 ------Your_model 0.048 0.041 0.024 0.064 Saturated model 0.000 0.000 0.000 0.000 Independence model 2.763 2.718 2.564 2.878
Model RMSEA LO 90 HI 90 PCLOSE ------Your_model 0.068 0.052 0.084 0.035 Independence model 0.222 0.216 0.229 0.000
Model AIC BCC BIC CAIC ------Your_model 172.462 173.608 599.608 519.928 Saturated model 132.000 133.327 626.590 534.329 Independence model 3354.678 3354.899 3437.109 3421.733
Model ECVI LO 90 HI 90 MECVI ------Your_model 0.143 0.126 0.166 0.144 Saturated model 0.109 0.109 0.109 0.111 Independence model 2.782 2.628 2.942 2.782
HOELTER HOELTER Model .05 .01 ------Your_model 350 447 Independence model 27 30
Execution time summary:
Minimization: 0.380 Miscellaneous: 0.149 Bootstrap: 0.000 Total: 0.529
290 Part 2 Structural Equation Model B
291 - - Correlation Coefficients - -
CS_OVER F1 F2 F3 F4 F5 F6 F7
CS_OVER 1.0000 .3859 .5337 .4116 -.3088 .2928 .2940 .2467 ( 1686) ( 1565) ( 1609) ( 1619) ( 1597) ( 1634) ( 1636) ( 1647) P= . P= .000 P= .000 P= .000 P= .000 P= .000 P= .000 P= .000
F1 .3859 1.0000 .7021 .7407 -.4852 .6388 .6066 .4491 ( 1565) ( 1650) ( 1602) ( 1598) ( 1585) ( 1612) ( 1621) ( 1626) P= .000 P= . P= .000 P= .000 P= .000 P= .000 P= .000 P= .000
F2 .5337 .7021 1.0000 .7096 -.4526 .6077 .4904 .4372 ( 1609) ( 1602) ( 1700) ( 1636) ( 1632) ( 1653) ( 1658) ( 1673) P= .000 P= .000 P= . P= .000 P= .000 P= .000 P= .000 P= .000
F3 .4116 .7407 .7096 1.0000 -.4342 .5743 .5002 .4268 ( 1619) ( 1598) ( 1636) ( 1711) ( 1623) ( 1660) ( 1664) ( 1673) P= .000 P= .000 P= .000 P= . P= .000 P= .000 P= .000 P= .000
F4 -.3088 -.4852 -.4526 -.4342 1.0000 -.3449 -.3048 -.2506 ( 1597) ( 1585) ( 1632) ( 1623) ( 1682) ( 1638) ( 1644) ( 1655) P= .000 P= .000 P= .000 P= .000 P= . P= .000 P= .000 P= .000
F5 .2928 .6388 .6077 .5743 -.3449 1.0000 .5265 .5571 ( 1634) ( 1612) ( 1653) ( 1660) ( 1638) ( 1717) ( 1680) ( 1686) P= .000 P= .000 P= .000 P= .000 P= .000 P= . P= .000 P= .000
F6 .2940 .6066 .4904 .5002 -.3048 .5265 1.0000 .4176 ( 1636) ( 1621) ( 1658) ( 1664) ( 1644) ( 1680) ( 1728) ( 1693) P= .000 P= .000 P= .000 P= .000 P= .000 P= .000 P= . P= .000
F7 .2467 .4491 .4372 .4268 -.2506 .5571 .4176 1.0000 ( 1647) ( 1626) ( 1673) ( 1673) ( 1655) ( 1686) ( 1693) ( 1740) P= .000 P= .000 P= .000 P= .000 P= .000 P= .000 P= .000 P= .
(Coefficient / (Cases) / 2-tailed Significance) ‘ . ‘ is printed if a coefficient cannot be computed
292 * * * * M U L T I P L E R E G R E S S I O N * * * *
Listwise Deletion of Missing Data
Equation Number 1 Dependent Variable.. CS_OVER cust satis - overall
Block Number 1. Method: Enter F1 F2 F3 F4 F5 F6 F7
Variable(s) Entered on Step Number 1.. F7 2.. F4 3.. F6 4.. F3 5.. F5 6.. F2 7.. F1
Multiple R .54734 Analysis of Variance R Square .29958 DF Sum of Squares Mean Square Adjusted R Square .29601 Regression 7 200.14226 28.59175 Standard Error .58358 Residual 1374 467.94240 .34057
F = 83.95278 Signif F = .0000
------Variables in the Equation ------
Variable B SE B Beta T Sig T
F1 -.009840 .027004 -.014670 -.364 .7156 F2 .350973 .025594 .489812 13.713 .0000 F3 .045969 .024968 .066625 1.841 .0658 F4 -.050000 .019042 -.070336 -2.626 .0087 F5 -.057323 .019459 -.098170 -2.946 .0033 F6 .044920 .027861 .047276 1.612 .1071 F7 .022011 .020563 .029941 1.070 .2846 (Constant) 2.024319 .176928 11.441 .0000
End Block Number 1 All requested variables entered.
293 Wed Dec 15 14:29:18 1999
Amos Version 3.61 (w32)
by James L. Arbuckle
Copyright 1994-1997 SmallWaters Corporation 1507 E. 53rd Street - #452 Chicago, IL 60615 USA 773-667-8635 Fax: 773-955-6252 http://www.smallwaters.com
******************************************** * Structural Model B * *------* * * ********************************************
Serial number 55501773
Structural Model B Page 1
User-selected options ------
Output:
294 Maximum Likelihood
Output format options:
Compressed output
Minimization options:
Technical output Standardized estimates Squared multiple correlations Machine-readable output file
Sample size: 1443
Your model contains the following variables
cs_over observed endogenous revpari observed endogenous
f1 observed exogenous f2 observed exogenous f3 observed exogenous f4 observed exogenous f5 observed exogenous f6 observed exogenous f7 observed exogenous
other1 unobserved exogenous other2 unobserved exogenous
Number of variables in your model: 11 Number of observed variables: 9 Number of unobserved variables: 2 Number of exogenous variables: 9 Number of endogenous variables: 2
Summary of Parameters
Weights Covariances Variances Means Intercepts Total ------Fixed: 2 0 0 0 0 2 Labeled: 0 0 0 0 0 0 Unlabeled: 8 21 9 7 2 47 ------Total: 10 21 9 7 2 49
The model is recursive.
Model: Your_model Computation of Degrees of Freedom
Number of distinct sample moments: 54 Number of distinct parameters to be estimated: 47 ------Degrees of freedom: 7
Minimization History
0e 12 0.0e+00 -4.4127e-01 1.00e+04 2.96540527289e+05 0 1.00e+04 1e 10 0.0e+00 -5.0243e-01 2.34e+00 1.53851730515e+05 15 1.07e+00 2e* 1 0.0e+00 -2.7700e-03 3.28e+00 6.70092425028e+04 6 1.09e+00 295 3e 0 2.5e+04 0.0000e+00 5.82e+00 1.07828142652e+04 5 9.40e-01 4e 0 2.6e+04 0.0000e+00 1.02e+00 8.78426152151e+03 6 0.00e+00 5e 0 2.4e+04 0.0000e+00 2.88e+00 3.07830203044e+03 2 0.00e+00 6e 0 2.4e+04 0.0000e+00 1.19e+00 1.29238805525e+03 1 1.26e+00 7e 0 2.4e+04 0.0000e+00 4.72e-01 5.36309430307e+02 1 1.28e+00 8e 0 2.4e+04 0.0000e+00 4.99e-01 2.02279336772e+02 1 1.27e+00 9e 0 2.4e+04 0.0000e+00 5.03e-01 7.93102165909e+01 1 1.23e+00 10e 0 2.4e+04 0.0000e+00 3.73e-01 5.13381868729e+01 1 1.16e+00 11e 0 2.4e+04 0.0000e+00 1.47e-01 4.90271902153e+01 1 1.06e+00 12e 0 2.4e+04 0.0000e+00 1.68e-02 4.90036264275e+01 1 1.01e+00 13e 0 2.4e+04 0.0000e+00 1.93e-04 4.90036234268e+01 1 1.00e+00
Minimum was achieved
Chi-square = 49.004 Degrees of freedom = 7 Probability level = 0.000
Maximum Likelihood Estimates ------
Regression Weights: Estimate S.E. C.R. Label ------
cs_over <------f1 -0.068 0.053 -1.288 cs_over <------f2 0.350 0.051 6.883 cs_over <------f3 0.090 0.049 1.820 cs_over <------f4 -0.017 0.037 -0.468 cs_over <------f5 -0.047 0.038 -1.235 cs_over <------f6 -0.035 0.055 -0.637 cs_over <------f7 0.049 0.041 1.196 revpari <---- cs_over 2.393 0.455 5.257
Standardized Regression Weights: Estimate ------
cs_over <------f1 -0.058 cs_over <------f2 0.279 cs_over <------f3 0.075 cs_over <------f4 -0.014 cs_over <------f5 -0.046 cs_over <------f6 -0.021 cs_over <------f7 0.038 revpari <---- cs_over 0.137 Means: Estimate S.E. C.R. Label ------
f1 5.131 0.028 186.311 f2 5.193 0.026 201.990 f3 4.976 0.027 185.788 f4 3.537 0.026 137.134 f5 4.866 0.032 154.356 f6 4.992 0.019 256.837 f7 5.641 0.025 225.760
Intercepts: Estimate S.E. C.R. Label ------
cs_over 2.410 0.344 7.007 revpari 80.307 1.963 40.908 296 Covariances: Estimate S.E. C.R. Label ------
f6 <------> f7 0.295 0.020 14.723 f5 <------> f7 0.645 0.034 18.757 f4 <------> f7 -0.241 0.025 -9.530 f3 <------> f7 0.401 0.028 14.566 f2 <------> f7 0.405 0.027 15.214 f1 <------> f7 0.453 0.029 15.756 f5 <------> f6 0.465 0.026 17.691 f4 <------> f6 -0.232 0.020 -11.616 f3 <------> f6 0.371 0.022 16.813 f2 <------> f6 0.358 0.021 16.900 f1 <------> f6 0.469 0.024 19.713 f4 <------> f5 -0.425 0.033 -12.951 f3 <------> f5 0.691 0.037 18.749 f2 <------> f5 0.710 0.036 19.706 f1 <------> f5 0.802 0.039 20.479 f3 <------> f4 -0.439 0.029 -15.328 f2 <------> f4 -0.451 0.028 -16.185 f1 <------> f4 -0.504 0.030 -16.775 f2 <------> f3 0.705 0.032 21.992 f1 <------> f3 0.782 0.035 22.494 f1 <------> f2 0.717 0.033 21.820
Correlations: Estimate ------
f6 <------> f7 0.421 f5 <------> f7 0.568 f4 <------> f7 -0.259 f3 <------> f7 0.415 f2 <------> f7 0.437 f1 <------> f7 0.456 f5 <------> f6 0.527 f4 <------> f6 -0.321 f3 <------> f6 0.494 f2 <------> f6 0.497 f1 <------> f6 0.607 f4 <------> f5 -0.363 f3 <------> f5 0.568 f2 <------> f5 0.607 f1 <------> f5 0.640 f3 <------> f4 -0.441 f2 <------> f4 -0.471 f1 <------> f4 -0.492 f2 <------> f3 0.710 f1 <------> f3 0.735 f1 <------> f2 0.702
Variances: Estimate S.E. C.R. Label ------
f1 1.094 0.041 26.851 f2 0.953 0.036 26.851 f3 1.035 0.039 26.851 f4 0.959 0.036 26.851 f5 1.433 0.053 26.851 f6 0.545 0.020 26.851 f7 0.900 0.034 26.851 other1 1.387 0.052 26.851 other2 450.482 16.777 26.851
Squared Multiple Correlations: Estimate ------
297 cs_over 0.080 revpari 0.019
Summary of models ------
Model NPAR CMIN DF P CMIN/DF ------Your_model 47 49.004 7 0.000 7.001 Saturated model 54 0.000 0 Independence model 9 45950.334 45 0.000 1021.119
DELTA1 RHO1 DELTA2 RHO2 Model NFI RFI IFI TLI CFI ------Your_model 0.999 0.993 0.999 0.994 0.999 Saturated model 1.000 1.000 1.000 Independence model 0.000 0.000 0.000 0.000 0.000
Model PRATIO PNFI PCFI ------Your_model 0.156 0.155 0.155 Saturated model 0.000 0.000 0.000 Independence model 1.000 0.000 0.000
Model NCP LO 90 HI 90 ------Your_model 42.004 23.438 68.055 Saturated model 0.000 0.000 0.000 Independence model 45905.334 45203.467 46613.479
Model FMIN F0 LO 90 HI 90 ------Your_model 0.034 0.029 0.016 0.047 Saturated model 0.000 0.000 0.000 0.000 Independence model 31.866 31.834 31.348 32.326
Model RMSEA LO 90 HI 90 PCLOSE ------Your_model 0.065 0.048 0.082 0.071 Independence model 0.841 0.835 0.848 0.000
Model AIC BCC BIC CAIC ------Your_model 143.004 143.660 Saturated model 108.000 108.754 Independence model 45968.334 45968.460
Model ECVI LO 90 HI 90 MECVI ------298 Your_model 0.099 0.086 0.117 0.100 Saturated model 0.075 0.075 0.075 0.075 Independence model 31.878 31.391 32.369 31.878
HOELTER HOELTER Model .05 .01 ------Your_model 414 544 Independence model 2 3
Execution time summary:
Minimization: 0.385 Miscellaneous: 0.122 Bootstrap: 0.000 Total: 0.507
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