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ANTICEDENTS OF INTENTION TO ADOPT WEB-BASED
COMPUTER AIDED FACILITY MANAGEMENT (CAFM) BY
FACILITY MANAGERS

presented by

SUPORNCHAI SAENGRATWATCHARA

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ANTICEDENTS OF INTENTION TO ADOPT WEB-BASED COMPUTER AIDED
FACILITY MANAGEMENT (CAF M) BY FACILITY MANAGERS

By

Supomchai Saengratwatchara

A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY
Department of Human Environment and Design

2007

ABSTRACT

ANTICEDENTS OF INTENTION TO ADOPT WEB-BASED COMPUTER AIDED
FACILITY MANAGEMENT (CAF M) BY FACILITY MANAGERS

By

Supomchai Saengratwatchara

Computer Aided Facility Management (CAF M) is a computerized
network system connects graphic and non-graphic information in a central and
comprehensive source of facilities information (Hayman & Ulrich, 1995). This system
increases accuracy through data validation, but remains flexible in reporting and inquiry.
It is believed that CAF M provides facility manages with the ability to analyze the
effective use of space more readily than ever.

Despite the development of Internet, the improvements of the system itself, and
the potential benefit and productivity gains from using it, studies have revealed that
facility professionals are not so willing to adopt CAF M application (FMLink, 2006).
Drawing upon Diffusion of Innovation Theory (DOI) (Rogers, 1995), the researcher has
developed a model describing the relationships between intention to adopt CAF M
application and five characteristics of an innovation led to the intention. The purpose of
this dissertation is to examine what factors that influence individuals FM professionals to
adopt CAF M application. To serve the purpose of the study accordingly, the original set
of characteristics of an innovation has been re-positioned by grouping perceived
trialability, perceived result demonstrability, and perceived visibility together and
labeling them as opportunities for experiencing CAF M. Eight hypotheses are proposed

to examine the relationships. Specifically, the researcher proposes that opportunities for

experiencing CAF M, consisting of perceived trialability, perceived result
demonstrability, and perceived visibility, will positively relate to perceived relative
advantage and perceived complexity. It is further proposed that perceived relative
advantage will fiirther positively relate to the intention to adopt CAF M, while perceived
complexity will negatively relate to the intention to adopt CAF M.

The researcher conducted stepwise regression analysis to test the hypotheses. All
hypotheses were supported, suggesting that perceived trialability, perceived visibility,
and perceived result demonstrability positively relate to intention to adopt CAF M system
indirectly through perceived relative advantage and perceived complexity and that
perceived advantage and perceived complexity have significant effects on F M
professionals’ intention to adopt CAF M application. Implications for future research and

FM professionals are discussed.

© Copyright by
SUPORNCHAI SAENGRATWATCHARA
2007

I dedicate this work to my parents, Ming and Ampha Saengratwatchara. They always
encouraged me to do well in school even though they never had opportunity to receive a
formal education.

I also would like to dedicate this dissertation to my wife Porngarm and my son Korn, who
sympathize, hearten, and help me throughout the learning experiences.

ACKNOWLEDGEMENTS

It would not have been possible to complete this work without the help of many
people. First, I am so grateful and would like to express my sincere appreciation to my
advisor Professor Jeffery Elsworth for stepping up as my major advisor, for his guidance
and for his assistance in the preparation of this dissertation. I am also very grateful to my
other three committee members, Professor Steve Weiland, Professor Linda Good for
thoughtful feedback on my research, and Professor Joan McFadden for all of kind and
thoughtful advices and editorial works through all the process. I could not be able to
reach the finish line without her. I also would like to extend my sincere thanks to Dr.
Karen Klomparens, Dean of the Graduate School at Michigan State University, for
helping me in searching for a major advisor.

I would like to thank all of the International Facility Management Association
members who provided the most insightful information used to complete this study.

Thanks to my Thai friends who encourage me through this learning experiences
especially, Mr.Sungwom Ngudgratoke for his kindness in helping me with statistical
analysis.

Most of all, I thank my wife Porngarm and my son Korn for their unwavering
love and support during the past six years that have made this possible. I would like to
acknowledge my parents and brothers and sisters who gave me the opportunities to get to

where I am today.

vi

TABLE OF CONTENTS

LIST OF TABLES .................................................................................. ix
LIST OF FIGURES ................................................................................... x
CHAPTER ONE
INTRODUCTION ................................................................................... 1
CHAPTER TWO
REVIEW OF LITERATURE ........................................................................ 8
The Field of Facility Management (FM) ................................................ 8
The Definition of Facility Management ........................................ 8
The Roles of Facility Managers ................................................. 11
Computer Aided Facility Management (CAF M) ...................................... 14
The Definition of CAFM ........................................................ 14
The Components of CAF M ..................................................... 15
The Evolution of CAFM ......................................................... 16
Generations l and 2: Technology Evolution .......................... 16
Generation 3: CAP M and CIF M ....................................... 17
Generation 4: the Explosion of the Internet .......................... 18
Generation 5 and beyond: High Speed Internet
Computers and Networks ............................................... 19
Research on CAFM ............................................................... 20
CAF M and its implementation ......................................... 21
CAP M and its benefits .................................................. 22
Theories of Adoption ..................................................................... 24
Diffusion of Innovation Theory (DOI) ....................................... 25
Research Model and Hypotheses ........................................................ 29
Research Model .................................................................. 29
Research Hypotheses ............................................................ 32
Opportunities for Experiencing CAF M ............................... 32
Perceived Relative Advantage and Perceived Complexity
Lead to Intention to Adopt CAF M .................................... 33
CHAPTER THREE
RESEARCH METHODOLOGY .................................................................. 36
Pretest ....................................................................................... 36
Participants and Procedure ....................................................... 36
Statistical Test for the Pretest .................................................... 37
Results of the Pretest .............................................................. 37
Main Study ................................................................................. 42

vii

Population and Sample ............................................................ 42

Procedure ............................................................................ 42
Survey Instrument development .................................................. 44
Dependent Variable ...................................................... 45
Independent Variables ................................................... 45
Mediators .................................................................. 45
Control Variables ......................................................... 46
Descriptive Statistic ................................................................. 46
Validity and Reliability of Scale ................................................... 48
CHAPTER FOUR
DATA ANALYSIS AND RESULTS .............................................................. 53
Results of Main Study .................................................................... 53
Opportunities for Experiencing CAF M ....................................... 58
Perceived Relative Advantage and Perceived Complexity
Lead to Intention to Adopt CAFM .................................... 59
CHAPTER FIVE
DISCUSSION AND CONCLUSION .............................................................. 62
General Discussion ........................................................................ 62
Implications for Future Researchers ..................................................... 64
Implications for Managers ............................................................... 65
Limitations ................................................................................. 66
Conclusion .................................................................................. 67
APPENDICES
Appendix A: Introduction E-mail & Reminder E-mails
Introduction E-mail for Pretest ................................................ 71
Introduction E-mail for Main Study .......................................... 72
Reminder E-mails for Main Study .............................................. 73
Appendix B: Consent Form and Questionnaire
Web-based Questionnaire ...................................................... 76
Appendix C: Summary of the Results
Summary of the Results ........................................................ 84
Appendix D: Human Subject Committee Approval
Human Subject Committee Approval ..................................... 104
BIBLIOGRAPHY ............................................................................... 106

viii

Table 1

Table 2

Table 3

Table 4

Table 5

Table 6

Table 7

Table 8

Table 9

Table 10

Table 11

TABLE OF TABLES

Descriptive Statistics of Pretest ........................................
Correlations of Pretest ...................................................
The Regression Analysis Results for the Pretest .....................
Descriptive Statistic ......................................................
Confirmatory Factor Analysis of Items ................................
Cronbach’s Alpha of the Constructs ....................................
Frequencies and Percentages of Variables ............................
Means and Standard Deviations of Variables ........................

Correlations of Variables ................................................

Regression Analysis Results for Opportunities

for Experiencing CAF M ................................................

Regression Analysis Results for Intention to Adopt CAFM

ix

........ 38

........ 39

........ 41

........ 47

........ 49

........ 52

........ 55

........ 56

....... 57

........ 59

60

Figure 1

Figure 2

Figure 3
Figure 4

Figure 5

TABLE OF FIGURES

A three-interrelated element model of People, Process, Place
(Developed by Facility Management Institute (FMI), a non-
profit education and research organization established by

Herman Miller Inc. in the late 19703) ......................................... 10
A diagram of the facility management definition provided by the

International Facility Management Association (IFMA) .. . . . . . . . . . . . 11
Diffusion of Innovations Theory (Rogers, 1983; 1995) .................... 27
Diffusion of Innovations Theory (Moore & Benbasat, 1991) ............. 28
Research Model .................................................................. 31

CHAPTER ONE

Introduction

Coping with rapidly accelerating technological change is one of the most
important challenges in today’s world, and Facility Management is not insulated from
such change. Facility Management (FM) is a discipline defined in the 19703 by the
Facilities Management Institute (FMI) to encompass the management of people, process
and place. It is known primarily by its effects, principally acquisition, design,
construction, maintenance, operation and support services for in—house customers and the
physical facilities (Gondeck-Becker, 2006). Effective facilities operations is the
responsibility of facilities managers, whose daily tasks may include long and short-term
facility planning, financial forecasting, real estate acquisition, interior, architectural and
engineering planning, maintenance and operations, telecommunications and security
(Thatcher, 2001).

With the influx of information has come a proportionate expansion of the role of
the facility manager. A facility manager now must be able to manage facility information
as well as technology related to PM work as part of their responsibilities (Brennan, 2002).

Information about facilities is used to support a number of other business processes such

as maintenance and operations, real estate/property, human resources, capital planning,
inventory services and information services.

Computer Aided Facility Management (CAF M) is a computerized network
system connects graphic and non-graphic information in a central and comprehensive
source of facilities information (Hayman & Ulrich, 1995). The system delivers a new,
robust set of computer-based tools that has automated many of these space, maintenance,
and asset tracking functions. Some facility managers have embraced this new technology
and begun to find significant benefits to implementing a CAFM system.

With the development of Internet and technology readily allows open databases,
some facility managers believe CAF M will become the best source for space, personnel,
asset, and maintenance information in the organization. In other words, the initial F M
users assert the CAF M system has become more needed, easier to use, and more cost
effective for managing facilities than it has been thought to be (F Mlink, 2006).

Despite improvements in the system itself and the potential benefit and
productivity gains from using it, studies suggest that less than 30% of FM professionals
managing facilities less than 250,000 sq.ft. are using CAFM and that those who manage
facilities larger than 250,000 sq.fi. have been less likely to add CAF M to their
responsibilities than those managing facilities less than 250,000 sq.fi. (FMlink, 2006).

Understanding what motivates individual FM professionals to adopt new
technology is the focus of this study. In this case, CAF M is the technology being studied
to determine why it has or has not been adopted by FM professionals. Specifically, the
research questions are: What influences FM professionals to adopt CAF M? Is it merely

the characteristics of the CAFM technology itself or are FM professionals also influenced

by other issues, such as their predisposed tendency to try out a new technology? If these
factors are indeed important in the intention to adopt CAF M by F M professionals, are
they interrelated? If so, how are they interrelated? And what are the mechanisms through
which they achieve their effects on the adoption decisions? These questions will be
explored by using Diffusion of Innovations theory (DOI), a theory that explains how the
characteristics of innovations, as perceived by individuals, can help to explain the
intention of adoption (Rogers, 1995).

Rogers (1983; 1995) identified five characteristics of an innovation: perceived
relative advantage, perceived compatibility, perceived complexity, perceived trialability,
and perceived observability. Moore and Benbasat (1991) argued that observability is too
broad a construct for use in many technology adoption contexts. In its place, they
proposed two more specific constructs: visibility and result demonstrability. This
argument has been empirically supported (e.g., Agarwal & Prasad, 1997; Plouffe,
Vandenbosch, & Hulland, 2001). In this study perceived visibility and perceived result
demonstrability will replace perceived observability.

D01 is regarded as an important theory to understand the adoption behavior of
potential adopters and to predict the adoption of technological innovations. Based on this
theoretical framework, researchers typically considered perceived innovation
characteristics of potential adopters as independent variables while the intention to adopt
as dependent variable. The explanatory power of those characteristics on the innovation
adoption was examined empirically (Adams, Nelson, & Todd, 1992; Agarwal & Prasad,
1997; Chau & Tam, 1997; Davis, 1989). Agarwal and Prasad (1997) found that the

innovation characteristics of visibility, compatibility, trialability, and voluntariness were

relevant in explaining current usage, while the only relevant innovation characteristics for
future use intention were relative advantage and results demonstrability.

Recent innovation studies have argued that some constructs in the diffusion of
innovations theory, such as compatibility, tn'alability and observability, relate to prior
technology experience or opportunities for experiencing the technology under
consideration (Oh, Ahn, & Kim, 2003; Agarwal, Sambamurthy, & Stair, 2000; Venkatesh
& Davis, 2000). Agarwal and his colleagues (2000) found that relevant prior experience,
mediated by general self-efficacy, affected perceived ease of use. Venkatesh and Davis
(2000) showed that experience moderated the relationship between subjective norm and
perceived usefulness in longitudinal research, and this was consistent with the result of
Venkatesh and Morris (2000).

Applying the diffusion of innovations theory and its relevant arguments to the
adoption of CAP M application, the researcher establishes a theoretical relationship
between the FM professionals’ intention to adopt CAFM application and factors that are
likely to influence their intention to adopt decisions. Specifically, the researcher has re-
positioned the original set of characteristics of an innovation developed by Rogers
(1995), proposing that opportunities for experiencing CAFM, consisting of perceived
trialability, perceived result demonstrability, and perceived visibility, will positively
relate to perceived relative advantage and perceived complexity. It is further proposed
that perceived relative advantage will further positively relate to the intention to adopt
CAF M, while perceived complexity will negatively relate to the intention to adopt
CAF M. By re-positioning the characteristics of an innovation, the research model will be

appropriate to better serve the purpose of the dissertation. These perceptions will be

assessed using the questionnaire with items adapted from the Perceived Characteristics of
Innovating scale (PCI), a well-established measurement instrument developed by Moore
and Benbasat (1991).

By selecting a population that is not confined to one organization or one event,
the information gathered through the research should be about the FM professional and
not specific to a particular organization in which these professionals are embedded. To
reach FM professionals who are expected to be involved in various functions of CAF M
application in a range of organizations and industries, members of the International
Facility Management Association (IFMA) were the target population of the study. Using
the members of IF MA as the population to be studied would provide a broad background
of experiences against which the perceptions of characteristics of innovation could be
assessed.

An online nation-wide survey was conducted, and the respondents were randomly
drawn from the IF MA membership directory. A large sample is necessary to ensure an
adequate number of FM professional respondents. The researcher expected to receive
approximately a 20-25% response rate, which is the average response rate for online
survey methods (Hamilton, 2003). The members were contacted by email survey and
asked to complete a short questionnaire, which recorded their perceptions of intention to
adopt CAF M.

The researcher used regression analysis for the statistical analysis. After
gathering the data an assessment was made as to whether the data meet the criteria
necessary to perform regression analysis. The proper assessment and any needed

transformation had been conducted before regression analysis was performed on the data

related to the eight hypotheses. Since the hypotheses are exploratory, a two-tail test
assessed the statistical significance of the results.

This study opens up new theoretical and empirical domains not yet explored in
PM field. Much of research has been conducted in the FM field, none of it, to the
researcher’s knowledge, has studied or analyzed individual F M professionals’
perceptions. Different degrees of perceptions of intention to adopt CAFM across
individual FM professionals are critically important for a better understanding of factors
that potentially motivate FM professionals when making adoption decisions. The
expected benefits from the investments in new technology are realized only when they
are adopted by their intended users and subsequently used.

The research study is important because it presents unusual approaches in
collecting and analyzing the data compared to other research conducted in the FM field.
There has been considerable research to date in FM and on CAF M, but most of it has
focused on single-site case studies using benchmarking as a method of collecting data
and proposing best practice principles of using CAFM. This study empirically examines
the research model using quantitative data collection method to analyze the collected data
using statistical approach. By initiating a different approach in doing FM research, this
study lays the groundwork for more ambitious empirical investigation designed to clarify
the intention to adopt CAFM among FM professionals and identify the factors that
determine that intention.

The dissertation consists of five chapters. This chapter is the introduction in
which project purpose and contribution are described. The second chapter contains the

literature review and the research hypotheses. The review includes definitions and a

discussion of the literature related to FM field and CAF M applications as well as the
review on the diffusion of innovations theory and its previous findings. Chapter three
presents the research design describing the sampling approach, measurement procedures,
and the data collection methods. Chapter four reports the results and data analysis. The
dissertation concludes with Chapter five containing a discussion of the results and

implications for FM professionals and future research.

CHAPTER TWO

Review of Literature

The first of two sections is the literature review discussing concepts and theories
that are important in developing the research study. The second section presents the
research model and hypotheses as well as the logic underlying the model.

In the literature review section, two areas of literature were reviewed in
developing this research study: 1) facility management (FM) and computer aided facility
management (CAF M) and; 2) Diffusion of Innovations Theory (DOI) (Rogers, 1983;
1995) and a brief description of other theories of adoption including the Technology
Acceptance Model (TAM) (Davis, 1989) and the Theory of Planned Behavior (Ajzen,

1985; 1991).

The Field of Facility Management (FM)

The Definitions of F acility Management

Facility management is a relatively new business and management discipline.
Only in the past 25-30 years has facility management become a recognized and required
process practiced by organizations that expend resources on people, their work

environment, and the ways they work. Facility management (FM) is a multidisciplinary

or trans-disciplinary profession drawing on theories and principles of engineering,
architecture, design, accounting, finance, management, and behavioral science (Springer,
2001). This multidisciplinary approach embraced by facility management professionals
has long been practiced by large organizations such as research, educational, health care
institutions and government, using different names such as operations and maintenance,

public works, facilities engineering, and physical plant administration.

The term "facility management" has been used since 1979 with the creation of the
Facilities Management Institute (FMI), a non-profit education and research organization
established by Herman Miller Inc. in Ann Arbor, Michigan. Facility management
encompasses the management of people, process, and place (Rondeau, Brown, &
Lapides, 1995). Particularly, it is the integration of an organization's people with its
process (work) into its place (facilities) where overlapping area represents facility
management. Figure 1 represents a three-interrelated element model of people, process,

and place developed by Facility Management Institute.

     

PROCESS

AVA

PEOPLE

   

Figure 1. A three-interrelated element model of People, Process, Place (Developed by
Facility Management Institute (FMI), a non-profit education and research organization
established by Herman Miller Inc. in the late 19708)

In 1982, the United States Library of Congress defined facility management as:
“The practice of coordinating the physical workplace with the people and work of the
organization; it integrates the principles of business administration, architecture, and the
behavioral and engineering sciences.” As FM profession continues to change and evolve,
and as the information technology (IT) has became very important in facility professions,
the International Facility Management Association (IFMA) then defines Facility
Management as a profession that encompasses multiple disciplines to ensure functionality
of the built environment by integrating people, place, process and technology (IFMA,
retrieved October 29, 2006). The IFMA definition of facility management will be
utilized throughout this research study. Figure 2 represents a diagram of the facility

management definition provided by the IF MA.

10

 

PROCESS PLACE

 

NW

 

PEOPLE TECHNOLOGY

 

 

 

 

 

 

Figure 2. A diagram of the facility management definition provided by the International
Facility Management Association (IFMA).

The Roles of Facility Managers

According to the definitions above, the mission of facility managers is to provide
quality and cost-effective service to in-house customers in support of the organization
business plan (Rondeau et al., 1995). The International Facility Management Association
(IFMA) has grouped the facility professions’ responsibilities under nine major function

areas (Rondeau et al.,1995):

1. Long-range facility planning (Strategic planning).
2. Annual facility planning (tactical planning).
3. Facility financial forecasting and management.

4. Real estate acquisition and/or disposal.

11

5. Interior space planning, work specifications, and installation and space
management.

6. Architectural and engineering planning and design.

7. New construction and/or renovation work.

8. Maintenance and Operations maintenance of the physical plant.

9. Telecommunications integration, security, and general administrative services
such as food service, records management, reprographics, transportation, mail

services, and etc.

A facility manager organizes and oversees budgeting, interviewing and hiring
consultants, set design, construction, furnishings, scheduling, space and office furnishing
standards, capital purchasing programs, and translates organizational customer facility
requirements into a cost-effective, environmentally safe, and aesthetically pleasing
workplace. Specifically, their role is to ensure that the customer and the organization
have an on-time and on-budget project with the best possible site, space, facilities,
furnishings, and support systems to serve their needs today and tomorrow (Rondeau et
al., 1995). The facility manager is a person who uses his or her corporate business
experience especially “people skills” to manage some or all of the nine job
responsibilities listed above and takes additional corporate business responsibilities on
occasion.

To survive in today’s business world facility management must handle
increasingly complex processes as Rondeau et al (1995) mentioned. The facility

manager must address diverse technological and economical changes in today’s practice.

12

In addition, Rondeau et al (1995) suggested some of the technological and economical
changes and they are:

0 Increasing use and reliance on technology including more computers,
telecommunications devices, and their support requirements.

0 Increasingly complex infrastructure systems such as telecommunication,
computer cabling, power, backup power, heating ventilation and air
conditioning (HVAC), lighting, human safety , security systems,
ergonomic, environmental and sustainability requirements.

0 Higher worker expectation for pleasant, comfort, ergonomic, secure, and
cost-effective environments with limited time, space, staff, and funds.

0 Competitive and economic pressures to reduce expenditures; increase
profits; and buy out, merge with, or take over competitor.

0 Pressure to mesh personnel and facility requirement with long-range,
regional, national, and international business issues, in addition to develop
and integrated strategic corporate business and facility plan.

0 Requirement to provide quality, integrated, in-house services in a timely,
coordinated, and cost-effective manner.

Moreover, Thatcher (2001) states that facility information is extremely important
for managing facility, as information has become the key to success in any business.
Thus one of the most important tasks for facility managers is to manage facility
information properly as well as to think about how to make the information accessible

and current when needed.

13

To ensure that quality FM services will be delivered, in a timely, and cost-
effective manner that fits the requirements of corporate culture and politics, including
customer and senior management service expectations, computer aided facility
management (CAFM) has an opportunity to play a critical role. The detail regarding this

software application is discussed subsequently.

Computer Aided Facility Management (CA FM

The Definition of CAFM

Computer-Aided Facility Management (CAFM) or Computer-Integrated Facility
Management (CIFM) refers to a software system that automates several facility
management tasks (Keller & Keller, 2004). The International Facility Management
Association (IF MA) defines CAF M as a high-tech tool used by facility professionals to
track and manage virtually any facility-related asset (Keller & Keller, 2004). This
technology is intended to reduce management costs by creating a central and
comprehensive resource of facilities information. Both facilities professionals and those
seeking facilities-related information can then use this resource. It supports day-to-day
facilities operations, from master planning to project design and budgeting; as well as
from construction to lease management. Such technology increases accuracy through
data validation, but remains flexible in reporting and inquiry. In sum, CAF M provides
facility managers with the ability to analyze the effective use of space more readily than

ever.

14

The Components of CAFM

A CAF M system usually includes both a graphic and non-graphic component

(Dean, 2004; Gabriel, 2003; Trudeau, 1996; Heyman, Ulrich, 1995; Boes, 1993). At the

heart of a CAF M system is a Computer-Aided Design (CAD) package which automates

design and drafting tasks. Planning and management software or non-graphic component

allows users to combine CAD and other non-graphic data to track information from bulk

data in order to perform various facility management-related tasks.

According to Teicholz and Noferi (2002), CAFM system can be described in six

primary components as follows:

1.

2.

Computer-Aided Design (CAD) for drafting and design tasks

Space and Asset Management for space inventory and space planning and
analysis, asset inventory, integrated CAD, occupancy information, stacking and
blocking, and move management.

Capital Planning/Facility Condition Assessment for tracking condition and
deficiencies of buildings (e.g., roofs, structural and mechanical systems, etc.) and
life cycle costs of renewal.

Maintenance and Operations for work order management, preventative
maintenance, stock/inventories, and other scheduling works.

Real Estate and Property Management for tracking information on property
portfolios with tenant and lease administration, transaction management, financial
modeling, and work management functions.

Support Technologies. This is a miscellaneous grouping for functions that

support and integrate the other FM-specific categories. Examples include project

15

management, document management, web-based applications such as extranets (a
private intemet over the Internet), reporting tools, IT infrastructure, and office
support tools such as email and spreadsheets.

CAF M software applications vary in their complexity and capabilities. Smaller
organizations may be satisfied with one or two CAD stations linked to a simple desktop
database. Others may wish to link all the graphic and non-graphic information involved
in facility management.

A review of the evolution of CAF M and its current context follows in order to

understand CAFM more profoundly.

The Evolution of CAFM

Teicholz (1994; 2001) attempted to divide the evolution of CAF M into five
distinct phases, covering almost 50 years, from the early 19603 to approximately 2005.
However, he made no attempt to go beyond this timeframe, simply because, according to
Teicholz, technology changes too rapidly.

Generations 1 and 2: Technology Evolution. Mainframe computers with
applications designed for facility managers existed in the early 19603, before the term
facility manager even existed. There were few vendors at this time, but solutions were
quite comprehensive and integrated. Moreover, mainframe FM software vendors
represented expensive investments for organizations and the integration of graphic and
non-graphic databases was complex.

Starting in the mid-19803, this centralized, integrated model for FM software

technology became fragmented with the widespread deployment and acceptance of

16

personal computers (PCs). PC applications, representing the second generation of FM
technology, were initially built around office automation software. However, no
standards existed for graphic or non-graphic databases and these “islands of automation”
rarely were designed for integration with other applications.

Generation 3: CAFM and CIFM. The third generation of CAF M, starting in the
early 19903, was characterized by robust integration between various FM graphic and
non-graphic applications, still using the PC as the primary hardware platform. The
earlier computer-aided facility management (CAFM) systems were desktop solutions
whereby data was moved over a local area network (LAN) to the desktop PC. The
CAF M software then processed the data and returned the results over the LAN to users.
This process tended to be slow and restricted their use to mostly inventory management
and reporting applications.

The next software improvement came as a result of new database management
systems (DBMSs). With the advent of client-server databases from companies such as
Oracle and Sybase, enterprise-wide CAF M systems (called computer-integrated facility
management or CIFM) significantly reduced the data transmission bottleneck and
resulted in increased links to external databases managed by non-F M groups such as
information technology, finance, and human resources.

Thus real-time space reports with occupancy information and occupancy cost
analyses started to be generated by FM systems. Linking FM data with mainstream
corporate databases in the beginning, resulted in making CAFM/CIFM more strategic in

nature and elevated the importance of facility managers and their role in managing spatial

l7

and work-related information. For example, organizations started to track costs
associated with space, which in turn led to space chargeback to the business units.

Generation 4: the Explosion of the Internet. The Internet represents the largest
telecommunications network that currently exists in the world. Any vendor that does not
use this network for all aspects of data communication and reporting will find it
increasingly difficult to exist. Initially, FM vendors used the Internet to post static
information by publishing HTML formatted reports from the client-server applications.
Next came some transaction server-based Web queries for functions such as entering or
querying the status of a work request or requesting a customized graphic or non-graphic
report from the software.

Today CAF M vendors have moved well beyond these simple data collection,
querying, and reporting functions. Many of the CAFM vendors seem to be writing their
own Java code to get their own “look and feel” to the graphic user interface. And
increasingly, entire FM applications have moved onto the Internet. A manifestation of
this is collaborative project Web sites which offer functions such as work flow, document
management, and redlining. Such software, mostly used for construction project
management, is changing how client and design consultants communicate.

The benefits of the Internet are clearly discernible and have been well defined and
understood. As process-based tools, such as work flow and technical document
management are better incorporated and integrated into CAFM, the benefits increase

significantly.

18

Generation 5 and beyond: High Speed Internet Computers and Networks. This
post-PC age refers to the imminent arrival of high speed Internet computers and networks
(both through cables and mobile) and the increasing embedding of microchips into assets.

In this generation, communication will be very high speed; it will be wireless as
well as on wires, new data formats such as sound and multimedia video will be included,
and there will be very hi gh-speed data networks connected to powerful hub computers.
The most immediate impact of these networks is expected to be on collaborative project
Web sites.

It is not surprising then, that the commercially available computer-aided facility
management (CAF M) software companies have eagerly sought to transfer their products
to Internet technology. The CAF M software business really began to evolve during the
early 19803 with the advent of PCs and the availability of PC-based and database and
computer aided design (CAD) systems. A number of companies have evolved to offer
integrated solutions to typical facilities information problems. Examples of the
application areas included in currently available CAFM system products are:

0 Space management
0 Asset management
0 Maintenance management
0 CAD drawing and image management
0 Project management
0 Project budgeting
The integrated CAFM systems are designed to work with standard database

management systems (DMBS) and generally include some method of displaying and

19

managing associated graphical information that is available from CAD system generated
drawings. There are two main areas of functions that must be addressed when the
Internet is enabling a CAFM solution. The first is the ability to organize; view, display,
and report information included in the backend database or application. The second, and
more difficult, is the ability to manipulate, manage, maintain, validate, and update the
information contained in that backend database system. The greater value can be
obtained by being able to actually collect and manage information on-line.

Technology is difficult to keep up with. It continues to evolve at a rapid pace.
Internet technology is considered a required and often a strategic technology (Teicholz,
2001). The facility managers now have a new tool to assist them in solving business
problems related to building and property management. Therefore, it is important for
facility managers to have a technology plan, which will most likely be built on Internet
technology. It will be incumbent on the astute facility managers to continue to study

further developments in Internet technology and its uses in FM.

Research on CAF M

Even though the field of facility management and CAF M are fairly new, the
prevalent use of CAFM has created a growing body of literature on two major streams:
CAF M and its implementation (e.g., Stryker, 1991; Keller & Keller, 2004) and CAFM
and its benefits (e.g., Hayman & Ulrich, 1995; Brennan, 2002) However, it is very
important to note that, however, the vast majority of research on CAF M has been

conducted using single-site case studies involving private corporations as well as public

20

sector institutions (Gabriel, 2003). Additionally, those research studies usually involve
proposing best practice principles of using CAFM and employ benchmarking as a method
to collect the data (e.g., Lomas, 1999; McDougall & Hinks, 2000), as opposed to studies
in other fields that use a variety of data collection methods and report the results analyzed

by different statistical analyses (Gabriel, 2003).

CAF M and its implementation. Teicholz (1995) described an implementation
process at Siemens-Pacesetter Inc. in California. The company was already convinced of
the benefits of integrated CAFM and was interested in how to implement this technology
with a minimum amount of disruption, using existing databases that were in place, and
minimizing the disruptive impact of ramp-up and implementation. Teicholz’s (1995)
paper also included a description of a rigorous cost benefits analysis performed to
determine both the quantitative and qualitative impact of CAF M.

Subsequently, Teicholz and Noferi (2002) reported some recent CAF M case
studies from universities that have successfully combined CAFM with business processes
to arrive at innovative solutions that meet their unique and increasing demands for
information. Those universities were, for example, University of Massachusetts at
Amherst and Iowa State University. Dean (2004) discussed a list of criteria for the
analysis of a CAF M product to establish an appropriate business case analysis. The list
featured guidance for CAF M implementation planning, as a good implementation plan is
a key to the success of the introduction of a CAFM. Stryker (1991) proposed six steps in
implementing CAFM, that begin with defining goals and developing an implementation
schedule. Similarly, BOMI Institute presented practical and step-by-step guides for

customizing a CAFM system (BOMI Institute, 2002) according to one’s specific needs.

21

These steps were necessary even though not all the issues and problems have been
identified and resolved.

In addition to the above studies, Gabriel (2003) reported a case study with the
University of Sydney where CAF M was introduced as a way to promote information
management on multi-site environments. The study also included a proposal of a more
decentralized and web-based CAF M framework as well as the possible application of the
proposed framework to a range of CAP M scenarios outside the context of the university
environment. Keller & Keller (2004) documented a process of implementing CAF M
systems in Dow Jones & Co., and found that the successful solution meant creating
balance between corporate business objectives, facility management objectives and
technology. A series of articles published in Building Owners and Management
International Institute (BOMI) website (BOMI Institute, July — September, 2003)
described how to set up and maintain a facility database as a way to successfully

implement CAFM systems.

CAFM and its benefits. The other stream of research studies in CAFM focuses
on benefits of using CAF M. For example, Hayman and Ulrich (1995) believed that with
a CAF M system a company should be able to enhance the value of its properties and
increase its profitability through reducing the cost of tenant improvements; lowering
architectural costs for space planning; and tracking and controlling operating expenses.
Similarly, an article from BOMI Institute (Anonymous, 1999) divided benefits of CAFM
into five categories. They are: cost-effective management of inventory, support for
individual projects, reporting information to others, budget preparation and justification,

and organize benchmarking data. In a more recent article, Watkins (2004) outlined the

22

business benefits for the implementation of a CAF M system. His list included, for
example, leverage customer service levels, centralization and control, asset management
and space management, and maintenance scheduling. He concluded that a CAF M system
typically represents only one percent of an operating and labor budget but has a
disproportionate impact on the 90 percent of the same budget relating to the
organization’s labor and ongoing operations.

In addition to the articles above, Brennan (2002) reported two major strengths of
new web-based CAFM systems. The first was the ability to house facilities data from
several sources in one data warehouse, resulting in the best source for space, personnel,
asset, and maintenance information in the organization. The other major benefit of web-
based CAFM applications is the cost. The current releases of client/server CAF M
software are priced according to the traditional “per license” method. This feature has
resulted in a new pricing structure wherein the web-based CAF M user can have an
unlimited number of people using the system for one set cost. He stated that CAFM
systems needed to be able to integrate with other systems in order to exchange data on a
real time basis with other departments.

Boes (1993) described how CAFM could be the best cost-cutting tool for a local
government. He specifically argued that the system generates cost savings, regardless of
its application, by enhancing management decision-making due to the delivery of timely
and accurate information. Later on, Boes (1994) expanded his arguments to healthcare
service, arguing that one of CAFM’s most useful roles is calculating the cost of space that
can be charged back to third-party payers. He further claimed that hospitals tended to

rely on guesswork and negotiation in calculating and substantiating the reimbursable

23

portion of their costs. This approach often led to liability for overcharges or to lost
revenues resulting from undercharging. Using a CAF M system to capture, document,
and display accurate reimbursement numbers would lead to a big improvement over the
current chargeback system.

In the following section, the researcher presents reviews of the theory used in the
research model, the diffusion of Innovations theory (DOI) (Rogers, 1983; 1995), as well
as other important theories in the area of technology adoption. These reviews provide the

basis of theorized relationships that are discussed subsequently.

Theories of A doption

Understanding the behavior of individuals has always been a concern for social
psychology researchers. Most studies concerned with the prediction of behavior from
attitudinal variables are conducted in the framework of the theory of planned behavior
(TPB) (Ajzen 1985; 1991) and, to a lesser extent, its predecessor, the theory of reasoned
action (TRA) (Fishbein & Ajzen, 1975). TPB is a general model that has been applied in
many diverse domains. The model posits that behavioral intention is a function of, in
addition to attitude, subjective norm, referred to the individual’s perceptions of general
social pressure to perform a given behavior (Ajzen, 1991), and perceived behavioral
control, defined as the perception of internal and external resource constraints on
performing the behavior (Ajzen, 1991). Along with these theories, the technology
acceptance model (TAM) (Davis, 1989) and diffusion of innovations theory (DOI)
(Rogers, 1983; 1995) are also widely supported, particularly in discussing the adoption of

technology.

24

Of the various models that information systems researchers have used to explain
or predict the motivational factors underlying users’ adoption of technology, the
technology acceptance model (TAM) of Davis and his colleagues (Davis, 1989; Davis,
Bagozzi & Warshaw, 1989) is perhaps the most widely applied (Yi, Jackson, Park &
Probst, 2006). Grounded in both TRA and TPB, TAM states that an individual’s system
usage is determined by behavioral intention, which is, in turn, determined by two
particular beliefs, perceived usefulness and perceived ease of use. Perceived usefulness
is defined as the prospective user’s subjective probability that using a specific application
system will increase his or her job performance within an organizational context.
Perceived ease of use refers to the degree to which the prospective user expects the target
system to be effortless (Davis, 1989; Davis et al., 1989). TAM initially included attitude,
but this was later dropped due to its weak role as a mediator between the beliefs and
behavioral intention.

While TAM has been empirically supported in much previous research (Davis,
1989; Adams et al., 1992; Taylor & Todd, 1995; Agarwal & Prasad, 1999; Lou, Luo, &
Strong, 2000), there are alternatives. An alternate theoretical framework for examining
technology use comes from Rogers’ (1995) diffusion of Innovations Theory (DOI). A

brief review of D01 is presented in the following section.

Diffusion of Innovations T heory (DOI)
Originating from sociology, diffusion of innovations theory is concerned with
understanding the process by which the use of innovations spread throughout a social

system (Rogers, 1995). The theory states that the rate of adoption is partially determined

25

by the perceived attributes of an innovation, called innovation characteristics. This
thinking has been validated in many studies of IT—based innovations (Prescott & Conger,
1995)

Rogers (1983; 1995) identified five characteristics of an innovation: its relative
advantage, compatibility, complexity, trialability, and observability (See Figure 3).
Perceived relative advantage is the degree to which an innovation is perceived to be
superior to its predecessor (Rogers, 1995). This perception has been widely shown to
have a positive influence on adoption and use intentions (Prescott & Conger, 1995).
Research has found this construct to be one of the best predictors of the adoption of an
innovation (Oh et al., 2003; Plouffe, Hulland, & Vandenbosch, 2001; Van Slyke, Lou, &
Day, 2002). Perceived complexity, which is the conceptual opposite of perceived ease of
use (Moore & Benbasat, 1991), is defined as the degree to which an innovation is viewed
as being difficult to use (Rogers, 1995). Empirical research validates the impact of
perceived complexity on potential users’ intentions to use a variety of IT innovations
including groupware (Van Slyky et al., 2002), smart card systems (Plouffe et al., 2001)
and information retrieval systems (Venkatesh & Morris, 2000).

Perceived compatibility is the degree to which an innovation fits with a potential
adopter’s existing values, beliefs and experiences. Perceived compatibility is widely
considered to have a positive influence on use intentions (Moore & Benbasat, 1991;
Rogers, 1995; Prescott & Conger, 1995). In fact, in some cases, perceived compatibility
has been shown to be the best perception-based indicator of use intentions (Van Slyke,
Belanger & Comunale, 2004). Finally, perceived trialability is the degree to which an

innovation may be experimented with before adoption. Innovations will be adopted and

26

implemented more often and more quickly if they can be tried prior to making a

commitment to purchase.

 

Perceived Relative
Advantage (+)

 

 

 

 

Perceived Complexity (-)

 

 

 

 

Perceived Compatibility (+) Intention to Adopt

 

 

 

 

 

 

 

Perceived Trialability (+)

 

 

Perceived Observability (+)

 

 

 

Figure 3. Diffusion of Innovations Theory (Rogers, 1983; 1995)

Rogers’ (1995) original conceptualization of the perceived innovation
characteristics included perceived observability, which represents perceptions of the
degree to which the results of using an innovation are visible (Rogers, 1995). However,
perceived observability has received equivocal support in empirical studies. A potential
explanation for this is offered by Moore and Benbasat (1991), who propose that
observability is better conceptualized as two separate constructs (see Figure 4) —- visibility
and result demonstrability. Visibility refers to the degree to which the use of an
innovation is apparent. In contrast, result demonstrability refers to the degree to which
the outcomes of the use of an innovation are apparent (Moore & Benbasat, 1991). This

distinction has been supported empirically (Moore & Benbasat, 1991; Agarwal & Prasad,

1997).

27

 

Perceived Relative
Advantage (+)

 

 

 

Perceived Complexity (-)

 

 

 

 

 

Perceived Compatibility (+)

 

 

Intention to Adopt

 

 

Perceived Trialability (+)

 

 

 

 

 

Perceived Visibility (+)

 

 

 

 

Perceived Result
Demonstrabilitv (+)

 

 

 

Figure 4. Diffusion of Innovations Theory (Moore & Benbasat, 1991)

Rogers’ diffusion of innovations theory has been widely supported for a variety of
IT innovations (Van Slyke et al., 2002; Ilie, Van Slyke, Green, & Lou, 2005). It is
regarded as an important theory to apply in order to understand the adoption behavior of
potential adopters and to predict the adoption of technological innovations. Based on this
theoretical framework, researchers typically considered perceived innovation
characteristics of potential adopters as independent variables so that the explanatory
power of those characteristics on the innovation adoption was examined empirically
(Adams et al., 1992; Agarwal & Prasad, 1997; Chau & Tam, 1997; Davis, 1989).

In a study investigating the factors influencing intentions to use a specific

groupware application — Lotus Domino discussion databases — Van Slyke, Lou and Day

28

(2002) found that perceptions of the relative advantage, complexity, compatibility and
result demonstrability of Lotus Domino have significant relationships with the adoption
intentions. He and colleagues (2006) examined the adoption of online e-payment by
business enterprises using diffusion of innovations theory. The findings indicated that
only perceived compatibility has significant influence on online e-payment adoption of
Chinese companies. Other research into IT innovations offers support for the importance
of users’ perceptions of the characteristics of an innovation in determining the

innovation’s subsequent use (Mathieson, 1991; Van Slyke et al., 2002; Yi et al., 2007).

Research Model and Hypotheses

Research Model

The theoretical model for the study is based on the diffusion of innovations
theory, as expressed in DOI rather than TAM for the following reasons. Although TAM
explains the relationship between perceived usefulness, perceived ease of use and attitude
very well, and has been empirically supported in much previous research (Davis, 1989;
Adams et al., 1992; Taylor & Todd, 1995; Agarwal & Prasad, 1999; Lou et al., 2000), it
has a high degree of generality (Mathieson, 1991). Specifically, TAM assumes that
beliefs about usefulness and ease of use are always the primary determinants of use
decisions. As a result, it is unlikely to provide richer perspective and detailed
information about factors users might consider when deciding to adopt technology in a
specific context (Mathieson, 1991).

The model proposed in this study concerns the intention to adopt a specific

technology among a specific group of users, thus TAM might not be able to provide the

29

desired information as needed. Toward this end, DOI appears to provide the underlying
structure for the theoretical model of the study as well as a theoretical description of
factors that might influence the intention to adopt technology (Ilie et al., 2005).

TAM and D01 share similarities in theoretical constructs. If new technology can
be thought of as a specific innovation, both theories share the view that the adoption of a
particular technology is determined by its perceived attributes. Furthermore, the
constructs employed in TAM are fundamentally a subset of the perceived innovation
characteristics; specifically perceived usefulness and perceived ease of use are
conceptually similar to relative advantage and complexity of DOI, respectively. Relative
advantage and perceived usefulness both encapsulate the degree to which a user feels that
the innovative but marginally improved technology (sometimes referred to as gadget
technology) is better than the current practice. Complexity is the opposite of perceived
ease of use. In this respect, D01 and TAM partially reconfirm each other’s findings.

The diffusion of innovations theory provides such constructs as compatibility,
trialability, result demonstrability, and visibility. These constructs are related to
experiences and/or opportunities for experiencing new technology before adoption.
While relative advantage is one of the best predictors of an innovation’s rate of adoption,
compatibility is of relatively little importance in predicting the same outcome (Rogers,
1995). Moreover, perceived compatibility might be difficult to measure since it involves
personal beliefs values. Therefore the researcher proposes that compatibility will be
excluded from the model. It is reasonable to think that these experience-related
constructs, which are perceived trialability, perceived result demonstrability, and

perceived visibility, will have a positive relationship to perceived relative advantage and

30

perceived complexity, and that perceived relative advantage will have a positive
relationship with the intention to ad0pt CAFM while perceived complexity will have a

negative relationship. The research model in Figure 5 demonstrates this perspective.

 

 

Perceived
Trialability

 
 

 

 

Perceived Relative

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Advantage
H4
Perceived Result Intention
Demonstrability H 5 to Adopt
Perceived
Complexity
Perceived
Visibility

 

 

 

Figure 5. Research Model

Behavioral intention to adopt, defined as a person’s subjective probability to
perform a specified behavior (Yi et al., 2006), is the dependent variable for theoretical
and practical reasons. According to Ajzen and Fishbein (1975), intention has a major
influence in mediating the effect of other determinants on behavior. Also, given that the
study utilized a survey-based cross sectional design, using intention instead of “actual
system usage” avoids the potential problem of retrospective analysis (Fichman, 1992).
Finally, even though the target technology in the study, CAFM, is now more commonly
recognized among FM managers, it is still considered an emerging technology. Thus, the

choice of intention to adopt over actual system usage as a dependent variable is desirable,

31

allowing a timely investigation of FM managers’ adoption intention even though there is

a growing number of organizations are adopting and using CAF M.

Research Hypotheses

Opportunities for Experiencing CAF M. Trialabiltiy is the degree to which an
individual may experiment with an innovation on a limited and low-risk basis (Rogers,
1995). New ideas that are tried incrementally, on an installment plan or through “pilot
tests,” are more likely to be adopted because experimentation can reduce anxiety and
uncertainty (Bender & Good, 2003). As mentioned earlier, observability consists of both
visibility and result demonstrability, with visibility defined as the degree to which the
innovation is visible and result demonstrability defined as the degree to which the results
of adopting/using the innovation are observable and communicable to others (Moore &
Benbasat, 1991). Trialability, result demonstrability, and visibility are related to the
opportunities for experiencing CAFM before adoption and use. If FM managers have
opportunities to be exposed to CAFM before adoption, then they have a better chance of
understanding its usefulness and its implications for their job, especially when tangible
results of CAF M are directly apparent, observable, and communicable to others.
Furthermore, the extents to which an individual has a chance to be exposed to the system
and perceives the results of using an innovation to be demonstrable help, he or she will be
able to determine whether the system is easy or difficult to understand and use.

By experiencing CAFM applications or by being exposed to CAF M before its
subscription, FM managers can build positive perceptions that may lead to the intention

to adopt the applications. Positive exploratory experiences before the adoption of CAF M

32

also help FM managers to obtain knowledge on how to use it and adopt it with less effort

and time. Therefore, the researcher suggests the following hypotheses:

Hypothesis 1 a: Perceived trialability will have a positive relationship with perceived

relative advantage

Hypothesis Ib: Perceived trialability will have a negative relationship with perceived

complexity

Hypothesis 2a: Perceived result demonstrability will have a positive relationship with

perceived relative advantage

Hypothesis 2b: Perceived result demonstrability will have a negative relationship with

perceived complexity

Hypothesis 3a: Perceived visibility will have a positive relationship with perceived

relative advantage

Hypothesis 3b: Perceived visibility will have a negative relationship with perceived

complexity

Perceived Relative Advantage and Perceived Complexity Lead to Intention to
Adopt CAFM. Relative advantage is the degree to which an innovation is perceived as
better than the innovation that it may replace. The greater the recognized advantages of
the new innovation, the quicker the innovation will be adopted. Little perceived relative
advantage may decrease interest in an innovation and reduce the speed with which it is

adopted. That is, if FM managers perceive that CAF M applications can help them

33

significantly improve their quality of work and the services provided for in-house
employees, they would be willing to adopt CAF M more quickly.

Previous research with other innovations has found relative advantage to be the
most important characteristic leading to adoption of an innovation among users (e. g.,
Plouffe et al., 2001; Landstrom, 1995; Charp, 2000). As such, the researcher expects the
same result in the context of CAFM adoption among FM managers. This leads to the

following hypothesis:

Hypothesis 4: Perceived relative advantage will have a positive relationship with

intention to adopt CAFM.

Complexity is the degree of difficulty in using and understanding an innovation.
New ideas that are simple to understand are adopted more rapidly than ideas that require
an individual to seek new skills and methods of understanding (Rogers, 1995). CAFM
applications consist of a high degree and great diversity of technology, affecting the way
FM professionals do their work and interact with other employees in organizations. As
noted earlier, CAF M applications have moved onto the Internet and may be very
complex. Its ability to organize, view, and report and update information contained in the
backend database requires FM professionals who currently use or want to use CAF M
applications to have some knowledge of the Internet and database management to be able
to collect and manage FM related information on-line. FM professionals might be
discouraged and less inclined to adopt CAFM applications due to the above complexity.

This leads to the final hypothesis.

34

Hypothesis 5: Perceived complexity will have a negative relationship with intention to

adopt CAFM.

35

CHAPTER THREE

Research Methodology

This chapter details the research methodology that was used to empirically test
the research hypotheses stated in the previous chapter. Included in this chapter are
descriptions of the pretest as well as the instrument used for the main survey of Facilities
Management (FM) professionals who are referred to as participants. Within both the
pretest and the posttest description are discussion about sample selection, data collection
process, variable operationalization, and scale description and validation. This chapter

concludes by discussing the statistical test selected for the survey.

Pretest

Participants and Procedure

A pretest was conducted in order to assess the probable response rates and check
face validity and content validity of the questionnaire. In addition, the pretest was to
ensure that respondents’ expertise with browsers and web-based materials matches or
exceeds the sophistication and complexity of the research instrument (Staton &

Rogelberg, 2001). Sixty-nine of IFMA members in Canada were randomly selected for

36

participation in the pretest. These individuals received an e-mail explaining the study and
a link to go to the pretest survey attached with the e-mail. The first and second e-mail
reminders were sent out to the same group of people one week and two weeks

respectively, after the initial email contact.

Statistical Test for the Pretest

The researcher conducted a regression analysis to examine the effect of
perception of characteristics of innovation on the intention to adopt CAF M applications.
If the statistical regression coefficient for the dependent variable is significant, this
indicates that the pretest participants indicated that they would support the adoption of
the CAP M applications. In addition, they indicated that they used the perceived
characteristics of innovation in deciding whether they will adopt CAFM applications.

The results of the pretest are reported in the subsequent chapter.

Results of the Pretest

The purposes of the pretest were to check face validity and content validity of the
questionnaire as well as to eliminate potential problems areas in the survey instrument.
The researcher used stepwise regression analysis to examine whether FM professionals
would be influenced by the perception of characteristics of innovation when making
adoption decisions. Cases were excluded listwise.

From the 69 selected participants, there were 25 responses, yielding 19 usable
questionnaires, for a response rate of 27.53%. The average age of the 19 respondents in

the final pretest data set was 47.4 years, and seventy-four percent were male. Most of

37

them were department managers who managed 1,000,000 — 2,500,000 sq. ft. facilities.

One completed high school, 4 went to some college, 3 had associate’s degrees, 7 had

bachelor’s degrees, and 5 had master’s degrees.
The tables 1 and 2 below present descriptive statistics including the means,

standard deviations, and correlations of variables used in the pretest.

Table 1.

Descriptive Statistics of Pretest

 

 

Variables Mean SD or
1. Trialability 4.79 1.562 .658
2. Result Demonstrability 5.12 1.132 .809
3. Visibility 4.03 1.426 .532
4. Relative Advantage 5.47 1.168 .971
5. Complexity 3.32 1.212 .899
6. Intention to Adopt CAFM 5.28 1.143 .941

 

38

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39

Table 3 shows the regression results for the pretest. Looking across this table,
result demonstrability appears to have a direct influence on both perceived relative
advantage and perceived complexity as the regression coefficients are statistically
significant (B = 1.201, Beta = 1.163, p < .05 for perceived relative advantage; B = -0.775,
Beta = -0.724, p < .05 for perceived complexity) while the other two perceptions,
trialability and visibility, do not.

As predicted, results indicate significant effects of perceived relative advantage
and perceived complexity on the intention to adopt CAF M, with perceived relative
advantage being the most significant determinant of F M professionals’ intention to adopt
the system (B = 0.795, Beta = 0.812, p < .001 for perceived relative advantage and B =
-0.209, Beta = -0.222, p < .05 for perceived complexity). These results indicate that FM
professionals’ intention to adopt CAF M is influenced by the apparent results of using the
system, which further leads to the importance of relative advantage and how complicated

it is to operate the system.

40

Table 3.

Regression Analysis Results for the Pretest

 

 

 

 

 

 

 

 

 

 

 

 

 

Relative Advantgge
Variables B Beta Standard t
Error
1. Trialability -0.364 -0.486 0.214 -1.699
2. Result Demonstrability 1.201 * 1.163* 0.342 3.512
3. Visibility -0.054 -0.066 0.208 -0.261
R2 .595
F (3,15) = 7.335
Valid N (listwise) 19
Complexity
Variables B Beta Standard t
Error
1- Trialability -0132 -0170 0.211 -O.626
2. Result Demonstrability _0.775* _0.724* 0.337 4.300
3- Visibility 0.083 0.097 0.205 0.403
R2 .634
F (3,15) = 8.670
Valid N (listwise) 19
Intention to Adopt CA FM
Variables B Beta Standard t
Error
1. Relative Advantage 0.795" 0.812“ 0.101 7.860
2. Complexity -0.209* -0.222* 0.097 -2.148
R2 .858
F (2,16) = 48.394
Valid N (listwise) l9

 

*Significant at 0.05 level
** Significant at 0.001 level

41

Main Study

Population and Sample

The population of interest was FM professionals in managerial level positions.
The sample was drawn from the directory of members of the International Facility
Management Association (IF MA). The population contained approximately 187,500
names along with email and work address information worldwide. Only the members
from the US or approximately 15,725 names were used in this study. The unit of analysis
was the individual. The selection of this population provided for the gathering of
information from a diverse group based on position in PM field. Additionally the
population may also feel some connection to the survey because of the IF MA

organization and therefore produce a higher response rate (Rea & Parker, 1997).

Procedure

The method of data collection was through web-based survey. Despite some
limitations of web-based surveys, they are considered a viable alternative to traditional
surveys (Dillman, 2000; Schonlau, Fricker, & Elloitt, 2002). Web-based surveys are
generally less expensive, faster, more convenient and provide higher data quality than do
mail surveys (Schonlau et al., 2002: Smith, 1997). A survey approach also gives
respondents an opportunity to think before completing the questions thus potentially

enhancing the reliability of data.

42

In attempting to reach a large sample, the web-based questionnaire is the most
economical approach (Sapsford, 1999). Noncompliance is expected to be higher with
this method, but the researcher is attempting to overcome that by choosing a sample that
may connect to the study because of the institute affiliation. Rea and Parker (1997)
suggest that selecting a “specialized” sample may result in a higher response rate.

The sampling and data collection techniques utilized were designed to guard
against the standard problems associated with web-based surveys. Web-based surveys
have been criticized for lack of control over the number of times the same individual can
take the survey (e. g., Schonlau et al., 2002). In this study, the researcher used a survey
application service provider (surveymonkey) that would screen respondents’ IP address
range and thus restricted them from responding to the survey multiple times. Another
criticism of web-based surveys has been the fact that users can skip questions. To ensure
high data quality, respondents would be required by the software to answer all survey
questions except the open-ended ones.

There are 15,725 IFMA members in the United States. By randomly selected
every 7th members from the list, the researcher was able to send an e-mail invitation
along with a cover letter indicating the nature and purpose of the study (See appendix) to
2,246 IFMA members in the United States. The message accompanying each email
survey assured respondents that their answers would be kept confidential and provided a
link to the online survey.

One week after the initial contact, a follow up e-mail was sent to the same group
of contacted people. Another e-mail was sent one week later as an attempt to achieve a

20-25 % response rate or approximately 360-400 completed surveys. The responses from

43

the survey were coded into a data file for statistical analyses. Invalid and incomplete
responses were removed. In addition, the demographic data from the participants
including e-commerce experience would be used to determine whether the sample was

representative of the target population.

Survey Instrument development

All items used in a survey questionnaire were from the measurement scale
developed and extensively tested by Moore and Benbasat (1991) and modified by Plouffe
et a1 (2001). The scale has long been used in previous research (e.g., Bender & Good,
2003; Oh et al., 2003; He et al., 2006) and shows high reliability and validity (Oh et al.,
2003). As the instrument of Moore and Benbasat (1991) was not designed for the CAP M
adoption, their instrument was modified by this researcher to be more closely applied to
the purpose of this study.

Respondents indicated their opinions by rating the items through the use of a
Likert scale. A seven-point scale with anchors ranging from “1 = Strongly disagree” to
“7 = Strongly agree” was used to answer the scale items with higher numbers indicating
high perception of trialability, result demonstrability, visibility, relative advantage,
complexity and a high intention to adopt CAFM.

In addition to the study variables, the survey included questions about whether the
respondents currently use CAF M applications and plan to use the applications in the
future. Also, the respondents were asked to provide reason(s) why they do or do not use

CAF M and why they do or do not plan to use CAF M. Finally, the survey includes

44

demographic questions about age, gender, educational background, size of the

respondents’ facilities managed, and respondents’ top responsibilities.

Dependent Variable

The dependent variable in this study is intention to adopt Computer Aided Facility
Management (CAFM). Intention to adopt CAFM is defined as a person’s subjective
probability to perform a specified behavior (Yi et al., 2006). Intention to adopt CAF M

will be measured by the respondent’s level of agreement or disagreement with six items.

Independent Variables

There are three independent variables in this study. They are perceived
trialability, defined as the degree to which an innovation may be experimented with
before adoption (Rogers, 1995), perceived result demonstrability, defined as the degree to
which the outcomes of the use of an innovation are apparent (Moore & Benbasat, 1991),
and perceived visibility, the degree to which the benefit of an innovation is apparent
(Moore & Benbasat, 1991).

Perceived trialability, perceived result demonstrability, and perceived visibility
were measured by the respondent’s level of agreement or disagreement with four items

for each construct.

Mediators

The mediators in this study are perceived relative advantage and perceived
complexity. Perceived relative advantage is the degree to which an innovation is

perceived to be superior to its predecessor (Rogers, 1995). Perceived complexity is

45

defined as the degree to which an innovation is viewed as being difficult to use (Rogers,
1995).
Perceived relative advantage and perceived complexity were measured by the

respondent’s level of agreement or disagreement with six and five items, respectively.

Control Variables

A number of control variables were included to try to control for items which
were believed to impact the dependent variables but were not the subjects of interest
(Neter, Kutner, Nachtsheim, & Wasserrnan, 1996). The researcher controlled for
respondent’s age, educational level (14 = some college; 16 = bachelors degree; 18 =
master’s degree; 22 = doctoral degree; codes reflect approximate years of education),
gender (male or female), size of managed facilities (measured in square feet), and priority
responsibilities (e. g., budget management, contract administration, and construction

management).

Descriptive Statistic

Table 4 presents descriptive statistic and item-total correlation of the 29 items.
Some items with low item-total correlation were excluded. These items were TR3, RD4,
and C01. However, even though RD3 has relatively low item-total correlation, its
content is reasonable to measure result demonstrability. Thus, this item is retained for

statistical analysis.

46

Table 4.

 

 

 

 

Descriptive Statistic
Item Statement Descriptive Statistic
Standard Item- Total

Mean Deviation Correlation

Trialability (TR)

1. I have had a great deal of opportunity to try the

web-based CAF M software application. 4.00 2.13 0.63

2. I know where I can go to satisfactorily try out

various uses of CAF M software. 4.28 2.09 0.66

*3. Before deciding whether to use web-based
CAFM software application, I would be able to

properly try it out. 5.57 1.58 0.36
4. I was permitted to use web-based CAF M on a
trial basis long enough to see what it can do. 3.75 2.01 0.65

 

Result Demonstrability (RD)
1. I would have no difficulty telling others about

 

 

 

 

my experience using web-based CAF M. 5.28 1.69 0.41
2. I believe I could communicate to others the

consequences of using the web-based CAF M. 5.20 1.68 0.41
3. The results of using web-based CAFM are

apparent to me. 5.19 4.13 0.29
*4. I would have difficulty explaining why using

web-based CAF M may or may not be beneficial. 3.17 1.79 -0.15
Visibility (10

1. I have seen what others do using web-based

CAFM. 4.24 1.99 0.75
2. I have seen many people using web-based

CAFM. 3.61 1.88 0.85
3. I have not seen many others using web-based

CAF M. 3.64 1.96 0.59
4. I have seen web-based CAF M in use outside my

organization. 4.26 2.09 0.67
Relative Advantage (RA)

1. Using web-based CAF M would enable me to

accomplish tasks more quickly 5.18 1.41 0.86
2. Using web-based CAF M would improve the

quality of my work. 5.21 1.39 0.92
3. Using web-based CAFM would make it easier

for me and may staff to do our jobs. 5.41 1.34 0.90
4. Using web-based CAF M would enable me to

work more effectively 5.42 1.35 0.94

 

(Table continues)

47

Table 4.

Descriptive Statistic (continued)

 

Item Statement Descriptive Statistic

Standard Item- Total
Mean Deviation Correlation

 

Relative Advantage (RA) (cont 7
5. Using web-based CAF M would give me greater

 

 

control over my work. 5.16 1.36 0.88
6. Using web-based CAF M would improve my job

performance 5.04 1.38 0.84
Complexity (C0)

*1. Using web-based CAF M is often frustrating. 4.02 1.29 0.17
2. I would find it easy to get web-based CAFM to

do what I want it to do. 3.59 1.26 0.60
3. I would find web-based CAFM easy to use. 3.27 1.24 0.81
4. Learning to use web-based CAF M would be

easy for me. 2.95 1.31 0.76
5. Overall, I believe that using web-based CAFM

is easy for me. 3.00 1.31 0.80

 

Intention to Adopt CAFM (IA)
1. I intend to use web-based CAFM when they are

available 5.21 1.46 0.86
2. To the extent possible, 1 would use web-based
CAF M to do various facility management tasks. 5.53 1.39 0.90
3. To the extent possible, I would use web-based
CAFM frequently. 5.47 1.40 0.90

4. It would be much better for me to use web-based
CAF M for facility management activities in

addition to traditional methods. 5.31 1.41 0.76
5. Overall, I like using web-based CAF M for

facility management. 5.07 1.33 0.75
6. I intent to use web-based CAF M in my job in the

future. 5.32 1.48 0.83

 

Validity and Reliability of Scale
Although the instrument developed by Moore and Benbasat (1991) has

established good validity and reliability, the validity and reliability needed to be

48

established with the instrument used in this study as the researcher has revised it and to

contribute further psychometric data for the instrument. The researcher conducted a

confirmatory factor analysis (CFA) by allowing the components to be correlated and

examining whether the items are significantly correlated with the component’s dimension

in which those items are supposed to measure. Confirmatory factor analysis is

appropriate for analyzing the structure of data items based on preconceived theory

because it identifies separate dimensions based on the degree to which the model fits to

the data by investigating goodness-of-fit statistics (Hair, Anderson, Tatham, & Black,

1995)

Table 5.

Confirmatory Factor Analysis of Items

 

Item Statement

T rialability ( T R)—F l

1. I have had a great deal of opportunity
to try the web-based CAFM software
application.

2. I know where I can go to satisfactorily
try out various uses of CAF M software.
4. I was permitted to use web-based
CAF M on a trial basis long enough to
see what it can do.

Result Demonstrability (RD)—F2

1. I would have no difficulty telling
others about my experience using web-
based CAF M.

2. I believe I could communicate to
others the consequences of using the
web-based CAFM.

3. The results of using web-based CAFM
are emparent to me.

F1 F2

0.79

0.78

0.71

0.85

0.89

0.36

49

Factor Loading
F3 F4 F5 F6

(Table continues)

Table 5.

Confirmatory Factor Analysis of Items (continued)

 

Item Statement
F1 F2

Visibility (IO—F3
1. I have seen what others do using web-
based CAF M.
2. I have seen many people using web-
based CAFM.
3. I have not seen many others using web-
based CAFM.
4. I have seen web-based CAF M in use
outside my organization.

Factor LoadinL

F3

0.81

0.93

0.68

0.74

F4

F5

F6

 

Relative Advantage (RA )—F 4

1. Using web-based CAFM would enable
me to accomplish tasks more quickly

2. Using web-based CAF M would
improve the quality of my work.

3. Using web-based CAF M would make it
easier for me and may staff to do our jobs.
4. Using web-based CAFM would enable
me to work more effectively

5. Using web-based CAF M would give
me greater control over my work.

6. Using web-based CAFM would
improve my job performance

Complexity (C0)—F 5

2. I would find it easy to get web-based
CAFM to do what I want it to do.

3. I would find web-based CAF M easy

to use.

4. Learning to use web-based CAFM
would be easy for me.

5. Overall, I believe that using web-
based CAFM is easy for me.

Intention to Adopt CAFM (IA)-—-F 6

1. I intend to use web-based CAF M when
they are available

2. To the extent possible, I would use
web-based CAFM to do various facility
management tasks.

0.88

0.93

0.93

0.97

0.89

0.85

0.69

0.94

0.82

0.43

0.88

0.97

 

50

(Table continues)

Table 5.

Confirmatory Factor Analysis of Items (continued)

 

Item Statement Factor LoadinL
F1 F2 F3 F4 F5 F6

 

Intention to Adopt CAFM (IA)—F 6
(cont ’)

 

3. To the extent possible, I would use
web-based CAFM frequently. 0.96
4. It would be much better for me to use

web-based CAF M for facility
management activities in addition to

 

traditional methods. 0.78
5. Overall, I like using web-based CAF M
for facility management. 0.72
6. I intent to use web-based CAF M in my

Job in the future. 0.83
F=Factor

The final scale (Table 5) consists of twenty-six items loading on six factors with
factor loading ranging from .36 to .97. The coefficient alpha was .871 indicating strong
reliability for the scale. For each respondent, the corresponding scale items were
averaged resulting in a single measure to be used in the regression analysis testing the
hypotheses. Larger numbers on the scale, except for the complexity, reflect higher
intention to adopt CAF M applications.

Individual Cronbach’s Alpha for each construct is presented in the Table 6 below:

51

Table 6.

Cronbach ’s Alpha of the Constructs

 

 

Constructs a
l. Trialability .807
2. Result Demonstrability .529
3. Visibility .862
4. Relative Advantage .966
5. Complexity .901
6. Intention to Adopt CAFM .945

 

52

CHAPTER FOUR

Data Analysis and Results

In this chapter, the researcher describes the data analysis procedures and presents
the results of the hypotheses tests. The chapter begins with the results from the pretest,
followed by the results from the main survey. The hypotheses that FM professionals’
perception of characteristics of innovation would relate to the intention to adopt CAFM
application were tested using data collected from the main survey described in the

previous chapter.

Results of the Main Study

Stepwise regression analyses were performed in order to determine the
significance and strength of each proposed effect. First, stepwise regression was
conducted for perceived trialability, perceived visibility, and perceived result
demonstrability against perceived relative advantage. Secondly, perceived trialability,
perceived visibility, and perceived result demonstrability were regressed against
perceived complexity. Thirdly, perceived relative advantage and perceived complexity
were regressed against intention to adopt CAFM. Cases were excluded listwise. All

three stepwise regression equations are shown below.

53

PRA=B0+BIPT+B2PD+B3PV

PRA = Perceived relative advantage

Bo = Constant

B.- 3 = Unstandardized Coefficients

PT = Perceived trialability

PD = Perceived result demonstrability
PV = Perceived visibility

PC=B0+B1PT+BZPD+B3PV

PC = Perceived Complexity

Bo = Constant

B.. 3 = Unstandardized Coefficients

PT = Perceived trialability

PD = Perceived result demonstrability
PV = Perceived visibility

IA = Bo + 31 PRA + B2 PC + B; Size + B4 ImptRes + Bs Age

1A = Intention to adopt

Bo = Constant

B. . 5 = Unstandardized Coefficients
PRA = Perceived relative advantage
PC = Perceived Complexity

Size = Size of facility managed
ImptRes = Important responsibility

Age = Age

Table 7 is a descriptive summary of the respondents including frequencies and
percentages of the non-metric variables used in the regression analysis. Two thousand
two hundreds and forty-six e-mails invitation were sent out with attached link to the
questionnaire. Four hundred and eighty-two e-mails were rejected by server, leaving a
total number of contacted e-mails of 1,764. Of 1,764 valid e-mails, a total of 514
questionnaires were returned, yielding a response rate of approximately 29.14%. One
hundred and ninety-one questionnaires were discarded from the analyses because of
missing values or incomplete surveys, leaving a total of 323 questionnaires (18.31%

effective response rate) to be used in this study.

54

Characteristics of the respondents indicate that 70.8 percent were male and 29.2
percent were females. The average age of the 323 respondents in the final data set was
43.7 years. Approximately twenty-eight percent of them were department managers who
managed facilities larger than 2,500,000 sq. ft. Six completed high school, 49 went to
some college, 34 had associate’s degrees, 137 had bachelor’s degrees, 83 had master’s
degrees, 6 had doctoral degrees, and 8 had professional degrees. Moreover, the results of
demographic in this study are relatively close to the 2003 research profile done by IFMA

(IFMA, 2003).

Table 7.

Frequencies and Percentages of Variables

 

 

Variables Class Frequency Percentag_e_
Age 40-49 122 37.9
50-59 112 34.8
Gender Male 228 70.8
Female 94 29.2
Education High School 6 1.9
Some College 49 15.2
Associate Degree 34 10.6
Bachelor Degree 136 42.2
Master Degree 83 25.8
Doctoral Degree 6 1.9
Professional Degree 8 2.5
Important Responsibility Maintenance and Operation 147 45.7
Management
Strategic Planning 55 17.1
Space Planning 36 11.2
Size of Facilities Greater than 2,500,000 sq. ft. 64 19.9
1,000,000 - 2,500,000 sq. ft. 58 18.0
100,000 — 250,000 sq. ft. 47 16.4

 

Further descriptive information including means and standard deviations and

correlation matrix for the variables are given in the Table 8 and 9. Listwise deletion of

55

cases was used in the correlations and caused the differing n sizes and correlation

significance levels among the variables.

Table 8.

Means and Standard Deviations of Variables

 

Variables

Mean

SD

 

a
1. Trialability 4.55 1.479 .681
2. Result Demonstrability 5.24 1.984 .530
3. Visibility 3.95 1.661 .860
4. Relative Advantage 5.25 1.275 .966
5. Complexity 4.83 1.250 .706
6. Intention to Adopt CAF M 5.33 1.259 .945

 

56

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57

Opportunities for Experiencing CA FM

Hypotheses 1, 2, and 3 stated that perceived trialability, perceived visibility, and
perceived result demonstrability would positively relate to perceived relative advantage
and perceived complexity. Table 10 displays results of the stepwise regression analyses.

The results were in the predicted directions. All three perceptions showed
significant effects on both perceived relative advantage and perceived complexity.
Specifically, perceived result demonstrability was highly significant (B = 0.147, Beta =
0.228, p < .001 for perceived relative advantage; B = -0.128, Beta = -0.225, p < .001 for
perceived complexity). Perceived visibility was also highly significant (B = 0.201, Beta
= 0.264, p < 001 for relative advantage; B = -0.126, Beta = -0.l86, p < .001). Finally,
perceived trialability has no significant effect on perceived relative advantage (B = .081,
Beta = .113, p > .05) and had a significant effect on a perceived complexity (B = -.158,
Beta = -.247, p < .001). These results indicated that all three perceptions or opportunities
for experiencing CAFM were important predictors in determining whether CAFM system
would provide job-related benefits for FM managers and whether it was too complicated
for them to adopt or not. Thus Hypotheses 1, 2, and 3 were supported.

The regression equations are presented below:

PRA=B0+BIPT+B2PD+B3PV

PRA“ = 3.214 + .105 PT' + .147 PD‘” + .199 Pv‘"

PC=Bo+BlpT+32PD+B3PV

PC" = 2.746 + .192 PT‘" + .127 919‘” + .137 W"

58

Table 10.

Regression Analysis Results for Opportunities for Experiencing CA FM

 

 

 

 

 

 

 

 

Relative Advantage
Variables B Beta Standard t
Error
1. Trialability 0.081 0.1 13 0.047 1.708
2. Visibility 0.201 * * 0.264“ * 0.047 4.318
3. Result Demonstrability 0.147M 0.228“ 0.036 4.119
R2 .234
F (5, 317) = 32.901
Valid N (listwise) 327
Complexity
Variables B Beta Standard t
Error
1. Trialability -0.158*** -0.247*** 0.041 -3.869
2. Visibility -O.126*** -0.l86*** 0.040 -3.141
3. Result Demonstrability -0.128*"‘* -0.225*** 0.031 -4.189
R2 .282
F (5, 317) = 42.239
Valid N (listwise) 326

 

*Approached significant at 0.10 level

** Significant at 0.05 level
***Significant at 0.01 level

Perceived Relative Advantage and Perceived Comlexig Lead to Intention to Adogt

£4111

 

According to Hypotheses 4 and 5, the researcher predicted that perceived relative

advantage and perceived complexity were significantly important factors for FM

professionals’ intention to adopt CAFM. In other words, respondents with high

perceived relative advantage and low perceived complexity toward CAF M should show

stronger intention to adopt CAF M. Similar expectation has been found in the D01

literature (Agarwal &Prasad, 1997; Moore & Benbasat, 1991).

59

In addition to the variables used in the regression equation described earlier, the
researcher added age, important responsibility, and size of facilities managed as control
variables to test these two hypotheses. Table 11 contains regression results related to

Hypotheses 4 and 5.
[A = Bo + B. PRA + B; PC + B3 Size + B4 ImptRes + B5 Age
IA" = .692 + .619 PM“ + .267 PC‘” + .057 Size" + .014 ImptRes - .052 Age

Table 1 1.

Regression Analysis Results for Intention to Adopt CA FM

 

Intention to Adopt CA FM

 

 

 

Variables B Beta Standard t
Error

1. Relative Advantage 0.578*** 0585*“ 0.038 15.027
2. Complexity -0.343*** -0.307*** 0.044 -7.764
3. Size 0.052” 0.087“ 0.019 2.692
4. ImptRes 0.012 0.024 0.017 0.728
5. Age -0.044 -0.034 0.042 -1.043
R2 .673
F (5, 317) = 129.939
Valid N (listwise) 321

 

*Significant at 0.10 level
** Significant at 0.05 level
"*Significant at 0.01 level
As hypothesized and consistent with previous research (Agarwal &Prasad, 1997;
Moore & Benbasat, 1991; Roger, 1995), the results indicated that both perceived relative

advantage and perceived complexity of the system had significant effect on FM

professionals’ intention to adopt CAFM (B = 0.578, Beta = 0.585, p < .001 for perceived

60

relative advantage; B = —0.343, Beta = -0.307, p < .001 for perceived complexity).
Hypotheses 4 and 5 were supported.

Additionally, it is interesting to learn from these results that size of facilities
managed by a FM manager showed a significant influence on FM professionals’ intention
to adopt CAFM (B = 0.052, Beta = 0.087, p < .05). Specifically, the larger the facility,
the more likely FM professionals adopt the system. However, age and important

responsibility of a FM manager were not significant.

61

CHAPTER FIVE

Discussion and Conclusion

In the final chapter, the researcher discusses the results of the study that aims to
explain factors affecting individual FM professionals’ intention to adopt CAFM
applications. The implications of the findings for both future researchers and FM

managers are also presented. The chapter concludes with limitations of the study.

General Discussion

This research analyzed a modified diffusion of innovations theory (DOI) model
with a sample of 323 IFMA members. All hypotheses were supported. The re-positioned
model was proven to be relevant and applicable to the intention to adopt CAFM
application.

The findings show that perceived trialability, perceived visibility, and perceived
result demonstrability positively relate to intention to adopt CAF M system indirectly
through perceived relative advantage and perceived complexity. This suggests that when
an innovation generates job relevant results that are readily discernable and visible and
when people are given opportunities to try an innovation, perceptions of relative
advantage are enhanced and the potential disadvantages of the complexity are

considerably reduced. Specifically, perceived trialability appears to have stronger

62

influence on perceived complexity than it does on perceived relative advantage. Being
able to try CAF M application before actual adoption appears to be helpful so potential
users to learn whether the application is easy or difficult to operate. However, having a
chance to try out the system might not be so important for them to realize its advantages,
for they can learn from other sources such as seeing other people using it or discussing
with others how they use it and its advantages and disadvantages.

Consistent with other studies (Plouffe et al., 2001; Landstrom, 1995; Charp, 2000;
Oh et al., 2003), perceived relative advantage and perceived complexity were found to
have significant effects on F M professionals’ intention to adopt CAF M application. If
FM professionals discover that CAF M application is easy to use and provides job-related
advantages, they are more willing to adopt CAF M application quickly.

Taking the results together, opportunities should be provided for allowing
potential users to understand CAF M system so that they would discover its work-related
advantages as well as be able to understand whether the system is easy or difficult to use.
While experiencing CAF M application, potential users then can make themselves open to
and ready for adoption of CAP M when they have new software under consideration.

Overall, the findings from this study significantly support the hypotheses that
opportunities for experiencing CAF M application affect perceived relative advantage and
perceived complexity, and that these two latter perceptions further affect intention to
adopt CAFM application by making potential users feel comfortable and ready to adopt

it.

63

Implications for Future Researchers

One major implication for FM researchers is the initial attempt to study individual
FM professionals’ perceptions using quantitative method and statistical analysis
approach. The researcher suggests that using different approaches, such as an online
survey as it was initiated in this study, to do FM research should be continued in order to
establish more ambitious empirical research in PM field.

The findings of this study make contributions to DOI theory. By re-arranging the
existing variables of the theory, the researcher felt that it would provide a more
appropriate and meaningful model for the purposes of the study as well as to FM
environment. Moreover, the re-arranged model empirically supported the idea that
opportunities for experiencing CAF M affect the information of positive perceptions
towards CAF M applications by making FM professionals feel comfortable and ready to
adopt it. Since this study was conducted to examine a particular technology involving a
specific user group, more research is needed to further explore whether or not the model
proposed in this study would be valid with different technologies and user groups. More
studies are also needed to collect data from multiple FM associations to reach higher
generalizability.

It is important to note that despite this study’s finding of significance, its
predictive power of perceived complexity is likely to be decreasing as this research
stream continues. Assuming that CAF M applications are likely to be more widely used,
their design and user interfaces would be created to improve and simplify user’s
interaction. As a result, many users expect the systems to be easy to use and simple in

design and function. If this assumption proves to be the case in the future, perceived

64

complexity then is a less effective predictor of users’ intention to adopt new technology.
Thus DOI theory might need to be adapted and/or re-examined accordingly.

The preliminary finding of the relationship FM professional’s perceptions of the
characteristics of an innovation and their intentions to adopt CAF M create many
possibilities for future researchers. Future research should also continue to examine the
external factors that impact a PM professional’s decision process to adopt CAF M
applications. For example, the findings from this study suggest that cost of
implementation process and size of facilities managed by a FM professional have
influence on their intention to adopt CAF M applications. A more complete model can be
developed by incorporating those factors as moderators to explore whether cost of
implementation and size of facilities would enhance or lessen the relationship.

Another possible stream for possible future research is to study the impact of
changes in perception and intention to adopt CAF M over a long period of time. This can
be accomplished by initially assessing intention to adopt CAF M, using a scale similar to
the one used in this study, and taking subsequent measurements at pre-specified time
intervals. Additional variables such as age and size of facilities managed can be included
to determine whether a change in those variables correlate with any change in the
relationship between the perceptions of characteristics of an innovation and intention to

adOpt CAF M.

Implications for Managers
The findings provide important managerial implications. One key advantage of

understanding the determinants of user intention is the opportunity that it presents for

65

organizational intervention. It is important to develop training interventions and
implementation strategies that illustrate and disseminate the potential advantages
associated with the target technology as evident by the significant effect of relative
advantage on intention to adopt the applications. Further, the findings suggest that
CAF M applications can be more successfiilly implemented if its tangible results are
readily apparent or if users have a chance to try them before making adoption decision.
Another implication for F M managers is that understanding the limitations and
causes of reluctance within the information system component (i.e., people, processes,
business policies and strategies, management) may better explain current limitations with
the use of CAP M systems than user’s familiarity with CAP M applications themselves.
Several comments from the open-ended questions indicate that CAF M application is a
cultural change for an organization and that most implementations fail because IT
component (i.e., hardware, software, and backbone infrastructure) leads the process,
rather than IS (i.e., people, processes, business rules, policies, etc) leads initiatives.
Therefore it is crucial to have active participation from top management as well as to

prepare people in other levels for the implementation and related processes.

Limitations
The study has some limitations that should be considered when interpreting the
results. First, the sample of the study was drawn from a single association. Given that
other FM associations might have different perceptions toward intention to adopt CAF M,
the findings may not generalizable to all FM professionals. Second, because the data

were collected at a single point in time and not longitudinally, measuring intention to

66

adopt CAF M would unavoidably involve retrospective analysis for some of the managers
who responded. In addition, the regression analyses were conducted in order to
determine the effects of each construct. Structural equation modeling, which shows the
effects of each construct from the perspective of the entire model, may also be used in
further research, along with a refinement of the research model. Finally, the
questionnaire attempted to receive insight information regarding FM professionals’
experience of using CAF M but its questions might be too general to ask FM
professionals’ experience of using CAF M. Richer information could be obtained by
posing more specific questions (e.g., for what type of tasks do you use CAFM?)
However, open-ended questions might take time to complete, thus could further
discourage respondents to answer them accordingly. Future researchers may need to
modify those open-ended questions to encourage the willingness of participants to
seriously answer them. This can be done by, for example, providing choice of reasons

rather than leaving blank space to fill out.

Conclusion
This study carries out an on-line questionnaire survey to explore how the
characteristics of innovation would affect FM professionals’ intention to adopt CAF M
systems. Research in FM field as well as Rogers’ (1995) and other prior scholars’
research on the characteristics of innovation have been reviewed, and a research model
based on DOI theory was modified to better assess its empirical applicability to

individual FM professionals. Exploratory factor analysis and Cronbach’s alpha were

67

used to refine the survey instrument and to assess its validity and reliability, while
stepwise regression analyses were conducted to test the hypotheses.

The findings from this study show that trialability, visibility, and result
demonstrability, grouped as opportunities for experiencing CAF M, are antecedents to
relative advantage and complexity and that relative advantage further positively relates to
while complexity negatively relate to intention to adopt CAFM system. In addition, the
results from the open-ended questions suggest that cost of implementation and difficulty
in preparing people for change might cause limitations with the use of CAP M
application.

By taking an integrative approach, the researcher was able to develop a rich
understanding of the mechanisms underlying intention to adopt CAF M application
among FM professionals. The factors that have been identified as important here should
be actively managed and manipulated to fully realize the expected benefits from the

investment in CAFM systems.

68

APPENDICES

69

APPENDIX A

INTRODUCTION E-MAIL & REMINDER E-MAILS

70

INTRODUCTION E-MAIL FOR THE PRETEST

Dear fellow IFMA members,

My name is Supomchai Saengratwatchara, an IFMA Michigan at Large member. I am a
PhD candidate in Human Environment Design and Management at Michigan State
University. I am writing this email to seek your help to do the pilot test for my
dissertation.

My dissertation title is “Antecedents of Intention to Adopt Web-based Computer Aided
Facility Management (CAF M) by Facility Managers.” The purpose of this study is to
examine factors that motivate FM professionals to adopt new technology, which in this
case is Computer Aided Facility Management (CAFM). The questionnaire will ask your
perceptions of intention to adopt CAF M your background and/or experience of using
CAFM, and some demographic questions. It will take approximately 15 minutes to
complete the questionnaire.

Since the purpose of the study focuses on FM professionals, your response is critically
important and I truly appreciate your help.

If you agree to fill out the questionnaire, please go to the following link:
http://www.survevmonkgv.com/s._a__sp?u=731243602357

Thank you very much for your help. You response will certainly strengthen my research
study.

Sincerely,

Supomchai Saengratwatchara

71

INTRODUCTION E-MAIL FOR THE MAIN STUDY

Dear fellow IFMA members,

My name is Supomchai Saengratwatchara, an IFMA Michigan at Large member. I am a
PhD candidate in Human Environment Design and Management at Michigan State
University. I am writing this email to seek your help to do the survey for my dissertation.

My dissertation title is “Antecedents of Intention to Adopt Web-based Computer Aided
Facility Management (CAF M) by Facility Managers.” The purpose of this study is to
examine factors that motivate FM professionals to adopt new technology, which in this
case is Computer Aided Facility Management (CAFM). The questionnaire will ask your
perceptions of intention to adopt CAF M, your background and/or experience of using
CAF M, and some demographic questions. It will take approximately 10-15 minutes to
complete the questionnaire.

Since the purpose of the study focuses on F M professionals, your response is critically
important and I truly appreciate your help.

If you agree to fill out the questionnaire, please go to the following link:
http://www.surveymonkev.com/s.aspx?sm=CkBK9SALd2kQ l DsUStms6w_3 d_3d

Thank you very much for your help. You response will certainly strengthen my research
study.

Sincerely,

Supomchai Saengratwatchara (Artee)

PhD. Candidate in Human Environment: Design and Management
Department of Human Environment: Design and Management
College of Communication Arts & Sciences

Michigan State University

E-Mail: saengrat@msu.edu
Tel: 765.749.5147

72

REMINDER E-MAIL FOR THE MAIN STUDY

Dear fellow IF MA members,

This is the first reminder for the FM research on CAF M applications adoption. If you
have already done the survey, please discard this e-mail and THANK YOU VERY
MUCH. If you have not taken the survey, please take a moment to complete the
survey. I CANNOT MOVE ON WITHOUT YOUR VALUABLE INPUT.

My name is Supomchai Saengratwatchara, an IFMA Michigan at Large member. I am a
PhD candidate in Human Environment Design and Management at Michigan State
University. I am writing this email to seek your help to do CAF M application adoption
survey for my dissertation.

Since the purpose of the study focuses on IFMA members and FM professionals, your
response is critically important and will certainly strengthen my research study. If you
agree to complete the questionnaire, please go to the following link:

http://www.surveymonkey.com/s.aspx?sm=CkBK9SALd21_<O1 DsU5tms6w°/o3d%3d

However, if you feel uncomfortable to participate in the study, you can OPT OUT by
using the following link:

http://www.survevmonkevcom/OptOutasg

Thank you very much for your time. I truly appreciated.

Sincerely,

Supomchai Saengratwatchara (Artee)

PhD. Candidate in Human Environment: Design and Management
Department of Human Environment: Design and Management
College of Communication Arts & Sciences

Michigan State University

E-Mail: saengrat@msu.edu
Tel: 765.749.5147

73

Dear fellow IF MA members,

This is the second reminder for the F M research on CAF M applications adoption. If you
have already done the survey, please discard this e-mail and THANK YOU VERY
MUCH. If you have not taken the survey, please take a moment to complete the

survey. I CANNOT MOVE ON WITHOUT YOUR VALUABLE INPUT.

My name is Supomchai Saengratwatchara, an IFMA Michigan at Large member. I am a
PhD candidate in Human Environment Design and Management at Michigan State
University. I am writing this email to seek your help to do CAF M application adoption
survey for my dissertation.

Since the purpose of the study focuses on IFMA members and FM professionals, your
response is critically important and will certainly strengthen my research study. If you
agree to complete the questionnaire, please go to the following link:

http://www.surveymonkey.com/s.aspx?sm=CkBK9SALd2kO1 DsUStms6w°/o3d%3d

However, if you feel uncomfortable to participate in the study, you can OPT OUT by
using the following link:

http://www.survevmonkev.com/Opt0ut.aspx

Thank you very much for your time. I truly appreciated.

Sincerely,

Supomchai Saengratwatchara (Artee)

PhD. Candidate in Human Environment: Design and Management
Department of Human Environment: Design and Management
College of Communication Arts & Sciences

Michigan State University

E-Mail: saengrat@msu.edu
Tel: 765.749.5147

74

APPENDIX B

CONSENT FORM AND QUESTIONNAIRE

75

 

CAFM Technology Adoption by IFMA Members and FM Professionals

1. INTRODUCTION

 

Dear fellow IFMA members:

My name is Supomchai Saengratwatchara. I am a Ph.D candidate in a Human Environment: Design and
Management at Midiigan State University. I am also a member of IFMA member, Michigan at- large. I am
writing this email to ask for your help in research concerning facility management professionals’ intention to
adopt web-based computer aided facility management (CAFM).

This study seeks to learn what factors will encourage facility managers to utilize a Computer Aided Facility
Management (CAFM) application in their work. Results from this study will be used to answer the following
questions: What influences FM professionals to adopt CAFM? Is it merely the characteristics of the CAFM
technology itself or are FM professionals also influenced by other issues, such as their predisposed tendency
to try out a new technology?

A better understanding of factors that potentially motivate FM professionals when making adoption decisions
is important because the expected benefits from the investments in new technology are realized only when
they are adopted by their intended users and subsequently used. Moreover, by knowing more about facility
managers’ needs and backgrounds, the researcher will be able to work with application developers in creating
the right application for the right task, which will further assist facility managers to work more effectively
and with less effort.

This study assesses FM managers’ intention to adopt web-based computer-aided facility management
(CAFM). Participation in this study usually takes between 10- 20 minutes and includes: (1) completing a
questionnaire assessing your experience with CAFM (2) completing a questionnaire assessing your intention
to adopt web-based CAFM, (3) answering some demographic questions.

This research is voluntary, that the participants can withdraw, or refuse to answer any particular questions.
If you agree to take a few minutes of your valuable time to share your experiences and your opinions, please
answer the informed consent below then click NEXT to enter the questionnaire. Your answers are completely
confidential and will be released only as summaries in the form of study's results. No lndividual’s answers will
be identified. If you would like to have the results of this study, please include your contact information at
the end of the questionnaire.

If you have any questions about this study, please contact the investigators:

Supomchai Saengratwatchara

PhD. Candidate

Department of Human Environments Design & Management
Michigan State University

East Lansing, MI 48824

Telephone: 765 -749 -5147

E- mail: saengrat@msu.edu

Jeffery Elsworth, Ph.D.

Assistant Professor

The School of Hospitality Business
Eli Broad College of Business
Michigan State University

243 Eppley Center

East Lansing, MI 48824-1121
Telephone: 517-353-9211

Fax: 517-432-1170

E-mail: elsworth@bus.msu.edu

76

 

5,5.
l

ecnnoicgy Adcption by lFi-lA i» .ers and FM Pi‘ofesgonals

 

If you have questions or concerns regarding your rights as a study participant, or are dissatisfied at any time
with any respect of this study, you may contact- anonymously, if you wish - Peter Vasilenko, Chair of
Behavioral, Social Science Institutional Review Board by phone: (517) 355-2180, fax: (517) 432-4503, e-
mail: irb@msu.edu., or regular mail: 202 Olds Hall, East Lansing, MI 48824-1047.

Thank you very much for helping this important research.
Sincerely,

Supomchai Saengratwatchara
Doctoral Student in Human Environments Design and Management

1. Please check one of these boxes to proceed:

O I have read the above and would like to participate.

O I would prefer not to participate at this time.

 

 

 

 

 

CAFM usage background

2. Have you heard about computer-aided facility management (CAFM)?

0 YES
0 NO

 

 

 

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3. Do you currently use CAFM in your facility management work?

0 YES
0 no

 

 

77

‘"%iTecthMQg Adophon byIFMA Menibenxand FM Pmfieswcnak

4. Part I (Continue)

 

4. How long have you been using CAFM or Web-based CAFM?

:3

5. Please select one CAFM application from the list your company currently uses.

MEAL, ,_ #4121

 

6. I use
0 CAFM (lncludlng DataBASE, Spread sheet and, etc.)
0 Web-based CAFM

5. Currently use CAFM not web-based CAFM

7. If you currently use CAFM, do you plan to upgrade to use web-based CAFM in
the near future?

l:l

8. Please specify why you D0 or DO NOT plan to upgrade to use web-based
CAFM

:l

 

 

78

”-3: Itch “Clo-3v .Lscrptisn ‘03.» IFi-TA i-lemDa's arr; Fill Pic‘e» 2115‘:-

 

6. Currently do not use CAFM or Web-based CAFM

9. If you currently do not use CAFM, do you plan to use either CAFM or web-
based CAFM in the near future?

|:El

10. Please specify why you do or do not plan to use either CAFM or web-based
CAFM
’ ’ :1

_ _,_*l

7. PART TWO

The following questions will help us to understand how you feel about using a web-based CAFM software
application.

EVEN IF YOU HAVE NEVER USED A WEB-BASED CAFM SOFTWARE APPLICATION, PLEASE ANSWER THE
QUESTIONS BASED ON HOW YOU THINK YOU WOULD USE ONE.

11. Please rate the following question by using scale 1 to 7 where 1: Strongly
Disagree and 7 = Strongly Agree

Strongly Moderately Slightly DT:;::e Slightly Moderately Strongly
Disagree Disagree Disagree nor Agree Agree Agree Agree
1. I have had a great deal of opportunity to try
the web-based CAFM software applicatlon.
2. 1 know where I can go to satisfactorily try out

various uses of CAFM software.

3. Before decldlng whether to use web—based
CAFM software application, I would be able to
properly try it out.

4. I was permitted to use web-based CAFM on
a trial basis long enough to see what it can do.
5. 1 would have no difficulty telling others about
my experience using web-based CAFM.

6. I believe I could communicate to others the
consequences of using the web-based CAFM.

7. The results of using web-based CAFM are
apparent to me.

a. I would have difficulty explaining why using
web-based CAFM may or may not be beneficial.

00000 COO
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00000000
00000000

 

 

 

A

Lilrlli Tecnnology Adoption by IFMA ivlembers and F‘vl Professionals

 

9. I have seen what others do using web-based ._ ._ . .
CAFM.

0. I h l ' b-b d
(11m. ave seen many peop e usmg we ase O O O O O O O
11. 1h n l b- O O O O 0
based giant seen many at ers us ng we 0 O
12. I have seen web-based CAFM in use 0 O O O O O 0

outside my organization.

12. Please rate the following question by using scale 1 to 7 where 1: Strongly
Disagree and 7 = Strongly Agree

Neither
Strongly Moderately Slightly Disa ree Slightly Moderately Strongly
Disagree Disagree Disagree nor gree Agree Agree

13. Using web-based CAFM would enable me to
accomplish tasks more quickly.

14. Using web-based CAFM would improve the
quality of my work.

15. Using web-based CAFM would make it
easier for me and my staff to do our jobs.

16. Using web-based CAFM would enable me to
work more effectively.

17. Using web-based CAFM would give me
greater control over my work.

18 . Using web-based CAFM would improve my
job performance.

19. Using web-based CAFM is often frustrating.

20. I would find it easy to get web-based CAFM
to do what I want it to do.

21. 1 would find web-based CAFM easy to use.

22. Learning to use web-based CAFM would be
easy for me.

23. Overall, I believe that using web-based
CAFM is easy for me.

00000000000
00000000000
00000000000
00000000000.-
00000000000
00000000000:
00000000000

13. Please rate the following question by using scale 1 to 7 where 1: Strongly
Disagree and 7 = Strongly Agree

N l he
Strongly Moderately Slightly 053;; Slightly Moderately Strongly
01 01 D' A
sagree sagree lsagree nor Agree Agree gree Agree
24. I intend to use web-based CAFM when they 0 O O O O O O

are available.

25. To the extent possible, I would use web- O O O 0

based CAFM to do various facility management
tasks.

0 O O
26. To the extent possible, 1 would use web- 0 O O O O O 0

based CAFM frequently.

80

 

v-x

rcfessionals

1-4134 Technology Adopt-on by IFi-lA i-lembers and FM '

 

27. It would be much better for me to use web-
based CAFM for facility management activities
in addition to traditional methods.

O I! l. O 1| 1. O
23. Overall, 1 like using web-based CAFM for O O O O O O O
O O O O O O O

facility management.

29. I intent to use web-based CAFM in my job
In the future

 

 

 

Please provide the following information about yourself. Please note that the lnforrnation will be kept
completely confidential, will never be linked to your name, and will be repOlted only in aggregate form.

14. Your Gender?

[IE]

15. Your age category?
I Lv]

 

16. The highest level of education you have completed is

[ I'J

 

17. What is your job function?

 

 

 

 

 

l Lvl
C"””TTT":J

18. Type of facility manage

1 H
r“ ‘ r ‘ ‘ 7

19. What is the size of your facility?
[ 1d

 

20. Please use the llst below to indicate your most Important responsibility as a
PM manager

 

81

 

 

 

 

CAFM Technology Adoption by IFMA Members and Fill Pro-3-5

 

 

21. To which IFMA chapter do you belong?

 

STATE

ZIP CODE

22. Comments:

 

 

 

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_ . x H,
_ ,

THANK YOU VERY MUCH FOR YOUR PARTICIPATION

23. If you would like to have the results of this study, please provide your e-mail
address below:

,_

 

82

 

 

APPENDIX C

SUMMARY OF THE RESULTS

83

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102

APPENDIX D

HUMAN SUBJECT COMMITTEE APPROVAL

103

MICHIGAN STATE

 

UNIVERSITY lnitiaIlRB
Application
June a. 2007 Determination
T, *Exempt*
0. Jeffrey D. ELSWORTH

243 Eppley Center

Re: "18 # X07-52‘I Category: EXEMPT 2
Approval Date: June 4, 2007

Title: ANTECEDENTS OF INTENTION TO ADOPT WEB-BASED COMPUTER AIDED FACILITY
MANAGEMENT (CAFM) BY FACILITY MANAGER

The Institutional Review Board has completed their review of your project. I am pleased to advise you that your
project has been deemed as exempt in accordance with federal regulations.

The IRS has found that your research project meets the criteria for exempt status and the criteria for the
protection of human subjects in exempt research. Under our exempt policy the Principal lnvestlgator
assumes the responsibilities for the protection of human subjects in this project as outlined in the
assurance letter and exempt educational material. The lRB office has received your signed assurance for
exempt research. A copy of this signed agreement is appended for your information and records.

Renewals: Exempt protocols do [191 need to be renewed. If the project is completed. please submit an
Application for Permanent Closure.

Revisions: Exempt protocols do go} require revisions. However, if changes are made to a protocol that may
no longer meet the exempt criteria, a new initial application will be required.

Problems: It issues should arise during the conduct of the research, such as unanticipated problems. adverse
events, or any problem that may increase the risk to the human subjects and change the category of review,
notify the IRB office promptly. Any complaints from participants regarding the risk and benefits of the project
must be reported to the IRS.

Follow-up: If your exempt project is not completed and closed after three years, the IRB office will contact you
regarding the status of the project and to verify that no changes have occurred that may affect exempt status.

Please use the IRB number listed above on any forms submitted which relate to this project, or on any
correspondence with the IRB office.

Good luck in your research. if we can be of further assistance. please contact us at 517-355-2180 or via email
at lRB@msu.edu. Thank you for your cooperation.

Sincerely.

Peter Vasilenko, Ph.D.
SIRB Chair

C: Supomchai Saengratwatchara
405 S Morrison Rd Apt#81
Muncle. W 47304

104

Name of Principal Investigator: Jeffrey D. Elsworth

Princi al lnves ator Assurance of An Ex P

JUN“ 2W

Title of Project: Antecedents of Intention to Adopt Web-based Computer Aided Facility Management (CAFM) by
Facility Manager

IRB #: X07-521

The Human Research Protection Program (HRPP) has deemed this project as exempt, in accord in federal
regulations of projects exempt from Institutional Review Board (IRB) review. As an exempt protocol, the
appropriate [RB will not be further involved with the review or continued review of the projects, as long as the
project maintains the properties that make it exempt.

Since the HRPP is no longer involved in the review and continued review of this project, it is the Principal
Investigator who assumes the responsibilities of protection human subjects in this project and ensures that
the project is performed with integrity and within accepted ethical standards, particularly as outlined by the
Belmont Report (see exempt educational materials).

The Principal Investigator assumes responsibility for ensuring that the research subjects be informed of the
research through a documented or undocumented consent process, if appropriate.

The Principal Investigator assumes the responsibility to maintain confidentiality of the subjects and the
data, and maintain the privacy of the subjects and protection of the data through appropriate means. If data
is anonymous, the investigators will make no attempt to identify any individuals.

The Principal Investigator assumes the responsibility that co-investigators and other members of the
research team adhere to the appropriate policies to protection human subjects, maintain confidentiality and
privacy, and adhere to accepted ethical standards.

If the Principal Investigator adds additional investigators to an exempt protocol, he/she may inform the
HRPP of the additions. This may be of particular importance to graduate students if the Graduate School
requires proof of IRB approval.

Any complaints from participants regarding the risk and benefits of the project must be reported to the
HRPP.

Since the Principal Investigator and co-investigators are charged with human subject protection and
adhering to ethical principles in exempt research, it is appropriate that investigators be trained in human
subject principles. The Principal Investigator and all members of the research team are required to
complete M SU IRB educational requirements or equivalent.

Any change in the protocol which may raise the project from exempt to an expedited or full review
category must be presented to the HRPP. If there is any question about a change in protocol die Principal
lnvesti gator should consult the Director of the HRPP. Failure to submit changes which raise the protocol
out of the exempt category will be considered non-compliance and will be subject to investigation and
action by the HRPP.

By signing below, the Principal Investigator assures that he/she will abide by the terms of this assurance and the
HRPP exempt policy.

3% O fire/g 5/30/07'

We I’Plincipal Investigator Date
05/05/05

105

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