REASONED ACTION VERSUS
PLANNED BEHAVIOR IN BUS USE
DISSERTATION
Presented in Partial Fulfillment of the Requirements for
the Degree Doctor of Philosophy in the Graduate
School of The Ohio State University
By
Puspa Man Joshi, M.S., MC&RP
* * * * *
The Ohio State University
2003
Dissertation Committee:
Professor Jack Nasar, Advisor
Approved By
Professor Richard Petty
Professor Phillip Viton
_________________________________
Advisor
Department of City and Regional Planning
ABSTRACT
Fishbein’s theory of reasoned action and Ajzen’s theory of planned behavior were
compared in their applications to the prediction of commuter’s bus riding intention and
behavior. According to Fishbein, behavioral intention is the antecedent of behavior; and
attitude towards behavior, and subjective norm are the determinants of behavioral
intention. In addition, behavioral beliefs and normative beliefs are the determinants of
attitude towards behavior and subjective norm respectively. Ajzen added perceived
behavioral control to the Fishbein model.
According to Ajzen, perceived behavior
control affects behavior directly, and it affects it indirectly through behavioral intention.
I hypothesized that in relation to bus ridership both theories would predict commuting
intention and behavior, but Ajzen’s theory of planned behavior would predict them
better. To test these hypotheses I surveyed 80 residents in Buckeye Village, a student
family housing complex owned by the Ohio State University.
The questionnaire
measured behavioral intention, attitude towards behavior, behavioral beliefs, normative
beliefs, subjective norms, perceived behavioral control and behavior in relation to riding
the Buckeye Village bus to go to campus.
The results showed that although both models worked, the Ajzen model predicted
bus-riding intention better than did the Fishbein model, but the Ajzen model did not
improve the Fishbein model's prediction of bus-riding. Behavioral beliefs and normative
ii
beliefs correlated significantly with attitude towards bus riding behavior and subjective
norm respectively.
iii
I would like to dedicate this dissertation to my late dad Moti Man Joshi and late
mom Chatra Laxmi.
iv
ACKNOWLEDGMENTS
First, I would like to thank my advisor Prof. Nasar for trusting me and helping
me from the beginning of this dissertation to its completion. For several years, Prof.
Viton was my advisor because I wanted to do thesis on a topic related to transportation
economics. I would like to thank him for his continued help and support even after I
changed my topic to transportation psychology. Another committee member, Prof. Petty
helped me to find a topic that is related to transportation psychology. I am obliged to him
for this help and agreeing to be in the committee despite his busy schedule.
Prof. Pearlman and Prof. Bertsch were always there to bail me out by providing
me internships when I almost had to leave the school for financial reasons. Other
professors in C & RP including Prof. Emeritus Miller, Prof. Guldman and Prof. Ravenau
had helped me to get through my 15 years in C & RP one way or the other. They all
deserve my acknowledgement and appreciation.
Had I not gotten the fellowship from the International Road Federation and
financial aid from the Roads Department of Nepal for my first year in the U.S., my desire
for further study in the U.S. would have been only a dream. My special thanks go to Mr.
Merron L. Latta, the former Vice President of IRF and Mr. S. B. Pradhanang, the former
Chief Engineer of Roads Department of Nepal. From the first day I entered OSU, Prof.
v
Nemeth (Now Emeritus) has been guiding me as an IRF advisor. His support has been
vital to my success in completing my PhD.
When I first came to OSU I was having difficulties in adjusting to the life over
here due to the vast cultural differences between Nepal and U.S. International Friendship
Inc. and its officer Phil Saksa, and OIE and its officer Kevin Harty have been a source of
support for me, and my family since then. They deserve to share my success.
My old colleague, Tim Thomas (now in Portland), despite my horrible English,
patiently helped me edit my various drafts. My younger son Ashish accompanied me to
distribute questionnaires and my older son Kiran helped me to edit the final draft. I must
not forget to thank them.
During my preliminary survey, five of the regular ride-sharers from the list
provided by Mid-Ohio Planning Commission spent their time answering questions from
my telephone interview. My former supervisor Derek Mair and colleagues at EMH&T,
Gahanna, and colleagues at OSU Transportation and Parking Services helped me answer
my preliminary questionnaires. For my final survey, the Buckeye Village management
approved my request to interview the Buckeye Village residents. In addition, there were
many residents who received the questionnaires and gladly filled and returned them on
time. All of the above deserve my appreciation.
I would like to express my gratitude to my sister Moti Maya, my brother-in-law
Krishna M. Pradhan, my uncles, aunties, cousins, their wives, and others who took care
of my late father during my stay in the U.S. Last but not least, thanks goes to my wife
Arun, my daughter Rummi, and my son-in-law Greg Dake for their best wishes to finish
my study.
vi
VITA
July 4, 1947………………………………………… Born – Patan, Nepal
1969……………………………………………….. B.A. Math and Newari language
Tribhuvan University, Nepal
1973……………………………………………….. B.Ed. Math, Institute of Education
Kathmandu, Nepal
1977……………………………………………….. B.E. Highway and Bridge
Engineering, Tung Chi University
Shanghai, China
1987……………………………………………….. M.S. Transportation Engineering
The Ohio State University
1991……………………………………………….. MC&RP
The Ohio State University
1977-1985…………………………………………. Assistant Engineer
Roads Department of Nepal
1987-Present……………………………………….. Student Service Representative
Transportation and Parking
Services, Ohio State University
Teaching Assistantships: Engineering Economics, Transportation Demand Forecasting,
Road Network Analysis (Department of Civil Engineering,
OSU), Transportation Economics, Physical Planning, American
Planning History (Department of City and Regional Planning).
Internships: Mid-Ohio Regional Planning Commission, Ohio Department of
Transportation and EMH&T (Engineering Consulting Firm), Gahanna, Ohio
FIELDS OF STUDY
Major Field: City and Regional Planning, Transportation Planning, Traffic Engineering
and GIS
vii
TABLE OF CONTENTS
PAGE
ABSTRACT --------------------------------------------------------------------------------------------------
ii
DEDICATION -----------------------------------------------------------------------------------------------
iv
ACKNOWLEDGMENTS --------------------------------------------------------------------------------
v
VITA ----------------------------------------------------------------------------------------------------------- vii
LIST OF FIGURES ------------------------------------------------------------------------------------------
xi
LIST OF TABLES -------------------------------------------------------------------------------------------
xii
CHAPTERS
CHAPTER 1
1. Introduction -------------------------------------------------------------------------------------------
1
1.1 Traffic Congestion Problem and Its Solutions -------------------------------------------------
2
1.2 Transportation Demand Management (TDM) Programs ------------------------------------
6
1.3 Mass Transit ----------------------------------------------------------------------------------------
10
1.4 Travel Demand Forecasting Models -------------------------------------------------------------
11
CHAPTER 2
2.1 Beliefs, Attitudes and Behavioral Intentional Models --------------------------------------
19
2.1.1 The Fishbein Behavioral Intention Model -----------------------------------------------
21
2.1.2 Other Behavioral Intention Models ------------------------------------------------------
27
2.1.3 Validations and Applications of the Fishbein Model ----------------------------------
31
2.1.4 Modifications of the FBIM -----------------------------------------------------------------
35
2.1.5 The Theory of Planned Behavior ----------------------------------------------------------
39
viii
2.1.6 Validations and Applications of the Ajzen’s model --------- --------------------------
45
2.1.7 Measurement Issues related to TPB -------------------------------------------------------
50
2.1.8 Thomas et al.’s Study ------------------------------------------------------------------------- 54
CHAPTER 3
3.1 The Problem Statement -----------------------------------------------------------------------------
58
3.2 The Purpose of Study ------------------------------ ------------------------------------------------- 61
CHAPTER 4
4.1 Method --------------------------------------------------- --------------------------------------------- 63
4.2 Description of Socio-economic Conditions of Respondents ---------------------------------
65
4.3 Questionnaire and The Pilot Study ---------------------------------------------------------------- 67
CHAPTER 5
5. Results of the Survey
5.1 Introduction -------------------------------------------------------------------------------------------- 72
5.2 Hypothesis testing ------------------------------------------------------------------------------------ 78
5.2.1 The Theory of Reasoned Action ------------------------------------------------------------
79
5.2.2 The Theory of Planned Behavior -----------------------------------------------------------
87
5.2.3 The Effect of External Variables -----------------------------------------------------------
90
5.3 Comparison of Mean Scores Across Respondents ---------------------------------------------
94
CHAPTER 6
6. Conclusions and Implications -------------------------------------------------------------------------- 97
LIST OF REFERENCES ------------------------------------------------------------------------------------- 104
APPENDICES
A. Tables from Thomas et al.’s Study ---------------------------------------------------------- 117
ix
B. Request Letter to Respondents (Flyer) ------------------------------------------------------ 121
C.
Survey Questionnaire -------------------------------------------------------------------------- 123
D.
Results of Regression and Probit Analyses ------------------------------------------------- 132
x
LIST OF FIGURES
FIGURES
PAGE
FIGURE 1
Behavioral Intention Model (Fishbein, 1980) ------------------------------------------------------
23
FIGURE 2
Path models for the theory of planned behavior (Ajzen, 1985) ---------------------------------- 40
FIGURE 3
Comparison of Regression Results of Fishbein and Ajzen Models in
predicting behavioral intention ----------------------------------------------------------------------- 73
FIGURE 4
Comparison of Regression Results of Fishbein and Ajzen Models in
predicting overt behavior ------------------------------------------------------------------------------ 74
FIGURE 5
Relationship between i) Sum of the belief-based attitudes and overall
attitude ii) Sum of the normative pressures and overall subjective norm ----------------------
75
FIGURE 6
Relationship between i) Sum of the belief-based attitudes and overall
attitude ii) Sum of the normative pressures and overall subjective norm ---------------------
xi
75
LIST OF TABLES
TABLE
PAGE
1.
Remarks About TDM Programs ------------------------------------------------------------------
9
2.
Validations and Applications of FBIM ----------------------------------------------------------
37
3.
Validations and Applications of ABIM ----------------------------------------------------------
49
4.
Definition of Terms ----------------------------------------------------------------------------------- 62
5.
Descriptive Statistics of Socio-economic Variables --------------------------------------------
66
6.
Descriptive Statistics of Attitudinal Variables ---------------------------------------------------
67
7.
R2 Values Obtained from Linear Regressions and ρ2 Obtained from Probit Analysis-----
77
8.
Bivariate Correlations between Attitude Towards Behavior (Ab) and
Behavioral Beliefs Multiplied by Their Outcome Evaluations (Sum Bi*Ei) ----------------
82
9.
Bivariate Correlations between Attitude Towards Behavior (Ab) and
Strength of beliefs (Bi) and Attitude Towards Behavior (Ab) and
Evaluations of outcomes ----------------------------------------------------------------------------- 84
10.
Bivariate Correlations between Subjective Norm (SN) and Normative
Beliefs Multiplied by Motivation to Comply (NBi*MCi) --------------------------------------
85
Bivariate Correlations between Subjective Norm (SN) and Normative
Beliefs (NBi) and Subjective Norm (SN) and Motivation to Comply (MCi) ---------------
86
12.
Bivariate Correlations Between All the Measured Variables ----------------------------------
92
13.
Mean Scores and Standard Deviations for Attitudinal Variables of
Bus-riders and Non Bus-riders --------------------------------------------------------------------
94
Mean Scores and Standard Deviations for Attitudinal Variables of
Respondents Across Socio-economic Groups ---------------------------------------------------
96
Summary Data Table of Simple Linear Regressions of Behavior (B) on Each of
Behavioral Intention (BI), Attitude Towards Behavior (Ab) and Subjective Norm (SN):
Simple Correlation Coefficient (R), R2, Ra2, F Values for R2,
Standardized Regression Coefficient β, df -----------------------------------------------------
133
11.
14.
15.
xii
16.
Summary Data Table of Binary Probit Analysis of Behavior (B) on Each of Behavioral
Intention (BI), Attitude Towards Behavior (Ab) and Subjective Norm (SN):
ρ2, Chi Square Values for ρ2, Standardized Regression Coefficient β, Values of
Log likelihood, df ---------------------------------------------------------------------------------- 134
17.
Summary Data Table of Multiple Linear Regression of Behavioral Intention (BI) on
Attitude Towards Behavior (Ab) and Subjective Norm (SN):
Multiple Correlation Coefficient (R), R2, Ra2, F Values for R2,
Standardized Regression Coefficient β, df -----------------------------------------------------
134
18
Summary Data Table of Binary Probit Analysis of Behavioral Intention (BI) on
Attitude Towards Behavior (Ab) and Subjective Norm (SN):
ρ2, Chi Square Values for ρ2, Standardized Regression Coefficient β, Values of
Log likelihood, df ---------------------------------------------------------------------------------- 134
19.
Summary Data Table of Simple Linear Regressions of Behavioral Intention (BI) on Each
of Attitude Towards Behavior (Ab), Sum of Belief-based Attitudes (Sum Bi*Ei),
Subjective Norm (SN), and Sum of the Normative Pressures (Sum NBi*MCi):
Multiple Correlation Coefficient (R), R2, Ra2, F Values for R2,
Standardized Regression Coefficient β, df -----------------------------------------------------
135
20.
Summary Data Table of Binary Probit Analysis of Behavioral Intention (BI) on Each
of Attitude Towards Behavior (Ab), Sum of Belief-based Attitudes (Sum Bi*Ei),
Subjective Norm (SN), and Sum of the Normative Pressures (Sum NBi*MCi):
ρ2, Chi Square Values for ρ2, Standardized Regression Coefficient β, Values of
Log likelihood, df ---------------------------------------------------------------------------------- 136
21.
Summary Data Table of Simple Linear Regression of Attitude Towards Behavior (Ab) on
Sum of Belief-based Attitudes (Sum Bi*Ei):
Simple Correlation Coefficient (R), R2, Ra2, F Values for R2,
Standardized Regression Coefficient β, df -----------------------------------------------------
137
Summary Data Table of Stepwise Linear Regression of Attitude Towards Behavior (Ab)
on A1 (Saving Money), A8 (Losing flexibility) and A9 (Helping to reduce air
pollution):
Multiple Correlation Coefficient (R), R2, Ra2, F Values for R2,
Standardized Regression Coefficient β, T Values for β, df ----------------------------------
137
Summary Data Table of Simple Linear Regression of Subjective Norm (SN) on
the Sum of the Normative Pressures (Sum NBi*MCi):
Simple Correlation Coefficient (R), R2, Ra2, F Values for R2,
Standardized Regression Coefficient β, df -----------------------------------------------------
137
Summary Data Table of Stepwise Linear Regression of Subjective Norm (SN)
on SN1 (Spouse), and SN3 (Best Friend):
Multiple Correlation Coefficient (R), R2, Ra2, F Values for R2,
Standardized Regression Coefficient b, T Values for b, df ----------------------------------
138
Summary Data Table of Simple Linear Regression of Behavior (B) on
Perceived Behavioral Control (PBC):
Simple Correlation Coefficient (R), R2, Ra2, F Values for R2,
Standardized Regression Coefficient β, df ----------------------------------------------------
138
22.
23.
24.
25.
xiii
26.
Summary Data Table of Binary Probit Analysis of Behavior (B) on
Perceived Behavioral Control (PBC):
ρ2, Chi Square Values for ρ2, Standardized Regression Coefficient β, Values of
Log likelihood, df ---------------------------------------------------------------------------------- 138
27.
Summary Data Table of Multiple Linear Regression of Behavior (B) on
Behavioral Intention (BI) and Perceived Behavior Control (PBC):
Multiple Correlation Coefficient (R), R2, Ra2, F Values for R2,
Standardized Regression Coefficient β, T Values for β, df ----------------------------------- 139
28.
Summary Data Table of Binary Probit Analysis of Behavior (B) on
Behavioral Intention (BI) and Perceived Behavior Control (PBC):
ρ2, Chi Square Values for ρ2, Standardized Regression Coefficient β, Values of
Log likelihood, df ----------------------------------------------------------------------------------- 139
29.
Summary Data Table of Simple Linear Regression of Behavioral Intention (BI) on
“The number of automobiles owned”:
Simple Correlation Coefficient (R), R2, Ra2, F Values for R2,
Standardized Regression Coefficient b, df -----------------------------------------------------
139
30.
Summary Data Table of Binary Probit Analysis of Behavioral Intention (BI) on
“The number of automobiles owned”:
ρ2, Chi Square Values for ρ2, Standardized Regression Coefficient β, Values of
Log likelihood, df ----------------------------------------------------------------------------------- 140
31.
Summary Data Table of Multiple Linear Regression of Behavioral Intention (BI) on
Attitude Towards Behavior (Ab), Subjective Norm (SN) and “The number of
Automobile owned”:
Multiple Correlation Coefficient (R), R2, Ra2, F Values for R2,
Standardized Regression Coefficient b, T Values for b, df ---------------------------------
140
Summary Data Table of Binary Probit Analysis of Behavioral Intention (BI) on
Attitude Towards Behavior (Ab), Subjective Norm (SN) and “The number of
Automobile owned”:
ρ2, Chi Square Values for ρ2, Standardized Regression Coefficient β, Values of
Log likelihood, df ---------------------------------------------------------------------------------
140
Summary Data Table of Simple Linear Regression of Behavioral Intention (BI) on
Perceived Behavioral Control (PBC):
Simple Correlation Coefficient (R), R2, Ra2, F Values for R2,
Standardized Regression Coefficient β, df -----------------------------------------------------
141
32.
33.
34.
Summary Data Table of Binary Probit Analysis of Behavioral Intention (BI) on
Perceived Behavior Control (PBC):
ρ2, Chi Square Values for ρ2, Standardized Regression Coefficient β, Values of
Log likelihood, df ---------------------------------------------------------------------------------- 141
35.
Summary Data Table of Multiple Linear Regression of Behavioral Intention (BI) on
Attitude Towards Behavior (Ab), Subjective Norm (SN) and Perceived
Behavior Control (PBC):
Multiple Correlation Coefficient (R), R2, Ra2, F Values for R2,
Standardized Regression Coefficient β, T Values for β, df ----------------------------------
xiv
141
36.
Summary Data Table of Binary Probit Analysis of Behavioral Intention (BI) on
Attitude Towards Behavior (Ab), Subjective Norm (SN) and Perceived
Behavior Control (PBC):
ρ2, Chi Square Values for ρ2, Standardized Regression Coefficient β, Values of
Log likelihood, df ----------------------------------------------------------------------------------- 142
37.
Summary Data Table of Simple Linear Regression of Behavior (B), Behavioral
Intention (BI), and Perceived Behavioral Control (PBC) on
Past Behavior (PASTB):
Simple Correlation Coefficient (R), R2, Ra2, F Values for R2,
Standardized Regression Coefficient β, df -----------------------------------------------------
142
38.
Summary Data Table of Binary Probit Analysis of Behavior (B), Behavioral
Intention (BI), and Perceived Behavioral Control (PBC) on Past Behavior (PASTB):
ρ2, Chi Square Values for ρ2, Standardized Regression Coefficient β, Values of
Log likelihood, df ----------------------------------------------------------------------------------- 143
39.
Summary Data Table of Multiple Linear Regression of Behavior (B) on Behavioral
Intention (BI), Perceived Behavior Control (PBC) and Past Behavior (PASTB):
Multiple Correlation Coefficient (R), R2, Ra2, F Values for R2,
Standardized Regression Coefficient β, T Values for β, df ----------------------------------
143
40.
Summary Data Table of Binary Probit Analysis of Behavior (B) on Behavioral
Intention (BI), Perceived Behavior Control (PBC) and Past Behavior (PASTB):
ρ2, Chi Square Values for ρ2, Standardized Regression Coefficient β, Values of
Log likelihood, df ---------------------------------------------------------------------------------- 144
41.
Summary Data Table of Multiple Linear Regression of Behavioral Intention (BI) on
Attitude Towards Behavior (Ab), Subjective Norm (SN), Perceived Behavior
Control (PBC) and Past Behavior (PASTB):
Multiple Correlation Coefficient (R), R2, Ra2, F Values for R2,
Standardized Regression Coefficient β, T Values for β, df ----------------------------------
148
42.
Summary Data Table of Binary Probit Analysis of Behavioral Intention (BI) on
Attitude Towards Behavior (Ab), Subjective Norm (SN), and Perceived Behavior:
ρ2, Chi Square Values for ρ2, Standardized Regression Coefficient β, Values of
Log likelihood, df ----------------------------------------------------------------------------------- 145
43.
Percentage distribution of respondents based on their positive, neutral or
negative responses related to Behavioral beliefs about outcome (Bi), Evaluation
of outcomes (Ei), Normative beliefs (NBi), Motivation to comply (MCi),
Attitude towards behavior (Ab), and Subjective Norm (SN), and Behavioral
Intention. ----------------------------------------------------------------------------------------------- 145
xv
CHAPTER I
INTRODUCTION
This dissertation rests on the belief that in a congested area if solo-drivers switch to
carpooling or bus riding, it can benefit society. It tests two psychological models—the
Fishbein model (Ajzen and Fishbein, 1980) and the Ajzen model (1985)—that may explain
individual’s choices to ride a bus. These models have been widely used in social psychology
and consumer research for predicting behaviors. The present study applies them to the
special case of public transportation, in particular students taking a bus to and from campus.
Only a few studies applied these models to travel behaviors. Notably, Thomas at al.
(Thomas 1976) tested the Fishbein model in prediction of women’s riding a bus to shop at a
mall. Extending that study, this study tests how well the Fishbein and Ajzen models work
for commuter trips, with the idea that if the models do work, it may have broader application
for transit planners.
While these models can predict individual behavior they can also explain the relation
between individual’s beliefs and their outcomes. As such, these models are applicable in
dealing with the social issues such as traffic congestion and air pollution due to automobiles
on the streets and highways which are partly the outcomes of traveler’s choice behavior.
Thus, although our interest is in aggregate demand, the modeling of individual behavior is
the core of all predictive models of aggregate behavior (Ben-Akiva and Lerman, 1985).
1
This chapter reviews Transportation Demand Management (TDM) programs that
include various ways to get people out of the auto mode and into shared riding, such as a
bus. The final section of this chapter briefly describes the travel demand forecasting models
and compares probabilistic models with Fishbein and Ajzen’s attitudinal models.
1.1 Traffic Congestion Problem and Its Solutions
In spite of efforts made by transportation professionals, congestion continues to
plague travelers especially commuters. Vehicle registration increased by 41% during 1980s
and vehicles per household increased from 1.94 to 2.24 (Levinson and Kumar, 1994). An
increase in the work force, particularly among women, increased traffic congestion (Takiyi,
1995). According to Johnson and Tinklenberg (2001), the increased mobility of society
caused travel to grow at almost the same rate of booming economy (about 4 percent per year
over the past 20 years), but the growth in roadway capacity has increased much less (about
0.3 percent per year). This discrepancy in demand for travel and supply in roads increased
congestion.
Beyond that, the infrastructure has deteriorated. As a solution to the problem,
economists have suggested a road pricing policy (Small, Winston and Evans, 1989). The
policy assumes that the best way to economize on maintaining and using an existing road is
to apply a user charge which equals the actual cost each user imposes on society through his
effect on the road’s condition and on the speed that other users can travel. The charge deters
the low-priority uses and accommodates high-priority uses with fewer deleterious effects
from crowding or pavement deterioration. Critics point out problems with the policy. They
2
say the behavioral values of time are highly complex and the values used in transport are
usually crude (Thomson, 1997). Nevertheless, the implementation of the policy might have
prevented the situation from worsening. The Federal Administration reported that 25% of
the Interstate network had deteriorated, and 42% of other links had deteriorated (Petit, 1993).
According to the Federal Highway 2001 Report (www.dot.fhwa.gov), in the year 2000 the
number of bridges rated structurally deficient was 30.8%. Governing (Jan. 1992) predicted
that approximately $33.1 billion dollars would be needed just to maintain current pavements
and traffic conditions in the year 2000. In 2001, FHWA provided $3.5 billion in funding for
approximately 3,000 bridge projects through the Highway Bridge Replacement and
Rehabilitation program (FHWA 2001 Report).
Traffic congestion causes delays in travel, contributes to the deterioration of air
quality, and increases transportation costs. According to Alcott et al. (1991), an estimated
reduction in peak period of 2 to 3% traffic in Tucson, Arizona equals a reduction of 405,000
mile traveled and five tons of carbon monoxide emitted. Flattau (1992) estimated the result
of energy waste due to traffic congestion and loss in productivity at $100 billion a year or
roughly 2% of the gross national product. In 1999, the nation lost an estimated $72 billion in
wasted time and fuel consumption due to road congestion (FHWA 2001 Report).
Reducing urban congestion can help air quality and energy conservation. It has
become a major goal of traffic management agencies (Howie, 1989). With growing concern
for the environment, authorities have increasingly used transportation programs to help meet
air-quality and energy goals (Gonseth, 1995). One such program is congestion pricing (CP).
Depending upon the extent of that use, congestion pricing charges road users for the use of
congested roads (U.S. DOT, 1992). This traffic system management technique spreads peak
3
hour traffic into non-peak hour and to less congested segments of the network by charging
road users who drive on the congested roads (Edelstein, 1991). This technique tends to
dislodge peak hour non-work trips because discretionary trips are much more elastic than the
non-discretionary trips (Levinson and Kumar, 1994).
According to an ITE journal (Feb. 2001), Midwestern states and Federal agencies
recently signed a memorandum of understanding to streamline the development of highway
projects while preserving environmental protections. The memorandum called for a) better
coordination of land use, growth and transportation issues, b) alternative strategies to
mitigate the environmental impacts of highways, and c) closer coordination of transportation
planning with the environment.
Autos dominate our roads because no other alternative mode can match their
convenience, privacy, independence, and flexibility (Zupan, 1992). According to the results
of survey by Angell and Ercolano (1991), some solo-drivers enjoy being alone during their
commute, and 48% of the solo drivers said they would not want to switch to other modes,
even with economic incentives.
According to Zupan (1992), the willingness of American motorists to tolerate delay
and inconvenience has been cultivated for generations. Not everyone likes the idea of
sacrificing individual choices for the greater good. Regarding the heavy congestion on the
roads, Littman (1995) and Ewing (1993) blame legislators for their biased policy against
alternative transportation, a catch-all term for alternatives to the single occupant vehicles.
These alternatives include carpool, vanpool, mass transit, bus and bicycles (Carter and
O’Connell, 1982). Thompson et al. (1993) also point out that large subsidies encourage
4
auto use and the organization of land uses that go with it, making the alteration of travel
behavior difficult to impossible.
Driving costs society about 60 cents per mile in "free" parking, road maintenance,
uncompensated accident costs, and pollution impact, more than double the social costs of
transit, van and car pooling, and many times more than the cost of bicycling or walking
(Littman, 1995). Ewing (1993) estimated the cost in delays, air pollution, parking costs, if
fully reflected in gas prices, would raise the cost of driving to more than $4.50 a gallon,
nearly four times its nominal cost.
Research suggests that beliefs, attitudes and behavioral intentions influence
behavior (Fishbein and Ajzen, 1973). Although a host of publications deal with bus
riding, few have examined the role of these psychological variables on the behavior of
commuters. This dissertation employed attitudinal and behavioral models to understand
why commuters choose or refuse to ride a bus, with the aim of identifying strategies that
would persuade drivers to ride a bus. The models theorize that behavioral intention is the
antecedent of behavior.
Bus riding is one kind of ride sharing in Transportation Demand Management
(TDM) programs.
Others include car pooling, van pooling and riding taxis with
unknown people by sharing the cost. Transportation Demand Management (TDM) also
uses park-and-ride, high occupancy lanes, and a guaranteed ride home. As this
dissertation deals with bus ridership, I will discuss TDM programs, with a particular
focus on mass transit.
5
1.2 Transportation Demand Management (TDM) Programs
TDM aims to shape travel demand whereas Traffic System Management (TSM)
aims to improve the efficiency of the transportation system (Steiner et al., 1992). TSM
alleviates congestion by improving the operation and coordination of transportation
services and facilities (AASHTO, 1992).
TDM programs alleviate congestion by
managing travel demand. These concepts have different, but complementary goals.
TDM goes beyond traditional transit and parking subsidy programs by enhancing
the use of high occupancy vehicles (Bhatt, 1991). It involves reducing vehicle trips by
changing the behavior of commuters from solo drivers to carpoolers (Ducca, 1992), van
poolers, bicycle riders, walkers and transit users (Glazer, 1993).
maximizes the use of existing facilities (Kraft, 1992).
It preserves and
In brief, TDM programs or
measures can reduce the demand on the road network by changing the choices made by
commuters or travelers (Zupan, 1992). TDM not only gets vehicles off the road, it also
frees up surface parking land in office and research parks that could be used for housing
(Hare and Honig, 1990).
The components of a TDM program depend on specific geographic and
demographic conditions, but they may include improvements to transit, bicycle and
pedestrian facilities, and incentives to reduce peak period driving (Littman, 1995).
Geographically, TDM may range from a specific site, a single employer with one
building, to a city with thousands of commuters (Ferguson, 1991).
6
The Institute of Transportation Engineers (ITE Board of Direction, 1991) has
supported TDM programs because these programs have alleviated traffic problems by
reducing solo driving. In support of TDM, the Federal government has required the
inclusion of congestion management plans in the transportation planning process (Orski,
1991). The public as well as private sector supports it because of its low cost. Although
TDM does not take as many vehicles off the roads as planners wish, it has a valuable
incremental effect (Kish and Oram, 1991). A survey in Seattle found that carpooling
reduced the percentage of solo-driving by commuters from 75.7% to 70.9% within one
year (Frederick and Kenyon, 1991).
Walking, bicycling, ride sharing and mass transit use often require more time than
driving alone, but under favorable conditions individuals using these alternatives may
experience less stress than single occupancy vehicle drivers.
Bus and rideshare
passengers can use their travel time to relax, read or work, and bicyclists and pedestrians
benefit from exercise (Littman, 1995). All these factors can contribute to TDM program
benefits.
The traditional way of fighting congestion by adding lanes in congested areas
tends to increase overall motor vehicle use; a phenomenon called generated traffic
(Littman, 1995). TDM programs can also reduce traffic congestion without creating this
problem.
In spite of the benefits, authorities found it difficult to enforce Traffic
Reduction Ordinance (one of the legal TDM measures) in suburbs, and many offices
move to suburbs for driving convenience (Frederick and Kenyon, 1991).
7
TDM strategy has had a modest impact on the travel decisions made by individual
travelers on a daily basis (Ferguson, 1991). Ewing (1993) argues that TDM programs
cannot even produce 4 to 6 % reduction in regional traffic volumes. Similarly, the
Institute of Transportation Engineers reports that the Regulation XV, an ordinance passed
in Los Angeles to reduce automobile emission, had only a marginal impact on aggregate
volume of trips, vehicle miles of travel and auto emissions (ITE, 64).
In general, while investigators such as Zupan (1991) consider TDM as a costeffective alternative, a study by ITE (1994) found that the cost of TDM ranges from $12
to $750 per employee, indicating that while some programs are cost effective, others are
not.
Table 1 presents some remarks made by investigators regarding feasibility,
implementation, marketing strategies and evaluation of Transportation Demand
Management programs.
Strategies that make TDM programs successful include 1)
coordination and cooperation among implementing agencies including labor unions
(Freas and Anderson, 1991; Steiner et al., 1992), 2) creation of a private and public
partnership (Hartje, 1991), and 3) establishment of a central clearing-house (Dagang,
1993). Some programs involve penalties, such as raising parking fees. Other programs
involve preferential treatment, such as allowing commuters with more than one person in
a car to use a special lane. Some results indicated that the penalty programs work better
than preferential programs, when these penalty programs are accompanied by other
programs for full effectiveness (Ben-Akiva and Atherton, 1977).
8
Others found a
reduction of employer subsidized parking had a positive influence on ridesharing
(Dagang, 1993).
Author
Alcott (1991)
Ben-Akiva and
Atherton (1977)
and Elsenar (1991)
Dagang (1993)
“ “
Elsenar (1991)
Ferguson (1991)
Flannelly et al.
(1991)
Freas and
Anderson (1991)
Grenzeback and
Woodle (1991)
Gillen (1977)
“ “
Glazer (1993)
Gordon and Peers
(1991)
Hartgen (1991)
Hartje (1991)
Orski (1991)
Small (1993)
Steiner et al.
(1992)
Remarks
We cannot expect to pursue commuters to change their transportation mode
easily when the external factors are unfavorable to them.
Penalty programs such as parking fee surcharges or restricted parking are more
effective than incentive programs such as preferential parking. However,
penalty programs should be accompanied with other programs.
Reduction of employer-subsidized parking is the most cost-effective TDM
measure.
Establishment of a central clearinghouse is necessary to promote TDM.
Before asking people to get out of their cars, the availability of alternative
transportation must be improved or provided.
Planners need sound evaluation techniques that accurately and reliably measure
the effectiveness of TDM because many agencies are fully implementing them
without proper knowledge of their effectiveness.
Expensive TDM programs cannot be pursued only based on achieving intangible
benefits such as employee morale or reduced tardiness.
Planners should work closely with labor unions in all phases of implementation.
Agencies that operate TDM should compete effectively with construction and
maintenance programs for state matching funds.
Only those individuals who are on the margin of relocation to switching modes
tend to switch.
Experiments based on economic incentives would fail to motivate solo drivers to
switch to carpool.
No single Measurement Of Effectiveness (MOE) appears to be clearly superior
for evaluating TDM programs. The best MOE may depend upon the
environment and the purposes of the TDM programs
Places where alternative modes are easily available and parking is plentiful,
TDM can influence only to a limited effect.
To make any progress in reducing congestion we must realize that the solutions
will be difficult and need cooperative planning.
Policies that promote the partnership between private and public sector may help
to solve the accelerating transportation crisis.
a) Little effort has been made to assess area wide effect of TDM.
b) Further experience and research is needed to make any firm conclusion
regarding TDM as the primary instrument of attaining and maintaining our
congestion reduction goals.
No single policy can be expected to succeed on all fronts. A combination is
needed to succeed.
Implementation of TDM could be improved through the coordination of these
major areas at the regional level to ensure that policies are consistent.
Table 1: Remarks about TDM Programs
9
Some investigators pointed out that programs based solely on intangible benefits to
the employers (Flannelly et al., 1991) or programs at the places with ample parking may not
work (Gordon and Peers, 1991). They believe that authorities could not persuade solodrivers to shift to other modes without necessary improvements on the alternative modes
(Allcott, 1991).
TDM programs influence the commuters’ behavior in different ways. Some
programs focus on a particular mode, such as carpooling or vanpooling, others do not.
For example, the Guaranteed Ride Home program tries to encourage alternative
transportation such as bus riding, carpooling or vanpooling, bicycling, walking, in
general. Other programs, such as raising parking fees or congestion pricing, try to
discourage solo driving. Now consider mass transit, an important component of
transportation demand management programs and the focus of this dissertation research.
1.3 Mass Transit
Public Transit or Mass Transit refers to a passenger transportation service, usually
local in scope, available to any person who pays a prescribed fare. It operates on established
schedules along designated routes or lines with specific stops, and it moves relatively large
numbers of people at one time (AASHTO, 1992). Examples include buses, light rails, and
rapid transit. Public Transportation differs in that it goes from one fixed point to another.
Like mass transit, it operates on a regular basis using vehicles that transport more than one
person for compensation, usually but not exclusively over a set route or routes (AASHTO,
1992). Buses that take riders from a parking lot to a stadium are an example.
10
An expansion of mass transit could provide benefits whenever freeway congestion
rises or a concern for environment and the availability of fuel increases (Takiye, 1995).
Unfortunately, almost 50 percent of commuters in the U.S. lack access to mass transit
(Warren and Vrebber, 1991).
Although mass transit has benefits, it has lost its market share. The share of transit
represented 12%, in 1960, 6% in 1980, 2% in 1990 (Takiyi, 1995), and about 2% in 2000
(Lieberman, 2002). Unfortunately, transit expenses have gone up. Since 1968, the rate of
annual wage increases of public transit employees has outpaced the average for all U.S.
industrial employees (Minkoff, 1984). Columbus, Ohio, the site of this dissertation research,
has also seen a decrease in ridership. According to the Columbus Dispatch (September 26,
2001), in 1960, in Columbus area, 71% of commuters used to drive alone and 14.3% of
commuters used public transit. However, in 2000, 84% of the commuters drove alone
and only 2.4% of commuters used public transportation.
Authorities often focus on changes in the system or technology. Though these
changes may work, they are costly. Transit planners should also consider promotional
activities, which may represent cost-effective ways to boost ridership. In planning to
increase ridership, one should understand travel demand. The next section discusses the
travel demand forecasting models.
1.4 Travel Demand Forecasting Models
Transportation professionals use travel demand forecasting models to predict and
explain travelers’ trip, mode, route, or destination choices. A model is a hypothesis about
11
the structure of the relationships among the variables of interest in a specified population
(Browne and McCallum, 2002). According to Chou (1986), mode choice was seen as one
of the most policy-relevant steps in the travel forecasting process. Early travel demand
models were calibrated to predict the trips on a large-scale highway system in
metropolitan areas for a long run (Stopher and Meyburg, 1975).
The variables
considered were transport supply and demographic, based on geographic location and
population in question (Thomas et al, 1976). These models were called aggregate models
as their unit of analysis was traffic zone rather than an individual traveler.
They
predicted the demands on highways satisfactorily.
When the focus of transportation investments has changed, the emphasis of
transportation policy has shifted from the long run to the short, from the large scale to the
small and from vehicles to the individuals (Stopher and Meyburg, 1975). Unfortunately,
the conventional models were not useful in predicting small scale travel demands.
According to Chou (1986), the conventional models have been criticized because i) they
are not policy oriented; ii) they are inflexible to changes; iii) they are uni-dimensional;
iv) their operational processes are cumbersome and expensive; and v) they are nonbehavioral and based on zonal-aggregate data. In addition, they did not deal explicitly
with economic forces and environmental outcomes (Thomson, 1997)
As a result, transportation investigators began to calibrate the behavioral models
based on consumer choice theory and psychological choice theory. According to Stopher
and Meyburg (1975), the consumer theory assumes that a person evaluates the utilities of
all available alternatives and makes the choice maximizing his or her utility. Utility is
defined as the index of attractiveness (Ben-Akiva and Lerman, 1985). The consumer
12
theory also assumes that consumers are rational in that they select the same alternative
given the exact situation and if consumers prefer alternative A over B and B over C, then
they prefer A over C (Consistent and transitive preferences). In transport, one can not
directly apply the utility models that deal with continuous choice (choosing the amount
of butter and milk) to the transport choice which is discrete (one can choose only one
mode to travel). A different analytical approach is applied.
The psychological choice theory assumes that since human nature is inherently
probabilistic one can only assign probabilistic values to the choice based on the measured
utilities. Consumer behavior models based on probabilistic theory can be categorized
into two groups: constant utility models and random utility models. The former assumes
that the consumer can measure the utility correctly, and the later assumes that one can
always expect some errors on utility score due to such unobserved attributes as
unobserved taste variations, measurement errors, instrument variables (Ben-Akiva and
Lerman, 1985). These probabilistic models hold the theorem of probability: 1) the
probability values assigned to the alternatives are always between 0 and 1, 2) the sum
total of the probability values assigned to all the alternatives available must be 1, and 3)
in the case of mode choice, the probability of selecting more than one mode
simultaneously is 0 (Ben-Akiva and Lerman, 1985). Most probabilistic mode choice
models are based mainly on random utility theory and their calibration makes an
assumption related to the error distribution. For instance, probit model, a probabilistic
behavioral demand model, assumes the distribution of errors as normal.
According to Stopher and Meyburg (1975), Warner (1962) was the pioneer to
apply the behavioral model in the transport field. This kind of model is disaggregate as
13
the basic unit of observation is the individual traveler. However, they are not strictly
disaggregate because a single set of parameters is estimated for a segment of population
(Stopher and Meyburg, 1975).
During the 1960’s and early 1970’s, probabilistic choice models proliferated
culminating in international conferences: One in 1973 on travel behavior and values, and
another in 1975 on behavioral travel demand (Stopher and Meyburg, 1975). These
conferences made an attempt to streamline the research in this area by evaluating the
strengths and weaknesses of past research and setting the guidelines for the future
research.
Since then investigators have successfully calibrated, tested, applied and
validated such models as multinomial and nested multinomial logit models for the
transportation choice (cf. Yun, 1990 for a review); and the use of probabilistic choice
models led to major advances in travel demand modeling (Yun, 1990). According to
Viton (1989), the study of these behavioral models provides tools for planners interested
in predicting travel demand and policy analysists interested in evaluating the desirability
of changes in transportation provision.
Researchers view the decision-making process of a traveler selecting a mode from
several available alternatives as complicated. Angell and Ercolano (1991) argue that many
factors affect the selection, including occupational title, income, as well as exposure to the
available modes of transportation. Horowitz and Sheth (1977) cite other factors affecting
the decision-making process: expense, comfort, pleasantness, reliability, time saving,
convenience, safety from crime, energy consumption, traffic problems, and pollution can
also affect the decision making process. Koutsopoulas et al. (1993) cite that travel time,
travel distance, type of road, travel speed, weather conditions, personal preferences
14
influence the mode choice. Saka (1993) argues that individuals do a comparative analysis
of cost of living close to employment and travel cost and choose the most cost-effective
alternative.
Depending upon the situation, the factors cited above may not have a large effect on
the choice of a particular mode. For example, Horowitz and Sheth (1977) found that
perceptions of economic advantages had minor roles in the determination of behavioral
predispositions toward ridesharing, and that demographic and travel characteristics did not
indicate whether a commuter drives alone or shares a ride.
In using probabilistic models, investigators incorporated not only demographic,
socioeconomic, and transportation system variables but also soft or attitudinal variables
such as comfort, convenience, and safety. According to Angell and Ercolano (1991), the
ability to gain information about the attitudes of commuters regarding various modes of
commuting available to them has proven valuable in tailoring programs that promote the
use of these alternatives.
The probabilistic models are not without problems.
Chou (1986) notes a
difficulty in interpreting the behavior of someone reporting a 70% chance of riding the
bus. It may mean that if someone plans to commute to work for 100 days, he or she will
ride it 70 days; or it may mean that each day the person has a 70% chance, but ends up
riding at a high percentage. Chou (1986) also contends that soft variables, such as
“inconvenience,” may have a different meaning to different individuals. Probabilistic
models also tend to ignore the questions of aggregation and definition of choice sets
(McFadden, 1975).
15
Two related attitudinal models—the Fishbein and Ajzen model—have proven
successful in predicting and explaining social behaviors (Next chapter will discuss these
two models in detail). Researchers have tested the models inside and outside the lab
environment and applied them towards many kinds of behavior such as career selection,
voting, family planning and quitting smoking. The Fishbein and Ajzen models share
some similarities with popular probabilistic behavior models:
1) Probabilistic Behavior (PB) models and the Fishbein and Ajzen (F&A)
models are “disaggregate” models.
2) Both types of models use compensatory decision rules. Compensatory models
are those in which a low value in one attribute can be substituted by a high value in
another. In non-compensatory mode choice models, decision rules such as dominance,
satisfaction, elimination by aspects are used to select the alternative (For detail, see
Chou, 1986).
3) Both types of models can predict as well as explain the behavior.
The F&A models also differ from the PB models:
1) Although both PB and F&A models are based on consumer theory (utility
maximization), F&A differs in that they assume linear relationships. Loui and Hartgen
(1975) contend that linear models should not be called behavioral models. They argue
that linear models may not reflect the reality because the traveler’s behavior with respect
to the attributes that influence this decision may be expected to be nonlinear (S-shaped).
Yet, in the test of Fishbein and Ajzen theories, probabilistic models can be applied when
the scores for behavioral intention or behavior are measured in a ratio scale and they hold
the theorems of probability theory.
16
2) The PB models use socioeconomic, demographic and transport system
variables, as well as soft variables such as comfort, convenience, and safety as a
substitute for an attitude and as direct influences on the choice behavior. The F&A
models assume that external variables such as those do not directly influence behavior or
behavioral intention. Other independent variables (beliefs, attitude, intention, distinctly
defined and based on cognition theories) directly influence behavioral intention and
behavior.
3) In the application of PB models, researchers select variables by intuition or
based on the past experience. In the A&F models, researchers elicit behavioral beliefs
and important referents from the respondents or individuals in the same population.
4) Few investigators have applied F&A models in the transportation field and
only one has tested it for transit choice: Thomas (1976).
Although Thomas et al. (1976) successfully applied Fishbein model more than 25
years ago and recommended further application of it to predict mode choice behavior,
since then, few investigators have tried to apply it in the transport area. Why have
transportation planners overlooked? Transformation of knowledge across disciplines
takes time and may not occur unless someone with a multi-disciplinary outlook takes the
initiative.
Behavioral scientists accept smaller correlation coefficients than do engineers. In
addition, psychologists disagree about whether the semantic differential scales in the
F&A models should be considered ordinal, or interval (Nunnally, 1967); thus, if
transportation engineers hear about these models they may avoid them because of the
17
uncertain specifications of the scale. Finally, for a complex decision making process,
they may consider a model without an exponential or integral function as inadequate.
However, Taaffe and Gauthier (1976) argue that regardless of their discipline,
individuals who work in the field of transportation should acquaint themselves with the
models of other disciplines to better understand the fundamental processes and relations
in any transport system.
This belief motivated me to apply attitudinal models to
understand the mode choice behavior of commuters.
The Fishbein Behavior Intention Model (FBIM) has proved useful in predicting
behavior (Kirking, 1980), and Thomas et al. (1976) found that it as compared to the
orthodox cost models could yield a better understanding of the travel demand process. My
dissertation applies Fishbein Behavioral Intention Model (FBIM) and a variation on it
(Ajzen Behavioral Intention model) to bus riding behavior. Both theories use attitudes and
assumptions about the relationship between attitudes and behavior. The next chapter
discusses these theories as they relate to models of modal choice.
18
CHAPTER 2
LITERATURE REVIEW
2.1 Beliefs, Attitude and Behavioral Intentional Models
Psychologists have offered different definitions of attitude.
Thurstone (1931)
defined an attitude as the amount of affect for or against a psychological object; Allport
(1935) defined it as a learned predisposition to respond to an object or class of objects in
a consistently favorable or unfavorable manner; Rokeach (1979) saw attitude as a
collection of beliefs organized around an object or situation; and Bem (1970) contended
that attitudes represent only likes and dislikes. However, many researchers agree that
attitude represents a person's evaluation of the object, in which the object refers to an entity
discernable to him or her (Ajzen & Fishbein, 1977). Put simply, an attitude toward an
object or behavior shows the individual’s feeling of favorableness or not towards that object
or behavior (Thurston, 1931). For transportation, Dobson (1975) defined attitude as a
behavioral intention. He argued that an individual’s preferences for the attributes of
transport services rely on attitudes.
While conventional wisdom suggests a positive behavior associated with a
positive attitude and a negative behavior associated with a negative attitude, many studies
show little to no correlation between attitude and behavior (Wicker, 1969). However,
19
Fishbein (1979) pointed out that attitude may predict overall behavior relevant to that
attitude but may not predict a specific behavior. For instance, suppose someone holds a
positive attitude towards a transit authority bus. The person may sign a petition to add a
route in his neighborhood, she may also vote for increasing the sales tax to finance the
authority, but she may never ride the bus.
Weigel and Newman (1976) believed that problems with the reliability and validity
of the methodology and behavioral criterion explain the lack of correspondence between
measures of attitude and behavior. For example, the use of a measure of general attitude to
predict a single specific behavior may fail because of a lack correspondence between the
measures.
Still, Dobson (1975) pointed out three potential benefits of attitudinal analysis:
1) it can be used for short-term forecasting and marketing programs,
2) it can have a potential role in transportation-system evaluation, and
3) it can help planners understand the decision process of traveler behavior.
Attitude modeling dates back to the 1930s (Golob, 1973), but Rosenburg’s
(1960) cognition summation theory helped attitudinal models gain popularity in
application. According to Rosenburg, attitude towards an object is the function of its
perceived instrumentality to achieve values and the importance of those values. His
theory suggests:
n
Aij =
∑
Pijk x Vik
i =1
Where Aij = affect aroused in individual i by object j, Pijk = perceived potency or
perceived instrumentality of object j for achieving or blocking value k for individual i,
20
Vik = rated value importance of the key value to individual; and n = number of salient
values.
Rosenburg’s model has the following implications: i) the product of perceived
instrumentality and rated value importance monotonically relate to attitude and can
explain it, ii) a number of salient values or attitudinal attributes affect attitude in a linear
additive manner, and iii) a statistically testable structure for modeling altitudinal
variables exists (Chou, 1986). Fishbein’s Behavioral Intention Model (FBIM) and Ajzen’s
Behavioral Intention Model (ABIM) adopted these properties.
2.1.1 Fishbein Behavioral Intention Model (FBIM)
Fishbein (1967) created a model that showed the relationship between beliefs,
attitudes, behavioral intention and overt behavior. According to Fishbein, an attitude
toward an object equals the sum of the evaluations of the attributes associated with that
object; and an attitude toward an act equals the sum of the evaluations of outcome
perceived as likely to follow from performance of that act. He assumed that people learn
directly or indirectly an association between the object and a given attribute and between
the behavior (act) and a given outcome and treat learning as probabilistic. He considered
beliefs as the relationships between objects and concepts and the belief strength as the
extent of the learned association between objects.
The Fishbein model relied on Dulany's theory (1961) of propositional control
(Kirking, 1980). Dulany applied this theory to verbal learning behavior through operant
conditioning in the laboratory. Dulany’s theory posited that in the context of studies of
21
verbal conditioning, the subject’s intention (BI) to make a particular response depends
upon four factors: 1) the subject’s hypothesis that the occurrence of the particular
response will lead to a certain event (Hypothesis of the distribution of
reinforcementRHd), 2) the subject’s evaluation of those events (AttitudeA), 3) the
subject’s belief about what he is expected to do in the situation (Behavioral
HypothesisBH), and 4) the subject’s motivation to comply (Mc). The theory can be
expressed as follows:
BI = [(RHd)(A)]w0 + [(BH)(Mc)]w1
Where w0 and w1 are beta weights.
(Source: Fishbein, 1967)
In theory, the Fishbein model can predict an individual's intention to perform any
behavior in a given situation (and thus the individual’s performance of behavior) by using
three independent variables: 1) his attitude toward performing the behavior in the situation
2) his perception of the norms governing that behavior, and 3) his motivation to comply
with those norms.
Adding norms and motivation to comply to Dulany’s behavioral
intention (BI), the Fishbein model is expressed as follows:
OB~BI = w1 x Ab + w2 x SN
Where OB denotes the Overt Behavior, w1 and w2 are weights.
BI denotes the Behavioral Intention.
Ab denotes an Attitude Towards Behavior.
SN denotes the Subjective Norm.
22
(1)
The wavy line between OB and BI means that BI can predict B only when the given
conditions (discussed below) are met.
Overt Behavior refers to observable acts (Fishbein & Ajzen, 1975). When our
interest involves seeing how many days per week a person rides a bike to work, riding a
bike to work represents an overt behavior. Behavioral Intention (BI) differs from overt
behavior. It refers to a person's subjective probability that he or she will perform the
behavior in question (Fishbein & Ajzen, 1975). For instance, when someone answers the
question, "Do you plan to ride your bike to work tomorrow?" the answer does not represent
the overt behavior. It merely shows the behavioral intention. The Fishbein model assumes
that behavioral intention represents an intervening variable between an attitude and overt
behavior. Figure 1 shows a schematic depiction of the model.
Ab (Attitude towards behavior) =
The sum of beliefs about outcome
multiplied by the evaluation of
those outcomes Sum BBi*EOi.
SN (Subjective Norm ) = Normative
belief regarding a referent multiplied
by the motivation to comply with
those referent. (NBi*MCi.).
BI
Behavioral
Intention
OB
Overt
Behavior
Fig. 1 BEHAVIORAL INTENTION MODEL (Ajzen and Fishbein, 1980)
23
Fishbein identified three factors that influence the correlation between OB and BI:
1) the specificity of the intentional measure,
2) the time between the measure of intention and the behavioral observation, and
3) the degree to which the individual can control carrying out the intention (Fishbein,
1973).
According to Fishbein, attitude has two components. The first component, known
as subjective probability, represents the probability that a chosen response will be followed
by some consequences. The second component denotes a subjective utility. To obtain the
attitude toward the performance behavior (Ab), one multiplies these two components. This
expectancy component BBi, denotes the belief that performing OB (behavior) leads to
outcome i and this value component EOi, represents the person's evaluation of outcome i.
The following mathematical model represents the relation:
n
Ab =
∑
(BBi x EOi )
(2)
i
Where n denotes the number of beliefs (meaning more than one belief and its
corresponding evaluation)
m
SN =
∑
(NBi x MCi )
(3)
i
NBi denotes a normative belief--a person's belief that "important others" think that
he or she should or should not perform behavior OB. MCi denotes the subject's motivation
to comply with the important other's wishes. Here again, m indicates that the number of
important referents can be more than one person or group. To obtain over the subjective
norm, one needs to sum up the scores.
24
One can express the model in its final form as:
n
B~BI = w1 x
∑
m
(BBi x EOi) + w2 x
i
∑
(NBi x MCi) (4)
i
(Fishbein, 1972)
The model states that 1) a person's intention to perform a behavior (BI) depends
upon that person's belief that performing that behavior will lead to certain consequences
multiplied by his or her evaluation of the value of those consequences, and 2) the person’s
belief about the norms influencing the provision of the behavior weighted by his or her
motivation to comply with those norms. Thus, the theory behind the model assumes that
people act or make decisions based on the overall behavioral and normative beliefs they
hold. Fishbein named this theory the Theory of Reasoned Action.
This model agrees with the social psychology theory that the strength of the
tendency to perform an act varies with 1) the strength of the expectancy that the act will be
followed by outcome and 2) the value of the outcome to the individual (Mazis et al., 1975).
Fishbein’s model excludes external variables such as demographic variables,
personal traits, and the situational variables as predictor variables because he contends that
if these variables have influence, they do so through attitudes and subjective norms. The
external variable can affect BI significantly only when external variable correlates with
predictor variable and predictor variable correlates with BI (Fishbein, 1972). Suppose that
income correlates with Attitude towards behavior (Ab).
But if Ab does not have a
significant weight on the equation, income would not correlate with BI even if income
25
correlates with Ab. On the other hand, if Ab had a significant weight (wi) in the overall
equation, one would expect income to correlate with BI. The same situation holds for SN.
Fishbein contended that an inclusion of external variables to predict behavior led to
inconsistent results.
For planners interested in understanding what factors influence intentions or
behaviors rather than just how to predict behavior, the Fishbein model has value. It
assumes that two major factors affect intention -- personal attitude towards performance
(Ab) and social norms (SN).
Ajzen and Fishbein (1980) reviewed several studies in laboratory and field setting
to test the validity of the model. These studies explained and predicted 1) weight loss, 2)
occupational orientation, 3) family planning, 4) consumer behavior, and 5) voting
behavior. They also showed that one could use FBIM to influence the behavior of
alcoholics by means of persuasive communication. Other research agrees. For example,
Hu (1995) conducted a study of intention to quit smoking in males in the workplace in
Southern Taiwan. She found that Fishbein model significantly predicted intention to
quit.
From their studies Fishbein and Ajzen concluded that one can predict overt
behavior from behavioral intention provided that one takes into account the three factors: a)
specificity of the intentional measure, 2) time, and 3) the degree of control.
26
2.1.2 Other Behavioral Intention Models
For planners like me interested in marketing programs, such as mass transit,
accurate prediction, and understanding of consumer intention is important. The F&A
models do a good job of this, but before chosing them to test in relation to transportation, I
evaluated other psychological models for behavior. Although several researchers have
developed models of the relationship between attitude and behavior (Miniard-Cohen, 1979;
Rosenburg, 1960; Sheth, 1974; Warshaw, 1980), none measure up to the Fishbein (1980)
model or Ajzen’s (1985) extension of the Fishbein model.
Sheth's model (1974) focused on product purchase situations. His model includes
four variables: 1) measurement of one’s perception of the object and its ability to satisfy a
need 2) perception of social connotations that the object posses 3) past satisfaction from
behavior which results from the object and 4) situational influences that one expect will
occur during the performance of behavior.
This model includes a measurement of
"consideration" instead of "intent," but "consideration" differs from intention. This model
measures attitude towards the object rather than attitude towards performance. Thus, in
contrast to Fishbein, the model uses the evaluation of brand 'y' instead of evaluation of
buying brand 'y'.
The Rosenburg (1960) model assumes that an attitude toward an object depends
upon the probability that the object yields good or bad outcomes and the degree of
satisfaction or dissatisfaction obtained from these outcomes. Rosenburg, like Rokeach
(1979), posits that values affect attitudes.
However, Rosenburg incorporates another
variable known as "perceived instrumentality." This variable represents the degree to
27
which one believes that engaging in a behavior will enhance or block the attainment of a
value. Rosenburg validated his model by successfully predicting various modes of travel,
menu items, and choices in restaurants.
Unfortunately, Rosenburg’s model does not
incorporate normative components. According to Fishbein, the social pressure, such as the
pressure of a spouse, a colleague or a supervisor, affects the behavior.
Warshaw’s (1980) model also focuses on product purchase behaviors. Like the
Fishbein model, it assumes that behavioral intention precedes behavior. However, the two
independent variables of this model include motivation and capability.
The major
assumption includes the intent of performing a specific behavior (riding a campus bus)
depends upon the formation of global intentions (bus riding).
According to
Danko-McGhee (1988), the Warshaw's model has shown superior cross-validity, meaning
the results are applicable to other samples from the same population (Raju et al., 1975).
The model yields stable predictions over different samples from the same population and
predicts behavior well. However, investigators question the convergent validity of the
proposed intention measure. An instrument has convergent validity if it relates to other
measures of the same behavior or construct (Cone and Foster, 1996).
Moreover,
Warshaw model requires a questionnaire with two sets of questions with corresponding
items where wording needs to be refined. This weakness in the survey instrument may
result in worthless data (Danko-McGhee, 1988).
The Miniard-Cohen model (1979) also looks similar to the Fishbein model, but
Miniard and Cohen contend that Fishbein failed to separate personal and normative
influences on one’s attitude. They argue that Fishbein model may count the same thing
twice. For instance, a person may have learned all the benefits of riding a bus from his
28
father and formed the positive attitude toward riding the bus. His father’s influence will be
reflected in his attitude toward the bus or bus riding. But the normative component also
would reflect this information. The Miniard-Cohen model includes three variables: attitude
toward the behavior, personal reasons for engaging in a behavior, and social reasons for
engaging in a behavior.
Miniard and Cohen manipulated personal and normative
influences and tested consumers' intentions to purchase different brands of products. They
successfully separated personal and normative components.
Although the Miniard and Cohen model predicted behavioral intention, the survey
instrument needs a lengthy instruction to respondents to make sure that they can separate
personal influences from normative influences. This extra reading can lower response
rate.
FBIM also has potential problems. The presence of correlation between the
components of the modelthe attitude and subjective normsrepresents one oftenmentioned criticism. Kirking (1980) contended that existence of correlation complicates
the matter if one aims to explain rather than predict behavior. As an alternative, Ryan
and Bonfield (1975) proposed a revised version of the model. Fishbein conceded the
existence of a correlation between components, but he did not regard it as a serious
problem because investigators found that the weights derived for the attitudinal and
normative components of Fishbein model vary in a manner logically predictable from
Fishbein’s theorizing (Burnkrant & Page Jr., 1982).
Another problem involves the existence of correlation among the salient beliefs
that construct the attitude. For instance, one may measure commuters’ beliefs about
traffic congestion and air pollution. These two beliefs may be correlated. In addition,
29
normative beliefs may be correlated to one another. One solution to such a problem
includes a factor analytic approach (Sheth, 1974) to eliminate such effects in his model.
Factor analysis assumes that the observed variables are linear combinations of some
underlying (hypothetical or unobservable) factors. It also assumes some of these factors
to be common to two or more variables and some others unique to each variable (Kim &
Mueller, 1978). Furthermore, factor analysis would dramatically increase the size of the
sample needed to analyze the results.
Raju et al. (1975) argued that the Fishbein model assumes attitude as the
summation of beliefs.
During the summation the positive components cancel out
negative components, but such an assumption may be false.
Although Raju et al.
believed that the disaggregated version would perform better they also realized that it
would result in too many independent variables.
They suggested using only the
orthogonal dimensions of the beliefs to limit the number of independent variables. In the
comparative study of predictive validation and cross-validation of the Fishbein,
Rosenberg, and Sheth models of attitudes, Raju et al. (1975) collected data on 243
respondents (students, student wives, and house wives) in the Champaign-Urbana area.
The researchers interviewed them regarding their beliefs, attitude, and intention of
purchasing a Pinto car (a product of Ford Motor Company) and purchasing of an
automobile. To test the Sheth model, Raju et al. (1975) first performed principal
component analyses on correlation matrices.
They used varimax rotation to obtain
rotated factor loadings and then used these loadings to obtain factor scores for each
individual variable. The analysis yielded three factors, which they labeled as: Quality,
Luxury and Sportyness (a dimension of Pinto). Raju et al. found that the Sheth model
30
had a high predictive validity and cross-validity, the Fishbein model had a lower
predictive validity but high cross-validity, and the Rosenberg model had a low crossvalidity. However, Fishbein contended that researchers should not try to eliminate the
correlation among the belief items because in the real world these beliefs may correlate
with one another. And the existence of correlation does not significantly affect the
predictive capability of the model.
Raju et al. (1975) contended that their own approach is a compromise between the
aggregated version (Fishbein’s model) and the completely disaggregated version (Sheth’s
model). They argued that the major advantage of their approach includes its ability to
capture the multi-colinearity among belief items. In addition, investigators learn which
beliefs group together under any given dimension. In spite of its strength, due to the use
of factor analysis, the compromised version requires a much larger sample than the
Fishbein model. According to MacCallum et al. (1999), although when communalities
are high (e.g., >0.7 or so), the sample size requirement is as small as 60, when
communalities are low (e.g., <0.4 or so) and factors are not well determined, the sample
size requirement may be well over 400. In addition, MacCallum et al. (2002) contended
that rules of thumb regarding sample size are invalid and misleading.
Moreover, the compromised model like most attitudinal models incorporate only
the personal factor (attitude towards object). However, Fishbein model incorporates both
personal factor as well as social factor (SN).
As a result, this model has been
successfully applied in the social psychology field and marketing field.
31
2.1.3 Validations and Applications of the FBIM
Researchers have tested the validity of FBIM successfully across a diverse range
of behavioral areas, but the literature records only a few applications of FBIM to
transportation behavior. This section reviews the application of FBIM in other fields.
Then, it details a study related to transportation conducted by Thomas et al. (1976).
To test two people's intentions to work in cooperation or competition, Ajzen and
Fishbein (1970) used the Prisoner's Dilemma game. In the Prisoner’s Dilemma game,
players make a choice between two alternative strategies: cooperation and defection. The
best strategy for the individual differs from the best joint strategy. Ajzen & Fishbein
asked students to play the Prisoner's Dilemma game and observed their intention and
behavior. They used two payoff matrices with a different cooperation index (CI) to
observe the influence of the cooperation index. Another independent variable included
the motivational orientation: cooperation, individualism, or competition. They also
observed the main effects of the order in which participants played the games, and the
gender of the players.
Both the motivational orientations and the cooperation index of the games
strongly influenced questionnaire responses and game behavior in the expected direction.
The cooperative motivational orientation produced the highest degree of cooperative
responses while the competitive orientation produced least cooperative responses.
However, neither the game order nor the players’ gender produced significant differences
in overt behavior. The results indicated that an individual's performance of cooperative
(or defecting) behavior depended on his or her intention to cooperate (BI) which
32
depended upon the individuals 1) attitude toward cooperating in the particular Prisoner’s
Dilemma situation (A-act) and 2) beliefs about what the other player expects him to do
(Normative Beliefs).
Subjects gave more importance to subjective norms when the
experimenter instructed them to work cooperatively, and less importance when instructed to
compete.
In a field study, Burnkrant and Page (1982) investigated the validity of the FBIM
for the behavior of blood donation. They administered a questionnaire over a two-week
period preceding the beginning of the blood drive. According to Burnkrant & Page
(1982), previous validity studies used single measures, regression or path analysis, but
they obtained multiple measures for variables that constituted the normative and
attitudinal components of the Fishbein model.
Using structural equation modeling, they performed a thorough investigation of
the Fishbein model, examining each component separately rather than assessing the
validity of the entire model. Their results rejected the first two hypothesis of the Fishbein
model: summation of behavioral beliefsΣ(BBixEOi)can predict attitudes towards
behavior (Ab) and summation of normative beliefsΣ(NBixMCi)can predict subjective
norm (SN). The results indicated a lack of discriminant validity between Σ(BBixEOi) and
Ab, and Σ(NBixMCi) and SN. An instrument has discriminant validity if it does not
relate to measures which theory would predict it to be independent (Cone and Foster,
1996). However, their results also indicated that the second part of Fishbein model
(attitude toward the behavior and subjective norm together predicting behavioral
intention) achieved convergent, discriminant, and predictive validity. Predictive validity
is a form of validity in which a psychological measure is able to predict some future
33
behavior.
They concluded that a single attitude toward behavior, measurable by
cognitive and affective measures, and a single normative construct, measurable by
general and specific normative measures, predicted intention. This one instance does not
rule out the utility of including and testing behavioral beliefs and normative beliefs in
relation to transportation behavior.
Green (1991) conducted a study related to transport. He sought to find the effects
of deregulation of state carriage bus services on the traveling public. To incorporate the
various effects on different types of users and different levels of bus service, he selected
four geographical areas within Plymouth, U.K.
He conducted two large-scale postal
surveys, one nine months before the deregulation and another three months after it. Later,
because of the information obtained from this survey, he selected a panel of respondents
and interviewed them. The results of the study indicated that people integrate their salient
beliefs to form attitudes and subjective norms. They hold intention based on attitudes and
norms and arrive at an overall judgment on behaving, supporting the theory of Fishbein.
In the context of travel decisions, Golob et al. (1973) noted a reverse causal link
from behavior to attitudes and beliefs (the phenomenon of cognitive dissonance). They
argued that planners need more complex models to know the consequences of changes in
policy variables and physical attributes. Further, they suggested that since behavior affects
attitude and vice versa, research should calibrate the new models that involve simultaneous
equations with explicit history of behavior, and incorporate softer variables such as comfort
and convenience.
34
2.1.4 Modifications of the FBIM
Some investigators in the marketing field contend that one can not directly apply
the Fishbein behavioral intention model in their areas of study, because of low correlation
between dependent and independent variables. Thus, marketing studies often use a
variation on the Fishbein theory. According to Mazis et al. (1975), investigators often
apply FBIM either without the normative component or with the replacement of
evaluation by importance in the attitudinal component.
A review of ten such marketing studies found the average multiple correlation
coefficient of BI on attitude and subjective norm as .60 and the average B-BI correlation
as .44 (Ryan and Banfield, 1975). Investigators considered these coefficients relatively
low when compared with the results in the field of social psychology, but Ryan and
Banfield (1975) concluded that the differences in the types of behaviors being tested
resulted in such low correlation. When King (1975) tested the Fishbein model for
predicting church attendance behavior, he found multiple correlation coefficient of BI on
attitude and subjective norm as .76 and B~BI correlation as .90. According to Ajzen and
Fishbein (1980), the focus of marketing research on the attitude towards object or product
rather than the attitude towards performance resulted in the low correlation or in the need
of modification in the model.
Some investigators added or modified components in the FBIM. Bennett & Harrell
(1975) added a new factor, known as the "confidence" of the respondents toward their
answers. They found that the inclusion of this "confidence" factor did not significantly
increase the value of R2.
Hackman & Anderson (1968) and Wyer (1970) added
35
"importance (I)" to the attitude equation such that A = BBiEOiIi. When they added this
component, the predictive ability decreased. Perhaps, the term evaluation may already
encompassed the importance of belief (Kirking, 1980).
Some investigators included situational or external variables in the model along
with an attitudinal variable, assuming that the circumstances in which people make their
decision may affect the decision independent of other components: Ab and SN. For
instance, when Beardon and Woodside (1976) conducted a study with inclusion of
situational variables but without a normative component, the results showed the increase in
the value of R2.
Table 2 displays some other studies conducted to validate or apply the FBIM, and
their results.
Most studies—Davidson and Jaccard (1975), King (1975), Sperber,
Fishbein, & Ajzen (1980), McNealey (1982), Wille (1993), Al-Yusuf (1995), Grant
(1995)—supported the Fishbein theory that behavioral intention mediates overt behavior,
and that attitude towards behavior and subjective norms together predict behavioral
intention. The results of the studies done by Kirking (1980), Kuijlen (1993), and OwensNauslar (1993) indicated that the Fishbein model explained only some portion of
behavior. When Hackman & Anderson (1968) added a new component, “importance
(I)”, in the model, the predictive ability of the model decreased. Danko-McGhee (1988)
found that the components of the Fishbein model did not mediate effects of all the
external variables. Fisher and Pathak (1980) also found an increase in R2 when they
included a situational variable in the model.
36
Investigators
Al-Yusuf (1995)
Beardon & Woodside (1976)
Brinburg (1979).
Investigators
Research Topics
Results and Conclusions
Perceptions
about
television
advertisements among women in Saudi
Arabia.
Inclusion of situational variables but
without a normative component.
The issue is the prediction of church
attending behavior (Kirking, 1980).
Research Topics
Supported FBIM.
R value went up.
Comparison of the FBIM with the one
suggested by Triandis (1977).
Results and Conclusions
Danko-McGhee (1988)
Parents’ advocacy behavior regarding
art education in Kindergarten
Davidson and Jackard (1975)
Family Planning activity.
Devires & Ajzen (1971)
Potential cheating behavior of college
students.
Voting
behavior
in
American
elections.
Inclusion of situational variables.
The intention to cheat significantly
correlated with self reported behavior.
Supported FBIM.
Nurses’ attitude toward gay AIDS
patients.
Added a new component “importance
(I)” in the attitude equation i.e. A =
bieiIi.
Supported FBIM.
Fishbein & Ajzen on Henkle
(1976)
Fisher & Pathak (1980)
Grant (1995)
Hackman & Anderson (1968)
Hornik (1970)
King (1975)
The choice to maintain a supply of
fictitious missiles or to divert resources
to the production of factories.
Specific church attendance behavior.
Kirking (1980)
The counseling behavior of pharmacists
Kuijlen (1993)
Complex consumer behavior mortgage
decision
McNealey (1982)
Columbus area school principal’s
behavior regarding art in the
curriculum.
Table 2: Validations and Applications of Fishbein Model
37
Her results supported Fishbein's
assumptions. However, she found that
external variables such as sex of parents,
sex of child and art education
background did effect both predictor
variables - Ab and SN.
Supported FBIM.
An increase in R2 value.
The predictive ability rather decreased,
which may be explained by the fact
that the term evaluation may already
encompass the importance of belief.
The correlation between the intention
and the behavior predicted by using
FBIM was .806.
The results showed that behavioral
intention was highly correlated with
reported behavior.
He concluded that the decision to
counsel is a complex phenomenon and
FBIM although helpful, explained only
a portion of counseling behavior.
An extended Fishbein model is not
adequate to explain and predict complex
consumer behavior mortgage decision
and suggested a scenario approach: A
study of complex consumer decision
with computer-assisted interviewing.
The results strongly supported that
attitude and subjective norm mediate
and predict intention.
(Continued)
Table 2 cont.
Schwartz and Tessler (1972)
The exclusion of external variables
Sheth (1974)
Beliefs and referents are regressed one
at a time.
To predict the future career selection.
Sperber, Fishbein & Ajzen
(1980). Wille (1993)
Wilson, Mathews and Hards
(1975)
Wyer (1970)
Yue (1995)
Mexican agriculture student’s attitude
towards summer fieldwork.
The use of nationally advertised
brands of toothpaste.
Added a new component “importance
(I)” in the attitude equation i.e. A =
bieiIi.
The pre-service teacher's reactions to
proposed nuclear power plants in
Taiwan
When they incorporated a component
personal norm in the model, this
variable was found to be the best
predictor of intention. However, one
problem with their study was the long
interval between the measurements of
intention and behavior. For such a
result, McNealey attributes the behavior
in question. Since the issues addressed
were a range of organ donation
activities, moral obligation might have
played important role.
This method yields high explanatory
power.
Supported FBIM.
Supported FBIM
They found .9 as the correlation
between intention and overt behavior.
The predictive ability rather decreased,
which may be explained by the fact
that the term evaluation may already
encompass the importance of belief.
He found that external variables such as
party affiliation, residential area, nuclear
locus of control, etc. were better able to
predict behavioral intention for
supporting nuclear power plants
Based on the literature review, I believe that depending upon the behavior of
interest, external or situational variables may directly influence the behavioral intention
or behavior. But the theory of reasoned action does not support such a view. However,
Kirking (1980), despite some limitations of the Fishbein model, suggested the application
of it to other fields of study to improve its generalizability.
discussion of inconsistencies can refine the models.
38
He pointed out that
2.1.5 The Theory of Planned Behavior
Ajzen (1985) extended the theory of reasoned action by adding the variable
perceived behavioral control. Perceived behavioral control refers to an individual’s
perception of ease or difficulty of performing the behavior. Ajzen’s theory assumed that
the individual reflects on past experiences and anticipates impediments and obstacles
(Ajzen, 1985). Ajzen referred to the model as the theory of planned behavior. Unlike
Fishbein’s theory, this theory incorporates non-volitional behaviors as well.
In the
model, the perceived behavioral control is independent from attitude or subjective norm
and reflects the beliefs regarding the possession of requisite resources and opportunities
for acting the target behavior. Figure 2 shows a schematic diagram of the theory of
planned behavior.
In the theory of planned behavior, perceived behavioral control affects behavior
directly and indirectly through intention. The theory assumes that perceived behavioral
control has motivational implications for behavioral intention. If people do not have
enough resource and opportunity to perform a behavior, they may not form an intention
or may only form a weak intention to perform the behavior in question even though they
evaluate the behavior positively and think that their important referents approve the
behavior. Bandura et al. (1981) provided empirical evidence that people’s confidence in
their ability to perform behavior strongly influences the performance of the behavior.
The influence of perceived behavioral control on behavior through intention shown on
the model reflects this motivational influence.
39
Ab
Attitude Toward Behavior
SN
BI
Subjective Norm
Behavioral
havior
OB
Behavior
PBC
Perceived Behavior Control
Figure 2 Path models for the theory of planned behavior
(Source: Madden, Ellen, & Ajzen, 1992)
In the case of commuting behavior, suppose a man has a positive evaluation of
riding a bus to work and his supervisor and spouse also like him to ride a bus. Then,
according to the theory of reasoned action, he will have a strong intention to take a bus to
work. However, according to the theory of planned behavior, if the person believes that he
does not have enough resources and opportunity to perform the behavior, he may not hold
the intention to act. For example, if bus service is not available in his residential area, the
person may not hold an intention to commute by bus.
Just because individuals have the resources and opportunity, does not mean that
they will form an intention to perform that behavior. They may hold negative attitudes
toward the behavior, or important referents may not approve of the behavior. For instance,
40
suppose a person hates to ride mass transit. She also believes that her husband, children
and supervisor do not want her to ride a mass transit. In such a situation, even if the bus
stops in front of her house, she may not form an intention to ride it. Thus, Ajzen contends
that perceived behavioral control is a necessary but not sufficient condition. Availability of
resource and opportunity works as a motivational component.
The model adds the direct link of perceived behavioral control to behavior. In
this version of the model, the perceived behavioral control also represents the actual
control a person has over performing the behavior. According to the theory of planned
behavior, this direct link between the perceived behavioral control and the behavior
exists only when a) the target behavior is not under full volitional control and b)
perceptions of control over the behavior are very close to the actual control.
Consider a scenario where one asks a question “How will you go to the airport?” to
four college students who will be flying for Spring Break. Suppose the students answer: a)
I will drive b) I will take a taxi cab c) if there is a bus on the weekend, I will take a bus d) I
will try to ask my roommate to give me a ride.
According to Ajzen, in the case of volitional behaviors, if a person does not act
according to her intention, it means she has changed her mind. However, in the case of
non-volitional behaviors, the person may not perform the act as intended, even when she
has not changed her mind. The external and internal factors may have prevented her
from performing the behavior. Internal factors include information, skills, abilities, will
power, emotions and compulsions, confidence and commitment. External factors include
time, money, opportunity, and dependence on others. In the above case, all four students
intend to perform four different behaviors with the same goal--arriving the airport in
41
time.
The way they answered indicated that they do not have the same level of
confidence in achieving their goals. The first and second students say they will do
something. They exhibit more confidence than the third and fourth students who will
take a bus if there is one, or who will ask for a ride.
According to Ajzen, people's behaviors exist on a continuum. On one extreme lie
behaviors under full volitional control such as a healthy person riding a bicycle to work
within a short distance and walking to a parking lot. On the other extreme lie nonvoluntary behaviors such as arriving at a destination by riding a plane service, or
avoiding congestion on a freeway during an accident. Most behaviors fit in-between the
two extremes. The higher the control we have over the performance of behavior, the
more volitional it is. The four students’ behaviors fall along the continuum.
Although driving a car or taking a taxicab may be considered volitional behaviors
because one can easily perform them, in the above case, their behavior strives to arrive at
the airport in time. Regardless of the mode they choose, either internal factors or
external factors may thwart the performance of behavior. The mechanical problem
(external) in the car may prevent the first student from driving to the airport and catching
his plane on time. Suppose someone tells the first student that the road to the airport is
slippery and then he decides to take a taxicab because of fear of driving on the rain. Here
the lack of skill (internal) prevents the person from performing the intended behavior. A
cab driver’s strike (external factors, dependence on others) will prohibit the second
student from riding a cab. Suppose the student who intended to ride a bus takes a cab
because of an uncertainty of availability of bus on Saturday. Here the lack of knowledge
(internal) stopped the action. On the other hand, suppose the student who intended to
42
ride a bus to save money changes her mind and takes a cab despite Saturday bus service,
the lack of will power (internal) keeps her from performing the behavior. If no public
bus runs to the airport, then the external factor prevented her from performing the
behavior.
Now consider the student who intends to get a ride from his roommate. If his
roommate has given him a ride to the airport without hesitation on previous several
occasions, then the student may have more confidence on performing the intended
behavior (reflection of past experience). Suppose he learns his flight coincides with a
football game and his roommate intends to watch the game, this new information stands
as an anticipated impediment and may lower his confidence over behavioral
performance. He might ask his roommate in advance, or buy a blank videocassette to
record the game so his roommate can watch after coming back from the airport. Here the
student initiates a plan. The more a person formulates a plan and tries to follow it, the
higher the control over behavioral achievement and the higher probability of success.
Thus, Ajzen named his theory as the theory of planned behavior.
Regarding the control variable posited in the Ajzen’s theory, some researchers
used self-efficacy as a substitute although it differs from perceived behavior control.
They did this because when someone has more control over the behavior, she will be
more confident in performing the behavior.
Conversely, it is assumed that more
confidence over the behavior means more control.
behavioral control differently.
Some authors termed perceived
For instance, Triandis (1977) called it “facilitating
conditions”, Sarver (1983) called it “the context of opportunity” and Liska (1984) called
it “resources.” Later, Ajzen (1991) argued that perceived behavioral control is most
43
compatible with Bandura’s concept of perceived self-efficacy.
Thus, perceived
behavioral control is the combined concepts of perceived difficulty and perceived selfefficacy (Hu, 1995).
Shifter and Ajzen (1985) applied the theory of planned behavior to study the
success at attempted weight reduction among college women. Researchers first assessed
the attitudes, subjective norms, perceived behavioral control, and intentions of
participants that related to the losing weight. After six months, they questioned the
participants about their actual behavior during the past six months. The results indicated
that intention depended upon all three variables: attitude towards behavior, subjective
norms, and perceived behavioral control. However, intention and perceived behavioral
control together had only moderate success in predicting the actual weight loss. Ajzen
argued that perceived behavioral control helps to improve the prediction of behavior only
when it reflects the actual control.
Shifter and Ajzen (1985) also tested the influence of external factors on weight
loss that include: a) Self-knowledge b) Planning c) Ego strength d) Health locus control
e) Action control and f) Competence. Some of the statements used to obtain the health
locus control statements are: a) “If I get sick, it is my own behavior which determines
how soon I get well again.” and b) “My good health is largely a matter of a good
fortune.” The results provided the evidence that the development of a plan to lose weight
and ego strength, which are assumed to increase to control over goal attainment, are
positively related to the weight loss.
In sum, according to the theory of planned behavior, perceived behavioral control
can predict intention not mediated by attitude and subjective norms and predict behavior
44
not mediated by intention. In the later case, the behavior in question must not be fully
volitional and the perceived behavioral control must reflect the actual control.
2.1.6 Validations and Applications of the Ajzen’s Model
Researchers have applied and tested Ajzen’s theory of planned behavior. Ajzen and
Madden (1986) conducted two experiments to examine the effect of perceived behavioral
control on intention and behavior.
The first experiment examined college student’s
attendance of class lecture as a target behavior. The investigators collected data at sixteen
regular class sessions and administered questionnaires to students to obtain the information
about attitude, intention and perceived behavioral control.
In the second experiment, the behavioral goal involved receiving the grade “A”
for course work. In this experiment, they collected data into two waves. A few weeks
after the start of spring semester, they administered a first questionnaire, which asked
students for their expectation with respect to getting an “A” in a particular course.
Toward the end of the semester, they administered the same question again assuming that
they had received enough information regarding their performance on the course. The
researcher used their actual grades in the final analysis.
The results of the two experiments supported the proposed theory of planned
behavior. The incorporation of perceived behavior control into the theory of reasoned
action increased the explanation of variation (greatly improved the prediction); and the
perceived behavior control, like the other two variables influenced the behavioral
motivation of a person.
45
The second experiment supported the hypotheses that the perceived behavioral
control significantly correlated with the target behavior.
It implied that perceived
behavioral control influenced behavior independent of effect of intention, but it applies only
when the behavior is not under volitional control and the perceived behavioral control
approaches the actual control. This experiment met these two conditions. Students could
not control obtaining "A". By the time of this experiment, the experimenters had provided
students with sufficient feedback permitting them to have relatively accurate assessment of
behavioral control.
Ajzen and Madden argued that perceived behavioral control has different effects on
behavior. At the time of second experiment, students knew that even if they tried to get an
"A", they could not achieve one. This lack of actual control, not the perception of it,
prevented students from getting an "A". Some of the specific control beliefs may constitute
"rationalization" or post hoc explanation for an inability to attain an intended grade.
Nevertheless, when the measured perceived behavioral control approaches the actual
control, an inclusion of it improves prediction of goal attainment.
In contrast to
expectation, the results showed that perceived behavioral control did not significantly
interact with attitude or SN to affect intentions or interact with intention to affect target
behavior. However, they contend that such a result confirms past research results. Past
research has only supported models that have the main effects of ability and motivation on
the task performance, not on the interaction between them. Whenever interaction was
found it was weak and marginally significant. They further argued that past research has
shown that linear models will usually perform adequately even when interaction occurs.
46
Another study focused on the relative effectiveness of the theory of reasoned action
and the theory of planned behavior (Madden, Ellen, & Ajzen, 1992). The researchers pretested students with open ended question to elicit some of their regular behaviors such as
“avoiding caffeine”, “doing laundry” and their control over these behaviors. Regarding the
control, they gave students examples of how internal and external control factors might
possibly prevent to perform the intended behavior.
In the second pretest, they asked other students to rate behaviors based on perceived
behavioral control. Madden et al. (1992) selected ten behaviors including “exercising
regularly” (low control), “doing laundry” (medium control), and “listening to an album”
(high control). They collected data in two waves. In the first wave, they questioned
students about their beliefs, attitudes, subjective norms, perceived control beliefs, and
intentions. In the second wave, they counted the number of times the students performed
these behaviors during the study period.
The results indicated that in addition to attitude and subjective norm, perceived
behavioral control independently influenced overt behavior. The results also indicated that
Ajzen’s theory of planned behavior explained more variation in behavioral intention and the
target behavior than did Fishbein’s theory of reasoned action.
Madden et al. (1992) argued that by assessing perceived behavioral control and
incorporating it in the model, one can increase the accuracy in prediction of intention and
target behavior. As intention and behavior have a strong relation, whenever one needs to
change behavior, one can do it through changing presentation. The result also indicated
that the higher the control over behavior, the less the influence perceived behavioral control
has over the behavior in question.
For low control, perceived behavioral control
47
significantly influenced overt behavior, and intention did not mediate perceived behavioral
control. For high control, perceived behavioral control did not correlate significantly with
overt behavior.
Therefore, for behaviors with low perceived control, in addition to
changing behavior indirectly through behavioral intentions, one may change behavior by
providing a mechanism for enacting plans to change actual control over the behavior.
Research has shown a wide range of applications of theory of planned behavior.
DeVellis, Blalock & Sandler (1990) applied it to predict participation in cancer
screening. A survey of 96 high-risk and 144 non-risk individuals found that perceived
behavioral control predicted intention better than attitudes and subjective norm in both
groups.
However perceived behavioral control was significant in predicting actual
behavior for the high-risk group only.
Researchers have written hundreds of dissertations and conference presentations on
the application of the theory of planned behavior (See Table 3 for some examples). Many
studies confirmed that the theory explains different kinds of behavior and that it predicts
better than the theory of reasoned action (Astrom, 1997; Fultz, 1997; Greene, 1999; Hu,
1995). Although the Ajzen model like the Fishbein model does not incorporate external
variables, some studies tested and found the direct influence of them on behavior. For
instance, Hu (1995) and Greene (1999) found that past behavior predicted behavior better
than behavioral intention did. Hu (1995) also found that external variables—priority of
quitting, previous attempt to quit smoking—significantly mediated the relationship between
attitude and behavior.
48
Investigators
Astrom, Anne-Kristine
Nordrehaug (1997)
Topics
Dental health behavior among
adolescents:
a
sociopsychological approach.
Beck, Judy (1997)
Teachers’ beliefs regarding the
implementation
of
constructivism
in
their
classroom.
Osten, Kevin Dee (1997)
Applying a derivative of the
theory of planned behavior to the
prediction of motivation to learn.
Fultz, Miriam
(1997)
Predicting voluntary turnover:
An application of the theory of
planned
behavior
(Military
Academy).
Flannery,
(1997)
Louise
Brenda
L.
The effects of individual,
contextual, and moral intensity
factors on environmental ethical
decision-making
(wastewater,
metal finishing).
Greene, Kimberly Faw
(1999)
Help-seeking intentions and the
theory of planned behavior.
Hu, Shu-Chen (1995)
A study of intention to quit
smoking in males in the
workplace in southern Taiwan:
An application and modification
of the theory of planned
behavior.
Table 3: Validations and Applications of Ajzen Model
49
Results or Conclusions
Provided empirical support for the TPB
with regard to the prediction of intention
and actual use of dental floss,
highlighting the non-volitional aspect of
this particular behavior. However, there
was a strong effect of prior use of dental
floss upon intention.
The attitude toward the behavior was the
greatest influence on teachers’ intent to
implement all five sub components of
constructivism
and
significant
differences existed between various
teacher populations for both intent and
the three constructs.
Showed
significant
relationships
between intentions and trainee attitudes,
subjective norms, and the perceived
control the subject felt they had to
perform the study behaviors or do well.
When predicting actual turnover
behavior, both hierarchical multiple
logistic and linear regression techniques
revealed a strong intention-behavior
relationship. These findings highlight
the importance of including all
behavioral alternatives when applying
expectancy-value models.
Manager’s attitudes toward the treatment
of hazardous wastewater, subjective
norms influence, perceptions of the
instrumentality of their respective
climates,
and
financial
cost
considerations significantly influenced
the managers’ decision intention
concerning the treatment of hazardous
wastewater.
TPB has been shown to be a useful
heuristic for explaining help-seeking
intentions. Being in distress and being
female were also found to link to
increased help seeking intentions.
TPB predicted intention to quit better
than the TRA. Priority of quitting and
previous quit attempt contributes
significantly to the TPB model.
(Continued)
Table 3 cont.
Lanigan, Mary Louise
(1997)
Applying the theories of
reasoned action and planned
behavior to training evaluation
levels.
The additional perceived control variable
within the theory of planned behavior
added to the prediction of actual
behavior and made it the more
appropriate theory to support the
Kirkpatrick model.
2.1.7 Measurement Issues related to theory of planned behavior
In regard to Ajzen’s model, researchers have raised a number of questions, which
Ajzen has ruled out. Some investigators indicated that belief measures only moderately
correlate with global measures of attitude, subjective norm, and perceived behavior.
Ajzen (1991) argued that the study of different subjects resulted in low correlation.
Belief measures may involve a reasoned response whereas global measure of attitude
may raise an automatic response.
The failure to evaluate the differences between
individual beliefs and global responses may have caused the low correlation. Some
researchers (Abelson, Kinder, Peters & Fiske, 1982; Ajzen & Timko, 1986) argued that
multidimensional (affective and evaluative) measure of attitude would raise the
correlation.
Maddux (1993) criticized that the measurement of perceived behavior control as
ambiguous. He complained that Ajzen did not make it clear if investigators should
measure control over behavior or the goal because Ajzen often assessed the likelihood
that if the person tried he could achieve a goal over time. According to Maddux, such
measures resulted in evaluating the outcome rather than self-efficacy. Fishbein and
50
Stasson (1990) argued that one should define perceived behavior control clearly before
its use. In recent applications, investigators (DeVellis, Blalock, Sandler, 1990; Godin,
Valois, Lepage, Desharnais, 1992) measured PBC as perceived difficulty (Hu, 1995).
Devellis et al. (1990) found that while perceived behavior control predicted
intention better than attitude and subjective norm, it did not support the direct link
between perceived behavioral control and participation behavior in cancer screening. But
in the Godin et al.’s study (1992) results showed that perceived behavior control not only
predicted the intention of quitting smoking but also the behavior. Despite the positive
results, Hu (1995) argued that according to the Ajzen’s suggestion, investigators should
measure both perceived self-efficacy and perceived difficulty. In her study, Hu used the
question, “How easy or difficulty do you think it would be to quit smoking in the next
month?” to measure the perceived difficulty. One of the eight questions she used to
measure perceived self-efficacy was, “If you were to quit smoking, how much do you
think you could avoid smoking when you feel tense or anxious?” She averaged the
values of perceived self-efficacy and perceived difficulty to obtain the perceived
behavior control. The results showed that perceived behavior control predicted intention
to quit best in the Ajzen model.
The measurement of intention also remains a controversial issue.
Although
behavioral expectation (self prediction) differs from behavioral intention (desire), no one
provided specific guidelines as to what should be measured while measuring intention.
Warshaw and Davis (1985) suggested that for the Fishbein’s model one should use
behavioral expectation instead of behavioral intention because behavioral expectation
predicts behavior better. However, Fishbein and Stasson (1990) argued that it matters
51
only for non-volitional behavior. Warshaw and Davis (1985) also believe that when
testing the theory of planned behavior, these two concepts should be differentiated
clearly and behavioral expectation should be measured. Their research results showed
that attitudes, subjective norm, and perceived behavioral control predicted behavioral
expectation rather than behavioral intention.
Netemeyer, Burton, and Johnston (1991) compared the theory of reasoned action
and the theory of planned behavior to see which one predicts better. They included two
behaviors, weight loss (low control) and voting (high control), in their research. They
considered behaviors with low volitional control such as weight loss as a goal-oriented
behavior because to achieve a weight loss people often have to perform more than one
behavior. They found that three independent variables posited by the theory of planned
behavior predicted behavioral intention better. They assumed that for the less volitional
behavior, the relation between behavior and attitude should be weaker. As predicted,
their research results showed that the theory of planned behavior predicted low volitional
behavior better.
The fourth issue involves the measurement of behavior. The theory of reasoned
action and the theory of planned behavior both pointed out the importance of the
correspondence in measurement when one wants to predict behavior from intention, or
intention from attitude. Since the theory of planned behavior incorporates perceived
behavior control investigators should make it clear whether they aim to measure behavior
or goal. If one aims at measuring the goal rather than behavior, then one should focus on
goal when assessing control beliefs and behavioral intention.
52
Researchers have used attitudinal variables to predict people's transport behavior
(Golob 1973, Dobson 1975, and Lovelock 1975). For example, Horowitz and Sheth (1977)
included attitudinal questions along with other questions related to socio-economic and
demographic conditions of commuters in their study of ridesharing. They found that the
study of attitudes toward ride sharing and driving alone could provide directions for ridesharing strategies. However, according to Thomas (1976), attitudinal studies conducted in
the field of transportation neither successfully developed nor adapted such a theory to
generalize across situation and have simple operational constructs compatible with survey
research and open to behavioral validation. He pointed out four kinds of problems with
these models.
1. While many attitude scales depend on correlation between ratings of attribute and
attitudes, the presence of correlation does not mean that cause exists.
2. Models that use averaging as opposed to summation for combination may not represent
the psychological process.
3.
Some rating procedures force subjects to scale an attribute when they lack the
knowledge about it.
4. The scales may lack adequate testing for external and internal validity.
When Thomas et al. (1976) applied the Fishbein model in their study to
transportation they found satisfactory results. According to Thomas (1976), the FBIM
provided a conceptual framework for the study of transport behavior. The following
section describes the Thomas et al. (1976) study, on which this dissertation is modeled.
53
2.1.8 Thomas et al.'s research.
Thomas et al. (1976) did an exploratory empirical study of belief systems and
their stability in the context of shopping by bus. The researchers selected 203 women as
their subjects on the basis of their mode use for shopping trips: women who used the bus
and who used walking as an alternative or relied on the few local shops (n=77), women
who had these options but were also regularly driven as passenger in a car (n=76), and
women who drove themselves by car (n=50).
a) Method
First, researchers interviewed 30 women (not part of the sample) to establish model
salient attribute beliefs and normative beliefs. Questions in the study included "What
would be the consequences/advantages/disadvantages for you of using the bus next week to
do your main shopping in Brentwood? (travel act 1). "What would be the consequences/
advantages-/disadvantages for you of not using the bus next week to do your main shopping
in Brentwood? (travel act 2). After a pilot study to identify salient behavioral beliefs and
normative beliefs for the questionnaire, the 40-minute interview asked about the trip,
shopping patterns, salient beliefs and evaluations of their beliefs and demographic
information.
A self-completed questionnaire also assessed attitude towards each attribute listed
in the set of salient beliefs, belief strength of attributes, normative beliefs and intention to
perform each act in the following week. Respondents also completed a travel diary with
specific information about the mode they used for shopping in the following week. One
54
month later, follow-up interviews repeated the procedures on the same women in group 1
(who used bus) and group 2 (who were driven as passenger).
b) The relationship between attributes and overall attitude
With all the women from the first stage interviews in the analysis, the relationship
between attributes and overall attitude showed that the multiple correlation between
Attitude towards behavior (Ab) and Subjective Norm (SN) with Behavioral intention (BI)
were r = 0.768 (act 1) or r = 0.725 (act 2) and between predictor variables and overt
behavior r = 0.734 (act 1) and r = 0.720 (act 2).
To predict behavioral intention when the analysis incorporated belief-based
attitude and belief-based (normative) subjective norm, the r = 0.629, without normative
beliefs r dropped to 0.416.
Although r values achieved statistical significance, the
proportion of the total variance explained did not exceed 30%. Noting that a study
related to birth-control done by Jaccard and Davidson (1972) had r as high as .75, the
investigators guessed that the poor result for transport occurred because behavioral
commitment plays a substantial role in such routinized travel acts (i.e. attitude is affected
by behavior).
When they tested the relationship between evaluations and belief strength and
overall attitude (Appendix A1 shows the results), they found that three groups of women
had different attitudes (significant main effect of user group, p <0.001, one- way analysis
of variance). The investigators also examined the belief strength in two ways. First, the
first seven item of each group from the set of salient beliefs were used. Second, the three
beliefs from the subjects' own idiosyncratic set were used. The researchers selected those
55
beliefs based on closeness to the mean number of beliefs (Appendix A2). The results
showed that r (.509) for “using the bus” exceeded r for “not using the bus” (r = 0.463).
However, in the case of idiosyncratic attribute sets, r for “not using the bus” exceeded
“using the bus.” Their results also indicated that the attributes of "not using the bus"
were more accurate than those of "using the bus" (Appendix A3).
During the period between two interviews, Thomas et al. found no significant
change of overall attitude based on the attributes and normative beliefs except some
increase in favorability of the non-bus mode. Indeed, they discovered several significant
changes in belief structure. For instance, for "using the bus", while "convenient shopping"
became more salient due to time effect, "waiting around for unreliable buses" and "carry
heavy shopping" became less salient. Another change included the significant reduction in
the evaluation of "cost of bus fares", but the evaluation of "waiting around for unreliable
buses" remained constant.
Despite an increase in the bus fare, the subjects’ attitudes towards bus riding
improved in this period. The investigators believed that the improvement in the bus
services produced such a result. They also pointed out that before the first survey, due to
the strike, the bus service stopped and as a result, the attitude towards riding the bus had
become less favorable; when the bus service resumed, the attitude gradually recovered.
Thomas (1976) contended that the FBIM seemed sensitive to such small changes.
Ajzen (1991) argued that an addition of past behavior in the theory of reasoned
action would improve prediction, but he believed that since perceived control behavior
mediates past behavior the model did not need to incorporate past behavior. To test this,
Ajzen & Driver (1991) conducted a study related to the leisure participation behavior and
56
Beck and Ajzen (1991) conducted a study related to dishonest action. In both studies, the
addition of past behavior increased the percentage of variance explained in future
behavior ranging from 5 to 32%.
Schlegel, D’Avernas, Zanna et al., (1992) and
Kashima, Gallois, and McCamish (1993) also found that past behavior had significant
effects on prediction related to drinking and condom use. Thus I included a test that
examined the influence of past behavior.
57
CHAPTER 3
3.1 The Problem Statement
Demand management programs such as bus riding and vanpooling, if successful,
not only benefit the users and providers, but also the community. Such programs help
alleviate congestion, conserve energy and reduce air pollution. However, these programs
failed in many places because most drivers do not want to give up their cars even when
offered an economic incentive to do so (Angell and Ercolano, 1991) and authorities lack the
knowledge of information on what would change the behavior and belief system of solodrivers.
Studies such as Thomas et al. (1976), Kirking (1987), Greene (1996), and Hu
(1995) suggest that FBIM and ABIM are useful models that may help investigators
understand what factors influence peoples' behavior. The present study aims to see
whether these models can predict the bus riding behavior of commuters. Planners can
gain understanding of a behavior by tracing its determinants back to the underlying
beliefs, and devise marketing technology that would help attract solo-drivers to
ridesharing, and especially to bus riding. Transportation professionals might be able to
use such information in persuading people to use alternatives to the car.
58
Marketing often tries to bolster sales by modifying attitudes of consumers to
influence their purchasing behavior. In a similar fashion, in transportation service sectors
such as bus, and train services, one might promote ridership of a particular mode by
changing attitudes.
The following simple and multiple regression equations represent the Fishbein
Behavioral Intention Model (FBIM) and the Ajzen Behavioral Intention Model (ABIM):
a) FBIM
i)
OB = w1BI+e1
ii)
BI = w2Ab + w3SN+e2
iii)
Ab = w4ΣBBi*EOi + e3
iv)
SN = w5ΣNBi*MCi + e4
(Source: Ajzen & Fishbein, 1980)
b) ABIM:
iii)
OB = w6BI+ w7PBC + e5
iv)
BI = w8Aact + w9SN + w10PBC+ e6
(Source: Ajzen, 1991)
Where:
OB
=
Behavior (Overt Behavior)
BI
=
Behavior Intention
Ab
=
Attitude towards the Behavior
SN
=
Subjective Norm
BBi =
Behavioral Belief
59
EOi
= Evaluation of Outcome
NBi
= Normative Belief
MCi = Motivation to Comply
wi
ei
= beta weights
=
error terms
The present research tested the following research questions regarding the transport
choice behavior of the commuters.
1) Does Behavioral Intention (BI) predict Behavior (OB)?
2) Will the linear combination of commuter Attitude toward bus riding behavior (Ab) and
Subjective Norm (SN) regarding the bus-riding act predict their behavioral intention
(BI)?
3) Does a positive correlation exist between attitude towards behavior (Ab) and behavioral
intention (BI)?
4) Does a positive correlation exist between Subjective Norms (SN) and Behavioral
Intention (BI)?
5) Will commuters' beliefs (BBi) about the outcome of bus riding multiplied by the
evaluation of those outcomes (OEi) correlate with their attitude toward the bus riding
behavior (Ab)?
6) Will commuters' beliefs (BBi) about the outcome of bus riding and the evaluation (EOi)
of those outcomes separately correlate with their attitude toward the bus riding behavior
(Ab)?
60
7) Will the sum of commuter's perception of beliefs (NBi) of important others regarding bus
riding behavior multiplied by the motivation to comply (MCi) with those referents correlate
significantly with Subjective norms (SN)?
8) Will the commuter's perception of beliefs (NBi) of important others regarding bus riding
behavior and the motivation to comply (MCi) with those referents separately correlate
significantly with Subjective Norms (SN)?
9) Will the linear combination of commuter attitude toward bus riding behavior (Ab) and
subjective norm (SN) regarding the bus riding act and perceived behavior control (PBC)
predict their behavioral intention (BI)?
10) Will the linear combination of perceived behavioral control (PBC) and behavioral
intention regarding bus-riding act (BI) predict bus-riding behavior (OB)?
11) Do demographic variables such as gender, age, and income directly influence
behavioral intention (BI) or behavior (OB)?
3.2 The purpose of study
I conducted this study to test two attitudinal models--Fishbein and Ajzen and
Ajzen—in the context of bus riding behavior of students from student housing complex
to campus. The study tested the Fishbein and Ajzen models to identify the factors that
influence commuters to choose or refuse to ride the bus. I assessed socio-economic
variables because I wanted to see if belief systems relative to bus riding differed across
socio-economic groups. Because some investigators such as Hu (1995) found that
external variables did influence behavioral intention or overt behavior I also wanted to
61
see if the demographic and socio-economic variables directly influence attitude,
behavioral intention, or behavior. Note that according to Fishbein and Ajzen models,
these variables do not directly influence attitudinal variables. Table 4 lists the variables,
defines them and describes how they were measured.
Variable label
Attitude toward bus riding
behavior (Ab)
Question number 22 on the
questionnaire (Appendix D).
Behavioral belief (BBi)
Definition
The degree of person’s
favorableness or unfavorableness
toward bus riding behavior.
Behavioral Intention (BI)
Average of Q. No. 19 through Q.
No. 21.
Overt Behavior (OB)
Answers from the second phase.
The probability that performing a
given behavior will result in a
given outcome.
Evaluation of outcome related to
the behavior.
The strength of each behavioral
belief (BBi) multiplied by the
outcome evaluation (EOi).
Perception of over all pressure
from important referents or
groups to perform or not to
perform the behavior of interest.
A person's belief that the
important other thinks he or she
should perform a specific
behavior.
The strength of each Normative
Belief (NBi) multiplied by the
Motivation to Comply (MCi)
A person’s subjective probability
that he or she will perform the
behavior in question.
The observable act (in this case,
self report of bus riding behavior).
Perceived Behavioral Control
(PBC)
Average of Q. No. 16 through 18.
The individual’s perception of
ease or difficulty of performing an
action.
Q. No. 1 through 10 (first part)
Outcome evaluation (EOi)
Q. No. 1 though 10 (second part)
Belief-based attitude toward
behavior (Sum BBi*EOi)
Subjective Norm (SN)
Q. No. 11 (first part, second not
used in the analysis)
Normative belief (NBi)
Q. No. 12 through 14 (first part)
Belief-based Subjective Norm
(Sum NBi*MCi)
How measured and scored
Scored on a bi-polar scale ranging
from good to bad, pleasant to
unpleasant, with responses
ranging from -2 to +2.
Scored on a bi-polar scale that1
ranges from –3 to +3.
Scored on a bi-polar scale that
ranges from –3 to +3.
Summed to obtain the total
belief-based attitude.
Scored on a bi-polar scale that
ranges from –3 to +3.
Scored on a bi-polar scale that
ranges from –3 to +3.
Summed to obtain the total
belief-based subjective norm.
Scored on a uni-polar scale that
ranges from 0 to 4.
The number of days to the
campus using the bus divided by
the total number of days to the
campus on the week of survey.
Scored on a uni-polar scale that
ranges from 0 to 4.
Table 4: Definition of Terms
1
According to Ajzen and Fishbein (1980), use of bipolar scale for measuring the belief strength is to give
an opportunity to a respondent to disagree with the statement which is not emitted by him or her.
62
CHAPTER 4
4.1 Method
Setting: Buckeye Village
Buckeye Village, a student-housing complex owned and managed by OSU, has
396 one and two bedroom apartments.
OSU students with family including single
parents can apply for an apartment, but OSU has also allocated a few apartments for the
faculty and cancer patients of OSU James Cancer Center. Buckeye Village is a 20 to 25
minute walk to campus and a 6 to 8 minutes ride on the Buckeye Village bus. The
Buckeye Village bus ran in a circular route from 6:30 a.m. until 12 a.m. on weekdays and
until 8 p.m. on weekends at the time of the survey. On weekdays, it ran every 15 min.
until 8 p.m. and every 30 minutes after 8 p.m. On weekends, it used to run every 30
minutes. Students and public could ride it for free.
Participants:
71 residents of Buckeye Village took part in the study. To get their response, I
distributed 85 questionnaires at random.
Buckeye Village Administration office
provided me with a list of apartments that included the list of vacant apartments and a list
of apartments in the Muskingum Court set aside for the faculty and hospital patients of
63
James Cancer center. The list contained 396 apartments that included 14 vacant and 11
occupied by non-students. The management office put my request as a flyer (Appendix
C) at every door, but I only went to the randomly selected 85 student apartments. Each
questionnaire had a code linking it to the apartment number.
I coded the 371 apartments from 1 through 371. For a confidence level of 95% with
a
population
of
371,
the
sample
size
had
to
be
57
(source:
www.surveysyst.com/sscalc.htm). On the assumption that I would get a response from at
least two third of the sample residents, I generated 85 random integer numbers.
Procedure
Over a three day period, I used a drop and retrieve method; and after a person
completed the questionnaire, I offered him or her a candy bar as a token of appreciation.
On the first day, I distributed 51 questionnaires and collected 28 back; some residents
were not at home. On the second day I distributed 21 more questionnaires and collected
20 questionnaires including some distributed on the previous day. On the third day, I
distributed 8 questionnaires and received 7 back. I collected another 16 questionnaires by
the end of Tuesday.
Although I intended to distribute 85 questionnaires, I only
distributed 80, because I found no residents at 5 of the randomly selected apartments
during my visits. Of the 80 questionnaires distributed, I received back 71. Thus I
obtained a response rate of about 89%.
For the second phase, I contacted the same respondents a week later by telephone
and asked how many days they went to the campus in the previous week (Oct. 4 through
64
Oct. 8) and how many days they rode the Buckeye Village bus. I returned to the
apartments of those who did not provide me the telephone number for the interview. In
the second phase, I learned that four questionnaires were not filled out by student
residents but by their spouses who do not go to campus. So I excluded those responses in
my analysis. In this phase I was able to contact and get the replies from all but one
respondent who responded in the first phase, for a response rate of 98.6%.
4.2 Description of the Socio-economic Conditions and Attitudinal Variables
Table 5 shows the demographic characteristics of the respondents. About one third
of the respondents were females. Nearly half of the respondents were less than 30 years
old and the rest were between 30 and 40 years old. Half of the respondents had two
members in the family. All but the two families had someone who worked outside the
home. About half had one member who worked outside the home, and the other half had
two or more people who worked outside the home. Most respondents were foreigners;
they had a median income in the range of $10,000 to $20,000 per year, and most
respondents owned one or more cars.
81.82% of respondents reported going to campus everyday the previous week of
the interview. 19.70% of respondents reported that they never rode the bus. 39.39%
reported that they rode the bus all five days of the week (Mean = 2.864, Std = 2.022).
Slightly more respondents reported using the bus than driving their car.
65
Percent
Gender:
Male
Female
Age :
30 or under
between 30 and 40
Household members:
1
2
3
4 or more
Number of working
Household members:
0
1
2 or more
Number of autos owned
0
1
2 or more
Income:
<10K
>=10K & <20K
>=20K
Number of days
To campus last week*:
1
2
3
4
5
Nationality
U.S.
Foreign
Number of days by bus
to campus last week:
0
1
2
3
4
5
Travel Mode
Drive Auto
Auto Passenger
Walk
Buckeye Village Bus
Bicycle
Others
Total cases
68.18
31.82
Mean (SD)
66
1.53 (0.50)
46.15
53.85
65
2.61 (0.91)
6.06
50.00
21.21
22.73
66
1.47 (0.56)
3.12
46.88
50.0
64
1.10 (0.43)
4.55
80.30
15.15
66
1.71 (0.66)
39.68
49.21
11.11
63
4.67 (0.83)
1.52
3.03
4.55
9.09
81.82
66
19.70
80.30
66
2.86 (2.02)
19.70
13.64
10.61
12.12
4.55
39.39
66
40.91
6.06
1.52
46.97
4.55
0
66
Table 5: Descriptive Statistics of Socio-economic Variables
66
Table 6 presents the description of attitudinal variables: Attitude towards
behavior, Subjective Norm, Perceived Behavioral Control, and Behavioral Intention.
Attitude ranges from -2 to +2 and the mean is 1.076. Thus the average attitude is biased
toward bus riding. However, the average subjective norm is less than zero meaning that
majority of the respondents do not feel pressured to ride the bus. The average perceived
behavioral control is 3.163 which is much greater than the mid point (2.00). The average
behavioral intention is 0.706 which means that majority of respondents intend to ride the
bus. One of the main reasons students moved to Buckeye Village because of the regular
bus service.
Thus, the results that show the biased attitude towards bus riding is
conceivable.
Name of the variable
Minimum
Maximum
Total cases
Mean (SD)
Ab (Attitude toward behavior)
-2
2
66
1.076 (0.982)
SN (Subject Norm)
-3
2
65
-0.846 (1.543)
PBC (Perceived Behavior Control)
1.25
4
66
3.163 (0.663)
BI (Behavioral Intention)
0
1
64
0.706 (0.310)
Table 6: Descriptive Statistics of Attitudinal Variables
4.3 Questionnaire and the pilot studies
To develop the final survey, I first conducted a survey adopting Ajzen and Fishbein
(1980) type questions to shuttle bus riding. I revised a questionnaire I had designed for
employees of the Ohio Department of Transportation (ODOT) who used the ODOT
67
shuttle bus to the downtown office. To revise it, I interviewed five ride-sharers obtained
from a list provided by the Mid-Ohio Regional Planning Commission and ten other
commuters who commuted by other modes. In an informal conversation, I asked them
about their beliefs, attitudes and intentions about ride sharing. At a consulting firm
where I worked I sought to find behavioral criteria (salient beliefs) related to commuter
behavior and behavioral outcomes. In addition, I sought to find the referents perceived as
important by employees.
From the results of the interviews, I created a questionnaire which I pilot-tested
on 20 people. The pilot test had four purposes:
1. To learn which of the beliefs on my list respondents perceive as salient beliefs
regarding rideshare program and rideshare behavior.
2. To find out if the idiosyncratic beliefs of respondents are included on my list.
3. To find important persons who influence respondents’ rideshare decision.
4. To ensure that survey respondents easily understand the questionnaire.
After modifications based on the pilot test, I did a second pilot test. The modified
questionnaire proved better than the first. The first pilot test revealed little variation in
the respondents’ answers to the motivation to comply. Most respondents circled the
neutral response. Removing that option in the second pilot test yielded a variation in the
response. The final questionnaire measured the following items.
1) behavioral intention on riding the bus
2) a direct measurement of attitude towards riding the bus
3) beliefs about the outcomes resulting from riding the bus
4) evaluation of those outcomes
68
5) perception of referents beliefs about whether or not riding the bus
should be performed
6) motivation to comply with the referents
7) a direct measurement of subjective norm
8) beliefs about riding a shuttle bus comprising a Likert attitude instrument
9) a measurement of attitude towards riding a shuttle bus
10) control beliefs regarding the BV shuttle bus riding, and
11) a measurement of past behavior.
Sample questions for each item appear below. Appendix D shows the full
instrument.
Ten questions, including the following example, assessed behavior beliefs of the
respondents related to bus riding behavior and their outcome evaluations.
If I ride the Buckeye Village bus next week, I will save money (gas cost, parking,
wear and tear etc.).
-------Very
Likely
---------Likely
----------Fairly
Likely
-----------Fairly
Unlikely
---------Unlikely
----------Very
Unlikely
Saving money is:
------------------Extremely good
------------Very good
------Good
-------Neither
-----Bad
----------Very bad
The other nine questions about beliefs and evaluation included:
1) Spending time waiting for the bus.
69
----------------Extremely bad
2) Having to deal with people having different personalities.
3) Relaxing while commuting (nap, read, chat, etc)
4) Having to worry about parking
5) Commuting in a crowded bus
6) Help reduce traffic on streets
7) Losing flexibility (i.e. I can’t arrive or leave when I want to)
8) Help reduce air pollution
9) Inconvenient for other errands (picking up or dropping off children, going to a bank)
One pair of questions assessed the molar Subjective Norm and motivation to
comply.
Most people who are important to me would want me to ride the Buckeye Village bus.
--------
Very
Likely
----------
-----------
Likely
Fairly
Likely
------------
Fairly
Unlikely
------------
-----------
Unlikely
Very
Unlikely
Generally speaking, I want to do what most people who are important to me think I
should do.2
-------Very
Likely
---------Likely
----------Fairly
Likely
-----------Fairly
Unlikely
-----------Unlikely
----------Very
Unlikely
In addition, three pairs of questions assessed normative beliefs and motivation to
comply for specific important referents: “my spouse (boyfriend or girlfriend),”
“neighbor,” and “best friend.”
Four questions assessed perceived behavioral control. For example, one asked:
2
In keeping with Fishbein (1980), this item was not used in the analyses. It is assumed that respondents
would want to comply with people important to them.
70
If I wanted to, I could easily ride the Buckeye Village bus next week.
----------------- ------Strongly Agree Agree
-------Neither
---------Disagree
-------------------Strongly Disagree
The other three questions asked about the difficulty, degree of control, and
number of events outside control.
Three questions, such as the one below, asked about behavioral intention.
I intend to ride the Buckeye Village bus in the next week.
----------Definitely
---------Probably
---------Not Sure
-------------Probably not
--------------Definitely not
One question asked about the attitude towards behavior.
Could you give your opinion very briefly about riding the Buckeye Village bus?
-----------------Very Favorable
----------Favorable
-------Neutral
-------------Unfavorable
--------------------Very Unfavorable.
The remaining nine questions asked about the socio-economic conditions of
respondents such as gender, age, and income.
At the end of the questionnaire, I mentioned that I would contact them the
following week to ask a few questions regarding their riding behavior.
71
CHAPTER 5
RESULTS OF THE ANALYSIS
5.1 Introduction
Recall that the theory of reasoned action predicted that subjective norm and attitude
towards the behavior affect behavioral intention, which affects the overt behavior. Figure 3
shows the relationships (through regression statistics) of Attitude towards behavior,
Subjective Norm and Perceived Behavior Control to Behavior Intention; and Figure 4
shows the relationship (through regression statistics) of Behavioral Intention and Perceived
Behavioral Control to Overt Behavior.
According to Murphy & Myors (1998), an estimate of the proportion of variance
(PV) in the dependent variable explained by the linear model (i.e. R2) above 0.10 represents
a medium effect, and PV above 0.25 represents a large effect (Table 2.2, Murphy &
Myors). The effect size refers to the magnitude of effect (Judd, Smith, and Kidder, 1991).
In my study, the values of PV are above 0.25 in all the regressions (Fig. 3, 4, 5 and 6)
indicating a large effect size. The positive direction of the effects indicates that as the
attitude towards riding the bus becomes more favorable, the intention to ride becomes more
likely. Similarly, the more one feels social pressure to ride the bus (SN) the more likely
one will intend to ride the bus.
72
At the molar level the results showed that Ab and SN were significant in
predicting BI, and BI was significant in predicting OB.
Although these findings
supported Fishbein’s theory of reasoned action, the theory of planned behavior, which
included perceived behavioral control, predicted behavioral intention better. The adjusted
R2 (Ra2 in Figure 3) increased from 36.3% to 42.7%.
Regarding the prediction of
behavior (Figure 4), the theory of planned behavior produced no improvement in
prediction. Ra2 decreased from 52.7% to 52.0% (The decrease is shown due to the
adjustment).
Ajzen model
( BI = B0 + B1*Ab + B2*SN+B3*PBC )
Fishbein model
( BI = B0 + B1*Ab + B2*SN )
Ra2=0.363
p<0.001
Ra2=0.427
p<0.001
Ab
(Attitude toward
behavior)
SN
(Subjective
Norm)
PBC
(Perceived
Behavioral
Control)
B1=0.044
Not sig.
B1=0.119
p<0.01
B2=0.049
p<0.01
B2=0.053
p<0.01
BI
(Behavioral
Intention)
B3=0.169
p<0.01
Figure 3. Ajzen model (left column of Ra2s) predicts behavioral intention better
than does Fishbein model (right column of Ra2s).
73
Recall that the molar variable Attitude toward behavior (Ab) consists of the sum
of behavioral beliefs multiplied by evaluation of each outcome (SumBBi*EOi); and the
molar variable Subjective Norm (SN) consists of normative beliefs multiplied by the
motivation to comply (Sum NBi*MCi). Figure 5 shows the relationships between the
micro-level predictors and the molar variables. Again, the results support both the
Fishbein and Ajzen models which agree on the importance of variables. The models also
predict that the sum belief-based attitudes (Sum BBi*EOi) and the sum of normative
pressures (Sum NBi*MCi) each predict behavioral intention. Figure 6 displays the
analysis for those predictions. Again, the results support both the Fishbein and Ajzen
models.
Ajzen model
Fishbein model
( OB = B0 + B1*BI + B2*PBC )
BI
(Behavioral
Intention)
PBC
(Perceived
Behavioral
Control)
(OB = B0 + B1*BI)
Ra2=0.520
p<0.001
Ra2=0.527
p<0.001
B1=1.04
7
<0 001
B1=1.01
3
<0 001
OB
(Overt
Behavior)
B2=-.027
Not sig.
Figure 4. Both the Ajzen model (left column of Ra2s) and the Fishbein model (right
column of Ra2s) have similar predictive power for behavior.
74
Sum (BBi*EOi)
(Sum of the
behavioral beliefs
multiplied by
outcome
B1=0.034
p<0.001
Ra2=0.305
p<0.001
Sum (NBi*MCi)
(Sum of the
normative beliefs
multiplied by
motivation to
comply)
B1=0.112
p<0.001
Ra2=0.521
p<0.001
Ab
(Attitude toward
behavior)
SN
(Subjective
Norm)
Figure 5. Relationship between i) Sum of the belief-based attitudes and overall
attitude towards behavior and ii) Sum of the normative pressures and overall
subjective norm.
Sum (BBi*EOi)
(Sum of the
beliefs multiplied
by evaluation of
outcome.
B1=0.010
P<0.001
Ra2=0.280
p<0.001
B=0.015
p<0.001
Sum (NBi*MCi)
(Sum of the
normative beliefs
multiplied by
motivation to
comply)
Ra2=0.411
p<0.001
BI
(Behavioral
Intention)
BI
(Behavioral
Intention)
Figure 6. Relationship between 1) Sum of the belief-based attitudes and behavioral
intention, and i1) Sum of the normative pressures and behavioral intention.
75
Fishbein and Ajzen used simple and multiple linear regressions in testing their
hypotheses and suggested other investigators to do so. Thus, many investigators have
used linear regressions in the application of Fishbein and Ajzen’s models, but there is a
problem in doing so. In this study, the scores for OB and BI are between 0 and 1. But
when the independent variables are plugged into the estimated regression equations, the
predicted values of dependent variables are occasionally less than zero or greater than
one. This occurs because of the wide range of the confidence interval (Aczel, 1993). The
probit model avoids this difficulty.
As a result, I reanalyzed the data with probit
models.
The results for the probit models using the Limdep software are tabulated in
Appendix D along with the results of linear regressions.
When probit analysis is
performed using the Limdep software, the result provides the value of the log likelihood
function when all the parameters are zero (α(0)), and the value of log likelihood function
at its maximum (α(β)). Using α(0) and α(β), I calculated ρ2 values (For detail, see BenAkiva and Lerman, 1985). ρ2 is defined as 1 - α(β)/α(0). McFadden has proposed ρ2as
an indicator of goodness of fit. I am using it as an indicator though the reader should be
aware that it is not directly comparable to R2 since it does not measure the same thing. It
appears that ρ2 is empirically biased downwards relative to R2 so one should not expect its
values to be comparable to R2. For a binary choice model, ρ2 must lie between 0 and 1.
The statistic used to test the null hypothesis that all parameters are zero is -2(α(0) - α(β)).
It is asymptotically distributed as χ2 with K degrees of freedom where K denotes the
number of independent restrictions on the parameters in computing α(0). The null
76
hypothesis is rejected when χ2 is “large” in the statistical sense. Tables 15 through 42 in
Appendix D present the results from the linear regression and the probit model and they
are discussed in the following sections. The results of probit model show Z values; the
results of linear regression show t values in the parameter tests. Thus, for the comparison
purpose, in the linear regression results, in addition to t and F values, Z values are shown.
The t and F values are transformed into Z values using the formula (Z = (df/log(1 +
(t2/df))))1/2(1 – (1/2df)))1/2) and (Z = (df/log(1 + (F/df))))1/2(1 – (1/2df)))1/2) respectively.
The formulas are obtained from Judd et al. (1991).
Most of the results from the linear regression and probit model are not
qualitatively different. All the ρ2 values are statistically significant except in one case (the
influence of “number of autos owned” on behavioral intention is not significant); all the
R2 values are statistically significant (Table 7). Moreover, the two sets of summary
measures are highly correlated, with r = 0.907, p<.01. All the estimated parameters have
the same sign in both models though in some cases they are significant in only one of the
models.
.
Dep. Variable
ρ2 (Probit)
Ind. Variables
2
R (linear reg.)
1
OB (Overt Behavior)
BI
0.355
0.527
2
OB
Ab
0.054
0.078
The correlation
3
OB
SN
0.075
0.111
between
ρ2
2
R
4
BI (Intention)
Ab & SN
0.175
0.363
and
5
BI
Ab
0.124
0.291
is 0.907
6
BI
SUM(BBi*OEi)
0.170
0.280
7
BI
SN
0.122
0.264
8
BI
SUM(NBi*MCi)
0.122
0.411
Table 7: R2 values obtained from linear regressions and ρ2 obtained from Probit model. (Continued)
77
Table 7 cont.
.
Dep. Variable
ρ2 (Probit)
Ind. Variables
2
R (linear reg.)
9
BI
AUTO
0.045
0.137
10
BI
Ab, SN & AUTO
0.181
0.308
11
OB
PBC
0.102
0.158
12
OB
BI & PBC
0.355
0.520
13
BI
PBC
0.140
0.335
14
BI
Ab, SN & PBC
0.208
0.427
15
OB
PASTB
0.465
0.687
16
BI
PASTB
0.301
0.713
17
OB
BI, PBC & PASTB
0.468
0.676
18
BI
Ab, SN, PBC, & PASTB
0.352
0.768
Number of samples (n) ranges from 64 to 66.
5.2 Hypothesis Testing
The analyses tested each of the predictors. In the case of linear models, following
Ajzen and Fishbein’s guidelines (1980), I used correlation coefficients to describe the
strength of relationship among variables and multiple correlation coefficients to look at
the variables affecting the dependent variable. I also used stepwise regression in my
analysis. Stepwise regression helps investigators to build a concise model by selecting
important variables to be included in a regression model (Mariza, 1986). It is performed
to retain important variables because a model with a large number of independent
variables is often difficult to interpret. Comparisons of the regression results and the
probit results indicated that most cases had qualitative agreement in the direction and
statistical significance of the effects.
78
5.2.1 The Theory of Reasoned Action
1) Does Behavioral Intention (BI) predict Behavior (OB)?
The analysis regressed BI as a predictor variable onto OB as a criterion variable.
The behavioral intention explained 52.7% (Ra2) of the total variance of OB; and the
regression coefficient was positive and significantly different from zero at the 5% level
(See Table 15, Appendix D for detail).
The analysis also examined the influence of attitude towards behavior (Ab) and
subjective norm (SN) on behavior separately to see if they individually predict behavior.
In linear model, Ab explained 7.8% (Ra2) and SN explained 11.1% (Ra2) of the variance
of OB both significant at the p<05 level. Thus, compared to Ab or SN, BI represents a
much stronger predictor of behavior. In both the above cases, the probit model (Table
16, Appendix D) agreed qualitatively.
2) Does the combination of commuters’ Attitude towards Behavior and
Subjective Norm predict their Behavioral Intention?
The analysis examined the influence of Ab and SN on behavioral intention (BI). For
the linear model, attitude towards behavior and subjective norm together explained 36.3 %
(Ra2) of the variance of BI. Both the coefficients were positive and significantly different
from zero (p<.05) (Table 17, Appendix D), but attitude towards behavior (standardized
regression coefficient (β) = 0.376) has greater influence on BI than does the subjective
norm (β = 0.342). The result of probit model (Table 18, Appendix D ) agreed qualitatively.
The results show that BI played a mediator role between OB, SN and Ab. The sum
total of the variations explained by Ab and SN on OB is just 18.9%, SN and Ab combined
79
(Multiple regression) explained 36.3% of total variation on BI, and BI explained 52.7% of
the variation on OB. A mediator variable is a separate variable that interprets or explains
the relation between dependent and independent variables. According to Evans and Lepore
(1997), researchers often use the terms moderation and mediation interchangeably, but
these terms are distinct processes. A moderator variable is a “third” variable that alters or
qualifies the relationship between two variables. The results of this analysis support
Fishbein and Ajzen’s contention that the pathway through which attitude and subjective
norm influences behavior is through their positive effects on behavioral intention. The
probit model (Table 18, Appendix D) also supported the mediator role of BI.
3) Does attitude towards behavior predict Behavioral Intention (BI) and does the
sum of behavioral beliefs multiplied by evaluation of outcome (Sum BBi*EOi) also predict
BI?
The analyses examined the influence of Ab and the total belief-based attitude on
behavioral intention separately. The total belief-based attitude (Sum BBi*EOi) represented
the sum of the commuter’s strength of beliefs about outcomes resulting from riding the bus
multiplied by the evaluation of those outcomes. The attitude towards behavior explained
29.1% (Ra2) and the total belief-based attitude explained 28.0% (Ra2) of the total variance of
behavioral intention to ride the bus and both coefficients were positive and significantly
different from zero (p<.05) (Table 19, Appendix D). The results of probit model (Table 20,
Appendix D) agreed qualitatively in both analyses.
4) Do Subjective Norm (SN) predict BI and total normative pressure (Sum
NBi*MCi) also predict BI?
80
The analyses examined the influence of SN and the total normative pressure (Sum
NBi*MCi) on behavioral intention separately. Total normative pressure refers to the sum
of the perceived normative beliefs relative to important referents multiplied by the
motivation to comply with those referents. Subjective norm explained 26.4% (Ra2) and the
total normative pressure explained 41.1% (Ra2) of the total variance of behavioral intention
to ride the bus and the regression coefficients in each case were positive and statistically
significant (p<.05) (Table 19, Appendix D). The findings confirmed that both subjective
norm and the total normative pressure predicted behavioral intention though the total
normative pressure explained 14.7% (Ra2) more total variance of behavioral intention than
did the subjective norm in the linear regression model. The probit model (Table 20,
Appendix D) agreed qualitatively.
5) Does the total belief-based attitude (Sum BBi*EOi) predict attitude towards the
bus riding behavior (Ab)?
The analysis regressed the total belief-based attitude onto attitude towards
behavior. Total belief-based attitude explained 30.5% (Ra2) of variance of Ab. The
regression coefficient was positive and significantly different from zero at the 5% level
(Table 21, Appendix D).
Recall that behavioral beliefs were measured through 10 items. To explore the
relative importance of each belief-based attitude to overall attitude towards behavior, I
constructed a ten by one correlation matrix. The result shown in Table 8 indicated that
the overall attitude significantly correlated with the belief based attitudes on six beliefs:
riding a bus means saving money, getting an opportunity to relax, avoiding parking
81
worry, helping reduce traffic congestion, losing flexibility to run errands, and helping
reduce air pollution.
A stepwise regression analysis was performed to see which belief–based attitudes
had the most influence in predicting attitude towards the behavior. The linear combination
of beliefs concerning saving money, losing flexibility and helping reduce air pollution
explained 35.3% (Ra2) of total variance of attitude towards behavior significant at the
(p<0.05) level. Among these three belief–based attitudes, helping to reduce air pollution
had the most influence on the attitude towards behavior (standardized regression
coefficients of helping to reduce air pollution, saving money, and losing flexibility are
0.372, 0.268, and 0.218 respectively--Table 22, Appendix D).
Belief based attitudes
Pearson Correlation Coefficient r
1. A1 = BB1*EO1 (Saving money)
0.478**
2. A2 = BB2*EO2 (A long waiting)
0.148
3. A3 = BB3*EO3 (Countering people with different personalities)
-0.083
4. A4 = BB4*EO4 (An opportunity to relax on bus)
0.423**
5. A5 = BB5*EO5 (Avoiding parking worry)
0.260*
6. A6 = BB6*EO6 (Riding a crowded bus)
0.037
7. A7 = BB7*EO7 (Helping to reducing traffic)
0.365**
8. A8 = BB8*EO8 (Losing flexibility to run errands)
0.313*
9. A9 = BB9*EO9 (Helping to reduce pollution)
0.500*
10. A10 = BB10*EO10 (Facing inconvenience)
0.026
# n = 66, except for saving money (n = 65) and facing inconvenience (n = 65)
** p < 0.01 Bonferroni adjusted, * p < 0.05 Bonferroni adjusted
Table 8: Bivariate Correlations Between Attitude Towards the Behavior (Ab) and Behavioral Beliefs
multiplied by their Outcome Evaluation (BBi*EOi). #
82
6) Do the behavioral beliefs (BBi) relating to the outcome of the behavior and
evaluation of those outcomes (EOi) correlate with attitude towards behavior?
To explore the association between each behavioral belief and attitude towards
behavior, and evaluation of outcome and attitude towards behavior, I constructed two
more one by ten correlation matrices. The results, shown in Table 9 for beliefs, indicated
that all the behavior beliefs correlated positively with attitude towards behavior at a
statistically significant level (p<.05). The overall attitude towards behavior is strongly
associated with the belief that if they ride bus they will save money, get an opportunity to
relax, avoid parking worry, help reduce traffic congestion, lose flexibility to run errands
and help reduce air pollution. For outcome of evaluation, only the evaluation of “helps
reduce air pollution” highly correlated with Ab (p<.05). Other evaluations of outcomes
such as saving money, encountering people with different personalities, avoiding parking
worry, helps reduce traffic congestion, and losing flexibility to run errands very
moderately correlated with attitude towards behavior at a statistically significant level
(p<.05). The attitude towards behavior is the function of sum of belief based attitudes,
so I performed the stepwise regression in that case.
Stepwise regression was not
performed to test the association between attitude towards behavior and individual belief
strengths, and the association between attitude towards behavior and individual outcome
of evaluations because the attitude towards behavior is the function of the sum of their
products.
83
Strength of beliefs relating behavior to outcome
Pearson Correlation Coefficient r
0.48**
1. BB1 (Saving money)
2. BB2 (A long waiting)
-0.10
3. BB3 (Encountering poeple with different personalities)
-0.08
4. BB4 (An opportunity to relax on bus)
0.41**
5. BB5 (Avoiding parking worry)
0.31*
-0.06
6. BB6 (Riding a crowded bus)
0.43**
7. BB7 (Helping to reducing traffic)
-0.27*
8. BB8 (Losing flexibility to run errands)
0.57**
9. BB9 (Helping to reduce pollution)
-0.17
10. BB10 (Facing inconvenience)
Evaluation of outcomes
1. EO1 (Saving money)
0.27*
2. EO2 (A long waiting)
0.15
3. EO3 (Encountering poeple with different personalities)
0.28*
4. EO4 (An opportunity to relax on bus)
0.22
5. EO5 (Avoiding parking worry)
0.29*
6. EO6 (Riding a crowded bus)
0.10
7. EO7 (Helping to reducing traffic)
0.29*
8. EO8 (Losing flexibility to run errands)
0.27*
9. EO9 (Helping to reduce pollution)
0.44**
10. EO10 (Facing inconvenience)
0.07
# n = 66 except belief related to facing inconvenience B10 (n = 65), evaluation of saving money E1 (n =
65), and evaluation of facing inconvenience E10 (n = 65).
** p < 0.01 Bonferroni adjusted, * p < 0.05 Bonferroni adjusted
Table 9: Bivariate Correlations Between Attitude Towards Behavior (Ab) and Strength of Beliefs (BBi),
And Between Attitude Towards Behavior (Ab) and Evaluations of Outcome (EOi)#
84
7) Does the sum of perceived normative pressures (Sum NBi*MCi) predict
Subjective norm (SN)?
The analysis regressed the sum of normative pressures variable onto subjective
norm. The total normative pressure explained 54.1% (Ra2) of the variation on subjective
norm and the regression coefficient was positive and statistically significant (p<.05)
(Table 23, Appendix D).
Recall that the perceived normative pressure was measured through three items.
To explore the relative importance of each normative pressure (NB*MC), I constructed a
three by one correlation matrix and evaluated the relationship (Table 10). The result
indicated that the perceived normative pressure of each important referent (a spouse, a
neighbor, and a best friend) significantly correlated with the overall subjective norm
(p<.05).
Belief based Subjective Norms
Pearson Correlation Coefficient r
a
1. SN1 = NB1*MC1 (Spouse)
(n = 52 )
0.612**
2. SN2 = NB2*MC2 (Neighbor)
(n = 63)
0.323*
3. SN3 = NB3*MC3 (Best Friend) (n = 62)
0.575**
a
13 respondents circled on the “Not Applicable” choice while answering the question related to social
pressure from the spouse.
** p < 0.01 Bonferroni adjusted, *p < 0.05 Bonferroni adjusted
Table 10: Bivariate Correlations Between Subjective Norms (SN) and Normative Beliefs
multiplied by Motivation to Comply (NBi*MCi)
A stepwise regression analysis indicated that a linear combination of perceived
pressure of a spouse and a best friend explained 53.6% (Ra2) of total variance of subjective
85
norm (p<.05) (Table 24, Appendix D). Best friend had a greater influence on subjective
norm than did influence of a spouse (standardized regression coefficient of a best friend =
0.169 and of a spouse = 0.113).
8) Do commuters’ perception of Normative Beliefs (NBi) of important others
regarding bus riding behavior and the motivation to comply (MCi) with those referents
separately correlate with Subjective Norm (SN)?
To explore the association between each normative belief and subjective norm,
and motivation to comply and subjective norm, I constructed two more one by three
correlation matrices (Table 11). The results indicated that each normative belief and each
motivation to comply (relevant to a spouse, a neighbor, and a best friend) positively
correlated with subjective norm (p’s<.05), but normative beliefs of spouse and best friend
had higher correlation coefficients than did neighbor.
Perceived Normative Beliefs (NBi)
1. NB1 (Spouse)
Pearson Correlation Coefficient r
(n = 52)
0.657**
2. NB2 (Neighbor)
(n = 63)
0.438**
3. NB3 (Best Friend)
(n = 62)
0.632**
Motivation to Comply (MC1)
Pearson Correlation Coefficient r
1. MC1 (Spouse)
(n = 57)
0.367*
2. MC2 (Neighbor)
(n = 64)
0.388**
3. MC3 (Best Friend)
(n = 63)
0.402**
** p < .01 Bonferroni adjusted, * p < .05 Bonferroni adjusted
Table 11: Bivariate Correlations Between Subjective Norms (SN) and Normative Beliefs (NBi)
and Motivation to Comply (MCi)
86
5.2.2 The Theory of Planned Behavior
9) Does the linear combination of Perceived Behavioral Control and Behavioral
Intention predict bus-riding behavior better than Behavioral Intention alone?
Recall that for testing the theory of planned behavior posited by Ajzen, the survey
measured Perceived Behavioral Control (PBC) through four questions. The average of
these raw scores is the perceived behavior control. First, the analysis regressed the
Perceived Behavior Control onto Behavior (OB). PBC explained 15.5% (Ra2) of total
variance on OB (p<.05) (Table 25, Appendix D). The probit model (Table 26, Appendix
D) agreed qualitatively.
When perceived behavior control was added to the Fishbein model in the linear
model, BI and PBC together explained 52.0% (Ra2) of variance of B significant at the
p<.05 level. The regression coefficient of BI (B=0.262) achieved statistical significance
(p<.05) level, but the regression coefficient of PBC (-0.027) did not achieve statistical
significance (Table 27, Appendix D).
The result implies that BI mediated all the
influence of PBC on behavior. The addition of PBC into the model did not increase the
explanation of variance of OB. The probit model (Table 28, Appendix D) agreed
qualitatively.
10) Does the linear combination of commuter’s Attitude toward bus riding
behavior (Ab), Subjective Norm
(SN) regarding the bus riding act and Perceived
Behavior Control (PBC) predict their Behavioral Intention (BI) better than Ab and SN
without PBC?
87
First, the analysis regressed the Perceived Behavior Control onto behavioral
intention. In the linear model, PBC explained 34.6% (Ra2) of the total variance of BI
significant at the p=.05 level (Table 29, Appendix D). The probit model (Table 30,
Appendix D) agreed qualitatively.
The regression of perceived behavior control onto behavioral intention found that
Ab, SN and PBC together explained 45.5% (Ra2) of variation of BI. The regression
coefficients of SN and PBC were positive and significantly different from zero (p<.05).
However, the regression coefficient of Ab did not achieve statistical significance,
showing the existence of strong correlation between Ab and PBC (Table 31, Appendix
D). These three variables explained 7.1% (Ra2) more total variance of BI than that
explained by Ab and SN. The standardized regression coefficient of PBC (β=0.678) is
much greater than those of Ab (β=0.140), and SN (β=0.316). Thus, PBC has a larger
influence on behavioral intention. The result of the probit model indicated that Ab, SN
and PBC together improved 20.8% (ρ2) of an initial log likelihood value of BI (p<.05).
The increase in improvement due to PBC is 3.3%. However, none of the regression
coefficients achieved statistical significance (Table 32, Appendix D). Note that each of
these variables (Ab, SN and PBC) was significant in predicting Bi when analyzed
individually.
Thus, the result indicates that there is a high correlation between
independent variables (Mariza, 1986). In this case, the results of probit model are
different from the regression results.
Interpreting the correlation coefficients: Thomas (1976) and Thomas et al.’s
(1976) study of women’s bus riding behavior for shopping found that the correlation
88
coefficients between sum of the belief-based attitudes and overall attitude towards
behaviors ranged from 0.353 to 0.526 and characterized the relatively low scores in relation
to other studies as due to the role of behavioral commitment in such routinized travel acts (a
feedback from behavior to attitude). This dissertation found a higher correlation coefficient
(0.562).
Regarding the multiple correlation coefficient (R) between attitude towards
behavior and subjective norm, and behavioral intention, Thomas et al.’s found 0.768. This
dissertation found 0.619. However, when Thomas et. al. (1976) considered the discrepancy
scores between ‘riding the bus’ and ‘not riding the bus’, R values increased to 0.818. The
discrepancy score is the score difference between the belief based attitude obtained from
beliefs related to ‘riding the bus’ and ‘not riding the bus’. The results were compatible with
decision theory that as the discrepancy increases the probability of choosing one of the
options tends towards unity (Thomas et al., 1976). Thus, they suggested that when a
behavior involves a choice between alternatives, the outcomes of both travel modes should
be assessed; and one should use the discrepancy scores.
Ajzen (1991) reported that the correlation coefficients for the relationship
between behavior and intention ranged from 0.21 to 0.78, with an average of 0.51. The r
for the present case (0.732) fits on the high end. Regarding the relationship between
behavioral intention and its three independent variables (Ab, SN and PB), Ajzen (1991)
reported correlation coefficients from 0.21 to 0.81. This dissertation had an r of 0.674,
again on the high end. Most past research on the theory of reasoned action or the theory
of planned behavior focused either on intent or behavior. This dissertation covered both
of them. Consequently, the results helped test the validity of both parts of the model and
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the link between them.
When Shifter and Ajzen (1985) tested the direct effect of
perceived behavioral control on weight loss, they did not find a significant effect. This
dissertation corroborated their result in relation to bus riding.
In the questionnaire, four items tapped the perceived behavioral control and three
items tapped the behavioral intention, but only one item assessed the attitude towards
behavior. Single item scales tend to be less reliable than multiple-item scales (Browne &
MacCallum, 2002). That lower reliability in measurement of attitude may have lowered
its correlation with other variables.
5.2.3 The effect of external variables
What effects did demographic variables and past behavior have on behavioral
intention and behavior? Recall that Hu (1995) included past behavior in her study and
found that it predicted present behavior. Researchers also continue to debate on whether
attitude causes behavior or behavior causes attitudes. According to Chou (1986), the
inclusion of past behavior in the model helps to check for feedback of behavior to choice
set. He used past experience rather than past behavior as a variable. Past experience
included past behavior and the learning about the behavior from watching tv or someone
performing it. My dissertation simply used past behavior.
11) Do the demographic variables such as gender, age, and income correlate with
Behavioral Intention or Behavior?
The Pearson correlations between attitude and behavioral intention and
demographic variables (age, gender, and income) revealed that the number of autos
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owned correlated negatively with all the attitudinal variables although none achieved
statistical significance (Table 12). Because number of autos owned is often used as an
independent variable in modal choice analysis (Ben-Akiva and Lerman, 1983), I
regressed it onto behavioral intention. The number of autos owned explained 13.7%
(Ra2) of total variance of BI (p<.05) (Table 33, Appendix D). When added to the
Fishbein model, the number of autos owned increased explanation of variance of BI by
only 2.5% (Table 33, Appendix D), and it achieved marginal significance (p=.07). Thus,
Fishbein’s claim that influences of external variables are mediated by behavioral beliefs
or normative beliefs held for this study of bus-riding behavior. The probit model (Table
34, Appendix D) agreed the result of the linear regression though the number of autos
owned was not significant when examined its influence on BI individually.
Multi-collinearity must be considered. Table 11 shows correlation coefficients
among the variables.
Note the possible multi-collinearity between attitude toward
behavior and perceived behavior control (r = 0.69), and behavioral intention and past
behavior (r = 0.82). Multi-collinearity increases the chance of a type-II error. Type II
errors occur when a statistically significant effect in the data may not appear in the test
statistic. According to Greene (1999), multi-collinearity exists when the correlation
coefficients exceed 0.7.
The high collinearity between Ab and PBC may explain why
the influence of attitude towards behavior became statistically insignificant once PBC
was added to the model. Now consider the effect of past behavior.
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OB
Past
Behavior
0.82** PASTB
Behavioral
Intention
0.72**
0.84** BI
Perceived
Behavioral
Control
0.38
0.45*
0.57** PBC
0.27
0.36
Norms
0.34
0.46*
0.52**
0.4
0.47 SN
Gender
0.05
0.06
0.05
0.09
0
Age
-0.03
-0.03
-0.07
-0.21
-0.23
-0.17
NHH
0.01
-0.02
0.02
-0.23
-0.28
-0.05
-0.25
NPWOH
0.02
-0.1
-0.06
-0.09
-0.1
-0.33
-0.15
-0.04
-0.28
-0.42
-0.39
-0.29
-0.35
-0.22
0.08
0.08
0.11
0.04
0
0.06
-0.06
-0.08
-0.19
0.12
0.01
0.21
Attitude
Towards
Behavior
0.53** 0.69**
Ab
Subjective
Autos
Income
0.12 Gender
-0.2 Age
0.50** NHH
0.14 NPW
0.28 Autos
0.44*
0.19
NHH = Number of household members.
NPWOH = Number of people working outside home.
* p<0.05 Bonferroni adjusted p-value, ** p<0.01 Bonferroni adjusted p-value
Table 12: Correlation Coefficients between important measured variables
The analysis regressed past behavior onto behavior, behavioral intention, and
perceived behavioral control. Past behavior explained 22.6% (Ra2) of the variance of
perceived behavior control, 71.3% (Ra2) of the variance of behavioral intention and 68.7%
(Ra2) of the variance of behavior, all at a statistically significant level (p<.05) (Table 37,
Appendix D. All the three correlation coefficients were positive and significantly different
from zero (p<.05). The probit model (Table 38, Appendix D) agreed qualitatively.
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When added to the Ajzen model, the linear combination of BI, PBC, and past
behavior explained 67.7% (Ra2) of the variance of behavior (p<.05) (Table 39, Appendix
D). Only the regression coefficients of past behavior was significant (p<.05). The probit
model (Table 40, Appendix D) agreed qualitatively. In the prediction of behavioral
intention, when past behavior was added to the Ajzen model, the linear combination of Ab,
SN, PBC and past behavior explained 76.8% (Ra2) of variance on behavioral intention
(Table 41, Appendix D).
Only the regression coefficients of past behavior achieved
statistical significance at the p<0.05 level. The probit model (Table 42, Appendix D)
agreed qualitatively in this case also.
In my study, past behavior correlated with the behavioral intention and behavior.
Although Ajzen (1991) argued that past behavior would contribute little to the prediction
of behavioral intention or behavior because perceived behavioral control should have
mediated the effect of past behavior, research has found that past behavior influences
behavioral intention or behavior (Bentler and Speckart, 1979; Fredricks and Dossett,
1983; Manstead, Praffitt, and Smart, 1983; Hu, 1995). This dissertation corroborated
these findings, but it dealt with a repetitive behavior that just showed the reliability of the
reported behavior.
The inclusion of my particular measure, past behavior, as an external variable
might not be justifiable. I measured the behavior of previous week as the past behavior.
Since the time lag is too short, I might have measured the same variable twice.
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5.3 Comparison of mean scores across respondents:
One set of t tests examined differences between riders and non-riders on the specific
items that made up behavioral beliefs, evaluations, normative beliefs and motivation to
comply. These analyses might help identify specific directions for promotional strategies.
Table 13 shows the statistically significant effects for the comparison of bus riders and nonbus riders and between the groups with different socio-economic backgrounds.
Travel mode
Bus
Mean(SD)
No-bus
Mean(SD)
BB1
Belief related to "Saving money"
2.26(1.00)
1.51(1.48)
BB4
Belief related to "An opportunity to relax"
1.68(1.49)
0.69(2.01)
BB7
Belief related to "Helping reduce congestion"
1.83(0.93)
0.60(1.65)
BB9
Belief related to "Helping reduce air pollution"
1.74(1.13)
0.94(1.73)
EO1
Evaluation of "Saving money"
2.35(0.80)
1.68(1.04)
EO2
Evaluation of "A long wait for the bus"
-0.21(1.67)
-0.29(2.07)
NB3
Normative belief regarding "Best Friend"
-0.52(1.66)
-1.55(1.54)
BI
Behavioral Intention
3.62(0.67)
2.12(1.21)
Ab
Attitude towards behavior
1.52(0.85)
0.69(0.93)
SN
Subjective Norm
0.40(1.96)
-1.11(1.75)
PBC
Perceived Behavioral Control
3.49(0.51)
2.87(0.65)
SumAb
Sum of the belief-based attitude (Sum Bi*Ei)
SumSN
Sum of the normative pressures (Sum NBi*MCi)
17.65(12.81)
7.03(17.62)
5.09(10.45)
-4.81(13.62)
Table 13: Variables with significant difference between bus riders and non-bus riders.
The table shows that bus riders were more likely to believe that bus riding results in
saving money, getting an opportunity to relax, helping reduce traffic congestion and air
pollution. Bus riders also evaluated “saving money” as more important than did non-bus
riders; and non-bus riders evaluated “riding a crowded bus” significantly worse than did
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bus riders. Regarding normative beliefs, bus riders believed their spouses likely to want
them to ride the bus, but non-bus riders believed their spouses unlikely to want them to ride
the bus. Although both groups believed their best friends were likely to want them to ride
the bus, the bus riders as compared to non-bus riders believed their friends as more
supportive.
Table 14 shows the statistically significant effects found for comparisons across
socio-economic groups. The top three comparisons show that respondents who owned two
cars or more evaluated “riding a crowded bus” much worse than did respondents who
owned one car or less. Those with more cars also reported less intention to ride the bus and
less positive attitude towards bus riding behavior than did respondents with fewer cars.
Male respondents evaluated “riding a crowded bus” significantly more unpleasant than did
their female counter parts. The next two comparisons show that males as compared to
females were less likely to believe that their best friends want them to ride a bus. The last
two comparisons show that respondents with 0 or 1 household member working outside the
home had a positive average subjective norm while respondents with two or more
household members working outside had negative average subjective norm. Higher income
respondents reported that they would face inconvenience if they ride a bus whereas lower
income respondents reported they would not.
Socioeconomic variables did have significant effects on micro-level variables.
Gender had significant effects on the evaluation of riding a crowded bus and normative
belief of the best friends. Males evaluated riding a crowded bus worse than did females.
Males as compared to females were less likely to believe that their best friends want them
to ride the bus. Number of working people outside the home affected the subjective
95
norm. Households with fewer people working outside tended to feel a marginal pressure
to ride the bus whereas households with more people working outside tended not to feel
pressure to ride the bus.
Although the socio-economic variables may not have
application to the full model, they do have value in suggesting promotional campaign.
Auto
1 or less
2 or more
Mean (SD)
Mean (SD)
EO6
Evaluation of "Riding a crowded bus"
-0.89(1.020
-1.80(1.13)
BI
Behavioral Intention
3.01(1.14)
1.67(1.25)
Ab
Attitude toward behavior
1.20(0.86)
0.40(1.35)
Gender
Male
EO6
Evaluation of "Riding a crowded bus"
NB3
Normative belief regarding "Best friend"
Female
Mean (SD)
Mean (SD)
-1.22(1.09)
-0.57(1.29)
-1.38(1.46)
-0.40(1.90)
Number of people working outside
SN
Subjective Norm
Low (one or less)
High (Two or more)
Mean (SD)
Mean (SD)
0.10(1.76)
-0.94(2.08)
Respondents' income
Low (<20k)
High (>=20k)
BB10
Belief related to "Facing inconvenience"
Mean (SD)
Mean (SD)
-0.04(1.97)
1.16(1.82)
Table 14: Mean Scores and Standard Deviations for Attitudinal Variables across
Socio-economic Groups
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CHAPTER 6
CONCLUSIONS AND IMPLICATION
My dissertation demonstrated the potential usefulness of the Fishbein and Ajzen
models for predicting and understanding commuters’ bus riding behavior. My results (both
linear regression and probit models) showed that the independent variables--behavioral
beliefs, normative beliefs, attitude towards behavior, subjective norm and perceived
behavior--predicted the intention to ride the bus.
The data also supported the idea that behavioral intention to ride the bus is the
antecedent of bus riding behavior. Additionally, the function of attitude towards bus riding
behavior, subjective norm related to the bus riding behavior and perceived behavioral
control that are linearly combined can predict the behavioral intention to ride the bus. In
the linear model, the behavioral intention alone explained 52.7% of the total variation on
reported behavior.
The linear combination of subjective norm and attitude towards
behavior explained 36.3% of total variation on behavioral intention, whereas the addition of
perceived behavior control explained 42.7% of total variation on behavior. In addition, the
sum of the belief-based attitude related to bus riding behavior explained 30.5% of the
attitude towards behavior and the sum of the normative pressures related to the important
referents explained 52.1% of the subjective norm related to the bus riding behavior. The
probit model agreed qualitatively.
97
The results may arise from the overlap of the measures of a repetitive behavior.
People who regularly ride the bus would report riding it a similar amount over consecutive
weeks. In essence, the question about bus-riding a week after the questionnaire measured a
similar thing to that measured by the question in the questionnaire. The two measures just
showed reliability in their reported bus-riding.
According to Thomas at al. (1976), when they compared their results with the
results of orthodox travel forecasting models, they obtained comparable predictions.
They contend that to operate those models the investigators require the evaluations of
alternative models, but the Fishbein model does not need that. In addition, the Fishbein
model (or Ajzen model) has the advantage that the factors used in the predictions are
elicited whereas in the orthodox models they are arbitrarily chosen. Fishbein and Ajzen
help planners understand better the underlying components of commuters’ behavior.
Although in the linear model, the Ajzen model explained the total variation on bus
riding intention 6% more than Fishbein model, it explained only 42% of the behavior
intention. Both models explained about 53% of the total variation of bus riding behavior.
The Fishbein and Ajzen models are based on the assumption that people are rational, but
not everyone acts rationally. Some intentionally and others unintentionally make irrational
decisions. Others may follow different decision rules. Measurement errors may also have
reduced the variation explained.
carelessly.
Perhaps some respondents answered the question
Perhaps some significant external variables, such as social status, habit,
perceived safety, and whether the respondent works on campus, were overlooked. Though
most variables had more than one measure for reliability, the measure of attitude towards
behavior had only one measure.
98
For various reasons respondents may have reported more favorable responses
toward riding the bus than they felt or than that representing the Buckeye Village residents.
They may have tried to please the experimenter, given socially desirable answers, and the
study may have had selectivity among those who chose to participate. These problems may
have arisen because the experimenter lived in Buckeye Village and some of the respondents
knew him. Future research could reduce these problems by having an experimenter
unknown to the respondents. In addition, all participants in my study could ride the bus for
free; and the bus went from their complex to the campus and back. Future research should
test the application of the model to paying riders on regular commuter bus or train systems.
It should also test the applications to other populations and locations.
For bus riding, perceived social status may have an effect, because people may
judge bus riding as lower in status. People may ride or not ride in part out of habit.
Thomas (1976) termed it “reverse effect” in that the behavior influenced the attitude.
Future research could examine the effect of habit as a prediction of behavioral intention.
The location of work place may also affect the choice. As the bus goes to and from the
campus, it would be convenient for students working on campus, but inconvenient for
students working elsewhere. However, perceived control might have captured it, and
most respondents reported that they had control over their behavior
If future work improves the findings and if the findings apply more broadly to bus
ridership or other mass transit, the results suggest that promotional strategies should focus
on attitude towards bus riding behavior for specific people and specific routes.
My
dissertation study found that non-riders had several incorrect salient beliefs about bus
riding. One should elicit the salient beliefs from the commuters or the potential commuters
99
and should attack those primary beliefs
Thus, the promotional activity should target the
specific needs of the people in that area.
According to Ajzen and Fishbein (1980), while formulating the promotional
activity, first, the beliefs targeted should be the primary beliefs. Second, it should be
positively correlated with the attitude towards behavior or subjective norm. Third, the
change in belief system should be such that it causes a shift in the attitude towards behavior
or subjective norm. In addition, the ultimate result should be the shift in behavior intention.
Then and only then, may the message alter the behavior. According to Ajzen (1985),
changes in behavior may also be achieved by changing the perceived behavioral control,
since it directly or indirectly affects behavior.
The ratings on specific items point to some directions for promotional activities
(See Table 12 and Appendix D17). When asked whether they would encounter people
with a different personality by riding the bus, 42.4% reported gave a positive reaction and
only 7.5% of the respondents evaluated encountering people of different personalities as
negative. Thus, promotion may not need to deal with this issue. In contrast, most
respondents judged riding the bus as inflexible for errands (66.7%) and inconvenient
(59.1%) and they evaluated these attributes negatively (75.8% and 78.8% respectively).
Thus, any promotional activities might do well to focus on the flexibility and
convenience of riding the bus. In addition, many respondents judged riding the bus as
crowded (48.5%) and most respondents judged crowding negatively, with males and
people with more than one car to do so more than others. Perhaps, promotion could
focus on the positive aspects of other people on the bus and focus the campaign toward
males and residents with more than one car.
100
According to Fishbein, if one intends to change the behavior, arguments in the
message must attack the primary beliefs about the performance of the behavior. Because
my dissertation focused on predicting and explaining the ridership, a detailed discussion of
how to persuade commuters to switch their travel mode is beyond the scope of this study.
Yet, this section briefly touches in this subject.
I found five beliefs significantly correlated with the attitude towards bus riding
behavior: 1) saving money, 2) having an opportunity to relax, 3) avoiding parking worry, 4)
helping to reduce traffic congestion, and 5) helping to reduce air pollution. If someone
already believes that riding the bus would help reduce air pollution, they might not need to
hear this again, but publicity might strengthen their belief through other evidence of the
benefits. The analysis identified one negative belief. Respondents felt that riding the bus
reduced their flexibility. Perhaps, promotional efforts should stress flexibility and creative
routing could add to the flexibility.
Ajzen and Fishbein (1980) presented three possible conditions during the reception
of a verbal or written persuasive communication. They are: a) acceptance b) yielding, and
c) impact.
Acceptance means a person strongly believes in the behavior and its
consequences. Yielding means the person changes to accepting the belief due to the
exposure of the message (Fishbein in Petty et al., 1981). Impact refers to the situation
where the presentation of argument may indirectly affect one or more beliefs, not explicit.
Some commuters may have accepted the belief that the bus riding behavior will
help to reduce the traffic congestion and air pollution. If their belief strength is not strong
enough, it may not have a strong influence on attitude and as a result, the resulting intention
remains weak. In such a situation, if the persuasive messages are well designed (with
101
evidences of such things happening in other places), then commuters may change those
beliefs into stronger beliefs, eventually influencing their attitudes and intentions. Such
messages may also lead other commuters to yield and it may impact others. According to
Ajzen and Fishbein (1980), the messages should stimulate receivers to think about the issue
under consideration, bring change in some of the primary beliefs, and hence in the
behavioral intention or behavior. While Ajzen and Fishbein (1980) acknowledge that other
factors, such as the source of communicator, should be considered in formulating the
promotional strategy, Fishbein in Petty et al. (1981) notes that the content of the message is
more important than the presenter.
This dissertation indicated that important others (spouse, neighbor and best friend)
influenced the subjective norm and subjective norms influenced behavioral intention. Thus,
these referents represent candidates as the persuasive communicators. This may suggest a
strategy of holding small meetings for neighbors and friends where transit planners both
listen to concerns and try to shape subjective norms in a positive direction. In addition, the
dissertation also indicated that perceived behavioral control indirectly influences behavior
through behavioral intention. Thus, another option to promote the ridership is by boosting
the perceived behavioral control of those who expressed that they had weak or no control
over the behavior. Giving them more information about the bus schedule may increase
their confidence level. But in this study most respondents expressed that they had a good
control over the behavior.
Once primary beliefs that underlie the attitude towards bus riding behavior,
subjective norms, and perceived behavioral control are figured out, and their correlations
with attitude and subjective norms are ensured, planners can use these beliefs for persuasive
102
communications to change the commuters’ intention to ride the bus. Further application of
the Fishbein model in other settings, modes of transit and populations should help identify
variables related to each situation and possible variables of more general applications.
Such a body of research can help transit planners promote ridesharing. Doing so can help
relieve traffic congestion, stress and air pollution associated with automobile commuting.
103
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APPENDIX A
(Tables from Thomas et al.’s study)
117
APPENDIX A (Source: Thomas, 1976)
a
Correlations between overall attitude towards use of a mode for the shopping trip
and the beliefs held about the outcomes of that mdoe.
MSB (sum of seven
most
ISB (sum of first
three
frequently elicited beliefs)
idiosyncrtic beliefs)
Act 1 (using the bus)
0.509
0.353
Act 2 (not using the bus)
0.463
0.526
a
All correlations are significant beyond p <
0.01
a
Correlations between overall attitude towards use of a mode for the shopping trip and
the beliefs held about the outcomes of that mode.
Shopping-trip mode
bus
not bus
MSB (seven most frequent)--intention
0.416
0.488
MSB (seven most frequent)--use of bus
0.361
-0.422
ISB (sum of first three)—intention
0.300
0.525
ISB (sum of first three)--use of bus
0.269
-0.471
a
All correlations are significant beyond p <
0.01
118
APPENDIX A (Source: Thomas, 1976)
Percentage of subjects in each user group who spontaneously elicited beliefs corresponding
to or similar to the modal salient beliefs listed, in response to 'using the bus next week to
do my main shopping means…'
Belief
Using the bus
group 1
stage 1 stage 2
39.0
26.9
46.8
43.3
26.9
38.8
46.8
26.9
19.5
21.0
6.5
3.0
Carrying heavy shopping
Getting to Brentwood and back quickly
Convenient shopping
Waiting around for unreliable buses
Cost of bus fares
Difficulty with children
Crowded buses
Having to keep an eye on the clock
Being out in the weather
No Parking problems
Not having to walk to Brentwood to shop
Not having to rely on uneconomical local shops
New individual beliefs not covered by
categories listed above.
(1)
(2)
(3)
23.4
18.2
16.5
7.5
28.6
19.5
31.4
18.0
31.2
7.8
10.5
1.5
group 2
stage 1
48.7
42.0
31.6
47.4
11.7
7.8
10.4
31.5
stage 2
44.3
31.4
35.6
18.5
17.2
5.8
8.6
37.2
5.2
10.0
29.0
35.8
27.5
5.2
1.3
4.3
group 3
stage 1
48.0
18.0
10.0
44.0
18.0
16.0
2.0
38.0
12.0
26.0
28.0
6.0
4.0
Percentage of subjects in each user group who spontaneously elicited beliefs corresponding
to or similar to the modal salient beliefs listed, in response to 'not using the bus next week
to do my main shopping means…'
Belief
Not using the bus
group 1
stage 1 stage 2
32.5
25.4
Carrying heavy shopping
Getting to Brentwood and back quickly
Convenient shopping
Difficulty with children
Being out in the weather
Going to Brentwood by car to shop
Walking to Brentwood to shop
Not having to carry heavy shopping
Cost of Petrol
No difficulty with children
Pleasing myself when I go shopping
Parking problems
Taking longer to get to Brentwood and back
Not waiting around for unreliable buses
Saving money on bus fares
Arranging for someone else to shop for me
Having to rely on uneconomical local shops
Going as a car passenger to Brentwood to shop
New individual beliefs not covered by
categories listed above.
(1)
(2)
119
9.1
3.9
0.0
3.0
18.2
20.9
52.0
50.8
28.6
13.0
14.3
6.5
48.1
29.5
16.4
10.5
9.0
43.3
35.1
2.6
9.0
group 2
stage 1
27.5
23.6
10.4
1.3
stage 2
10.0
37.2
22.8
1.4
32.8
22.8
17.1
22.9
23.6
5.2
31.4
12.9
35.5
58.0
18.6
79.8
25.0
1.3
1.4
group 3
stage 1
18.0
24.0
70.0
32.0
36.0
8.0
4.0
28.0
16.0
12.0
14.0
2.0
APPENDIX A (Source: Thomas, 1976)
a
Mean values of evaluation and strength of belief for the sets of modal salient beliefs for
each group of women, act 1- 'using the bus next week to do my main shopping in Brentwood'
Mean
evaluation
Belief
BU
BU/CP CD
Carrying heavy shopping
-1.3
-2.2
-2.2
Getting to Brentwood and back quickly
1.9
1.4
1.2
Convenient shopping
2.0
1.9
1.9
Waiting around for unreliable buses
-1.5
-1.9
-2.2
Cost of bus fares
-1.0
-1.3
-0.6
Difficulty with children
-0.2
-0.5
-0.8
Crowded buses
-1.2
-1.3
Having to keep an eye on the cloce and
make sure I'm in good time
-0.4
-0.8
-0.9
Being out in the weather
-0.9
-1.0
No Parking problems
1.2
1.6
Not having to walk to Brentwood to shop
1.6
Not having to rely on uneconomical local shops
1.9
1.6
Mean belief
strength
BU
BU/CP
0.4
1.0
1.7
1.4
2.2
1.8
1.1
1.2
1.0
1.1
-0.2
0.4
0.7
1.1
0.9
1.4
1.1
1.7
1.5
CD
1.6
-0.7
0.0
0.6
0.8
0.7
1.0
1.8
1.3
1.2
1.6
Key: BU = bus users; BU/CP = bus users/car passengers; CD = car drivers.
The evaluation scales are scored from +3 (extremely good) to -3
(extremely bad). The belief
strength scales are scored from +3 (extremely likely) to -3 (extremely unlikely).
a
a
. Mean values of evaluation and strength of belief for the sets of modal salient beliefs for
each group of women, act 2- 'not using the bus next week to do my main shopping in Brentwood'
Mean
Mean belief
evaluation
strength
Belief
BU
BU/CP CD
BU
BU/CP
Walking to Brentwood to shop
-0.8
-1.2
-0.8
0.5
-0.2
Carrying heavy shopping
-1.3
-2.2
1.0
0.0
Taking longer to get ot Brentwood and back
-1.2
1.4
Convenient shopping
2.0
1.9
1.9
-0.2
1.3
Not walking around for unreliable buses
1.6
2.2
2.4
0.8
1.4
Saving money on bus fares
1.4
1.8
1.6
1.8
Difficulty with taking my children shopping with me
-0.2
-0.5
-0.2
-0.4
Arranging for someone to shop for me
-1.2
-1.5
Being out in the weather
-0.9
0.9
Having to rely on uneconomical local shops
-1.4
-1.4
1.1
-0.5
Going to Brentwood by car to shop
2.3
Not having to carry heavy shopping
2.4
Getting to Brentwood and back quickly
1.4
1.2
0.9
Cost of petrol
-1.4
No difficulty with taking my children
shopping wih me
1.1
Pleasing myself when I go shopping and
not worrying about the time
2.2
Parking problem
-0.6
-1
0.2
Going as a passenger in a car to Brentwood
2
to shop
2.4
Key: BU = bus users; BU/CP = bus users/car passengers; CD = car drivers.
a
The evaluation and belief strength scales are the same
as table 6.
120
CD
-1.2
2.6
2.0
2.7
1.9
2.3
1.1
-0.4
2.4
0.2
2.7
APPENDIX B
(Flyer)
121
Sept. 28, 1999
2601 Muskingum Ct.
Dear Buckeye Village Residents:
I am a graduate student from Nepal and live in Muskingum Court. I have chosen
the Buckeye Village bus as my research topic, aiming to understand in detail why a
resident rides the Buckeye Village bus or not. I will be conducting my survey this
weekend at the Buckeye Village. I will randomly pick the apartment numbers. If the
numbers so picked include yours, I will come to your apartment and request you to fill
out my questionnaire. It will take around 10 minutes. I hope I will have your full
support. Thanks in advance.
If you will be out of town this weekend, please let me know. My telephone
number is 688-9624.
Yours truly,
Puspa Man Joshi
Ph.D. Student
City and Regional Planning
122
APPENDIX C
(Survey Questionnaire)
123
Oct. 1, 1999
2601 Muskingum Court
Dear Buckeye Village Resident:
I am a graduate student from Nepal and have chosen the Buckeye Village bus as
my research topic, aiming to understand in detail why a resident rides the Buckeye
Village bus or not. To complete my research, I need 10 minutes of your time. As I am
without financial support for my project, I can only offer “Thanks” and a small gift for
your help.
Please complete the enclosed, anonymous, confidential questionnaire and I will
come to pick it up after half an hour. Thank you very much.
Yours truly,
Puspa Man Joshi
Ph.D. Student
City and Regional Planning
Ph. No. (614) 688-9624
124
The following are belief statements about riding the Buckeye Village bus.
Please circle the statement, which most closely matches your feeling.
Beliefs about the behavior
1. If I ride the Buckeye Village bus next week, I will save money (gas cost, parking,
wear and tear etc.).
-------Very
Likely
--------Likely
--------Fairly
Likely
----------Neither
------------Very good
------Good
-------Fairly
Unlikely
--------Unlikely
--------Very
Unlikely
Saving money is:
------------------Extremely good
-------Neither
-----Bad
----------- ------------Very bad Extremely
bad
2. If I ride the Buckeye Village bus next week, I will have to spend time waiting for the
bus.
-------Very
Likely
--------Likely
--------Fairly
Likely
----------Neither
-------Fairly
Unlikely
--------Unlikely
--------Very
Unlikely
Spending time to wait for the bus is:
------------------Extremely good
------------Very good
------Good
-------Neither
-----Bad
----------Very bad
------------Extremely
bad
3. If I ride the Buckeye Village bus next week, I will have to deal with people having
different personalities.
-------Very
Likely
--------Likely
--------Fairly
Likely
----------Neither
-------Fairly
Unlikely
--------Unlikely
--------Very
Unlikely
Dealing with people having different personalities is:
----------Extremely
enjoyable
----------Very
enjoyable
------------ -------- -------------- ------------- ----------Enjoyable Neither Unenjoyable
Very
Extremely
unenjoyable unenjoyable
125
4. If I ride the Buckeye Village bus next week, I will relax (nap, read, chat, etc.) while
commuting.
-------Very
Likely
---------
--------Fairly
Likely
Likely
----------Neither
-------Fairly
Unlikely
--------Unlikely
--------Very
Unlikely
Relaxing while commuting is:
------------Extremely
pleasant
----------Very
pleasant
-----------Pleasant
-------Neither
------------ ------------- ----------Unpleasant
Very
Extremely
unpleasant unpleasant
5. If I ride the Buckeye Village bus next week, I will not have to worry about parking
hassle.
-------Very
Likely
--------Likely
--------Fairly
Likely
----------Neither
-------Fairly
Unlikely
--------Unlikely
--------Very
Unlikely
Not having to worry about parking hassle is:
-----------------Extremely good
------------Very good
------Good
--------Neither
-----Bad
----------- -------------Very bad Extremely
bad
6. If I ride the Buckeye Village bus next week, I will be commuting in a crowded bus.
-------Very
Likely
--------Likely
--------Fairly
Likely
----------Neither
-------Fairly
Unlikely
---------Unlikely
--------Very
Unlikely
Riding a crowded bus is:
-----------------Extremely good
------------Very good
------Good
--------Neither
-----Bad
----------Very bad
-------------Extremely
bad
7. If I ride the Buckeye Village bus next week, it will help reduce traffic on streets.
-------Very
Likely
--------Likely
--------Fairly
Likely
----------Neither
126
-------Fairly
Unlikely
---------Unlikely
--------Very
Unlikely
Reducing traffic on streets is:
---------Extremely
important
----------- ----------Very
Important
important
-------Neither
-------------- ------------- ----------Unimportant
Very
Extremely
unimportant unimportant
8. If I ride the Buckeye Village bus next week, I will lose flexibility (i.e. I can’t arrive or
leave when I want to).
-------Very
Likely
---------
--------Fairly
Likely
Likely
----------Neither
-------Fairly
Unlikely
--------Unlikely
-----Bad
----------Very bad
--------Very
Unlikely
Losing that flexibility is:
------------------Extremely good
----------very good
------- --------Good Neither
----------------Extremely bad
9. If I ride the Buckeye Village bus next week, I will help reduce pollution.
-------Very
Likely
---------
--------Fairly
Likely
Likely
----------Neither
-------Fairly
Unlikely
--------Unlikely
--------Very
Unlikely
Reducing pollution is:
-----------------Extremely good
-----------Very good
-----Good
-------Neither
----Bad
----------Very bad
--------------Extremely
bad
10. If I ride the Buckeye Village next week, it will be inconvenient for other errands
(such as picking up or dropping off children, going to a bank, etc.)
-------Very
Likely
-------Likely
--------Fairly
Likely
----------Neither
-------Fairly
Unlikely
--------Unlikely
--------Very
Unlikely
Inconvenience for errands is:
---------------Extremely good
----------very good
------Good
-------Neither
127
----Bad
----------Very bad
----------------Extremely bad
11. Most people who are important to me would want me to ride the Buckeye Village
bus.
-------Very
Likely
---------Likely
----------Fairly
Likely
-----------Fairly
Unlikely
-----------Unlikely
----------Very
Unlikely
Generally speaking, I want to do what most people who are important to me think I
should do.
-------Very
Likely
---------Likely
----------Fairly
Likely
-----------Fairly
Unlikely
-----------Unlikely
----------Very
Unlikely
12. My spouse (boyfriend or girlfriend) would want me to ride the Buckeye Village bus.
-------Very
Likely
---------Likely
----------Fairly
Likely
-----------Fairly
Unlikely
-----------Unlikely
------------------Very
Not
Unlikely Applicable
Generally speaking, I want to do what my spouse, boyfriend or girlfriend thinks I should
do.
-------Very
Likely
---------Likely
----------Fairly
Likely
-----------Fairly
Unlikely
-----------Unlikely
------------------Very
Not
Unlikely Applicable
13. My neighbors would want me to ride the Buckeye Village bus.
-------Very
Likely
---------Likely
----------Fairly
Likely
-----------Fairly
Unlikely
-----------Unlikely
----------Very
Unlikely
Generally speaking, I want to do what my neighbors think I should do.
-------Very
Likely
---------Likely
----------Fairly
Likely
-----------Fairly
Unlikely
128
-----------Unlikely
----------Very
Unlikely
14. My best friend would want me to ride the Buckeye Village bus.
-------Very
Likely
----------
----------Fairly
Likely
Likely
-----------Fairly
Unlikely
------------
----------Very
Unlikely
Unlikely
Generally speaking, I want to do what my best friend thinks I should do.
-------Very
Likely
----------
----------Fairly
Likely
Likely
-----------Fairly
Unlikely
------------
----------Very
Unlikely
Unlikely
15. If I wanted to, I could easily ride the Buckeye Village bus next week
-----------------Strongly Agree
------Agree
-------Neither
---------Disagree
-------------------Strongly Disagree
16. For me to ride the Buckeye Village bus next week would be
------------Very Easy
------Easy
--------Neither
--------Difficult
-------------Very Difficult
17. How much control do you have over riding the Buckeye Village in the next week?
--------------------Complete Control
---------------Much Control
--------------------Not Much Control
--------------Little Control
------------No Control.
18. How many events outside your control could prevent you from riding the Buckeye
Village bus in the next week.
------------Very Many
------Many
-----Some
----Few
-----------Very Few
19. I intend to ride the Buckeye Village bus in the next week.
-----------Definitely
----------Probably
---------Not Sure
--------------Probably not
--------------Definitely not
20. I will try to ride the Buckeye Village bus in the next week.
-----------Definitely
---------Probably
---------Not Sure
--------------Probably not
129
--------------Definitely not
21. I will make an effort to ride the Buckeye Village bus in the next week.
-----------------Definitely True
-----True
-----------Not Sure
------False
-----------------Definitely False.
22. Could you give your opinion very briefly about riding the Buckeye Village bus?
----------------Very Favorable
----------Favorable
-------Neutral
-------------Unfavorable
Now I would like to ask a few questions about you.
1. Gender:
M ______
F ______
2. When were you born?
_____ a) After 1970
_____ b) 1970-1950
_____ c) before 1949
3. Number of household members.
1 ____
2 _____
3 _____
4 or more ____
4. Number of household members who work outside home.
1 _____ 2 _____
3 or more _____
5. Number of autos in your household.
0 _____
1 _____
2 or more _____
6. Household income.
_____a) less than $15,000
_____b) $15,000 to $30,000
_____c) Above $30,000
7. How do you usually go to campus?
_____a) Drive auto
_____b) Auto passenger (with family member)
_____c) Walk
_____d) The Buckeye Village Bus
_____e) Bicycle
_____f) Others
130
--------------------Very Unfavorable.
8. How many days (Monday through Friday) did you go to campus this week?
0___ 1____ 2____3___ 4____ 5____
9. Of those, how many days did you ride the Buckeye Village bus?
0___ 1___ 2 ___ 3 ____ 4____ 5_____
* Next weekend I plan to come to ask you one simple question—How
many days did you ride the Buckeye Village bus from Monday Oct. 4 through Friday
Oct. 8?
131
APPENDIX D
(Results of Regression and Probit Analysis)
132
________________________________________________________________________
t-valueb (Zf.)
p-level R2 (Ra2)
Goodness-of-fitc (F(1,62df) = 71.175, Zf = 4.519)
.000 (-.101)
Constant (n = 64)
BI (Behavioral Intention)
.731 (1.013)
.000
.281
.000
.534 (.527)
-1.088
8.437 (4.530)
Goodness-of-fit (F(1,64df) = 6.646, Zf = 1.651)
.000 (.484)
Constant (n = 66)
Ab (Attitude towards behavior)
.303 (.132)
.013
.000
.013
.092 (.078)
6.416 (1.630)
2.543
Model
Betaa
________________________________________________________________________
OB Predictions:
.004
.125 (.111)
Goodness-of-fit (F(1,63df) = 8.998, Zf = 1.904)
Constant (n = 65)
.000 (.652)
12.719
.000
SN (Subjective Norm)
.354 (.076)
3.000
.004
___________________________________________________________________________________
a
Standardized regression coefficients (Regression coefficient).
b
Zf. is t value transferred into Z using the formula: Zf = (df/(log(1 + (t2/df))))1/2(1- (1/(2df)))1/2
c
Zf. is F value transferred into Z using the formula: Zf = (df/(log(1 + (F/df))))1/2(1- (1/(2df)))1/2
(Source for formulas: Table 18.1, Judd, Smith & Kiddler, 1991)
Table 15: Summary Data Table of Simple Linear Regression of Behavior (B) on Each of Behavioral
Intention (BI), Attitude Towards Behavior (Ab) and Subjective Norm (SN)
_________________________________________________________________________________
Model
Beta
Z-value
p-level
_________________________________________________________________________________
OB Predictions:
McFadden’s ρ2 value: .355
Constant
-1.989
BI (Behavioral Intention)
3.369
Log likelihood function
-28.16063
30.954, Zf =5.564
Chi-squaredd (1df)
-3.899
.000
4.833
.000
Restricted log likelihood
Significance level
-43.63755
.000
McFadden’s ρ2 value: .054
Constant
-.383
.353
Ab (Attitude towards behavior)
Log likelihood function
-41.27605
Chi-squared (1df)
4.723, Zf = 2.175
-.168
.866
2.163
.031
Restricted log likelihood
Significance level
-43.63755
.030
d
Zf. is Chi-squared value transferred into Z using the formula: Zf = (Chi-squared)1/2
(Source for formula: Table 18.1, Judd, Smith & Kiddler, 1991)
Table 16: Summary Data Table of Binary Probit Analysis of Behavior (B) on Each of Behavioral
(Continued)
Intention (BI), Attitude Towards Behavior (Ab) and Subjective Norm (SN).
133
Table 16 cont.
__________________________________________________________________________________
Model
Beta
Z-value
p-level
__________________________________________________________________________________
McFadden’s ρ2 value: .075
Constant
.587
2.937
.003
SN (Subjective Norm)
.278
2.473
.013
Log likelihood function
-40.38494
Restricted log likelihood -43.63755
Significance level
.011
Chi-squared (1df)
6.505, Zf = 2.550
__________________________________________________________________________________
_________________________________________________________________________________
Model
Betaa
t-value (Zf.)
p-level R2 (Ra2)
_________________________________________________________________________________
BI Prediction:
.000
.384 (.363)
Goodness of fit (F(2,60df) = 18.675, Zf = 2.646 )
Constant (n = 63)
.000 (.604)
11.427
.000
.376 (.119)
3.239 (2.043)
.002
Ab (Attitude towards behavior)
SN (Subjective Norm)
.342 (.053)
2.945 (1.870)
.005
________________________________________________________________________________
a
Standardized regression coefficients (Regression coefficient).
Table 17: Summary Data Table of Multiple Linear Regression of Behavioral Intention (BI) on
Attitude Towards Behavior (Ab) and Subjective Norm (SN):
_________________________________________________________________________________
Model
Beta
Z-value
p-level
_________________________________________________________________________________
BI Prediction:
McFadden’s ρ2 value: .175
Constant
.514
1.565
.118
.393
2.018
.044
Ab (Attitude towards behavior)
SN (Subjective Norm)
.273
1.958
.050
Log likelihood function
-32.55713
Restricted log likelihood -39.46396
Significance level
.001
Chi-squared (2df)
13.814, Zf = 3.717
__________________________________________________________________________________
Table 18: Summary Data Table of Binary Probit Analysis of Behavioral Intention (BI) on
Attitude Towards Behavior (Ab) and Subjective Norm (SN):
134
_________________________________________________________________________________
Model
Betaa
t-value (Zf.)
p-level R2 (Ra2)
_________________________________________________________________________________
BI Predictions:
Goodness-of-fit (F(1,62df) = 26.826, Zf = 3.099)
Constant (n = 64)
.000 (.524)
.550 (.173)
Ab (Attitude towards behavior)
.000
.000
.000
.303 (.291)
10.949
5.179 (3.103)
Goodness-of-fit (F(1,60df) = 24.751, Zf = 2.987)
Constant (n = 62)
.000 (.598)
.540 (.010)
Sum (BBi*OEi)
.000
.000
.000
.292 (.280)
14.619
4.975 (2.991)
Goodness-of-fit (F(1,61df) = 23.245, Zf = 2.909)
Constant (n = 63)
.000 (.740)
SN (Subjective Norm)
.525 (.082)
.000
.000
.000
.276 (.264)
21.455
4.821 (2.916)
.000
.424 (411)
Goodness-of-fit (F(1,47df) = 34.551, Zf = 3.336)
Constant (n = 49)
.000 (.697)
20.638
.000
.651 (.015)
5.878 (3.344) .000
Sum (NBi*MCi)
__________________________________________________________________________________
a
Standardized regression coefficients (Regression coefficient).
Table 19: Summary Data Table of Simple Linear Regressions of Behavioral Intention (BI) on Each of
Ab, Sum of Belief-based Attitudes (Sum Bi*Ei), SN, and Sum of Normative Pressures Sum (NBi*MCi)
135
Model
Beta
Z-value
p-level
_____________________________________________________________________________________
BI Predictions:
McFadden’s ρ2 value: .124
Constant
.645
.539
Ab (Attitude towards behavior)
Log likelihood function
-34.58061
Chi-squared (1df)
9.767, Zf = 3.125
.279
.780
3.040
.002
Restricted log likelihood -39.46396
Significance level
.002
McFadden’s ρ2 value: .130
Constant
.231
.359
Sum (BBi*OEi)
Log likelihood function
-34.33177
Chi-squared (1df)
10.264, Zf = 3.204
1.161
.245
2.996
.003
Restricted log likelihood -39.46396
Significance level
.001
McFadden’s ρ2 value: .122
Constant
.969
SN (Subjective Norm)
.369
Log likelihood function
-34.63231
Chi-squared (1df)
9.663, Zf = 3.109
4.134
.000
2.918
.004
Restricted log likelihood -39.46396
Significance level
.002
McFadden’s ρ2 value: .116
Constant
.887
4.267
.000
.659
2.906
.004
Sum (NBi*MCi)
Log likelihood function
-34.50952
Restricted log likelihood -39.46396
Significance level
.002
Chi-squared (1df)
9.909, Zf = 3.148
__________________________________________________________________________________
Table 20: Summary Data Table of Binary Probit Analysis of Behavioral Intention (BI) on Each of
Ab, Sum (Bi*Ei), SN, and Sum (NBi*MCi )
136
_________________________________________________________________________________
Model
Betaa
t-value
p-level R2 (R a2)
_________________________________________________________________________________
Ab Predictions:
Goodness-of-fit (F(1,62df) = 28.657)
.000
.316 (305)
Constant (n = 64)
.000 (.646)
4.998
.000
.562 (.034)
5.353
.000
Sum (BBi*EOi)
_________________________________________________________________________________
a
Standardized regression coefficients (Regression coefficient).
Table 21: Summary Data Table of Simple Linear Regression of Attitude towards Behavior (Ab) on
Sum of Belief-based Attitudes (Sum Bi*Ei):
_________________________________________________________________________________
Model
Betaa
t-value
p-level R2 (R a2)
_________________________________________________________________________________
Ab Prediction:
Goodness-of-fit (F(3,61df) = 12.633)
.000
.383 (353)
Constant (n = 65)
.000 (.626)
3.656
.001
A1 (Saving Money)
.268 (.066)
2.389
.020
A8 (Losing Flexibility)
.215 (.066)
2.065
.043
A9 (Reducing Pollution)
.372 (.096)
3.414
.001
_________________________________________________________________________________
a
Standardized regression coefficients (Regression coefficient).
Table 22: Summary Data Table of Stepwise Linear Regression of Attitude Towards Behavior (Ab)
on A1 (Saving Money), A8 (Losing flexibility) and A9 (Helping to reduce air pollution):
_________________________________________________________________________________
Model
Betaa
t-value
p-level R2 (Ra2)
_________________________________________________________________________________
SN Prediction:
Goodness-of-fit (F(1,48df) = 58.804)
.000
.551 (.541)
Constant (n = 50)
.000 (-.491)
-2.599
.012
.742 (.112)
7.668
.000
Sum (NBi*MCi)
_________________________________________________________________________________
a
Standardized regression coefficients (Regression coefficient).
Table 23: Summary Data Table of Simple Linear Regression of Subjective Norm (SN) on
the Sum of the Normative Pressures (Sum (NBi*MCi))
137
_________________________________________________________________________________
Model
Betaa
t-value
p-level R2 (Ra2)
_________________________________________________________________________________
SN Prediction:
Goodness-of-fit (F(2,47df) = 29.304)
.000
.555 (.536)
Constant (n = 50)
.000 (-.559)
-2.450
.018
SN1 (Pressure from Spouse)
.487 (.113)
4.788
.000
SN3 (Pressure from Best Friend)
.439 (.169)
4.314
.000
________________________________________________________________________________
a
Standardized regression coefficients (Regression coefficient).
Table 24: Summary Data Table of Stepwise Linear Regression of Subjective Norm (SN)
on SN1 (Spouse), and SN3 (Best Friend)
_________________________________________________________________________________
Model
Betaa
t-value (Zf.)
p-level R2 (Ra2)
_________________________________________________________________________________
OB Prediction:
.000
.346 (.335)
Goodness-of-fit (F(1,64df) = 9.00, Zf = 1.905)
Constant (n = 66)
.000 (-.154)
-1.004
.319
Perceived Behavior Control
.588 (.273)
5.726 (3.382)
.000
_________________________________________________________________________________
a
Standardized regression coefficients (Regression coefficient).
Table 25: Summary Data Table of Simple Linear Regression of Behavioral Intention (BI) on
Perceived Behavior Control
_________________________________________________________________________________
Model
Beta
Z-value
p-level
_________________________________________________________________________________
OB Prediction:
McFadden’s ρ2 value: .102
Constant
-2.021
-2.424
.015
Perceived Behavior Control
.748
2.860
.004
Log likelihood function
-39.18449
Restricted log likelihood -43.63755
Significance level
.003
Chi-squared (1df)
8.906, Zf = 2.984
________________________________________________________________________________
Table 26: Summary Data Table of Binary Probit Analysis of Behavior (OB) on Perceived
Behavior Control
138
_________________________________________________________________________________
Model
Betaa
t-value (Zf.)
p-level R2 (Ra2)
_________________________________________________________________________________
OB Prediction:
.000
.536 (.520)
Goodness-of-fit (F(2,61df) = 35.179, Zf = 3.459)
Constant (n = 64)
.000 (-.039)
-.215
.831
BI (Behavioral Intention)
.756 (1.047)
7.007 (3.955)
.000
PBC (Perceived Behavioral Control)
-.042 (-.027)
-.393 (-0.258)
.696
_________________________________________________________________________________
a
Standardized regression coefficients (Regression coefficient).
Table 27: Summary Data Table of Multiple Linear Regression of Behavior (OB) on Behavioral Intention
(BI) and Perceived Behavior Control (PBC)
_________________________________________________________________________________
Model
Beta
Z-value
p-level
_________________________________________________________________________________
OB Prediction:
McFadden’s ρ2 value: .355
Constant
-1.814
-1.933
.053
BI (Behavioral Intention)
3.470
4.129
.000
PBC (Perceived Behavioral Control)
-.788
-.220
.826
Log likelihood function
-28.13630
Restricted log likelihood -43.63755
Significance level
.000
Chi-squared (2df)
31.000, Zf = 5.568
_________________________________________________________________________________
Table 28: Summary Data Table of Binary Probit Analysis of Behavior (OB) on Behavioral Intention (BI)
and Perceived Behavior Control (PBC)
_________________________________________________________________________________
Model
Betaa
t-value (Zf.)
p-level R2 (Ra2)
_________________________________________________________________________________
BI Prediction:
.000
.346 (.335)
Goodness-of-fit (F(1,62df) = 32.791, Zf = 3.367)
Constant (n = 64)
.000 (-.154)
-1.004
.319
Perceived Behavior Control
.558 (.273)
5.726 (3.372)
.000
_________________________________________________________________________________
a
Standardized regression coefficients (Regression coefficient).
Table 29: Summary Data Table of Simple Linear Regression of Behavioral Intention (BI) on Perceived
Behavioral Control (PBC)
139
_________________________________________________________________________________
Model
Beta
Z-value
p-level
_________________________________________________________________________________
BI Prediction:
McFadden’s ρ2 value: .140
Constant
-2.104
-2.459
.014
Perceived Behavior Control
.870
3.148
.002
Log likelihood function
-33.95232
Restricted log likelihood -39.46396
Significance level
.001
Chi-squared (1df)
11.023, Zf = 3.320
_________________________________________________________________________________
Table 30: Summary Data Table of Binary Probit Analysis of Behavioral Intention (BI) on Perceived
Behavioral Control (PBC)
_________________________________________________________________________________
Model
Betaa
t-value (Zf.)
p-level R2 (Ra2)
_________________________________________________________________________________
BI Prediction:
.000
.455 (.427)
Goodness-of-fit (F(3,59df) = 16.412, Zf = 2.497)
Constant (n = 63)
.000 (.147)
.854
.396
.140 (.044)
1.007 (0.658)
.318
Ab (Attitude towards behavior)
SN (Subjective Norm)
.316 (.049)
2.862 (1.821)
.006
PBC (Perceived Behavioral Control)
.365 (.169)
2.777 (1.770)
.007
_________________________________________________________________________________
a
Standardized regression coefficients (Regression coefficient).
Table 31: Summary Data Table of Multiple Linear Regression of Behavioral Intention (BI) on
Attitude towards Behavior (Ab), Subjective Norm (SN) and Perceived Behavior Control (PBC)
_________________________________________________________________________________
Model
Beta
Z-value
p-level
_________________________________________________________________________________
BI Prediction:
McFadden’s ρ2 value: .208
Constant
-.023
-1.005
.315
.160
.660
.509
Ab (Attitude towards behavior)
SN (Subjective Norm)
.272
1.907
.057
PBC (Perceived Behavioral Control)
.575
1.597
.110
Log likelihood function
-31.24712
Restricted log likelihood -39.46396
Significance level
.001
Chi-squared (3df)
16.434, Zf = 4.054
_________________________________________________________________________________
Table 32: Summary Data Table of Binary Probit Analysis of Behavioral Intention (BI) on
Attitude towards Behavior (Ab), Subjective Norm (SN) and Perceived Behavior Control (PBC)
140
_________________________________________________________________________________
Model
Betaa
t-value (Zf.)
p-level R2 (Ra2)
_________________________________________________________________________________
BI Prediction:
.002
.151 (.137)
Goodness-of-fit (F(1,62df) = 11.032, Zf = 2.091 )
Constant
(n = 64)
.000 (1.015)
10.167
.000
The number of auto owned
-.389 (-.283)
-3.321 (-2.093) .002
_________________________________________________________________________________
a
Standardized regression coefficients (Regression coefficient).
Table 33: Summary Data Table of Simple Linear Regression of Behavioral Intention (BI) on
“The number of auto owned”
________________________________________________________________________________
Model
Beta
Z-value
p-level
_________________________________________________________________________________
BI Prediction:
McFadden’s ρ2 value: .045
Constant
1.398
2.891
.004
The number of auto owned
-.729
-1.851
.064
Log likelihood function
-37.67457
Restricted log likelihood -39.46396
Significance level
.058
Chi-squared (1df)
3.579, Zf = 1.892
_________________________________________________________________________________
Table 34: Summary Data Table of Binary Probit Analysis of Behavioral Intention (BI) on
‘The number of auto owned’
________________________________________________________________________________
Model
Betaa
t-value (Zf.)
p-level R2 (Ra2)
_________________________________________________________________________________
BI Prediction:
.000
.417 (388)
Goodness-of-fit (F(3,59df) = 14.088, Zf = 2.332 )
Constant (n = 63)
.000 (.778)
7.244
.000
.317 (.100)
2.677 (1.709)
.010
Ab (Attitude towards behavior)
SN (Subjective Norm)
.322 (.050)
2.814 (1.792)
.007
The number of auto owned
-.197 (-.142)
-1.847 (-1.196) .070
________________________________________________________________________________
a
Standardized regression coefficients (Regression coefficient).
Table 35: Summary Data Table of Multiple Linear Regression of Behavioral Intention (BI) on
Attitude Towards Behavior (Ab), Subjective Norm (SN) and “The number of Automobile owned”
141
_________________________________________________________________________________
Model
Beta
Z-value
p-level
_________________________________________________________________________________
BI Prediction:
McFadden’s ρ2 value: .181
Constant
.885
1.411
.158
.356
1.768
.077
Ab (Attitude towards behavior)
SN (Subjective Norm)
.258
1.844
.065
The number of auto owned
-.315
-.699
.484
Log likelihood function
-32.31212
Restricted log likelihood -39.46396
Significance level
.003
Chi-squared (3df)
14.300, Zf = 3.781
_________________________________________________________________________________
Table 36: Summary Data Table of Binary Probit Analysis of Behavioral Intention (BI) on
Attitude Towards Behavior (Ab), Subjective Norm (SN) and “The number of Automobile owned”
_________________________________________________________________________________
Model
Betaa
t-value (Zf.)
p-level R2 (Ra2)
_________________________________________________________________________________
OB Predictions:
Goodness-of-fit (F(1,64df) = 143.784, Zf = 5.699 )
Constant (n = 66)
.000 (.105)
BPAST (Past Behavior)
.832 (.869)
.000
.049
.000
.692 (.687)
2.007
11.991 (5.717)
.000
.000
.000
.718 (.713)
9.068
12.563 (5.835)
BI Predictions:
Goodness-of-fit (F(1,62df) = 157.831, Zf = 5.814)
Constant (n = 64)
.000 (.330)
BPAST (Past Behavior)
.847 (.637)
PBC Predictions:
.000
.238 (.226)
Goodness-of-fit (F(1,64df) = 19.938 )
Constant (n = 66)
.000 (2.691)
21.047
.000
BPAST (Past Behavior)
.487 (0.788)
4.465 (2.737)
.000
__________________________________________________________________________________
a
Standardized regression coefficients (Regression coefficient).
Table 37: Summary Data Table of Simple Linear Regression of Each of Behavior (OB), Behavioral
Intention (BI), and Perceived Behavioral Control (PBC) on Past Behavior (PASTB)
(Continued)
142
__________________________________________________________________________________
Model
Beta
Z-value
p-level
___________________________________________________________________________________
OB Predictions:
McFadden’s ρ2 value: .465
Constant
-1.296
BPAST (Past Behavior)
3.033
Log likelihood function
-23.33046
Chi-squared (1df)
40.614, Zf = 6.373
-3.733
.000
5.307
.000
Restricted log likelihood
Significance level
-43.63755
.000
BI Predictions:
McFadden’s ρ2 value: .301
Constant
-.536
-1.838
.066
BPAST (Past Behavior)
2.211
4.365
.000
Log likelihood function
-27.59338
Restricted log likelihood -39.46396
Significance level
.000
Chi-squared (1df)
23.741, Zf = 4.872
__________________________________________________________________________________
Table 38: Summary Data Table of Binary Probit Analysis of each of Behavior (OB), Behavioral Intention
(BI), and Perceived Behavioral Control (PBC) on Past Behavior (PASTB)
_________________________________________________________________________________
Model
Betaa
t-value (Zf.)
p-level R2 (Ra2)
_________________________________________________________________________________
OB Prediction:
.000
.691 (.676)
Goodness-of-fit (F(3,60df) = 44.725, Zf = 3.794 )
Constant (n = 64)
.000 (.087)
.573
.569
BI (Behavioral Intention)
.113 (.156)
.769 (0.504)
.445
PBC (Perceived Behavioral Control)
-.019 (-.013)
-.219 (-0.144)
.827
PASTB (Past Behavior)
.743 (.774)
5.493 (3.259)
.000
_________________________________________________________________________________
a
Standardized regression coefficients (Regression coefficient).
Table 39: Summary Data Table of Multiple Linear Regression of Behavior (OB) on Behavioral
Intention (BI), Perceived Behavior Control (PBC) and Past Behavior (PASTB)
143
_________________________________________________________________________________
Model
Beta
Z-value
p-level
_________________________________________________________________________________
OB Prediction:
McFadden’s ρ2 value: .468
Constant
-1.406
-1.391
.164
BI (Behavioral Intention)
.639
.509
.611
PBC (Perceived Behavioral Control)
-.427
-.110
.913
PASTB (Past Behavior)
2.669
2.962
.003
Log likelihood function
-23.19424
Restricted log likelihood -43.63755
Significance level
.000
Chi-squared (3df)
40.887, Zf = 6.394
_________________________________________________________________________________
Table 40: Summary Data Table of Binary Probit Analysis of Behavior (OB) on Behavioral
Intention (BI), Perceived Behavior Control (PBC) and Past Behavior (PASTB)
________________________________________________________________________________
Model
Betaa
t-value (Zf.)
p-level R2 (Ra2)
_________________________________________________________________________________
BI Prediction:
.000
.783 (.768)
Goodness-of-fit (F(4,58df) = 52.454, Zf = 4.011 )
Constant (n = 63)
.000 (.186)
1.700
.094
.159 (.050)
1.797 (1.165)
.078
Ab (Attitude towards behavior)
SN (Subjective Norm)
.091 (.014)
1.221 (0.797)
.227
PBC (Perceived Behavioral Control)
.115 (.053)
1.309 (0.853)
.196
PASTB (Past Behavior)
.689 (.519)
9.380 (4.857)
.000
__________________________________________________________________________________
a
Standardized regression coefficients (Regression coefficient).
Table 41: Summary Data Table of Multiple Linear Regression of Behavioral Intention (BI) on
Attitude Towards Behavior (Ab), Subjective Norm (SN), Perceived Behavior Control (PBC)
and Past Behavior (PASTB)
144
__________________________________________________________________________________
Model
Beta
Z-value
p-level
__________________________________________________________________________________
BI Prediction:
McFadden’s ρ2 value: .352
Constant
-1.065
-.971
.331
.231
.851
.395
Ab (Attitude towards behavior)
SN (Subjective Norm)
.127
.773
.439
PBC (Perceived Behavioral Control)
.228
.559
.576
PASTB (Past Behavior)
1.837
3.151
.002
Log likelihood function
-25.55926
Restricted log likelihood -39.46396
Significance level
.000
Chi-squared (4df)
27.810, Zf = 5.273
_____________________________________________________________________________________
Table 42: Summary Data Table of Binary Probit Analysis of Behavioral Intention (BI) on Attitude
Towards Behavior (Ab), Subjective Norm (SN), PBC, and Past Behavior (PASTB)
Riding the Buckeye Village bus means:
1. Saving money
a) Belief-BB1 (strength)
2. A long waiting
a) Belief-BB2
b) Evaluation EO1
b) Evaluation EO2
3. Countering people with different
Personalities
Percentage
Percentage
Percentage
positive (%)
neutral (%)
negative (%)
87.9
9.1
3.0
92.3
7.7
0.0
78.8
0.0
21.2
4.5
22.7
72.7
a) Belief-BB3
42.4
30.3
27.3
b) Evaluation EO3
47.0
45.5
7.5
4. An opportunity to relax on bus
a) Belief-BB4
74.2
9.1
16.7
b) Evaluation EO4
93.9
4.5
1.5
5. Avoiding parking worry
a) Belief-BB5
90.9
3.0
6.1
b) Evaluation EO5
97.0
1.5
1.5
a) Belief-BB6
48.5
13.6
37.9
3.1
33.3
63.6
a) Belief-BB7
72.7
18.2
9.1
b) Evaluation EO7
86.4
10.6
3.0
a) Belief-BB8
66.7
10.6
22.7
0.0
24.2
75.8
a) Belief-BB9
74.2
15.2
10.6
b) Evaluation EO9
90.9
9.1
0.0
a) Belief-BB10
59.1
16.9
24.2
6. Riding a crowded bus
b) Evaluation EO6
7. Helping to reducing traffic
Congestion
8. Losing flexibility to run errands
b) Evaluation-EO8
9. Helping to reduce pollution
10. Facing inconvenience
b) Evaluation EO10
3.0
18.2
78.8
_______________________________________________________________________________________________
Table 43: Percentage distribution of respondents based on their positive, neutral or negative responses
for the statements related to beliefs, attitudes, behavioral intention and past behavior
(continued)
145
Table 43 Cont.
____________________________________________________________________________________________
__________
11. Attitude toward behavior Ab
72.7
21.2
6.1
12. Spouse as an important referent
a) Normative belief-NB1
61.5
38.5
b) Motivation to Comply MC1
78.9
21.1
a) Normative belief-NB2
27.0
73.0
b) Motivation to Comply MC2
23.4
76.6
a) Normative belief-NB3
25.8
74.2
b) Motivation to Comply MC3
47.6
52.4
15. Subjective Norm SN
43.1
56.9
16. Perceived Behavioral Control PBC
95.5
4.5
17. Behavioral Intention BI
65.2
34.8
18. Past Behavior PASTB
60.4
39.6
13. Neighbor as an important referent
14. Best Friend as an important referent
.
146