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Journal of Organizational
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Behavior-Based Safety and
Working Alone
a
Ryan Olson & John Austin
a
a
Western Michigan University, USA
Available online: 12 Oct 2008
To cite this article: Ryan Olson & John Austin (2001): Behavior-Based Safety and
Working Alone, Journal of Organizational Behavior Management, 21:3, 5-43
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EXPERIMENT
Behavior-Based Safety and Working Alone:
The Effects
of a Self-Monitoring Package
on the Safe Performance
of Bus Operators
Ryan Olson
John Austin
ABSTRACT. Experimental evaluations of Behavior-Based Safety (BBS)
processes applied with lone workers are scarce. Clinical and organizational researchers alike have studied the effectiveness of self-monitoring
as a performance improvement strategy, but further work is needed to determine the power of such interventions for improving safe behavior and
Ryan Olson and John Austin are affiliated with Western Michigan University.
Address correspondence to Ryan Olson, 3308 Miami Avenue, Kalamazoo, MI
49048 (E-mail: ryan.olson@wmich.edu).
The authors would like to thank Adam VanAssche and Lisa Olson for implementing critical aspects of the intervention.
They would also like to recognize Alicia Alvero and Scott Traynor for their helpful
input regarding the design of the study.
Journal of Organizational Behavior Management, Vol. 21(3) 2001
 2001 by The Haworth Press, Inc. All rights reserved.
5
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JOURNAL OF ORGANIZATIONAL BEHAVIOR MANAGEMENT
to explore the best practices for using such processes with lone workers.
In the current study, four male bus operators (20.5 years average experience) self-monitored their safe performance and received feedback
based on self-monitoring data. Dispatch supervisors used radio communication to prompt participants to complete self-monitoring forms and also
conducted special observations of participants to measure target performances. Both operators and supervisors were unaware of experimental
observers who measured the performance of each participant by riding on
busses as passengers. A multiple baseline design across performances was
used to assess the effects of the intervention on four performance targets.
The intervention resulted in a 12.3% increase in safe performance for the
group, with individual increases in performance ranging from 2% to 41%
for specific target performances. The results are discussed in terms of the
value of BBS processes for employees who work alone and the research
needed to determine the components of self-monitoring processes that are
most critical for generating improvements in safe performance. [Article copies
available for a fee from The Haworth Document Delivery Service: 1-800-HAWORTH. E-mail address: <getinfo@haworthpressinc.com> Website: <http://www.HaworthPress.com>
© 2001 by The Haworth Press, Inc. All rights reserved.]
KEYWORDS. Self-monitoring, behavior-based safety, safe driving,
lone workers, bus transit safety, bus operator performance
Over the past 20 years behavioral research in the field of Behavior-Based Safety (BBS) has grown steadily. Some of the first conceptual
articles discussing the application of behavior analysis technology to improve occupational safety were published in the late 1970’s (e.g., Smith,
Cohen, H., Cohen, A., & Cleavland, 1978). The first experimental applications of behavioral technology applied to occupational safety occurred during the same time period (Komaki, Barwick, & Scott, 1978; Smith, Anger, &
Uslan, 1978; Sulzer-Azaroff, 1978). The central foundation of all BBS research since these early applications has been the identification and measurement of safe and at-risk behaviors and conditions, and the use of
behavioral technology to increase the frequency of those safe behaviors
and conditions. The body of research has demonstrated the effectiveness of
many different intervention packages designed to achieve these effects.
Studies have evaluated experimentally the effectiveness of training
(Cohen & Jensen, 1984; Komaki, Heinzmann, & Lawson, 1980; Reber &
Wallin, 1984; Reddell, Congleton, Huchingson, & Montgomery, 1992),
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Experiment
7
goal setting and/or prompts (Austin, Alvero, & Olson, 1998; Berry,
Geller, Calef, R. S., & Calef, R. A, 1992; Engerman, Austin, & Bailey,
1997; Fellner & Sulzer-Azaroff, 1986; Ludwig & Geller, 1991, 1997;
Phillips, Sutherland, & Makin, 1994: Reber & Wallin, 1984; Reber,
Wallin, & Chhokar, 1990; Saarela, 1989), verbal and graphic feedback
(Alavosius & Sulzer-Azaroff, 1986, 1990; Babcock, Sulzer-Azaroff, &
Sanderson, 1992; Chhokar & Wallin, 1984; DeVries, Burnette, &
Redmon, 1991; Fellner & Sulzer-Azaroff, 1984; Komaki, Heinzmann, &
Lawson, 1980; Nasanen & Saari, 1987; Phillips, Sutherland, & Makin,
1994; Sulzer-Azaroff & de Santamaria, 1980), contingent incentives and
reinforcement (Austin, Kessler, Riccobono, & Bailey, 1996; Fox, Hopkins, &
Anger, 1987; Komaki, Barwick, & Scott, 1978; McAfee & Winn, 1989;
Petersen, 1984), and self-monitoring procedures (McCann & SulzerAzaroff, 1996) at increasing safe behaviors and conditions. For a recently
published, more thorough, review of BBS in manufacturing settings, see
Grindle, Dickinson, and Boettcher (2000). For a review of the impact of
BBS on injury rates, see Sulzer-Azaroff and Austin (2000).
Studies by Geller and colleagues have clear relevance when discussing driving safety. For example, Ludwig and Geller (2000) described a
series of seven studies designed to improve the safe driving of pizza deliverers. The interventions they evaluated included public and private feedback, corporate policy changes, commitment card strategies, participative
and assigned goal setting, competition and rewards, and involving the
deliverers as community intervention agents. Ludwig and Geller (2000)
reviewed these seven studies in terms of the multiple intervention level
(MIL) hierarchy. The MIL is characterized by a continuum of intervention intrusiveness and cost, where the least intrusive and most inexpensive interventions tend to reach the most people and the most intrusive
and most costly interventions tend to impact the fewest people. The
MIL is not unlike other discussions of treatment intrusiveness in applied behavior analysis (e.g., Meinhold & Mulick, 1990), but the MIL
specifically applies these concepts to organizational behavior. Ludwig
and Geller (2000) recommended that least intrusive interventions be applied to create large-scale change and that those individuals who remain
unaffected by non-intrusive interventions should be exposed to successively more intrusive interventions.
Although discussed in theory by the MIL, experimental evaluation of
self-monitoring procedures to improve safe behavior is a relatively new
development. The field of BBS is growing and reports of successful commercial applications with lone workers have begun to surface (e.g.,
Krause, 1997; Pettinger, Click, & Geller, 2000). The research base exam-
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JOURNAL OF ORGANIZATIONAL BEHAVIOR MANAGEMENT
ining the best practices for improving the safe performance of lone workers is small, however, self-monitoring has been widely used in other
contexts as a behavior change technique.
SELF-MONITORING
Richman, Riordan, Reiss, Pyles, and Bailey (1988) conducted a
study that demonstrated the power and utility of self-monitoring procedures for improving organizational performance. Richman et al. (1988)
used in-service training, self-monitoring, and self-monitoring plus
feedback to improve the on-schedule and on-task performance of staff
at a residential setting for persons with mental disabilities. A multiple
baseline design across groups was used to assess the effects of the different intervention phases. Three months prior to the study, participants
were informed that a special project was going to take place where
staff/client interactions would be observed. Under this guise, experimental observers collected data for on-schedule and on-task behavior
for the duration of the study. After baseline data were collected, an
in-service training session was held to review job responsibilities that
included topics related to on-schedule and on-task performance. During
the self-monitoring phase, staff members carried individual schedule
cards during the workday and self-recorded the extent to which they
were on-schedule and on-task during the shift. These self-recorded data
were handed in at the end of each shift. For the self-monitoring plus
feedback component, supervisors provided periodic on-the-spot feedback regarding target performances while the self-monitoring procedure continued as before. Two houses participated in the study and were
labeled A and B. For house A, on-schedule behavior averaged 50%,
50%, 80%, and 94% across baseline, in-service, self-monitoring, and
self-monitoring plus feedback conditions respectively. For house B,
on-schedule behavior averaged 39%, 39%, 75%, and 81% across baseline, in-service, self-monitoring, and self-monitoring plus feedback
conditions respectively. For on-task behavior, baseline for both houses
combined was 28%. In-service increased the on-task performance of
staff in house A to 36% but did not affect the performance of staff in
house B. For house A, on-task behavior averaged 72% and 88% for
self-monitoring and self-monitoring plus feedback respectively. For
house B, on-task behavior averaged 77% and 80% for self-monitoring
and self-monitoring plus feedback respectively.
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Self-monitoring, as part of intervention packages, has also been used
to improve academic performance (Dean, Malott, & Fulton, 1983;
DiGangi, Maag, & Rutherford, 1991; Kneedler & Hallahan, 1981; Lan,
1996; Stecker, Whinnery, & Fuchs, 1996), to improve the performance
of teachers (Browder, Liberty, Heller, & D’Huyvetters, 1986), to improve the performance of athletes (Kessler, 1985; Srikameswaren,
1992; Whelan, Mahoney, & Meyers, 1991), to increase interactions between staff and patients at an institution (Burgio, Whitman, & Reid,
1983), and to help individuals stop smoking and reduce their caloric intake (Moinat & Snortum, 1976). Only some of the research listed above
was conducted with adults and targeted “workplace” performance.
However, self-monitoring procedures are potentially relevant across a
broader scope of organizational behavior for people who work alone
and for people who work in groups. This broader scope includes support
for improving the quality, quantity, or timeliness of the performance of
salespeople or consultants working outside of the home office with clients. People working in teams could use self-monitoring procedures in
concert with peer feedback to track progress on long-term projects or to
target specific “team relevant” skills. In relation to the topic of the current paper, self-monitoring procedures could complement or substantially improve the current performance management strategies utilized
to support and improve the performance of people operating any number of different vehicles in the general transportation and product delivery industries. For example, the first author has been exploring
supplementary performance measurement systems for student pilots
during the early phases of flight training. An especially risky phase of
flight training involves the first series of solo flights without an instructor on board. As part of the exploratory research mentioned above, volunteer students have been self-monitoring aspects of landing performance on
dual (with an instructor) and on solo (without an instructor) flights. This
produces data that would otherwise not be available and could potentially improve learning and performance, thereby reducing risk. Students have reported that the procedure has enhanced the learning
process. If these self-monitoring procedures were used in combination
with instructors rating the same performances, such systems could
prompt feedback and coaching for specific critical performances. Down
the road when these students become professional pilots working in the
cockpits of planes for major airlines, self-monitoring procedures and
peer feedback and discussion could be used to target critical crew resource management skills. With these potential applications in mind,
we can predict that self-monitoring procedures will probably prove
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valuable across a wide variety of contexts. However, as self-monitoring
research and practices expand in organizational settings, we should also
predict limits to the effectiveness and relevance of self-monitoring procedures in organizational settings.
Self-monitoring procedures have contributed to performance improvement across many settings and represent a set of methods that may
be especially relevant for improving learning and performance in workplace environments. However, the question of which components of
self-monitoring procedures are most critical for generating behavior
change is still being explored. For example, the extent to which
self-monitoring data need to be reliable is not clear. Some research suggests that self-monitoring procedures produce performance improvement even when the self-recorded data are not accurate (Hayes &
Nelson, 1983; McCann & Sulzer-Azaroff, 1996). However, when
self-monitoring data are more reliable, effects seem to be enhanced
(Baskett, 1985; Kanfer, 1970; McCann & Sulzer-Azaroff, 1996). It
would be useful to know whether training participants to reliably
self-monitor is a worthy investment. An additional consideration related to the effectiveness of self-monitoring procedures is identifying
the behavioral functions of the stimuli generated by such procedures.
Some of the potential behavioral functions of stimuli produced by
self-monitoring processes include: (a) an antecedent function (i.e., informational or task clarification), (b) a consequence function (conditioned reinforcement or punishment), (c) a rule generating function
(i.e., contingency specifying, function-altering stimuli are evoked), and
(d) a conditioned establishing operation function.
When a participant is asked to record aspects of his or her behavior,
looking at the form, and filling it out may clarify performance expectations or prompt the most appropriate performance. Based upon subsequent observations of behavior with respect to such informational
stimuli, we would say that participants “know” or “do not know” the
safe manner in which to behave (Skinner, 1953). If self-monitoring
functions primarily as information or a prompt it would make sense to
ask participants to self-monitor at the beginning of the workday or just
prior to opportunities to perform. Antecedents without consequences
are likely to have only temporary effects due to habituation or extinction (Daniels, 1989). Therefore, it would also make sense to ensure that
positive consequences were correlated with the antecedent process and
that the self-monitoring procedure was periodically changed.
Aspects of self-monitoring processes may also function as consequences. Scoring oneself high or low may function as analogs to rein-
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11
forcement or punishment for the desired performance depending upon
the quality of the most recent relevant performance (Malott, R., Malott,
M., & Trojan, E., 1999). Thinking of the potential consequence function of self-monitoring procedures may also explain in part why compliance with self-monitoring processes is normally less than perfect.
Because performance varies, scoring aspects of one’s own performance
might sometimes reinforce and sometimes punish filling out a self-monitoring form.
Due to the fact that management systems utilize numerous performance management strategies, filling out self-monitoring forms may
also cause participants to generate rules related to those strategies. If an
organization regularly uses aversive consequences to discourage unsafe
practices, filling out a self-monitoring form might evoke rules such as,
“If I improve this performance, I can avoid punishment from my supervisor (because the performances on this form are what he/she cares
about right now).” Schlinger (1993) proposed that a rule such as this
one might produce behavioral effects because it specifies contingencies
and alters the function of stimuli in the immediate environment. For example, the rule above specifies a new contingency (i.e., my supervisor
will punish me if I don’t improve these behaviors on the form) and
might alter the function of stimuli in the immediate environment (a previously ineffective stop sign now evokes behavior that results in a complete stop).
Another way of accounting for the effects of verbal behavior describing contingencies is the concept of the conditioned establishing operation (CEO). An establishing operation is a stimulus or procedure that
has at least two effects; it (1) momentarily alters the effectiveness of a
reinforcer or punisher, and (2) momentarily alters the frequency of behavior that has been correlated with the consequence whose effectiveness has been altered (Michael, 1993). Michael has delineated three
types of CEOs with specific characteristics, but discussing these types
is beyond the scope of this paper. In most cases, CEOs alter the effectiveness of conditioned reinforcers or punishers, and given the fact that
most organizational performance is maintained and shaped by such
consequences, we should consider the CEO concept a potentially important motivational variable. When approaching a relevant opportunity to perform, a rule statement related to the performance being
self-monitored might be evoked. The covert verbal behavior, or perhaps
the stimulus that evoked the covert behavior, may then function as a
CEO that alters the effectiveness of salient consequences. For example,
a bus driver may perform rolling stops at stop signs because the brakes
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squeal less than when he/she performs a complete stop. Participating in
a self-monitoring procedure that targeted complete stopping might
cause the sight of a stop sign and/or evoked rule statements to function
as a CEO that momentarily alters the value of the squealing sound, making it less aversive (weakening motivation to escape or avoid the squealing). Alternatively, CEOs could momentarily establish the squealing
sound as an effective reinforcer, thereby evoking behavior (firm foot
pressure on the brakes) that had produced that consequence in the past.
It is likely that performance improvement generated by self-monitoring
procedures is caused by a complex set of contingencies and behavioral
mechanisms. Considering these mechanisms and explanatory concepts
may guide future research and help discover the most effective practices.
With self-monitoring research in BBS being scarce, the field may require
more studies that demonstrate the effectiveness of self-monitoring procedures to improve safe performance before technical questions can be addressed. Below we review two applications of self-monitoring procedures
to improve safe performance that informed the design of the current study.
BBS APPLICATIONS OF SELF-MONITORING PROCEDURES
Preventing Cumulative Trauma Disorders
McCann and Sulzer-Azaroff (1996) used a behavioral approach to
prevent cumulative trauma disorders with employees who spent much
of each workday typing in an office setting. Part of the intervention
package required typists to self-monitor performance along particular
behavioral dimensions. Participants were divided into two groups
where one group monitored hand and wrist position and the other monitored posture. Each participant was exposed to conditions in the following sequence: (a) baseline, (b) training and self-monitoring, and
(c) feedback, goal setting, and reinforcement. During training, participants were taught discriminations between safe and at-risk performance
and were required to pass a discrimination test with a score above 80%
correct. Self-monitoring procedures required participants to estimate
the percentage of time they performed target behaviors safely. During
the final phase of intervention participants met prior to each session and
were given both graphic and verbal feedback based on levels of safety
observed by experimenters on the previous days. The graphic feedback
was in the form of transparencies that, when laid over the participants’
self-monitored data, revealed the accuracy of participants’ reports. Ex-
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13
perimenters guided participants as they set goals to ensure that goals
were not set higher than the highest data point from the previous session. And finally, praise was provided for progress and attainment of
goals.
The study produced consistent improvements in safe performance
across all participants with moderate to high improvements during the
training and self-monitoring phase, and very high improvements during
the feedback, goal setting, and reinforcement phase. Posture ultimately
improved to near perfect levels for all participants in the posture group.
Hand and wrist position improved to levels clearly above baseline for
all participants in the hand/wrist position group.
Participants were not initially given information about the accuracy
of their self-estimations of safe performance. Without accuracy information participants achieved acceptable levels of agreement between
self-monitored data for posture and experimenter data for posture.
However, self-monitoring data for hand and wrist position did not agree
with experimenter data at this stage. Researchers postulated that the
“gross motor” nature of the movements involved with posture made the
behavior easier to self-monitor than the “fine motor” hand and wrist position movements, which resulted in the different agreement levels between posture and hand/wrist position. The goal setting, feedback, and
reinforcement phase increased the agreement between self-monitoring
data and experimental data for hand and wrist position. The reinforcement component (verbal praise) was contingent upon performance improvement and accurate self-estimations of performance. The researchers
reported that high agreement between typists and experimenters was associated with enhanced performance improvement of safe hand and wrist
position.
Improving the Safe Performance of Bus Operators
Krause (1997) reported a Behavioral Science Technology, Inc. (BST)
consultation effort with a public transportation system where self-monitoring procedures were utilized. Thirty drivers and several supervisors
participated in the project. Interviews with drivers were utilized to develop a checklist that contained 34 performances. Drivers estimated
their safe performance on these 34 targets once or twice daily and plotted their own data on graphs. Every two weeks a supervisor rode with
each driver and collected data using the same checklist.
When the intervention was initially implemented drivers reported
high percent safe scores that did not agree with supervisors’ scores of
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driver performance. Supervisors discussed these discrepancies with
drivers and plotted the self-monitoring data and supervisor data together on feedback graphs. Over a period of 20 weeks, supervisor data
trended upward and driver data began to trend downward slightly to almost match supervisor data. Agreement between employees and supervisors appeared to take place over time and Krause (1997) reported a 66%
decline in injuries and accidents in the organization over the 20-week
time period. However, the project did not employ an experimental design
and did not include any formal assessment of the reliability of either supervisor or driver data. Therefore, the degree to which driver’s behavior
actually changed because of the intervention could not be evaluated.
In order to evaluate experimentally the degree to which self-monitoring procedures can improve the safe performance of lone workers, a demonstration study similar in design to the McCann and Sulzer-Azaroff
(1996) study is needed. The current study was an attempt to synthesize
aspects of Krause (1997) with McCann and Sulzer-Azaroff (1996) and
experimentally evaluate the effectiveness of self-monitoring procedures
for improving the safe performance of bus operators.
METHOD
Participants and Setting
A public transportation system serving two midwestern cities with a
combined estimated population of 160,000 was the sponsoring organization for the study. In addition to operating and maintaining 17 bus
routes, the organization operated rail and other public transportation
systems. Within the bus system an operations supervisor managed the
performance of seven dispatch supervisors, who in turn supervised 65
bus and other vehicle operators. A university campus route serving a
campus of approximately 26,000 students was the location for the study
where two to eight busses operated from 7 a.m. to 12 midnight on weekdays. The bus route consisted of two directional patterns, each lasting
about 30 minutes, and served all major campus locations including
on-campus housing.
Four experienced drivers who worked a 10-hour shift (about 6:30 a.m.
to about 4:30 p.m.) were selected by the operations supervisor to participate in the study (male, ages approximately 40-50; average experience
20.5 years, range: 19-23 years). Organizational leadership was inter-
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15
ested in this shift because of its duration and the busy pedestrian and
traffic conditions of the university campus.
Prior to the study, the transit system used five methods to motivate
safe driving after drivers were initially trained upon hire. These methods were (a) a $25 bonus for all drivers who worked an entire quarter
without having a preventable collision, (b) a 7 step progressive discipline program for moving violations and preventable collisions, (c) hiring private investigators to monitor drivers receiving serious complaints,
(d) yearly safety awards at a banquet, and (e) bi-monthly general performance evaluations by dispatch supervisors. The operations supervisor reported that this general management strategy had produced a plateau in total
collisions per year that had remained relatively stable over the past five years.
Dependent Variables
Dependent variables were identified through an assessment that included a review of one year of collision reports from the organization’s
records. Passenger and pedestrian injury reports were also reviewed but
were so infrequent that no patterns could be discerned from them. Given
the high pedestrian traffic conditions of the campus bus route, it is
likely that risk for these kinds of events was high when compared to
other routes within the transit system. The degree to which acceptable
IOA could be achieved was the final consideration for the selection
of dependent variables. Performances observed were divided into
three categories: (a) loading/unloading passengers, (b) bus in motion,
and (c) complete stop. Bus in motion performances related to cornering
safely and maintaining adequate following distance were excluded
from the study due to ceiling effects.
Loading/unloading performances included bus stopping position, remaining motionless for two seconds after an unload/load instance, and
mirror checking. The assessment discovered that 20% of preventable
collisions had occurred at loading zones and another 12% of preventable collisions had occurred at parking lots or driveways. Checking mirrors was identified as a behavior that may have helped prevent 56% of
the collisions reviewed in the assessment. The route involved in the
study passed through six major campus parking lots. Moreover, many
loading zones were located near parking lot exits and various other
throughways.
Bus stopping position was defined as “bus doors must remain shut
until the bus is completely stopped, and the bus should be positioned so
no cars can pass on the right.” Observers scored this performance by
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watching the front doors of the bus as it slowed. If the bus was still moving when the doors separated, or if a car could pass on the right, the performance was scored at-risk. Two seconds motionless was defined as
“the bus should remain motionless for at least two seconds after the last
loading/unloading passenger either steps behind the yellow line on the
bus, steps off the bus to the right, or steps clear of the front left corner of
the bus.” Observers were instructed to count “one-thousand one,
one-thousand two,” to themselves to measure this performance and
used a wristwatch to periodically calibrate the pace of their counting. If
the observer was able to reach “two” before the bus moved the performance was scored correct while any movement before the observer
reached “two” was scored as at-risk. Mirror checking was defined as
“the driver should visually check both side mirrors after loading/unloading passengers as the bus pulls out of a loading zone.” Observers
were instructed to mark this performance as correct if both mirrors were
checked before or as the bus started moving. Checking mirrors after the
back of the bus cleared the original load/unload location was scored
at-risk. From the driver’s right hand side of the bus in the second row of
forward facing seats his eyes were visible in the center mirror and head
movement could be viewed. If a driver looked in the general direction
of either mirror it was assumed he checked that mirror.
Complete termination of forward motion at stop signals is a legal requirement and was considered an important safe performance for the
campus route. Drivers making complete stops have a better opportunity
to scan traffic and pedestrian conditions at busy intersections. There
were over 20 stop signals during each 30-minute loop regardless of the
direction the bus was traveling. Rolling stops and jumping a traffic signal were scored as at-risk. The observation technique that achieved reliability for complete stops involved picking out an outside object like a
pole and watching it as the bus slowed. If the outside object stood still in
the observer’s field of vision the performance was scored as safe.
A percent safe score for each dependent measure was calculated by
counting the number of correct scores and dividing that number by the
total number of observations for that dependent measure, and then multiplying by 100. An overall percent safe score for each observation session was also calculated in a similar fashion.
Observers and Observation Procedures
The first author and two undergraduate research assistants worked as
experimental data collectors over the course of the study. Undergradu-
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17
ate research assistants were selected on the basis of good performance
in an organizational psychology class, interest, and availability. All
three researchers worked for research credits at Western Michigan University. Observers sat at the driver’s right hand side of the bus in the second row of forward facing seats about 10 feet from the driver’s chair.
Bus schedules were marked with color codes to locate participants and
each driver was identified by their color code throughout the study for
confidentiality reasons. As an additional measure of confidentiality color
codes were changed to participant numbers for this paper. Each driver
was generally observed at least once each day for at least 30 minutes (i.e.,
one directional loop of the route). However, observers were required to
monitor at least 10 instances of loading/unloading of passengers per session, resulting in some sessions longer than 30 minutes. On average, there
were 10 or more load/unload instances and over 20 stops observed each
session.
Once or twice each week all four participants were observed by two
observers to assess IOA, which was calculated by dividing the number
of agreements by the number of agreements + disagreements, and then
multiplying by 100. During reliability sessions, the first author was the
primary observer. To protect the independence of observations, the observer sitting on the right hand seat next to the window used a three-ring
binder with the left cover held upright to block the visibility of the data
sheet. The observer sitting on the left hand seat covered his/her data
sheet with his/her right arm and hands (all observers were right handed).
Methods to Minimize Driver Reactivity to Experimental Observers
Participating drivers were not informed of experimental observers until a post-experiment debriefing.1 However, it was odd for passengers to
ride an entire loop of the route without arriving at a destination and drivers occasionally asked questions. Observers, who were earning research
credits for participation, were instructed to answer such questions by saying “I’m collecting a survey for a class.” Surveys on bus ridership had
taken place recently and this proved to be an effective strategy. To further
reduce the possibility of untoward interactions with drivers observers
were instructed to wear headphones when collecting data by themselves.
Independent Variables
Project Kick-Off Meeting. After an initial baseline phase intervention began with an hour and a half meeting at the transit station hub that
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consisted of an introduction to BBS and the rationale for piloting such a
process at the transit system, an introduction to and rationale for a
self-monitoring process, and finally a description of the details of running the project. The meeting was conducted by a doctoral student (male,
age 26) not involved in data collection and by the operations supervisor.
The student was introduced as an external safety consultant without mentioning his ties to the university. Participants were informed that their input about the project would be solicited at a post-project lunch and that
organizational leadership had signed an agreement that information obtained during the project could not be used for disciplinary purposes.
Drivers were also told that one or two additional meetings with the consultant would be scheduled over the next few weeks. Immediately after
the kick-off meeting, the student consultant and the operations supervisor
met with dispatch supervisors to introduce them to the project. Supervisors were not informed of the presence of experimental observers.
Self-Monitoring. Three different self-monitoring forms were used
over the course of the study and were introduced during meetings at the
transit hub (see Experimental Design section below). Drivers used these
forms twice each day during their 10-hour shift to estimate the percentage of time they performed each of the target performances safely.
Blank squares were provided on the form for writing estimations, which
is one strategy suggested by research to avoid shaping respondent answers (Schwarz, 1999). At the drivers’ request the locked drop box for
self-monitoring forms was located in the drivers’ lounge at the transit
system hub. Drivers were also told that they would be prompted twice a
day by their dispatch supervisors via radio when it was time to self-monitor.
Feedback. The first author generated daily color-coded individual
and group graphs based on self-monitoring data from the previous day.
A research assistant posted a new set of graphs each evening between 8
and 9 p.m. in the drivers’ lounge near the drop box and collected completed self-monitoring forms. Each driver was asked to initial the group
graph at the conclusion of each shift to demonstrate that the feedback
had been viewed.
Supervisor Prompts and Observations. Dispatch supervisors were instructed to prompt participating drivers via radio twice each day to use
the self-monitoring forms and record the date and time of their prompts
on a chart posted in the dispatch office. In addition to delivering
prompts, supervisors conducted special observations of drivers using a
data sheet (identical in format to experimental data sheets) limited to the
performances currently being self-monitored by drivers. Experimental
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Experiment
19
observers arranged to measure performance concurrently with supervisor observations. On these occasions, experimental observers boarded
the bus prior to the supervisor visit and left the bus one or two stops after
the supervisor left the bus. This procedure was added to the design of
the study as a type of probe, where performance changes generated by
the presence of a supervisor could be measured and compared to data
collected on the same day without supervisor presence. To create this
comparison, each driver was observed for an additional session on the
same day either before or after the supervisor probe was completed.
Independent Variable Integrity. Three measures of independent variable integrity were calculated. Percentage of compliance with the
self-monitoring procedure was calculated by counting the actual number of self-monitoring forms completed by each driver, dividing that
number by the expected number of completed self-monitoring forms
for each driver (two per day), and then multiplying by 100. Percentage
of compliance with feedback procedures was calculated by counting the
number of days each driver signed the feedback graph, dividing that figure by the number of days the driver was expected to sign the feedback
form, and then multiplying by 100. And finally, the percentage of supervisor compliance with delivering prompts was calculated by counting
the number of prompts recorded on the supervisor form, dividing that
figure by the number of prompts that were expected to be given, and
then multiplying by 100.
Experimental Design
A multiple baseline design across performances was used to assess
the effects of the intervention. Intervention began after a baseline of 9 to
11 sessions for each individual driver (group baseline sessions totaled
13 because individual driver baseline sessions were obtained across different days). Intervention was first implemented for complete stop performance and lasted for eight workdays while baseline conditions
continued for the remaining dependent variables. Phase two added the
performance of remaining motionless for two seconds after loading/unloading passengers and lasted for five workdays while baseline conditions continued for the remaining dependent variables. The third and
final phase of intervention introduced checking mirrors and bus stopping position. After five more working days using this final form, the
route stopped running for the semester and the study was concluded.
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RESULTS
Group Performance
Group performance was calculated by creating a group percent safe
score for each day of the experiment for each dependent measure. For
practical purposes, average improvement percentages for each dependent measure were then summed and divided by four to obtain a simple
overall improvement percentage. Using this method, the group improved safe driving by an average of 12.3% over baseline conditions.
The dependent variable realizing the largest improvement for the group
was complete stop, which improved by an average of 21.2% (range:
14%-41%). Two seconds motionless after loading/unloading passengers improved by an average of 11.8% (range: 3%-19%), mirror check
improved by an average of 10% (range: 3%-15%), and bus stopping position improved by an average of 6.2% (range: 2%-12%). Figure 1 represents the grouped data (i.e., averaged across the four drivers for each
day of data collection) for each of the four dependent variables in the
multiple baseline design. As can be seen during visual inspection of
group data, calculations of average “improvement” may not indicate
clear effects, especially with regard to the last phase of the intervention.
For example, visual inspection does not indicate any clear effect for bus
stopping position (patterns in intervention data closely resemble patterns in baseline data). For mirror check, an up trend in the data suggests
an effect, but more data would be required to draw this conclusion.
Individual Performance
The results of individual participants are presented in order of largest
to smallest overall improvement. The word “improvement” is used
throughout the discussion of individual performance, although it should
be understood that small average increases for specific dependent measures do not necessarily indicate clear effects of the intervention. For
example, average improvement percentages for phase 3 of the intervention are based on only 3 to 4 data points and should therefore be interpreted conservatively. Overall improvement percentages for each
participant are necessarily influenced by this characteristic of the experiment, and should also be interpreted conservatively. Several alternatives for computing overall improvement were considered, but for
practical purposes, the same method used to calculate overall group improvement was applied to individual participants. Percentages related
21
FIGURE 1. Group results in multiple baseline design format. Closed circle data
points are experimenter data averaged for each day of the experiment and
open circle data points are self-monitoring data averaged for each day of the
experiment.
COMPLETE STOP
86.5%
Percent Safe
100
80
60
40
20
0
68%
46.8%
1
3
5
7
9
11 13 15 17 19 21 23 25 27 29
Days
LOAD/UNLOAD 2 SECONDS MOTIONLESS
Percent Safe
100
60%
80
60
40
20
0
57.1%
45.3%
3
1
5
7
9
11 13 15 17 19 21 23 25 27 29
Days
LOAD/UNLOAD MIRROR CHECK
Percent Safe
100
99%
80
60
40
20
0
58.3%
1
3
5
68.3%
7
9
11 13 15 17 19 21 23 25 27 29
Days
LOAD/UNLOAD STOPPING POSITION
100
Percent Safe
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Experiment
95.5%
80
60
40
20
0
79%
72.8%
1
3
5
7
9
11 13 15 17 19 21 23 25 27 29
Days
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JOURNAL OF ORGANIZATIONAL BEHAVIOR MANAGEMENT
to individual performance have been rounded to the nearest whole number to make them easier to read.
Participant 1 improved by an average of 14% over baseline levels.
The largest average improvement was for two seconds motionless with
a 19% improvement (baseline, 43% safe; intervention, 62% safe). Bus
stopping position improved 8% (baseline, 70% safe; intervention, 78%
safe), mirror check improved 15% (baseline, 73% safe; intervention,
88% safe), and stopping improved 14% (baseline, 63% safe; intervention, 77% safe). A supervisor probe on the first day of phase two of the
intervention created systematic effects on the performance of participant 1. Complete stop and two seconds motionless, which were being
self-monitored, improved to over 20% above the levels measured on the
same day without supervisor presence. Mirror check and bus stopping
position, which were still under baseline conditions, did not change in
the presence of the supervisor. For a graphic display of these data see
Figure 2.
Participant 2 improved by an average of 13% over baseline conditions. His largest average improvement was for complete stop with a
41% improvement (baseline, 51% safe; intervention, 92% safe). This
improvement stands out as the most clear and dramatic effect of the intervention procedures. Bus stopping position improved 3% (baseline,
49% safe; intervention, 52% safe), two seconds motionless improved
3% (baseline, 28% safe; intervention, 31% safe), and mirror check also
improved 3% (baseline, 38% safe; intervention, 41% safe). For a
graphic display of these data see Figure 3.
Participant 3 improved by an average of 12% over baseline conditions. His largest average improvement was for mirror check with a
15% improvement (baseline, 65% safe; intervention, 80% safe). Bus
stopping position improved 12% (baseline, 81% safe; intervention,
93% safe), two seconds motionless improved 12% (baseline, 47% safe;
intervention, 59% safe), and complete stop improved 9% (baseline,
38% safe; intervention, 47% safe). For a graphic display of these data
see Figure 4.
Participant 4 improved by an average of 10% over baseline conditions. His largest average improvement was for complete stop with a
19% improvement (baseline, 38% safe; intervention, 57% safe). Bus
stopping position improved 2% (baseline, 94% safe; intervention, 96%
safe), two seconds motionless improved 5% (baseline, 66% safe; intervention, 71% safe), and mirror check improved 15% (baseline, 58%
safe; intervention, 73% safe). For a graphic display of these data see
Figure 5.
23
FIGURE 2. Participant 1 results in multiple baseline design format. Closed circle data points are experimenter data, open circle data points are self-monitoring data, closed triangles are experimenter data during supervisor probes, and
open triangles are supervisor data.
COMPLETE STOP
100
Percent Safe
60
40
20
0
Percent Safe
100
63%
77%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Sessions
LOAD/UNLOAD 2 SECONDS MOTIONLESS
100
83
80
60
40
20
0
43%
62%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Sessions
LOAD/UNLOAD MIRROR CHECK
100
Percent Safe
100
89
80
80
60
40
20
0
88%
73%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Sessions
LOAD/UNLOAD STOPPING POSITION
100
Percent Safe
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Experiment
80
60
40
20
0
70%
78%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Sessions
JOURNAL OF ORGANIZATIONAL BEHAVIOR MANAGEMENT
FIGURE 3. Participant 2 results in multiple baseline design format. Closed circle data points are experimenter data, open circle data points are self-monitoring data, closed triangles are experimenter data during supervisor probes, and
open triangles are supervisor data.
COMPLETE STOP
100
Percent Safe
100
80
100
51%
92%
60
40
20
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Sessions
LOAD/UNLOAD 2 SECONDS MOTIONLESS
Percent Safe
100
80
28%
31%
57
55
60
40
20
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Sessions
LOAD/UNLOAD MIRROR CHECK
Percent Safe
100
80
93
38%
64
60
40
20
0
41%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Sessions
LOAD/UNLOAD STOPPING POSITION
100
100
91
80
Percent Safe
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24
60
40
20
49%
52%
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Sessions
25
FIGURE 4. Participant 3 results in multiple baseline design format. Closed circle data points are experimenter data, open circle data points are self-monitoring data, closed triangles are experimenter data during supervisor probes, and
open triangles are supervisor data.
COMPLETE STOP
100
Percent Safe
80
76
67
60
40
20
38%
47%
0
1 2 3 4 5 6
7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Sessions
LOAD/UNLOAD 2 SECONDS MOTIONLESS
100
86
70
Percent Safe
80
60
40
20
59%
47%
0
1 2 3
4 5 6 7
8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Sessions
LOAD/UNLOAD MIRROR CHECK
100
100
86
Percent Safe
80
60
40
80%
65%
20
0
1
2 3 4 5 6 7
8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Sessions
LOAD/UNLOAD STOPPING POSITION
100
80
Percent Safe
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Experiment
100
75
60
40
93%
81%
20
0
1
2 3 4
5 6 7
8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Sessions
JOURNAL OF ORGANIZATIONAL BEHAVIOR MANAGEMENT
FIGURE 5. Participant 4 results in multiple baseline design format. Closed circle data points are experimenter data, open circle data points are self-monitoring data, closed triangles are experimenter data during supervisor probes, and
open triangles are supervisor data.
COMPLETE STOP
Percent Safe
100
80
100
80
60
38%
40
57%
20
0
1
2 3
4
Percent Safe
5 6
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
Sessions
7 8
LOAD/UNLOAD 2 SECONDS MOTIONLESS
100
80
60
71%
40
66%
20
0
3 4
1 2
5 6
7
8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
Sessions
LOAD/UNLOAD MIRROR CHECK
Percent Safe
100
80
60
40
58%
20
73%
0
1 2
3 4
5 6
7 8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
Sessions
LOAD/UNLOAD STOPPING POSITION
100
Percent Safe
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26
80
60
96%
40
94%
20
0
1
2 3 4
5 6
7 8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
Sessions
Experiment
27
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Results of Self-Monitoring Estimations
It should be noted that drivers estimated their performance for an entire day with two self-observations, and experimenters only sampled
their behavior between 30 minutes to 60 minutes each day. Therefore,
the comparison between experimenter and self-monitoring data is not
an exact comparison. Drivers’ self-estimations are plotted as open circles on individual results figures.
For participant 1, the average of percent safe estimations across all
intervention phases was 72% (complete stop, 79%; two seconds motionless, 67%; mirror check, 94%; bus stopping position, 81%). His actual overall percent safe score, as calculated from experimental observations, was 73% (complete stop, 77%; two seconds motionless, 62%;
mirror check, 88%; bus stopping position, 78%). The largest discrepancy between his self-monitoring data and experimenter data occurred
for mirror check, with a difference of 6%. The smallest discrepancy occurred for complete stop, with a difference of 2%.
For participant 2, the average of percent safe estimations across all
intervention phases was 98% (complete stop, 99%; two seconds motionless, 100%; mirror check, 100%; bus stopping position, 81%). His
actual overall percent safe score as calculated from experimental observations was 53% (complete stop, 92%; two seconds motionless, 31%;
mirror check, 41%; bus stopping position, 52%). The largest discrepancy
between his self-monitoring data and experimenter data occurred for two
seconds motionless, where the difference was 69%. The smallest discrepancy occurred for complete stop, where the difference was 7%.
For participant 3, the average of percent safe estimations across all
intervention phases was 78% (complete stop, 85%; two seconds motionless, 99.9%; mirror check, 100%; bus stopping position, 100%). His
actual overall percent safe score as calculated from experimental observations was 65% (complete stop, 47%; two seconds motionless, 59%;
mirror check, 80%; bus stopping position, 93%). The largest discrepancy between his self-monitoring data and experimenter data occurred
for two seconds motionless, where the difference was 40.9%. The
smallest discrepancy occurred for bus stopping position, with a difference of 7%.
For participant 4, the average of percent safe estimations across all
intervention phases was 74% (complete stop, 82%; two seconds motionless, 18%; mirror check, 100%; bus stopping position, 100%). His
actual overall percent safe score as calculated from experimental observations was 71% (complete stop, 57%; two seconds motionless, 71%;
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JOURNAL OF ORGANIZATIONAL BEHAVIOR MANAGEMENT
mirror check, 73%; bus stopping position, 96%). The largest discrepancy between his self-monitoring data and experimenter data occurred
for two seconds motionless, where the difference was 53%. The smallest
discrepancy was for bus stopping position, where the difference was 4%.
Independent Variable Integrity
Group compliance with the rule to fill out two estimations of safe
performance each day was 76.5%. During phases one, two, and three of
the intervention, compliance was 91.5%, 72.5%, and 60.5% respectively. Group compliance with the rule to sign the feedback graph at the
end of each shift was 58.8%. During phases one, two, and three of the
intervention, compliance was 43.3%, 52%, and 85.5% respectively.
Drivers received 68.3% of the supervisor prompts via radio that were
planned. During phases one, two, and three of the intervention, supervisor compliance with the prompting procedure was 66%, 81.5%, and
57.5% respectively. Individual participants received at least one prompt
on 88.3% of the days during the project, and received two daily prompts
on 48.3% of the days during the project. Independent variable integrity
for individual participants is summarized in Table 1.
Reliability
A total of 99 experimental observations of driver performance took
place over the course of the study. Two independent observers collected
data simultaneously for 30 sessions (30.3% of total sessions). The average agreement percentage was 89.8% (range: 70-100). IOA scores were
calculated for each dependent variable for every IOA session. Agreement scores under 80 percent were limited to 11 out of 120 total IOA
calculations. Table 2 shows ranges of IOA scores for each dependent
variable over the course of the study.
Debriefing
At the conclusion of the study the participants met with the operations supervisor and student consultant for lunch and debriefing. A survey was administered to the drivers to investigate issues related to the
study and solicit their opinions about the process, and afterwards, participants were informed about experimental observers and were each
provided with a coded summary of self-monitoring results and the average percent improvement for each individual as observed by experi-
Experiment
29
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TABLE 1. Independent Variable Integrity
Participant and Variable
Phase One
Phase Two Phase Three
All Phases
Participant 1
100.0
100.0
33.0
Feedback
Self-Monitoring
50.0
33.0
67.0
82.0
50.0
Supervisor Prompts
80.0
66.7
66.7
73.0
Overall IV Integrity
76.7
66.6
55.6
68.3
83.0
Participant 2
100.0
69.0
67.0
Feedback
Self-Monitoring
83.0
75.0
100.0
85.0
Supervisor Prompts
58.3
87.5
50.0
65.0
Overall IV Integrity
80.4
77.2
72.3
77.7
Participant 3
Self-Monitoring
83.0
63.0
67.0
73.0
Feedback
40.0
75.0
75.0
62.0
Supervisor Prompts
58.3
75.0
50.0
62.0
Overall IV Integrity
60.4
71.0
64.0
65.7
Participant 4
Self-Monitoring
Feedback
83.0
38.0
75.0
68.0
0.0
25.0
100.0
38.0
Supervisor Prompts
80.0
75.0
62.5
73.0
Overall IV Integrity
54.3
46.0
79.2
59.7
mental observers. Participant responses to this information were positive.
After discussing all questions that were raised during debriefing the operations supervisor left the room while consent was obtained for the use of
data.
Survey results showed that participants believed their self-monitoring estimations were accurate to slightly high. Participants also identified which performances had actually changed the most and which had
changed the least over the course of the study. Drivers were given opportunities in the survey to describe why they thought their performance had improved or stayed the same. Comments on this topic were
informative and will be presented when relevant in the discussion section. Participants rank-ordered aspects of the project from most to least
useful in the following order: (1) being able to share opinions about the
project, (2) talking with co-workers about safety and aspects of the
route, (3) meetings to discuss the project, (4) using self-monitoring
forms, (5) graphs of safe performance, (6) process not attached to disci-
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TABLE 2. Inter-Observer Agreement Percentages for Each Dependent Variable
Average % IOA
Range % IOA
Sessions < 80%
Bus Stopping Position
Dependent Variables
93.2
70.0-100
2
2 Seconds Motionless
90.0
73.0-100
0
Mirror Check
84.1
70.0-100
8
Complete Stop
91.8
77.2-100
1
pline in any way, (7) supervisors observed the same behaviors we did,
and (8) more frequent contact from supervisors. All four participants
recommended extending the use of customized self-monitoring processes to other parts of the organization for both new and experienced
drivers. They also responded favorably to having the union participate
in choosing target behaviors.
DISCUSSION
The results of the study suggest that a self-monitoring package can
change the safe performance of bus operators. Furthermore, the study
represents a rare empirical evaluation of lone worker performance.
However, because of the small number of participants and short duration of the study, it cannot be concluded that changes in safe behavior
led to an important decline in collisions. All four participants were “collision free” for five weeks, but the transit system as a whole had three
separate months without collisions in 1997.
The overall effects of the intervention were small to moderate (12.3%
overall average improvement; individual performance improvement on
specific targets range: 2% to 41%). This may have been due in part to
the lack of participant involvement in activities such as performance
target selection and design of the process (i.e., “buy-in” activities). The
fact that participants were aware of the short-term nature of the project
may have also contributed to this effect and, for some participants, may
have been the reason for low treatment compliance. Perhaps some did
not take the procedures “seriously” because the process was presented
as temporary rather than permanent. Only future research can answer
these questions conclusively. However, the results of this study do sug-
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31
gest that there are limits to the effectiveness of self-monitoring interventions (i.e., not all behaviors improved; certain antecedent conditions
or additional variables may be necessary to ensure that all behaviors improve). Indeed, it may be very difficult to ensure that self-monitoring
packages produce substantial behavior change.
The Cumulative Benefits of Small Effects
In the current study the intervention phases were relatively short,
with the entire intervention lasting only three weeks. Without any opportunity to significantly involve participants or to allow participants to
become familiar with the new process, a 12.3% improvement in overall
safe performance was achieved. It is possible that larger effects would
occur under more supportive circumstances. This fact notwithstanding,
we should consider the possible practical importance of the level of behavior change observed in the current study. Mawhinney (1999) noted
that it is important to consider how small to moderate improvements
would impact an organization over time. To investigate this issue we
will consider the potential impact of the changes made by participant 3,
whose overall average improvement of 12% was not clearly visible to us
in graphic form until after mean lines were added (see Figure 4). As suggested by Mawhinney (1999), we agree that “cumulatively large benefits
can result from incrementally small intervention effects” (p. 83).
On the campus route there were usually about 10 instances of loading/unloading passengers every 30 minutes. During a ten-hour shift
with a regular flow of passengers, each driver could stop to unload or
load 200 times each day. During baseline conditions, participant 3
checked both side mirrors 65% of the time when loading/unloading passengers. This would represent 130 safe mirror checks out of 200 opportunities each day. During brief intervention conditions, he checked both
side mirrors 80% of the time. This would represent 160 safe mirror
checks out of 200 opportunities each day. During one month performing at baseline levels participant 3 would achieve 2080 safe mirror
checks out of 3200 opportunities whereas one month of intervention
level performance would achieve 2560 safe mirror checks out of 3200.
So a 15% average improvement on checking mirrors could result in as
many as 480 fewer at-risk load/unload instances each month. If the remaining 64 drivers working in the transit system were also participating
and improved to similar levels (assuming similar passenger rates), the
transit system could realize 31,200 fewer at-risk behaviors each month
and 374,400 fewer at-risk behaviors each year. Managers and practitio-
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ners applying BBS with lone workers should find moderate effect sizes
promising, especially when maintained over longer periods of time.
Behavioral Functions of the Self-Monitoring Package
The effect size and variability produced in this study are similar to effects and variability generated with antecedent interventions targeting
safety (e.g., Austin, Alvero, & Olson, 1998; Engerman, Austin, &
Bailey, 1997; Ludwig & Geller, 1997; Streff, Kalsher, & Geller, 1993).
Although the performance targets of the above studies are not identical
to the targets in the current study, all seem to involve a common safety
dilemma where immediate and probable consequences support risky
performance, while delayed or improbable consequences fail to support
safe performance. Given the similar behavioral underpinnings of performance targets, it is interesting to note that the self-monitoring package generated effects similar in magnitude to purely antecedent strategies. In
contrast to antecedent strategies, most safety studies that use programmed consequences have demonstrated much larger changes in behavior (e.g., Austin, Kessler, Riccobono, & Bailey, 1996; SulzerAzaroff & de Santamaria, 1980). These results, combined with the effect size and variability issues discussed above, leads us to believe that
our self-monitoring procedure might have served an important antecedent function.
One may also argue, in line with Hayes and Nelson (1983), that the
whole self-monitoring package (the instructions, the sheets, the
prompts, and posted feedback) made more effective the natural consequences of the particular performances we measured. That is, monitoring complete stops, for example, could have made the potential
consequences of behaving unsafely (e.g., colliding with student pedestrians or other vehicles) more salient. One driver’s answer to a survey
question highlights this issue. When explaining why some of his behavior did not change very much, participant 2 circled the statement “accidents/collisions just don’t happen often enough to warrant any extra
effort to prevent them.” Alternatively, participant 1 reported the following with regard to the self-monitoring package, “It caused me to consider the effects on others (students) of my errant behavior (rolling
stops).” Aversive outcomes like collisions, as horrific as they may be,
tend to be too improbable to motivate safe behavior. In addition, the safest way of doing things often requires the person to endure immediate
aversive conditions (taking longer to complete a task, wearing uncomfortable personal protective equipment, etc.). Reports such as those
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33
from participant 1 suggest that the self-monitoring package, in some
cases, overcame the contingencies favoring risk-taking by making the
consequences for unsafe acts more salient. From a molecular analytic
perspective, the self-monitoring package may have generated CEOs
that altered the effectiveness of direct-acting reinforcers or punishers
(Malott, R., Malott, M., & Trojan, E., 1999; Michael, 1993).
Discussion of Individual Performance
It was hoped that very consistent effects would be observed across participants, or at least systematic improvements related to the degree to
which participants complied with intervention procedures. However,
each participant’s largest improvement was not necessarily the most accurately self-estimated performance. Among individual participants
there were very small to very large improvements for specific target performances. Understanding these individual differences in performance
requires a consideration of the accuracy of each participant’s self-monitoring estimations, the extent of exposure to the independent variables
(i.e., independent variable integrity) for each participant, the self-report
data obtained from each participant, and anecdotal information obtained
by experimental observers. Because aggregate data can obscure intervention effects and relationships between variables, we chose to provide
more detailed analyses of the data for participants 1 and 4.
Participant 1 Performance
Participant 1 realized the greatest average improvement (14%) and
the most consistent improvements of any participant. He was also the
most accurate self-estimator of safe performance. Upon visual inspection of his data, it is clear that his estimations closely tracked his actual
performance (see Figure 1). The data from participant 1 support the
findings of McCann and Sulzer-Azaroff (1996), suggesting that the
greatest improvements in safe performance occur when participants are
most accurate in their self-estimations.
Overall independent variable integrity for participant 1 was 68.3%
(Phase one, 76.7%; Phase two, 66.6%; Phase three, 55.6%). The decline
in integrity percentages was largely the result of decreased participation
in self-monitoring procedures (he was 33% compliant during phase
three). This may explain the sharp drop in his performance on bus stopping position during the last two days of intervention (see Figure 1).
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Anecdotally, participant 1 appeared to be deliberate and conscientious and seemed to take great pride in his profession. It is possible that
certain “personality characteristics” (i.e., verbal responses to questions
on valid psychometric instruments) could predict the effectiveness of or
compliance with self-monitoring procedures in some cases. Participant
1 also responded very systematically to supervisor observations, during
which his performance on the variables being self-monitored was about
20% higher than his performance on the same day without supervisor
presence. During supervisor observations, dependent variables that
were not being self-monitored remained at baseline levels. This effect
suggests relatively low reactivity to experimental observers as compared to reactivity to supervisor presence.
Participant 4 Performance
Participant 4 achieved the smallest overall improvement for the
group (10%) and also had the lowest overall independent variable integrity of all participants (59.7%). The clearest effects for this participant
occurred during the first phase of intervention. During baseline conditions he seemed to come to a complete stop only when he was forced to
do so by traffic conditions. His typical pattern of performance was to
roll slowly through stop signs. This distinctive performance during
baseline made behavior changes observed on the first day of intervention very dramatic. The deterioration of this improvement in performance was also distinctive as it gradually returned to baseline levels
over the six sessions of phase one (see Figure 5). Contributing to this effect may have been the fact that participant 4 did not ever sign the feedback graph during phase one of the intervention, suggesting he did not
view the graphs, which may have eliminated a consequence component
from the self-monitoring package.
Another clear effect achieved during the first phase of the study for
this participant occurred during the supervisor probe. He scored 30%
higher on complete stops when the supervisor was present than he did
when the same performance was measured on the same day without the
presence of a supervisor. In addition, the baseline dependent measures
all showed slightly lower performance with the supervisor present than
they did without the presence of the supervisor, showing that the participant was reactive only to the performance being self-monitored.
At the onset of phase two, participant 4’s performance dropped to 20%
and 16% safe on 2 seconds motionless and complete stop respectively. At
that time we hypothesized that this might represent counter-controlling
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behavior in response to the intervention procedures (Ludwig & Geller,
1999; Skinner, 1953). However, this low performance did not continue
beyond the first day of phase two of the intervention.
We selected participant 4’s performance to analyze because it demonstrates the fact that grouping data across behaviors within a single participant can conceal true effects; just as effects can be concealed by grouping
the performance data of several individual participants (as in group research). To clarify, we consider his performance relative to the accuracy
of his estimations in more detail. Although his estimations were the least
accurate and his overall performance changed the least of all participants,
the accuracy of participant 4’s self-estimations did not seem to systematically vary with his performance improvement. The smallest discrepancy
between his self-estimations and experimenter data occurred for bus
stopping position with an average difference of 4%. However, this performance improved by only 2% over baseline levels (see Figure 5). In
contrast, his estimations differed from experimenter data by 25% for
complete stop, where he realized his greatest improvement (19% over
baseline levels). Another large discrepancy occurred for two seconds motionless, which improved by only 5%, where his self-estimations were
53% lower than experimenter data. If we were to look only at his overall
behavior change and his overall estimations (i.e., data grouped across behaviors), the data would suggest a clear relationship between his estimations and the resulting behavior change (i.e., he had the smallest overall
effect size and reported the least accurate data), especially in light of participant 1’s results discussed above (i.e., who reported the most accurate
data and had the largest overall effect size). However, as one can see from
the more detailed analysis that considers each behavior singly, the relationship between estimations and behavior change is far from clear. It is
logical that accuracy of self-estimations would affect the degree to which
behavior changes. However, the degree of agreement between self-monitoring and experimenter data in our study did not predict the degree of improvement for each participant on particular performance targets.
Accuracy is only estimated by assessing IOA (Johnston & Pennypacker,
1993). Therefore, with regard to the importance of the accuracy of
self-monitoring data, we have only scratched the surface of the topic.
FUTURE RESEARCH
There are many unanswered questions regarding applications of
self-monitoring procedures to improve the performance of lone work-
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ers. The most important question in terms of safety is the effectiveness
of these kinds of procedures at reducing accidents and/or injuries.
Krause (1997) reported a 66% reduction in accidents and injuries in his
application with bus drivers, but methodological issues prevent drawing firm conclusions about the degree of behavior change generated by
the self-monitoring procedure. While we were able to successfully
build upon the consultation effort reported by Krause (1997) by experimentally evaluating behavior change, we were not able to answer questions about accident/injury reduction. In order for researchers to draw
conclusions about accident/injury reduction, it may be necessary to
partner with companies or consulting firms implementing behavior-based safety processes with lone workers on a large scale. Academic
parties could ensure reliable assessment of behavior change and companies or consultation firms could ensure a large-scale implementation
with a long duration that could impact accident/injury rates. An additional question that still remains unanswered is the extent to which accuracy of self-monitoring influences its effectiveness (McCann &
Sulzer-Azaroff, 1996). Such research could begin by training participants to accuracy at the beginning of a study and then assessing drift and
concurrent performance levels with confederate observers or some
other unobtrusive measurement system over time. Some electronic
forms of driving performance measurement are now becoming available that might be of use in such research. The issues discussed above
are excellent research questions, however, both require that behavior
change be produced reliably by a self-monitoring procedure. It is hoped
that the successes and failures of the current study will inform the development of self-monitoring procedures that can reliably produce substantial changes in safe behavior.
Several methodological improvements are needed in order for extensions and/or replications of this work to (a) more clearly assess behavior
change and (b) generate greater behavior change. First, baseline and intervention phases of longer duration would allow researchers to discern
behavioral effects more reliably. This is especially relevant for the third
phase of the intervention that targeted mirror check and bus stopping
position, which lasted for only a few sessions. Even if methodological
improvements indicate no effect for any one dependent measure, this
enables researchers to draw stronger conclusions about the “boundaries” of the effectiveness of an intervention. In other words, researchers could then explore questions about why some performances
improved while others did not. Research of this kind would be costly in
terms of labor and time, but would enable researchers to draw stronger
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conclusions about behavioral changes. Second, potentially more powerful intervention variables should be tested. An obvious choice would
be the addition of valuable consequences that were dependent upon
some aspect of driver performance (e.g., participation or performance
levels as assessed by observers). In addition to adding more powerful
consequences to such interventions, we feel that there are at least two
less costly or less intrusive conditions that might add power to the intervention: these are (a) participants selecting performance targets of personal value or interest and (b) evaluating an implementation that was
perceived as permanent rather than temporary. We discuss these two issues, as well as research that might reveal the behavioral functions of
stimuli generated by self-monitoring procedures, below.
A key component missing from the current study was the absence of
employee participation in the design stages of the project and other activities said to generate “buy in.” Both Krause (1997) and McSween
(1995) heavily promote employee participation in BBS processes. Aspects of such employee involvement may function as motivational variables where the value of consequences related to safety improvement is
increased and behaviors correlated with those improvements are more
frequently evoked. When behavior is analyzed on a molecular scale
(i.e., behavior is analyzed in terms of its immediate antecedents and
consequences), Michael’s (1993) taxonomy of CEOs may be relevant.
Participant comments on the debriefing survey suggest that employees may make the greatest improvements when they “value” the target
performance. Technically, values can be defined as a set or constellation of conditioned reinforcers (Malott, R. Malott, M., & Trojan, 1999).
Participant 2 improved complete stops by 41% and his survey comments regarding the self-monitoring process emphasized this specific
target performance. He wrote, “Complete stops are important. A lot can
happen in a short amount of time at an intersection. Really have to stop
completely to see the whole picture.” These results and self-report comments concur with McSween (1995), who suggested that learning experiences prior to the onset of a BBS initiative may be important
strategically and that individuals’ values should be incorporated into
performance improvement initiatives. Future research should examine
more closely this potential relationship between employee “buy-in” activities and the effectiveness of and compliance with treatment protocols.
Whether self-monitoring procedures with lone workers tend to function primarily as antecedents, as does a prompt for safety belt use, is an
interesting research question. Researchers have suggested that self-moni-
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toring is effective as a result of an individual applying consequences contingent upon his or her own behavior (Kanfer, 1970). Future research
could explore this question by requiring the self-monitoring to take place
either just before (antecedent function), or just after (consequence function) a work shift and/or by measuring the use of self-delivered consequences through talk-aloud procedures. If self-monitoring tends to
function primarily as an antecedent, practitioners should emphasize the addition of programmed reinforcement to ensure prolonged effectiveness.
Future researchers should also consider the clinical literature on self-monitoring. For example, some clinical research suggests that the power of
self-monitoring procedures is enhanced when participants monitor the frequency of undesired, rather than desired, performance (Kopp, 1988).
DISCUSSION AND CONCLUSION
Strengths of the current study include measures of independent variable integrity, collection of self-report measures at the conclusion of the
study, and supervisor probes. In some cases IV integrity measures provided insight into unusual patterns in the data. The survey instrument
gave participants a chance to express their opinions about aspects of the
process, and gave us a chance to collect information about covert behavior that may have impacted their performance. Supervisor probes affected performance systematically across all participants and may
represent one method for assessing participants’ understanding of the
target performances. For some participants the probes demonstrated
that they understood and were capable of performing the target behaviors at high percent safe levels. For participant 2, however, the probe
showed that he may not have understood or discriminated certain target
behaviors. His performance improved when the supervisor was present,
but only to 55% and 64% for 2 seconds motionless and mirror check, respectively. In general, the probes demonstrated that supervisor presence
was a more powerful intervention than self-monitoring, and that participants improved only the behaviors that supervisors were observing.
Weaknesses of the current study include the relatively short duration
of the intervention, the absence of meaningful outcome measures (also
due to the short duration), the small number of participants, the lack of
employee “buy-in,” and the small to moderate effect size of the intervention. The study was cut short because the particular bus route terminated
for summer break, so we could not determine whether performance
changes maintained, improved, or deteriorated over time. As mentioned
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previously, future researchers should consider methods that provide more
time for the stabilization of performance under each experimental condition.
In addition to concerns about the ability to assess behavior change, a reduction in collisions or injuries is not possible unless more participants are involved over long periods of time. With regard to “buy-in,” our participants
started off “cold” with self-monitoring procedures. This feature does not resemble real world BBS consultation efforts where employees are often
heavily involved in the planning stages of the process. In the current study,
participant “buy-in” was compromised for the sake of ensuring dependent
variables that could be measured reliably, although it is possible that using
video cameras to tape lone worker performance could expand the range of
dependent measures that can be reliably evaluated. An intervention that included more salient motivational operations (e.g., employee participation in
the planning stages; expression of support from upper managers), more powerful antecedent prompts (e.g., the presence of a video camera), or more
powerful consequences (e.g., incentives or praise contingent upon reaching
participation goals) may have generated larger performance improvements.
The results of the current study suggest that self-monitoring is a promising strategy for improving the safe performance of lone workers such as
bus operators. However, these types of interventions are difficult to study
and future advances in improving the safe performance of lone workers
must address important methodological challenges. These challenges include (a) participant reactivity to experimental observers, (b) reliability
of behavioral/performance data, (c) scale and duration, and (d) expense.
NOTE
1. The authors obtained approval to conduct the research from the Human Subjects
Institutional Review Board at Western Michigan University. The proposal was submitted under the category of exempt from full review because the measurement system
targeted public behavior.
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