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This is to certify that the
thesis entitled

PROMOTING SUSTAINABILITY: MENTAL MODELS RESEARCH
TO INFORM THE DESIGN OF A CAMPUS RECYCLING
PROGRAM

presented by

LAUREN K. OLSON

has been accepted towards fulﬁllment
of the requirements for the

Master of degree in Community, Agriculture,
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PROMOTING SUSTAINABILITY: MENTAL MODELS RESEARCH TO INFORM
THE DESIGN OF A CAMPUS RECYCLING PROGRAM

By

Lauren K. Olson

A THESIS
Submitted to
Michigan State University
in partial fulﬁllment of the requirements
for the degree of
MASTER OF SCIENCE

Department of Community, Agriculture, Recreation, and Resource Studies

2008

ABSTRACT
PROMOTING SUSTAINABILITY:
MENTAL MODELS RESEARCH TO INFORM THE DESIGN OF A CAMPUS
RECYCLING PROGRAM
By

Lauren K. Olson

The Michigan State University (MSU) initiative for comprehensive and campus-
wide recycling program has a goal Of increasing recycling participation. A coupled
strategy to facilitate recycling will require more knowledgeable students, faculty, and
staff, as well as a thoughtfully designed, well structured, and institutionalized program.
This research speaks to the charge of educating the MSU community, and to a lesser
extent the physical characteristics of the program. The mental models approach was used
to inform the design of outreach efforts by quantifying the level Of knowledge held by
student and faculty subjects about recycling concepts. Responses were elicited by way Of
broad, Open-end interviews associated with mental models research. The visual
representation Of subjects’ recycling knowledge as mental models and statistical analysis
Of their scored responses, revealed distinct speciﬁc differences between students and
faculty as well as common trends. Both students and faculty were aware of broad
concepts of different materials and separation, but generally lacked knowledge of speciﬁc
details. Knowledge was generally poor in terms of speciﬁc beneﬁts of recycling; yet the
disincentives of participation were relatively well understood. With a more targeted and
systematic outreach program, the audience will (theoretically) have improved knowledge
upon which they can base decisions to recycle more items, and—importantly—to recycle

these items correctly.

In memory Of Chickpea

iii

ACKNOWLEDGEMENTS

I would like to thank the Ofﬁce of the Vice President of Finance and Operations for the

funding and assistance that made this work possible.

iv

TABLE OF CONTENTS

LIST OF TABLES ........................................................................................................ vii
LIST OF FIGURES ....................................................................................................... ix
1. Introduction ................................................................................................................. 1
2. Facilitating Pro-Recycling Behavior Change ............................................................... 5
2.1 Situational Incentives to Recycle ........................................................................... 5
2.1.1 Antecedent Strategies ..................................................................................... 5
2.1.2 Consequence Strategies .................................................................................. 9
2.2 Personal Incentives to Recycle ............................................................................. 14
2.3 Knowledge as a Precursor to Recycling Behavior ................................................ 17
3. Mental Models .......................................................................................................... 22
3.1 Mental Models: Historical Roots ......................................................................... 27
3.2 Mental Models: Recent and High Proﬁle Applications ......................................... 28
3.3 Applying the Mental Models Approach ............................................................... 38

4. Promoting Sustainability: Mental Models Research to Inform the Design of a Campus
Recycling Program ........................................................................................................ 43
4.1 Introduction ......................................................................................................... 43
4.2 Methods .............................................................................................................. 48
4.2.1 Study Area .................................................................................................... 49
4.2.2 Subjects ........................................................................................................ 49
4.2.3 Design .......................................................................................................... 50
4.2.4 Analysis ........................................................................................................ 52
4.3 Results ................................................................................................................. 53
4.3.1 General Trends ............................................................................................. 54
4.3.2 Gender Differences ....................................................................................... 57
4.3.3 F aculty/Student Differences .......................................................................... 58
4.3.4 Campus Facilities Differences ....................................................................... 59
4.4 Discussion ........................................................................................................... 59
4.4.1 Statistical Results Implications ..................................................................... 60
4.4.1.1 Practical and signiﬁcant results .............................................................. 61
4.4.1.2 Signiﬁcant, but not practical results ........................................................ 62
4.4.2 Recommendations ........................................................................................ 65
4.5 Conclusion .......................................................................................................... 68

5. Mental Models Research to Inform the Design of a Campus Recycling Program:

Report to the Ofﬁce of the Vice President of Finance and Operations ............................ 93
5.1 Executive Summary ............................................................................................. 93
5.2 Introduction ......................................................................................................... 95
5.3 Methodology ....................................................................................................... 97

5.3.1 Subjects ........................................................................................................ 99

5.3.2 Analysis ...................................................................................................... 100
5.4 Findings ............................................................................................................ 102
5.4.1 Where to Recycle at MSU‘7 ......................................................... 102
5.4.2 What is Recycled at MSU? ......................................................................... 102
5.4.3 How to Recycle at MSU? ............................................................................ 105
5.4.4 Why Recycle at MSU? ................................................................................ 105
5.4.5 Why Don’t People Recycle at MSU? .......................................................... 106
5.4.6 Alternatives to Recycling at MSU? ............................................................. 106
5.4.7 Gender Differences ..................................................................................... 106
5.4.8 Student/Faculty Differences ........................................................................ 107
5.4.9 Differences Across Campus Facilities ......................................................... 107
5.5 Recommendations ............................................................................................. 108
5.5.1 Statistical Analyses ..................................................................................... 108
5.5.1.1 Student and Faculty Speciﬁc Recommendations .................................. 109
5.5.1.2 Gender Differences .............................................................................. 110
5.5.1.3 Differences Across Campus Facilities .................................................. 111
5.5.2 General Trends ........................................................................................... 112
5.6 Conclusion ........................................................................................................ l 15
BIBLIOGRAPHY ....................................................................................................... 141

vi

LIST OF TABLES

Table 4.1: The frequency of, and mean level of understanding for, 116 concept areas
across male (M) and female (F) undergraduate students on the MSU campus. Means
were compared using chi-square tests at the p=0.05 signiﬁcance level ........................... 70

Table 4.2: The frequency of, and mean level of understanding for, 116 concept areas
across male (M) and female (F) faculty members on the MSU campus. Means were
compared using chi-square tests at the p=0.05 signiﬁcance level. .................................. 73

Table 4.3: The frequency of, and mean level of understanding for, 116 concept areas
comparing faculty members and undergraduate students on the MSU campus. Means
were compared using chi-square tests at the p=0.05 signiﬁcance level ........................... 76

Table 4.4: The frequency of understanding for 116 concept areas comparing
undergraduate students living in four MSU residential areas, namely Akers (Ak),
Butterﬁeld (Bu), Holmes (Ho), and Hubbard (Hu) Halls ................................................ 79

Table 4.5: Mean levels of understanding for 116 concept areas comparing undergraduate
students living in four MSU residential areas (Akers, Butterﬁeld, Holmes, and Hubbard
Halls). Means were compared using chi-square tests at the p=0.05 signiﬁcance level... 82

Table 4.6: Mean levels of understanding for 116 concept areas comparing faculty
working in academic buildings judged to be “recycling friendly” (F) and “recycling
unfriendly” (U). Means were compared using chi-square tests at the p=0.05 signiﬁcance
level. ............................................................................................................................. 85

Table 5.1: The frequency of, and mean level of understanding for, 116 concept areas
across male (M) and female (F) undergraduate students on the MSU campus. Means
were compared using chi-square tests at the p=0.05 signiﬁcance level ......................... 118

Table 5.2: The frequency of, and mean level of understanding for, 116 concept areas
across male (M) and female (F) faculty members on the MSU campus. Means were
compared using chi-square tests at the p=0.05 signiﬁcance level. ................................ 121

Table 5.3: The frequency of, and mean level of understanding for, 116 concept areas
comparing faculty members and undergraduate students on the MSU campus. Means
were compared using chi-square tests at the p=0.05 signiﬁcance level......... ................ 124

Table 5.4: The frequency of understanding for 116 concept areas comparing

undergraduate students living in four MSU residential areas, namely Akers (Ak),
Butterﬁeld (Bu), Holmes (Ho), and Hubbard (Hu) Halls .............................................. 127

vii

Table 5.5: Mean levels of understanding for 116 concept areas comparing undergraduate
students living in four MSU residential areas (Akers, Butterﬁeld, Holmes, and Hubbard
Halls). Means were compared using chi-square tests at the p=0.05 signiﬁcance level. 130

Table 5.6: Mean levels of understanding for 116 concept areas comparing faculty
working in academic buildings judged to be “recycling friendly” (F) and “recycling
unfriendly” (U). Means were compared using chi-square tests at the p=0.05 signiﬁcance
level. ........................................................................................................................... 133

viii

LIST OF FIGURES

Figure 4.1: Expert mental model for recycling on the MSU campus. ............................. 88

Figure 4.2. Comprehensive student mental model. Percent values indicate frequency of
understanding across subjects. Hatched boxes indicate concepts mentioned by subjects
that were not present in the expert model ....................................................................... 89

Figure 4.3. Comprehensive faculty mental model. Percent values indicate frequency of
understanding across subjects. Hatched boxes indicate concepts mentioned by subjects

that were not present in the expert model ....................................................................... 90
Figure 4.4. Example mental model elicited from a MSU undergraduate student. .......... 91
Figure 4.5. Example mental model elicited from a MSU faculty member. .................... 92
Figure 5.1: Expert mental model for recycling on the MSU campus. ........................... 136

Figure 5.2. Comprehensive student mental model. Percent values indicate frequency of
understanding across subjects. Hatched boxes indicate concepts mentioned by subjects
that were not present in the expert model. This model employs a color-coding scheme to
differentiate the levels of knowledge into three categories; the color red signiﬁes 0—33%
interviewed understood the subject, yellow signiﬁes 34-66%, and green signiﬁes 67-
100%. Images in this thesis are presented in color. ..................................................... 137

Figure 5.3. Comprehensive faculty mental model. Percent values indicate frequency of
understanding across subjects. Hatched boxes indicate concepts mentioned by subjects
that were not present in the expert model. This model employs a color-coding scheme to
differentiate the levels of knowledge into three categories; the color red signiﬁes 0-33%
interviewed understood the subject, yellow signiﬁes 34-66%, and green signiﬁes 67-

100%. Images in this thesis are presented in color. ..................................................... 138
Figure 5.4. Example mental model elicited from a MSU undergraduate student. ........ 139
Figure 5.5. Example mental model elicited from a MSU faculty member. .................. 140

ix

1. Introduction

A product’s life cycle begins with the extraction of raw materials, which are
shaped into goods, sold, used, and then disposed of, usually in either an incinerator or a
landﬁll (McDonough and Braungart 2002). Before disposal, a used product can
potentially be collected for recycling, and subsequently made into a new products (Strong
1997). Formal recycling programs have gained popularity in the developed world
because of wide-ranging concerns about landﬁlling and incinerating wastes, including the
possible cumulative and long-term effects to the environment and human health. Despite
their popularity, many communities have had considerable difﬁculty with designing and
implementing robust recycling programs; in other words, programs that encourage pe0ple
to divert the maximum amount possible from the waste-stream for recycling collection.

Recycling programs facilitate the collection of materials from consumers. The
stage where the consumer is given the choice to recycle or not, is perhaps the most
important stage of the recycling process, because the amount collected is strictly
dependent on the consumer’s involvement. For this reason, programs designed to engage
consumers in recycling activities are given considerable study.

In terms of its history, recycling became an important cause for governments and
institutions in the mid 1980’s. Since then, recycling rates in the United States have
doubled from 16.2% in 1990 to 32.5% in 2006 (Environmental Protection Agency 2007).
In 1988, when the Journal of Composting and Organics Recycling (also known as
Biocycle) started publishing yearly reports on “The State of Garbage in America,” less
than 1,000 curbside programs were known to exist (Simmons, Goldstein et al. 2006). In

the latest report of 2004 data, 7,689 curbside programs were known to exist in 41 of 50

American states (Simmons, Goldstein et a1. 2006). It is worth noting that these ﬁgures
include neither community drop-off locations nor universities or colleges that provide
recycling services.

Beyond residential communities, recycling at university campuses grew from the
fringes to become a mainstream practice around the same time as governmental
(municipal) programs, with the University of Colorado at Boulder creating the ﬁrst
position for a campus-wide recycling coordinator in 1984 (Keniry 1995). This
university’s move recognized the need for an institutionalized program comprised of
dedicated facilities and full-time custodial staff because of the belief that successful,
large-scale recycling demands more than volunteer student efforts (Keniry 1995). In
1993, the National Recycling Coalition, the main national recycling trade association,
recognized the occupational association of College and University Recycling
Coordinators, signaling that campus recycling needed to be treated with the same level of
seriousness as municipal recycling programs (Lounsbury 2001).

At Michigan State University (MSU), the site of this research, recycling has been
an institutionalized activity since 1990. Like the University of Colorado at Boulder,
MSU also has on staff a full-time recycling coordinator in MSU’s Ofﬁce of Recycling
and Waste Management. In the ﬁrst year of its program in 1990, MSU recycled 200 tons
of paper, which increased to more than 1,600 tons in 2006 (Link 2007).

However, MSU’s recycling program is not perfect. Beyond paper products—
which includes white paper, mixed paper, cardboard, and newsprint—other types of
recycling are not currently available on a campus-wide scale. In another example, some

academic and residential halls have a system in place for the collection of batteries. This

system for collection is uncoordinated and non—existent in other buildings and does not
give students, faculty, or staff the option to participate. In general, expanding the amount
and variety of materials collected for recycling will give these stakeholders more
opportunities to divert material from the waste-stream to recycling. Providing recycling
collection points is only half of the solution; the other is increasing the rate of
participation in the recycling program.

MSU community is comprised of students, faculty, and staff1 whose activities,
sometimes inadvertently, yield abundant amounts of waste. In 2007, undergraduate
students made up the largest segment of the campus community with 36,072 people
representing the student body; graduate students were the second largest segment of the
MSU population (9,973). Due to the sheer number of students—46,045 individuals
strong—they have the greatest potential to create a large amount of waste, and. thus to
divert materials to recycling.

In addition to students, faculty members are also important in increasing recycling
rates because they represent a much more stable population who can contribute to a
lasting culture of waste reduction, without frequent interventions. There are
approximately 4,800 faculty members on the MSU campus. While the majority of waste
produced by faculty members seems to consist of paper products, they also generate
waste in the form of hazardous and laboratory wastes. Their role in waste reduction will
have perhaps the greatest impact—and long-term effect—in terms of reducing MSU’s

environmental footprint.

 

' University staff were also recognized as important participants in any campus-wide recycling initiative.
This group was involved in a parallel, focus group based study of programmatic needs (Hansen, McMellen
et a1. 2008).

For MSU to have a successful recycling program, institutional support of
infrastructure (i.e. consistent bins and collection points) must be coupled with buy-in and
increased participation from students, faculty, and staff to change or enhance their waste
reduction and waste recovery (i.e. recycling) practices accordingly.

Because all recycling programs are reliant on individual participation, many
previous studies of recycling have focused on strategies to motivate recycling behavior.
None of these strategies should be viewed as universally effective, however some have
been shown to work better than others in terms of encouraging participation in recycling
collection.

There has been some study of how to speciﬁcally tailor recycling programs to an
audience on the basis of demographic variables such as income, ethnicity, and gender
(Goldenhar and Connell 1992; Howenstine 1993). Nevertheless, the tailoring of
recycling programs has been largely understudied in recycling literature, particularly
when it comes to understanding the current state of knowledge possessed by today’s
recycler.

This paper outlines a mental models approach for assisting with communication
and education efforts based on what people know and do not know about the tOpic of
recycling at an institute of a higher education. The objectives of this research project
were to: (1) quantify the recycling knowledge of non-expert populations (students and
faculty) and identify relevant gaps or lack, and (2) provide recommendations to ﬁll gaps
in recycling knowledge, correct misconceptions, and reinforce correct beliefs. Overall,

this research seeks to provide a foundation for informing recycling outreach plans.

2. Facilitating Pro-Recycling Behavior Change

Encouraging recycling behavior generally involves programs that use either
situational or personal incentives. Situational incentives provide an extrinsic motivation
to encourage recycling (Bagozzi and Dabholkar 1994). Personal incentives, by contrast,
are internal motivations that deﬁne recyclers from non-recyclers. The notion is to
increase these attributes that motivate recyclers, these attributes include: attitudes, beliefs,

perceived control, and level of ascribed responsibility (Schultz 2002).

2.1 Situational Incentives to Recycle

Situational programs may come before (i.e. antecedent to) the recycling behavior,
or they may be enacted as a result (i.e. consequence of) of recycling behavior (Porter,
Leeming et al. 1995). For the remainder of this thesis, these will be referred to as

“antecedent strategies” and “consequence strategies”, respectively.

2. 1.1 Antecedent Strategies

As an incentive to promote recycling behavior, antecedent strategies for recycling
are designed to increase recycling behavior by altering a variable prior to the
performance of the target behavior (e.g. collecting recyclables, delivering recyclables to a
collection center) (Schultz, Oskamp et a1. 1995). The goal is to provoke new instances of
recycling participation, rather than increasing community and individual recycling or
compliance rates of those already participating (see Section 2.1.2, Consequence

Strategies). Common antecedent strategies include persuasive and educational prompts,

environmental alteration, the solicitation of personal or community commitments, and
goal setting (Schultz, Oskamp et a1. 1995).

Persuasive and educational prompts are designed to help individuals remember to
recycle (Austin, Hatﬁeld et a1. 1993; De Young 1993; Lord 1994). Information included
in prompts can be presented before and during2 the recycling program and is made
available by direct-to-recycler means, commonly taking the form of printed notices. In a
study integrating environmental alteration (see below), Austin, Hatﬁeld et al. (1993) used
prompts as informative cues in copy rooms at Florida State University. Some prompts
were placed within sight of recycling and trash receptacles, while others were not.
Compared with the control condition (i.e., the absence of persuasive prompts), all
prompts were shown to increase recycling rates, with prompts in closest proximity to
recycling and trash receptacles increasing compliance rates most dramatically. The
speciﬁc prompts tested included information about what (i.e. types of recyclable
products) and how (i.e. preparation and sorting of materials for recycling) to recycle.
Overall, Austin, Hatﬁeld et a1. (1993) argue that successful prompts must include a
speciﬁc request or appeal to recycle and be within close proximity to the intended
recycling area; the requests themselves should be convenient for the consumer to do.

Environmental alteration is a second technique to increase the rate of recycling by
making the act of recycling logistically easier. These techniques include decreasing the
physical distance between containers and consumers, providing dedicated containers for
curbside programs, and coordinating curbside recycling pick-up with garbage pick-up

(Porter, Leeming et a1. 1995). Derksen and Gartell (1993), for example, demonstrated

 

2 These strategies are conducted during the beginning stages of the program because not everyone starts
recycling at the same time, hence, until enough people have begun recycling to the satisfaction of program
managers, the intervention may continue.

that the most important determinant for recycling was access to a structured,
institutionalized program allowing for easy and convenient recycling. Concern for the
environment was positively correlated with both recycling and non-recycling behavior;
but only those groups who beneﬁted from environmental alteration showed an increase in
recycling rates. It was concluded in this study that environmental concern cannot
overcome logistical barriers of recycling (Derksen and Gartrell 1993).

Another form of environmental alteration is coordinating curbside recycling pick—
up so that it is more convenient for participants recycling programs. In a study of waste
management programs across Canada, F errara and Missios (2005) found that weekly (vs.
biweekly) recycling opportunities had a positive impact on recycling for glass, aluminum,
and toxic chemicals; on the other hand, it had little effect on the recycling intensity of
newspaper, plastic bottles, tin cans, and cardboard. Overall, recycling appears to be more
appealing when materials do not need to be stored for an extended period of time,
perhaps causing people involved in weekly recycling programs to recycle a wider variety
of materials. It is noteworthy that the communities with high curbside recycling intensity
increased the amount of toxic chemicals (i.e. some paints and solvents) correctly
recycled. These materials must to be taken to a pr0per disposal facility within the
community; they are not available for curbside collection. This suggests an unintended
spill-over effect of recycling curbside.

In the end, environmental alteration is considered by many to be a very effective
technique in terms of leading to durable behavior change and increasing recycling rates

(DeYoung 1986; Derksen and Gartrell 1993; Howenstine 1993; Boldero 1995; Lee,

DeYoung et a1. 1995; Oskamp, Zelezny et al. 1996; Ludwig, Gray et a1. 1998; Barr
2004)

A third kind of antecedent approach is known as commitment strategies, which
aim to increase recycling rates through the collection of personal pledges to recycle. This
strategy typically takes the form of verbal or written promise to recycle (Bryce, Day et a1.
1997). The commitments can be publicly announced to the community as displayed
signs or published names in a newspaper (Burn and Oskamp 1986; DeLeon and F uqua
1995). The strategy of publicized written commitments typically leads to action,
compared to no commitment or a verbalized commitment (Bryce, Day et al. 1997). In
one study by Burn and Oskamp (1986), households who committed verbally to recycle
and publicly displayed a window sticker, alerting others to their decision, increased their
recycling rates by a factor of four. However, since the study only lasted two months, the
durable behavior is unknown.

Goal setting is a fourth incentive to increase recycling rate, and is one that has not
received much attention in recent years. This strategy typically involves an organization
or community setting a goal of diverting a higher proportion of materials from the waste-
stream to recycling. To provide a reasonable goal, program managers need information
about the amount of recycling taking place, as well as how much more waste could be
recycled—based on the current system. In a nationwide study of recycling programs that
were both municipal in scale and voluntary by nature, F 012 (1991) found goals were an
effective tool to increase recycling rates in voluntary programs, but not institutionalized
municipal programs. Most programs choose a goal of at least 25% higher than the

current diversion rate of recyclables (F012 1991).

2.1.2 Consequence Strategies

Consequence strategies are aimed at those who are already recycling, with
incentives or penalties based on the extent to which recycling takes (or does not take)
place. Techniques typically include providing pe0ple with feedback, tangible rewards,
and penalty structures (P. Wesley Schultz 1995; Porter, Leeming et a1. 1995).

The ﬁrst of these techniques, feedback, provides people with information about
how they as an individual, household, community, or institution are performing. This
information is often accompanied with measures of how much other people are recycling.
In one study of this technique, DeLeon and F uqua (1995) combined feedback with an
antecedent, commitment strategy (see above). Three treatments were compared: one
group of subjects made a public commitment to recycle with their names published in the
local paper. A second group of subjects received weekly feedback based on the amount
they recycled. The third group of subjects made both a public commitment and received
feedback. A fourth group served as the control group, and received no experimental
treatment.

Compared to the amount recycled by the control group, the commitment-only
group recycled similar amounts (DeLeon and F uqua 1995). The combined commitment
and feedback group recycled the most, followed by the feedback-only group. This study
indicates that combining feedback with public commitment strategies results in
signiﬁcant increases of recycling rates. As this was a relatively short study, durability of

the change beyond the eight-week study period is unknown.

Along similar lines, Goldenhar and Connell (1991) conducted a large-scale study
to examine the combined effects of a public feedback strategy and an educational (i.e.
antecedent) strategy on attitudes, beliefs, and behaviors regarding the recycling of
newspaper at the University of Michigan. In this study, all ﬁrst year students who lived
in campus dormitories were asked to complete a pre-test questionnaire during their
summer orientation program (Goldenhar and Connell 1991). Then, students were
assigned to one of far different treatment groups for the ﬁve month duration of the
study; the treatments were: (1) educational posters that included a series of messages
debunking recycling myths (such as the garbage crisis, the environmental impact of
recycling) placed in recycling areas; (2) feedback in the form of posters that displayed,
using bar graphs, how much each dorm had recycled, including information about the
pounds of newspaper recycled per student; (3) a combined educational and feedback
treatment group; and (4) a control group with no treatment.

In this study, the feedback group, as well as the combined treatment of feedback
and education, proved to be more effective than the education-only or the control group
in increasing recycling amounts. It is worth noting that the treatments were measured
solely on the amount of newspaper recycling. The results may have been biased in that
some students may not regularly read newspapers, and therefore do not have the
opportunity to recycle newspapers (Goldenhar and Connell 1991). Also, because the
recycling behavior was self-reported on a Likert scale, students may have over-reported
recycling rates because of the well-documented tendency to base attitudes on the pro-

environmental context of the survey, known as context effect (Tourangeau, Rasinski et al.

10

1989). Additionally, the altruistic nature of recycling (see section 2.2) may cause
subjects to act in self-interest rather than truthfully report behavior (Guagnano 2001 ).

Rewards are a second commonly applied technique to increase recycling rates.
This strategy involves—as the name suggests—giving tangible incentives to peOple for
their participation in recycling activities. Rewards can take the form of ﬁnancial
payments or opportunities to take part in contests, rafﬂes, or lotteries (Schultz, Oskamp et
al. 1995). Because those who do not recycle often want rewards, rewards can be
successful in increasing recycling rates (Vining and Ebreo 1990). However, problems
tend to arise when rewards are removed. In a meta-anyalsis of recycling literature
detailing situational incentives, Porter et a1. (1995) found that the recycling rates returned
to the baseline levels once rewards were removed in all nine studies reviewed. Another
problem with reward-based strategies is the cost of the rewards versus the revenue
generated from recycling. Usually, it is simply not cost effective to offer rewards over
the long term (Porter, Leeming et a1. 1995).

In a study comparing of lotteries to direct ﬁnancial rewards, Diamond and Loewy
(1991) found that the most signiﬁcant attitude change toward recycling occurred among
subjects who were rewarded with lottery tickets for recycling glass and newspaper. It is
worth noting that if the lottery winners did not expect to win, their attitude was positive
toward recycling with or without rewards. In the same study, small sums of money
(direct rewards) were given for recycling, either to an individual or a group.
Individualistic rewards promoted a greater amount of recycling than collective reward

programs. Diamond and Loewy (1991) note that when pe0ple attribute their actions to

11

extrinsic rewards, attitudes toward the behavior are likely to become less positive, as
future decisions about recycling will be made without a reward.

Penalties are deﬁned as negative economic incentives to recycle (Porter, Leeming
et al. 1995). As the third form of consequence technique, they are the direct opposite of
reward-based strategies (see above). Speciﬁc strategies that are considered penalties
include unit-pricing on garbage pick-up and deposits on recycled materials that can only
be recovered when an item is returned (e.g. bottle deposits).

Ferrara and Missios (2005) examined the connection between waste management
policies and recycling behavior. Because many communities set different policies, the
authors collected and examined data from several communities. Across Canada, 12
municipalities were studied through interviews with community members about which
materials they recycled, and data about waste management program details, recycling
program details, and household characteristics were collected (F errara and Missios 2005).
Curbside recycling was available for all the households studied, which was an important
constant variable. In this study, F errara and Missios (2005) found that user fees (price-
per-bag of garbage) signiﬁcantly increased the recycling intensity. This program is close
to the true marginal pricing of trash disposal because households have the option to
recycle more or reduce waste. The study discovered poor recycling in communities
whose waste management policies allowed residents a free number of garbage bags; these
policies almost completely offset the effect of the user fee system. Households act
strategically to ensure their garbage output is not over the limit, yet do not try to decrease
it any further—through increased waste reduction or recycling—because there is little

motivation to go beyond what is required. Similar studies examining unit pricing for

12

waste disposal and it’s relation to recycling include Van Houtven and Mon°is (1999),
Jenkins, Martinez et al. (2003), and lsely and Lowen (2007).

In a different study, Hong and Adams (1999) examined household demographics,
waste management policies, recycling rates and waste production in Portland, Oregon.
Unlike Ferrara and Missios (2005) who studied price-per-bag systems, Hong and Adams
(1999) concentrated on volume-based waste systems. In this study, households pay a
weekly fee for a container that holds a speciﬁed volume of waste. Fees were based on
the number of gallons each container held, with the price per unit of volume discounted
for larger volumes. In all households in the study, curbside recycling was offered to the
residents free of charge to reduce their volume of waste by diverting appropriate
materials for curbside recycling.

The price of the trash container did not appear to affect the amount of materials
recycled by subjects. In other words recycling amounts did not increase as the fee
charged for larger trash containers increased. However, the volume-based waste pricing
system was thought to create a quasi incentive for more sustainable practices (e.g.,
reduce, reuse) because the amount of garbage disposed of could not exceed the physical
constraints of the container. Overall, the demand for trash pick-up was inelastic, so any
price increase for garbage disposal would cause very little decrease in trash output. This
emphasizes the need to connect education programs and economic incentives to
encourage community members to decrease garbage production and increase recycling
rates.

In the end, consequence strategies typically work best when they are applied in

combination with an antecedent strategy. Depending on the community, marginal pricing

l3

systems may not be feasible due to the restraints of the garbage pick-up provider or
conveniently accountable in an institutional setting, such as university campus or ofﬁce
buildings. Feedback presents another logistical challenge, as recycling must be recorded
based on household or another unit of housing measure. Rewards, although gratifying,
present challenges for creating lasting behavior change, as rewards cannot be given

indeﬁnitely for every recycling action.

2.2 Personal Incentives to Recycle

Personal incentives are assessed through observation to correlate recyclers’
motives and reasons with their high participation in recycling activities; vice-versa, those
that do not recycle are studied to understand their underlying motives and reasons for
non-participation. These motivations are intrinsic (vs. extrinsic) and arise out of active
participation in an ongoing activity, and in turn provide a strong internal attribution and
justiﬁcation for acting (DeYoung 1986). These factors involved in the personal choice to
recycle are commonly investigated using applications of the Theory of Reasoned Action
including The Theory of Planned Behavior, analysis of the satisfaction derived from
recycling activities, and understanding of norms as described in Schwartz’s model of
altruistic behavior (Schwartz 1977).

One technique employed to understand the reasons for why people recycle is the
Theory ofReasoned Action (F ishbein and Ajzen 1975). It examines social consequences
and attitudes as necessary components for conceptualizations of human behavior. This
theory relies on the use of behavioral intervention to produce some action. Behavioral

interventions may include attitude (anticipated consequences of an action and evaluation

14

of those consequences) and subjective norm (normative expectations of the actions and
acceptance of those actions). The Theory of Reasoned Action, and a theory derived from
this model, called the Theory of Planned Behavior (Ajzen 1991), is used to explain
recycling behavior and provide a conceptual framework to change norms and attitudes to
induce recycling (Taylor and Todd 1995; Chueng, Chan et al. 1999; Tonglet, Phillips et
al. 2003; Valle, Rebelo et al. 2005).

An example of the application of Theory of Reasoned Action to recycling is a
study by Barr and Gilg (2005) in the context of developing policies for better waste
management. Using a mailed survey, Barr and Gilg (2005) compared recycling
frequency with the variables from the Theory of Reasoned Action. These variables
included for the purposes of this study included: behavioral intention and willingness to
carry out certain activities. Barr and Gilg (2005) conducted a path analysis using
multiple regressions. The path analysis traces inﬂuences of variables that affect the
participant’s willingness to recycle and their recycling behavior; those were found to be
(in order of importance): environmental protection, active concern for waste and
recycling, acceptance of the norm to recycle, convenience and efforts, and norm
awareness (Barr and Gilg 2005). The authors suggested that their results should be
included into policy advertisements or social campaigns to increase recycling rates.

A second possible personal incentive is the satisfaction pe0ple may ﬁnd in
recycling. De Young (1986) suggested that motivations to please the self are critical to
encouraging recycling, unlike an altruistic model that suggests selﬂess concern for others
causes recycling. According to the satisfaction model, pe0ple may start recycling and

then continue because, fundamentally, it made them feel satisﬁed and happy. To

15

examine this model, students at the University of Michigan were surveyed to understand
what level of satisfaction (if any) the students derived from the monthly curbside pick-up
of recyclables (DeYoung 1986).

The results of the survey revealed that the students receive the most satisfaction
from frugality, deﬁned as the careful use of resources and the avoidance of waste. Strong
feelings of frugality were directly correlated with participation in recycling and reuse
activities. Being a participant in society showed a high correlation with recycling rates,
which reflects the demands of recycling as a difﬁcult activity. Regarding this ﬁnding, De
Young (1986; p.446) noted, “recycling is sometimes portrayed as a primitive, time-
consuming, and inconvenient behavior—hardly an appropriate behavior for a
technologically advancing society.” Although people may have these feelings, people
appeared to gain satisfaction from acting in ways that make a difference to society as a
whole.

A third possible technique for understanding why people recycle is the use of
Schwartz ’s model of altruistic behavior, which seeks to explain why injunctive (i.e.
personal) norms of altruism often do not lead to corresponding behavior. Altruistic
behaviors are deﬁned as any activity that most people agree upon as being morally
normative, yet not everyone acts in accordance with the norm (Hopper and Nielsen
1991).

Recycling is an altruistic behavior because it is guided by the norm that recycling
is the morally correct activity that warrants participation, yet not everyone does
participate. Recycling is costly to the individual due to the time and energy involved in

separation, storage, and transportation. Often, recycling yields no immediate rewards, yet

16

has beneﬁts to society as a whole (Hopper and Nielsen 1991). Because recycling is
generally believed to be a good idea, and there is a distinct population that does not
recycle even though it is believed to be a good idea—recycling is by deﬁnition an
altruistic behavior. This kind of behavior can be modiﬁed (in theory) by persuasion to
behave accordingly.

Schwartz’s (1977) model attempts to explain why social norms that would result
in behavior change do not become personal norms resulting in behavior change.
According to this theory, awareness of consequences and ascription of responsibility are
the variables, , connecting personal norms to the ascribed behavior. Bratt (1999) applied
Swartz’s model to recycling; the model proposed that the relationship between personal
norms and recycling behavior would be higher for those that assumed the consequences
of recycling were high. However, those who assumed the consequences of recycling to
be low still had a strong relationship between personal norms and recycling, which
caused Bratt (1999) to question the applicability of Schwartz’s theory to recycling.

Guagnano, Stern et al. (1995) conducted a study of behavioral change associated
with recycling using a survey based on Schwartz’s model, and attempted to pull together
external and internal factors. External factors are physical structures, social institutions,
and economic forces. lntemal factors are general and speciﬁc attitudes, beliefs,
information, and behavioral intention. The resulting model predicts a context for
effective recycling programs using education or information intended to improve the

knowledge as important predictors of behavior change.

2.3 Knowledge as a Precursor to Recycling Behavior

17

While knowledge about recycling may not be the sole motivator for recycling,
lack of this knowledge is generally viewed as an important barrier to recycling (Schultz
2002). The more knowledgeable an individual is about what items are recyclable, how to
prepare items for recycling, and where to go to recycle, the more likely the individual
will recycle (De Young 1989; Vining and Ebreo 1990; Gamba and Oskamp 1994; Scott
1999).

The goal of techniques to increase knowledge, such as communication and
educational interventions, is to help people understand how they can help with the
environmental problem and why it is important to do so (De Young 1993). By providing
speciﬁc knowledge about the issue and cause, these techniques provide an avenue for
durable change by prompting the actors to modify their behavior. Another perspective is
that once people have information about how to proceed, they are more able move
forward and act according to the needs of the situation (De Young 1993). Finally,
increased knowledge is believe to create a durable behavior change.

These objectives are particularly important considering that lack of knowledge
about the recycling program is a common trait of non-recyclers. Howenstine (1993)
studied students at Northeastern Illinois University, and found that the most widely cited
reasons non-recyclers gave as an explanation for their behavior were a lack of convenient
curbside recycling in their neighborhood and a lack of knowledge about where to take
recyclables. Even though the city of Chicago had more than 100 scattered drop-off sites,
79% of the respondents did not know the location of even one (Howenstine 1993).

Vining and Ebreo (1990) found similar characteristics that deﬁne non-recyclers in

Champaign and Urbana Illinois. Based on a questionnaire given to community members,

18

recyclers tended to be better informed about overall recycling procedures and acceptable
materials than non-recyclers. Non—recyclers were unsure of their own knowledge of what
can be recycled and how to begin recycling materials compared to recyclers.

Scott (1999) studied four communities in the greater Toronto area, which all had
access to curbside recycling for over a decade, to determine why some of the community
members recycle, while others recycle less—or not at all. Knowledge of the curbside
recycling program was positively correlated with the number of materials recycled. Scott
(1999) suggested that recycling departments distribute up-to-date information about
eligible materials for recycling, proper techniques for curbside pick-up, and schedules for
special collections to help increase recycling rates.

Overall, the importance of knowing what can and cannot be recycled should not
be underestimated in the design of a successful recycling program. Lack of knowledge
not only results in lower recycling intensity (thus lower diversion rates) and collection
efﬁciency, but the resulting trial-and-error approach increases contamination levels in
collected recyclables (Scott 1999). In the end, the good intentions of an uncertain public
can reduce the efﬁciency and efﬁcacy of recycling programs.

De Young (1989) explored the differences between recyclers and non-recyclers
and found that both groups viewed recycling in a very positive light. The interviews
suggested that consequence-based strategies such as monetary rewards or punishment
would not cause non-recyclers to recycle; yet social pressure to comply with behavioral
norms will motivate people to recycle. Even if the norm to recycle gives people a reason
to recycle it, does not mean they possess all the required skills. New recyclers are

probably not familiar enough with recycling to know how to carry out the activity

19

successfully, and if they become overwhelmed with the task, recycling may continue as a
non-activity. In the end, recycling education programs, according to De Young (1989),
should concentrate on basic information needed, such as how much time and space to
allot to carry out the activity successfully, what can be recycled, how the materials must
be prepared, and where to go for assistance.

The good news is that lack of knowledge about how to recycle appropriately can
be overcome through educational and communication campaigns. The bad news is that
many of these campaigns fail. Meneses (2006) suggests that most recycling education
programs fail because the organization focuses on what they want to transmit—based on
rather ill-conceived notions about the information that they assume people need—
rather than on what content people actually need. Frequently, these programs tell
audiences what environmentalists want to communicate rather than taking into account
an individual’s information processing model. By information processing model,
Meneses (2006) refers to an individual’s understanding of programs based on ecological
consciousness and personal beliefs about recycling.

The main focus of Meneses (2006) study was to ﬁrst determine the recycling
beliefs of an audience, which are identiﬁed as knowledge about the “how, what, and
why” of recycling. Similarly, Howenstine (1993) and Goldenhar and Connell (1992) also
studied how to speciﬁcally tailor recycling education programs to an audience on the
basis of demographic variables such as income, ethnicity, and gender. Nevertheless, the
tailoring of educational and communication campaigns has been largely understudied in

the recycling literature, particularly when it comes to understanding the current state of

20

knowledge possessed by the current recycler. Addressing this gap is the objective of the

remainder of this thesis.

21

3. Mental Models

The objectives of this research are to (1) characterize the knowledge of members
of the MSU community (i.e., students and faculty) to identify knowledge gaps or lack
thereof, and (2) to provide recommendations for outreach based on the latent knowledge
of the audience.

The emphasis upon knowing the audience is due to the history of most recycling
education programs failing to increase recycling rates. These programs fail because an
organization (e. g., the group responsible for sustainability or waste management efforts in
cities, communities, universities, corporations, etc.) focuses on the messages they want to
transmit, which are often based on ill-conceived assumptions about the information that
people will need to make better informed choices (Meneses 2006). Educational programs
should focus instead on including speciﬁc content people actually need to undertake a
desired activity. Often, an organization’s outreach efforts tell their intended recipients
only what they either want to communicate or think that pe0ple need to know, rather than
taking into account the information and decision support needs of those that they are
trying to reach (Meneses 2006).

An explanation for the current general practice of recycling outreach could be
explained by the outreach plans as part of a public relations effort of an organization to
establish the legitimacy of recycling and create an understanding with the target
population that causes them to act (Stalhofer and Isaac 2002). In public relations, the
linear model of communication within information theory is one of the most popular, yet

widely criticized, theories (Austin and Pinkleton 2006). The linear model prescribes

22

publicity-based communication programs where messages are scattered through multiple
channels with the hope that they ultimately reach the receiver. One can think of this as a
“shotgun approach” to communication where, hopefully, at least a few of these messages
will (1) ﬁnd their intended recipient, and (2) are helpful in terms of informing choices.
Although a message itself may gain a lot of exposure this way, either because it is
repeated several times or in several different ways, it does not necessarily mean that
anyone will receive, understand, or learn from it, let alone act differently because of it
(Austin and Pinkleton 2006).

Instead of using the time and resource-intensive approach of scattering multiple
messages, the mental models approach provides a systematic methodology to understand
what the intended recipient does or does not know about the given issue, in this case
recycling. This way, communicators can design and implement more effective (in terms
of their ability to address knowledge gaps) outreach plans to encourage and foster
recycling behavior.

This issue of inadequate research of the target audience is not unique to recycling
and environmental education campaigns. Fischhoff, Bostrom et al. (1993) describe the
issue of ill-conceived outreach plans as they apply to the discipline of “risk
communication”: Risk communicators often make strong statements about other
people’s competence to manage risks, based solely on anecdotal observation. In risk
communication efforts, the communicator is presumed to know exactly what people
actually know, what they need to learn, what they want to hear, and how they will

interpret the message (Fischhoff, Bostrom et a1. 1993). This view has been proven wrong

 

3 The stated aim of risk communication is to supply pe0ple with the information they need to make
informed decisions about risks to their health. safety. and environment (Morgan, Fischhoffet al. 1992).

23

in many contexts. The mental model approach summarized in a recent text by Morgan,
Fischhoff et a1. (2002) was, therefore, developed for the purposes of helping risk
communicators better understand their audience and to create better outreach efforts for
them. While sustainability issues are not generally viewed as a “risk problem”, outreach
plans aimed at fostering behaviors such as recycling must address many of the same
challenges faced in risk communication.

The mental models approach begins with the creation of an “expert model” that
depicts the current state of knowledge surrounding a particular issue (recycling in this
case). Expert models can be thought of as a snapshot of up-to-date knowledge
surrounding a particular issue or problem, and these concepts are captured in the nodes of
the model (Morgan, Fischhoff et al. 2002).

Although the model is called the “expert” model, it is really the product of
insights from many different experts. In its truest form, the expert mental model is
developed collaboratively with a diverse group of subject-area experts who are
interviewed to gather their collective knowledge. Researchers must also consult experts
for input on further iterations throughout the model’s development process. During
development, researchers can enrich draft models with concepts from the literature for
further consideration by the experts. A full range of expertise is vital to ensure results
that predict with accuracy the state of knowledge about a topic.

An expert model based on the knowledge from multiple experts, and on the
current literature about a problem, can be difﬁcult to represent in a cohesive form
(Morgan, Fischhoff et al. 2002). However, these authors offer alternative strategies to

create a map of linked concepts that can become the expert model. One strategy is the

24

assembly method, in which the expert model is “assembled” by listing all the relevant
factors associated with the issue of interest and then ﬁguring out how they are
interrelated. Another strategy is the materials/energy balance method that incorporates
the physical properties of the issue, such as the factors that affect the amount of material
available for human contact. A third approach to creating an expert model is through the
scenario method, in which the model is formed based on the chain(s) of events leading to
a particular event. Lastly, there is the template method for creating an expert model, a
strategy that works best for issues that involve similar, recurrent exposure and effects
processes. For example, this method could be used to build a model that illustrates the
exposure processes preceding effects processes, and includes environmental,
physiological, and behavioral dimensions for each step. In either case, the concepts
elicited from experts and through a literature review are represented in hierarchical order
with arrows indicating the relationship between general concepts and more speciﬁc
content.

The expert model is used to develop an Open-ended interview protocol that will
eventually be administered to other subjects, typically non-experts whose behavior or
knowledge managers would like to modify. The expert model is used as a base for the
protocol to ensure that all relevant topics will be covered. The scope of the research
helps determine the type of subjects who will be recruited to participate in the Open-
ended interviews. Each interview is a purposefully open-ended process to allow subjects
to express both correct and incorrect knowledge and beliefs. Typically, a sample of
between 20 and 30 subjects reveals most of the latent knowledge and beliefs held by a

particular population with substantial conﬁdence (Morgan, Fischhoff et al. 2002). These

25

responses are analyzed to develop an individual mental model for each subject. When
mental models are compared with one another, the graphical representations of
knowledge indicate where knowledge gaps, or areas of confluence in knowledge, exist
between and within the non-expert and expert groups (Morgan, Fischhoff et al. 2002).

An analysis of the interviews and their resulting mental models also allows
researchers to better understand which gaps in people’s knowledge may need to be
addressed through education (and which ones may not). All responses are compared to
the expert model, which serves as the basis for measures of comparison of subjects to
experts. The simplest method of analysis is quantifying how frequently pe0ple talk about
the concepts (Morgan, Fischhoff et al. 2002). A more complex analysis looks at
frequency patterns, and the content and accuracy of what is said about each concept
(Morgan, Fischhoff et al. 2002). An even more elaborate analytical option places similar
concepts into categories and develops a measure of the relative frequency for those
categories of data.

Often, subjects will suggest concepts that are not represented on the expert model.
In these cases, it may be necessary to sort the provided concepts. Morgan, Fischhoff et
al. (2002) suggest the categories of: (l) misconceptions; (2) peripheral beliefs, which are
correct but not particularly relevant to the topic (although people believe they are); (3)
indiscriminate beliefs, correct as far as they go, but not speciﬁc enough to be very useful;
and (4) background beliefs, so basic that they do not explicitly show up in the expert
model, even if they represent facts important to determining its operation.

Analysis of the resulting individual mental models reveals which knowledge gaps

may need to be ﬁlled, which correct beliefs need to be reinforced, and which

26

misconceptions ought to be corrected. These results become the basis for designing an
informed communication effort. Later, beliefs captured in the interviews can be made
into a questionnaire to conﬁrm their prevalence on a larger, p0pulation-wide scale. Using
the results of the interviews and surveys, communicators working closely with decision
makers or policy analysts must decide which incorrect beliefs are most in need of
correcting, and which knowledge gaps are most in need of ﬁlling (Morgan, F ischhoff et
a1. 2002). In the ﬁnal stage of a complete exploration of the mental models method,
communications that are ultimately designed based on the analysis are tested and reﬁned

both before and after communications are disseminated to the audience.

3.1 Mental Models: Historical Roots

Before their application to the ﬁeld of risk communication, or recycling in the
case of this research, mental models ﬁrst appeared in psychology. The work of Kenneth
Craik is cited for ﬁrst introducing the concept of mental models. He sketched a theory in
which an individual’s thoughts and knowledge could be characterized according to a
“small scale model” of external reality (Craik 1943). Craik argued that people mentally
“try-out” various alternatives in a small-scale model when making decisions. This allows
an individual to conclude which alternative is best, react to future situations before they
arise, utilize the knowledge of past events when dealing with the present and future, and
in every way react in a much fuller, safer, and competent manner to situations (Craik
1943)

The theory outlined by Craik (1943) formed the basis of the iconic models of

knowledge, which started appearing in the late 1970’s. At the time, the term “mental

27

model” was coined by Johnson-Laird (2004) as a framework to represent how individuals
understand a variety of phenomena. Johnson-Laird (2004), for example, detailed the use
of knowledge-based mental models by experts and naive persons (non-experts) on topics
including: mechanics, hand-held calculators, and electrical circuits. In all early mental
model studies, research involved two-dimensional spatial models that made inferences
about how people learn to make—and then use—mental simulations of phenomena,
either as a series of images or as a mental model (Johnson-Laird 2004).

Mental models research, as described by Morgan, F ischhoff et al. (2002),
encompasses drawing forth and interpreting knowledge-based mental models to design
risk communications. This research method has been applied in a wide variety of risk
management contexts, including radon in homes (Bostrom, F ischhoff et al. 1992), nuclear
energy sources for space missions (Maharik and Fischhoff 1993), climate change

(Bostrom, Morgan et al. 1994), and wildﬁres (Zaksek and Arvai 2004).

3.2 Mental Models: Recent and High Proﬁle Applications

Bostrom, F ischhoff et al. (1992) ﬁrst used “modern” mental models research to
understand non-expert knowledge of residential radon hazards. In their study, a single
interviewer elicited data from 24 subjects, who had been recruited from several civic
clubs. Half of the subjects were female, half were male, and all were adults. Though
diverse, no claims were made as to the degree the subjects actually represented the
community. The interviews lasted 45 minutes on average, and contained two types of

questions: nondirective and directive.

28

The nondirective stage of the interview began with broad questions meant to elicit
any knowledge a subject possessed about radon. The interviewer next asked subjects for
detailed information about each comment until their responses were exhausted. In the
directive stage of the interview, subjects were asked to sort through 36 black and white
photographs and describe the photograph’s relation to radon, if any. Only half of the
photographs dealt directly with a major issue of radon hazards.

The interviews were taped and transcribed for accuracy, and responses were
compared to the expert state of knowledge, represented in the expert mental model. Only
when subjects explicitly mentioned a part of the expert model was it explored further as
part of their individual mental model interview. Any concepts generated that did not ﬁt
into the expert model were coded either as misconceptions, peripheral beliefs,
indiscriminate beliefs, background beliefs, or valuations. The ﬁnal interviews offered
very few new concepts.

The results from the mental model interviews of non-expert subjects were
analyzed for completeness (how much the respondents knew), accuracy (the proportion
of their beliefs that were correct), and speciﬁcity (either how detailed or general those
beliefs were). The directive portion of the interviews, which involved sorting
photographs, was designed to elicit more detailed latent knowledge than the non-directive
interview segment, and contributed signiﬁcantly to higher accuracy for the non-expert
mental models. Accuracy was primarily established as a measure of concurrence, i.e. the
percentage of concepts in a subject’s model that also appeared in the expert model.
Accuracy was also measured as the product of completeness and concurrence, i.e. the

pr0portion of a respondent’s concepts appearing in the expert diagram. This study

29

included a measure of speciﬁcity, which was calculated as the ratio of speciﬁc (more
detailed) concepts given by the subject to the ninnber of speciﬁc concepts in the expert
model. A second measure of specificity was made by calculating the ratio of general
concepts offered by the subject compared to the expert model.

The subjects’ responses were then analyzed based on each category of questions
and the different interview types (nondirective and directive) in order to understand the
effects of different ways of conducting mental models interviews. Overall, subjects’
responses were much more general than those of experts. Both parts of the interview
process resulted in eliciting about half of the concepts in the expert model. Measures of
concurrence and completeness were strongly correlated in subjects’ interviews. In other
words, those who simply said more were more likely to give a higher percentage of
correct information. Measures of completeness show that the second directive portion of
the interview (picture sorting) elicited more latent knowledge than the non-directive
portion (open-end questions), but measures of speciﬁcity showed that the picture-sorting
portion also resulted in more wrong concepts.

Subjects tended to mention general concepts more frequently than speciﬁc
concepts. All of the general concepts were mentioned in at least one interview (except
that radon can be found in the gas supply), yet some speciﬁc concepts were not
mentioned at all. Subjects were more aware of exposure than effects. Generally, radon
was understood to be bad for human health but for unknown reasons. Common
misconceptions were that radon originates from garbage, mining practices, or industrial
waste. More misconceptions were developed from the use of the nondirective segment,

in which photos of animals, plants, blood and food may have caused subjects to

30

erroneously derive the effects of radon. The results indicate that people’s understanding
of radon hazards is incomplete and incoherent, in the sense that knowledge was scattered
in inconsistent terms.

The goal of the mental models study carried out by Maharik and F ischhoff (1992)
was to investigate people’s responses to the risks of using nuclear energy in space
exploration. The expert model had a general layout of events leading to an accident and
the subsequent short- and long-term human health affects. Exposure of radioactive
materials was deﬁned in the categories of a space system failure, the release process of
radioactive materials, transport processes of radioactive materials through the atmosphere
and physical environment, and human exposure. In all, the exposure diagram contained
113 concepts including both general and speciﬁc concepts.

The subjects recruited for interviews were members of organizations active in
promoting environmental or peace-related topics in the Pittsburg, PA area. Members of
activist organizations were chosen because they are (1) generally opinion leaders in the
community, and (2) often the target of communication efforts. Of the 30 subjects
interviewed, half of the subjects were male, half were female, and most were college
educated.

Once again, these interviews contained two stages: directive and non—directive
prompts. The directive stage involved open—ended interviews that asked respondents to
elaborate upon each comment they had made during the broad, open-ended interview.
Each interview took 60-80 minutes, and the same researcher interviewed all subjects.
The interviewer attempted to direct respondents without inﬂuencing the answers to avoid

“reactivity,” or the tendency to lead the subject’s responses.

31

The subjects’ responses were compared to the expert model for the completeness
of knowledge (i.e. awareness of the concepts in the expert model). The results were ﬁrst
reported by the frequency which different concepts were mentioned. In the category of
aerospace system failure, all level 1 concepts of the expert model were mentioned by at
least one subject, while the level 2 concepts were harder to imagine and, as a result, were
mentioned less frequently. Release processes concepts showed a general confusion
among subjects about the energy sources derived from different nuclear materials and
technologies that may be used during space missions (namely, radioisotope
thermoelectric generators (RTGs) and ﬁssion reactors).

Plutonium was mentioned most frequently as the energy source for both RTGs
and ﬁssion reactors, yet plutonium is used only is RTGs while uranium is the source of
energy in ﬁssion reactors. Also, of the 30 subjects interviewed, only one subject
correctly predicted that a nuclear explosion could happen only with ﬁssion reactors, and
not with RTGs. The fact that transport processes entail a chance of explosion of the
nuclear energy power source was familiar to most subjects, and the authors believe that
general knowledge of environmental issues enhanced the ability to accurately identify it.
Personal exposure to radioactive material was divided into three main routes: inhalation,
ingestion, and external exposure. Most subjects mentioned external exposure, which may
be avoided by seeking shelter, but did not mention exposure close to the launch site, such
as direct heat blasts. Ingestion and inhalation pathways were also poorly understood.

The effects category of concepts in the expert model focused on personal health

eﬂects. Cancer deveIOpment, e.g., in the lungs or bone marrow, was a well-known

32

concept to about two-thirds of the subjects. Other effects were mentioned, but a detailed
understanding of the health effects following exposure to plutonium was largely absent.

Respondents also mentioned 65 concepts that were not on the expert model.
These concepts were classiﬁed as background knowledge and beliefs (correct concepts
that may help clarify the expert model), general concepts (correct, but too broadly
deﬁned to clarify the process), peripheral concepts (correct, but only conditionally or
marginally relevant), wrong concepts (misconceptions), and concepts outside the model’s
boundaries. The most commonly reported misconception was that radioactive material is
released as radiation only because of an accident, and not by the radioactive material
itself at all times (which is the correct statement).

Performance of the subjects was measured using four different summary statistics.
The ﬁrst was completeness, which as noted above, is the pr0portion of the concepts in the
expert model also mentioned by a subject. Respondents were able to reproduce 28% of
the total expert mental model and 51% of the general concepts. For concurrence, which
is the percentage of subjects’ concepts that appeared on the expert model, the mean was
0.67 for all subjects. Accuracy, the third measure, is computed as a product of
concurrence and completeness, giving higher scores to subjects who were able to say
right things and to say many of them. This ﬁgure was highly correlated with the subjects
who had high concurrence. The fourth measure was speciﬁcity, or how detailed people’s
knowledge was compared to the expert model. Speciﬁcity measures showed that subjects
had a more general oriented knowledge than experts.

Overall, Maharik and F ischhoff (1992) argue that knowing about the audience is

the essential element to a well crafted risk communication effort. The communication

33

effort should help clarify the situation, by meeting one of two desired outcomes, namely
reducing conﬂicts between non-experts and experts because all parties have a better
understanding of the topic, or sharpening conﬂicts by clarifying the basis of any
disagreements between non-experts and experts.

In a third study, Bostrom, Morgan et al. (1994) set out to understand the current
state of lay knowledge about climate change. At a Pittsburg, PA auto show, a single
interviewer (with the exception of one interview conducted by a research assistant)
interviewed all subjects (n=3 7). The subjects were randomly recruited at the event, and
were somewhat representative of the state of Pennsylvania’s adult education level. The
interview protocol was much like the other mental models studies outlined here (e. g.
Morgan, F ischhoff et al. 2002) in that it was open-ended, and responses were compared
to an expert model for accuracy. In addition to the interview, subjects were asked to
perform two additional tasks. The ﬁrst task was to list the contributions to climate
change on separate cards, arrange the cards in order of importance, and explain why they
ranked them in that order. The second task asked subjects to describe US. policy
towards mitigating the effects of climate change.

All interview questions were placed into one of ﬁve categories: deﬁnitions,
exposure processes, effects processes, judgments, and decisions. In terms of the
deﬁnitions, the layperson perspective was that “climate” and “weather” is basically the
same thing. The greenhouse effect was commonly equated with global warming, and
related to the ozone layer. While the subjects’ responses were inconsistent with each
other and with incorrect deﬁnitions, it was evident that their knowledge about the key

terms in climate change was incomplete. As to the exposure effects of climate change,

34

subjects overwhelmingly mentioned ozone depletion as a cause, which is incorrect. But,
in the card sorting task, subjects correctly indicated that auto emissions are a contributor
to climate change.

Subjects also presented a wide range of concepts related to the effects of climate change,
many of which were included in the expert model. For example, subjects were able to
correctly identify a number of second- and third-order effects, long-term effects, and
some feedback 100ps. As with the deﬁnitions, ozone and UV-related health effects were
predominantly mentioned, although the questions asked were about climate change.

Tasks were given after the open-ended interview, subjects were asked to respond
to a variety of closed ended questions. First, they were asked to make judgments, a little
more than half of those interviewed agreed that the greenhouse effect does exist.
Subjects also generally agreed (78%) that climate change is a real phenomenon. Then
subjects were asked whether the greenhouse effect was good, neural or bad, perhaps
taking advantage of the general inability of most subjects to differentiate between
“greenhouse effect” and “climate change.” Most subjects (75%) believed that the
greenhouse effect is in fact “bad”, when it is the natural phenomenon that allows life to
exist.

In this beginning stage of research determining what people understand about
climate change, the results indicate that subjects had many misconceptions. These
misconceptions largely revolved around the physical mechanisms of climate change and
how climate change differed from ozone depletion. Overall, subjects agreed that climate
change was a threat that deserved action and national attention, but their knowledge

limited their abilities to distinguish between effective and ineffective strategies.

35

Finally, Zaksek and Arvai (2004) used mental models to study non-expert (i.e.
homeowner) and expert knowledge regarding wildﬁre risk in British Columbia (B.C.),
Canada. The study area’s forests had accumulated large amounts of highly ﬂammable
logging debris, causing the forest service to prescribed burnings close to residences as
part of a combination of wildﬁre threat reduction practices. The expert model (called the
technical model in this study) was created through an extensive literature review plus
consultation with wildﬁre experts across North America.

As with other mental model approaches, an interview protocol—based on the
technical model—was deveIOped for the non-expert interviews. Unlike other examples
of mental models studies, the investigators also conducted mental model elicitations from
local wildﬁre experts". This was done to also test local experts—people that residents in
the local community would go to for advice—against the technical model. Also unlike
any previous risk studies that have been the subject of mental models research, the
technical model developed by Zaksek and Arvai (2004) included both risks and beneﬁts.

The non-expert subjects interviewed in the study were residents of the forest
district surrounding the community of Whistler, BC. (n=26). Local experts interviewed
included subjects that were employed by the provincial government as ﬁre managers
(n=6). A single researcher (the ﬁrst author, Zaksek) conducted all interviews, which
typically lasted between 1 and 2 hours. At the beginning of each open-ended interview,
questions were broad with neutral prompting until a subject’s knowledge was exhausted.
Then, interview questions became more speciﬁc to elicit the individual’s complete mental

model of the wildﬁre risk scenario.

 

4 This group did not include any of the experts that were interviewed during the development of the
technical model.

36

Each subject’s interview resulted in an individual mental model, to represent
where concepts were also present in the technical diagram. Concepts that were noted by
the subjects, but were not reﬂected in the technical diagram were also included.

In terms of analysis, each of the subject’s responses were coded according to a
ﬁve-point scheme (from 0 through 4), with higher scores reﬂecting more accurate
comprehension of the concept; these codes were:

4 = concept discussed without prompting and understood by subject;

3 = concept discussed when prompted and understood by subject;

2 = concept discussed without prompting but misunderstood by subject;

1 = concept discussed when prompted but misunderstood by subject;

0 = subject was unable to answer interview question.

Using these codes, subjects’ responses were compared using an analysis of variance
(ANOVA) that tested for statistically signiﬁcant differences between experts and non-
experts.

Statistical analysis revealed the differences in expert knowledge compared to the
knowledge represented in the technical diagram. Non-experts had knowledge of the
“mainstream” causes of wildﬁre, as well as a number of the potential risks to the
environment and quality of life of resident. Generally, non-experts seemed to lack any
knowledge about how ﬁres help reduce the risk of future ﬁres. Experts had more
knowledge than non-experts about the majority of the concepts (5 of the 9 categories of
concepts). The largest statistical differences were observed for the knowledge of
conditions that facilitate wild land ﬁre ignition and a detailed understanding of wildﬁre’s

environmental beneﬁts. Experts had a more developed understanding of these categories

37

compared to non-experts. Only half of the experts were able to identify prescribed ﬁres
and ﬁre suppression as factors that inﬂuence wildﬁre ignition and spread. Local experts
did not outperform non-experts (statistically speaking) in knowledge of a majority of the

concepts, including speciﬁc concepts.

3.3 Applying the Mental Models Approach

All of the studies noted here (Bostrom, Fischhoff et al. 1992; Maharik and
Fischhoff 1992; Bostrom, Morgan et al. 1994; Zaksek and Arvai 2004) similarly
implemented the mental models approach to better understand public risk perceptions.
Interview delivery was consistent with the mental models approach detailed by Morgan,
Fischhoff, et a1. (2002), all interviews used Open-ended protocols based on the expert
model. The interviewer’s delivery of questions was also important to ensuring the
objectivity of the Open-ended process. Marharik and F ischhoff ( l 992) described it as
reactivity: directing the subject without inﬂuencing the answers. Bostrom, Fischhoff et
al. (1992) described the open-ended interview delivery as a valuable way to gain insight
on layperson perspective, while minimizing the imposition of the scientiﬁc perspective
on respondents’ conceptualizations. The open-ended interviews format is a strong part of
the mental models approach because it allows the expression of both incorrect and correct
beliefs without directing the subjects.

It is noteworthy that the subjects of the interviews do not necessarily have to be
laypersons, although this population is usually associated with mental models research.
Marharik and Fischhoff (1992) focused their attention on activist populations, who were

working on either environmental or peace tOpics to understand what they did and did not

38

know about nuclear energy sources for space exploration. Zaksek and Arvai (2004)
interviewed local experts and non-expert residents to understand their knowledge of
wildﬁre. Interviewing speciﬁc populations can allow for comparison between different
populations, as illustrated by Zaksek and Arvai (2004), with their comparison of local-
expert to layperson (non-expert) knowledge.

These studies also show that mental model interviews allow for the incorporation
of additional tasks before or after the interview to enrich the results. Marharik and
F ischhoff ( 1992) asked subjects value judgments and opinion questions regarding the use
of nuclear energy sources in space. The results showed that most subjects were skeptical
of using the technology, they felt that it is unsafe, and believed that it cannot be used in a
sufﬁciently safe manner. These value judgments are important insights into
understanding the audience (particularly the activist audience the research is trying to
understand), but are considered as separate from the mental model interviews. Bostrom,
Morgan, et a1. (1994) asked subjects to write the causes of the climate change on cards,
one cause per card, then order them from most important to least, and to explain why.
The interviewers also asked a series of close-ended questions about United States policy
toward climate change and gathered judgments on greenhouse effect as good, bad, or
neutral. These questions allowed for further insight into the subjects’ ability to apply
knowledge to form policy decisions and to make value judgments. Zaksek and Arvai
(2004) used similar tasks as Bostrom, Morgan, et al. (1994) to understand the subjects’
familiarity and judgments of wildﬁre management Options by asking subjects to name all

the options for wildﬁre management, and rate the effectiveness of each option.

39

In addition to general measures used in some studies, all studies in this literature
review reported results for the proportion of subjects that understood concepts. Zaksek
and Arvai (2004) used an additional method of analysis for understanding results, a 5-
point coding scheme. This coding scheme improves the mental model’s ability to deﬁne
the accuracy and latency of knowledge of each concept. This coding scheme is a
derivation of the measures used by Bostrom, Fischhoff et al. (1992) for generalizing all
subjects’ accuracy and speciﬁcity of all concepts. Maharik and F ischhoff (1993) used a
similar coding method in the form of a structured knowledge test. This test was created
to measure the prevalence of certain interview results for a larger population. The
application of this coding scheme to the results of the mental models interviews, rather
than for a knowledge test for results, allows—in my view—for greater clarity than the
coding schemes developed by Bostrom, Fischhoff et al. (1992) and those included in
Morgan, Fischhoff et al. (2002).

The strengths and weakness of the application of mental models to inform
outreach merit discussion. Of particular concern, in terms of the weaknesses of the
approach, is the small sample size. Communication efforts are likely to be widely
dispersed over a much greater number of pe0ple than those interviewed to inform the
decision of included and excluded concept areas. Although Morgan et al. (2002) has
found that the frequency of new concepts added by subjects’ decreases substantially by
15 to 20 subject interviews, the matter of a large sample size is still a preference of many
researchers. Another weakness of the technique, in all, is the time needed to complete the
full process. Particularly, recruiting and interviewing subjects can be labor some

considering the time required (an hour or more) to participate.

40

Mental models approach to outreach has strengths as well, which set the method
apart of other techniques. In particular, mental models research can gather information
about how well as concept is known, without using the terminology. Since mental
models interviews use broad questions and neutral prompting, the exact term does not
need to be used in the question (or deﬁned for the subject) as it would in a survey
question. Additionally the results of these interviews, the frequency at which concepts
were known and unknown by the subjects, have direct implications to outreach because
communicators or public relations managers can refer to the results to decide the
emphasis of outreach. Because the results can be shown visually, managers without
statistical or highly technical expertise can understand the results.

The method allows for a full range of concepts within the topic area to be covered
in each interview, emphasizing depth over breadth. In other words, unlike a survey that
may ask more subjects about fewer concepts (compared to mental models research), the
mental models approach asks fewer subjects about more concepts (compared to typical
short survey research). Speciﬁcally, the emphasis of depth allows for the results to reveal
which concepts are unknown, rather an approach of survey research that may involve
researchers anecdotally choosing which exact concept areas should be covered in the
survey. Using the mental models method, the researchers have less inﬂuence over the
results, granted the expert model represents the current knowledge about the situation.

Mental models have been used in a variety of situations. However, as of the time
of this review, no other studies—to my knowledge—have been conducted on the topic of
recycling. Zaksek and Arvai’s (2004) application of mental models research to wildﬁre

was the ﬁrst study of a situation that involved both clear risks and beneﬁts. Although

41

recycling is a non-risk situation, it has clear drawbacks and opportunities (akin to risks
and beneﬁts). Additionally, the issues examined in the environmental communication
and risk communication ﬁelds are relatively similar. In both ﬁelds, communicators
typically proceed by developing messages that communicate information that they
believe the audience needs to know, rather than what they actually need to know.

All of the examples outlined above used similar methods to those outlined by
Morgan, Fischhoff, et al. (2002) for the elicitation of latent knowledge through mental
models interviews. In each case, the interview results were used as the basis of well-
informed communication efforts aimed at adding, reﬁning, replacing parts, generalizing
people’s existing mental models surrounding an issue. Recycling offers an opportunity to
integrate a new method into the design of informational interventions so that
communicators can create outreach materials based on what the audience knows and
needs to know. I draw upon these previous studies in my work to determine the
appropriate focus for out reach and communications aimed at increasing recycling rates at

an institution of higher education.

42

4. Promoting Sustainability: Mental Models Research to Inform the Design of a

Campus Recycling Program

4.1 Introduction

In 20065, Americans collectively generated roughly 228.0 million metric tons of
garbage (Environmental Protection Agency 2007); this is up from 216.2 metric tons in
2000 and 186.2 million metric tons in 1990. One way to keep pace with increasing rates
of waste production is for manufactures to redesign their products to include less
packaging, to use easily recyclable components for their products, and to design products
with a longer functional lifespan. Another way to keep pace with higher annual tonnages
of waste is by promoting reuse of products to customers, as illustrated in the recent
popularity of reusable shopping bags and reﬁllable plastic water bottles.

A third, and perhaps the most prominent way of reducing the high output of
garbage is through increased recycling of products and packaging. While recycling
recovery rates have doubled from 16.3% in 1990 to 32.5% in 2006 (Environmental
Protection Agency 2007), there is still room for improvement. For example, according to
the Environmental Protection Agency, about 50% of all paper and paperboard products
are not recycled. These products account for the largest component of garbage in
America by weight (33.9% of all waste). A common approach used to increase recycling
rates is to motivate consumers’ recycling habits so they will be diverting more recyclable

materials (like paper products) from garbage to recycling collection.

 

5 The most recent year for which data about the amount of waste produced in the United States is available.

43

University and college campuses are particularly noteworthy in discussions about
increasing recycling rates. Each day, they are responsible for creating massive quantities
of waste that, to a large extent, could be captured in a well functioning recycling
program. Many everyday campus activities produce waste; these include the widespread
use of white and colored paper, magazines, softbound books, cardboard, containers and
utensils used by food services, plastic used in laboratories, used batteries, outdated
electronic equipment, and the list goes on and on.

On the positive side, attractive reasons for universities to recycle include reducing
the need to dispose of garbage and increased revenue from the sale of recyclables. Most
importantly, however, many universities take their recycling programs seriously because
of their important leadership role in society. In short, many university and college
campuses take pride in being at the forefront of the sustainability movement (Pike,
Shannon et al. 2003). Recycling programs provide good evidence of—and a good
opportunities for public relations around—sustainability practices.

To this end, the National Recycling Coalition, the primary US recycling trade
association, recognized—in 1993—the occupational association of College and
University Recycling Coordinators (CURC), signaling to the industry that campus
recycling needed to be treated with the same level of seriousness as municipal recycling
programs (Lounsbury 2001). Following suit, many higher education institutions around
the country have since developed recycling programs.

Detailed information regarding (1) how many campuses have full-scale recycling
programs, (2) the amount that college and universities recycle each year, and (3) the

amount of garbage generated—is not readily available. However, we know that at

44

Michigan State University (MSU), 27,594 cubic meters of garbage were sent to the
landﬁll in 2007. Currently, recycling takes place at MSU in many ways. One example is
the recycling of a range of paper products; at MSU, the recovery of paper has increased
by eightfold (200 tons to 1,600 tons) since the inception of the program in 1990 (Link
2007). However, the MSU recycling program only recycles paper products on a regular
and completely campus-wide scale. Recycling of other materials is largely ad-hoc and
scattered. For this reason, the administration of MSU decided to expand the campus
recycling program by way of increasing the amount and variety of recyclables collections
points that are routinely collected in more areas on campus. This decision motivated the
research reported here, which was aimed at identifying ways to create a more successful
recycling program at MSU.

Despite the ubiquity of recycling programs in higher education institutions,
program managers have considerable difﬁculty designing and implementing programs
that divert the maximum possible amount from the waste-stream for recycling collection.
The initial stage of collection—when the consumer has the choice to recycle or not—is
perhaps most important stage of a recycling program because these materials are needed
for the recycling to begin. The consumer causes the most variability in the recycling
system in terms of both its efﬁciency and its efﬁcacy (e.g., in terms of sorting correctly
between materials, recycling whenever possible). Because a recycling program’s success
is highly dependent on the consumer’s involvement, programs designed to increase
consumer engagement in recycling activities warrant study to (1) inform potential
subjects of the beneﬁts of recycling and (2) engage them in recycling the correct

materials at the correct locations.

45

These objectives are particularly important considering that lack of knowledge
about a recycling program is a common trait of non-recyclers. While knowledge about
how to recycle may not be the sole motivator of recycling, lack of this knowledge is
generally viewed as an important barrier to recycling (Schultz 2002). The more
knowledgeable an individual is about what items are recyclable, how to prepare items for
recycling, and where to go to recycle——the more likely the individual is to correctly take
part in the activity (De Young 1989; Vining and Ebreo 1990; Gamba and Oskamp 1994;
Scott 1999)

The good news for attracting people to recycling programs is that a lack of
knowledge about how to recycle appropriately can be overcome through educational and
communication campaigns. The bad news is that, historically, many of these campaigns
have failed. Most recycling education programs fail because organizations (e.g., groups
responsible for sustainability or waste management efforts in cities, communities,
universities, corporations, etc.) focuses on the message they want to transmit, based on
rather ill-conceived notions about the information that they assume people need (Meneses
2006). Often, communication strategies don’t take into account key characteristics—cg,
areas where people have a clear understanding of issues as well as key deﬁciencies—of
the audience they are trying to reach (Meneses 2006). Educational programs should
focus instead on speciﬁc content people actually need to complete the desired activity.

These communication plans are most likely a public relations effort of an
organization to establish the legitimacy of recycling and create an understanding with the
target population that causes them to act (Stalhofer and Isaac 2002). In public relations,

the linear model of communication is one of the most popular and criticized theories

46

(Austin and Pinkleton 2006). The linear model prescribes publicity-based
communication programs where messages are scattered through multiple channels to
ultimately reach the receiver. One can think of this as a “shotgun approach” to
communication where the hOpe is that at least a few of these messages will be (1) helpful
and (2) ﬁnd their intended recipient. Yet, just because a message gains a lot of
exposure—either because it is repeated several times or in several different ways—it does
not mean that anyone will receive, understand, learn from, or act differently because of it
(Austin and Pinkleton 2006).

Rather than deferring to the time and resource intensive process of scattering
messages, one approach for empirically identifying the knowledge gaps that the audience
needs addressed is known as mental models research. This approach, which is outlined
by Morgan, F ischhoff et al. (2002), helps communicators to better understand their
audience by undertaking a systematic analysis of the information they currently possess
as well as their information needs. Based on the knowledge gained from mental models
research, communicators can design and implement more effective outreach plans, in this
case, for recycling programs. With a more targeted and systematic outreach program, the
audience will (theoretically) have improved knowledge upon which they can base
decisions to recycle more items, and—importantly—to recycle these items correctly.
Recycling managers can use resources to educate individuals about the materials and
practices that are unknown or misunderstood, while using less resources to enforce what
the audience understands. Finally, the mental models approach offers an alternative to
other ill-conceived messaging strategies (e.g., fear-based messaging, altruistic appeals)

that recycling programs currently use to boost recycling rates.

47

Mental models research, as described by Morgan, Fischhoff et al. (2002),
encompasses the elicitation and interpretation of knowledge for the design of
communications. This method has been applied to a wide variety of management
contexts, including radon in homes (Bostrom, Fischhoff et al. 1992), nuclear energy
sources for space missions (Maharik and F ischhoff I993), climate change (Bostrom,
Morgan et al. 1994), and wildﬁres (Zaksek and Arvai 2004).

There has been some study of how to speciﬁcally tailor recycling programs to an
audience on the basis of demographic variables such as income, ethnicity, and gender
(Goldenhar and Connell 1992; Howenstine 1993). Nevertheless, the tailoring of
recycling programs has been largely understudied in recycling literature, particularly
when it comes to understanding the current state of knowledge possessed by today’s
recycler.

This paper reports the use of a mental models approach for assisting with
communication and education efforts based on what people know and do not know about
the topic of recycling at the setting of a higher education institution. The objectives of
this research project were to: (1) quantify the knowledge of non-expert populations
(students and faculty) and identify relevant gaps or lack, and (2) provide
recommendations to ﬁll gaps in recycling knowledge, correct misconceptions, and
reinforce correct beliefs. Overall, this research seeks to provide a different approach to
environmental communication plans by understanding and incorporating the audience’s

needs.

4.2 Methods

48

4.2.1 Study Area

This research was conducted on the main campus of Michigan State University
(MSU), which is located in East Lansing, Michigan. At the time of this study (the Spring
and autumn of 2007), a total of 46,045 students were enrolled at MSU; 36,072 of these
were in undergraduate degree programs and 9,973 in graduate programs. In addition to

these students, approximately 4,800 faculty members work on campus.

4.2.2 Subjects

Because of their size and inﬂuence on the amount of waste generated (and
potentially recycled), students and faculty6 were identiﬁed by the university
administration as the ﬁrst target populations for the new campus recycling initiative.

Undergraduates living on campus were one focus of this study, due to their
experience with a wide-range of campus recycling options. Student subjects were
recruited via mail, 250 letters were sent by the administration’s ofﬁce to each residential
hall. In collaboration with residential hall administration, four residential halls were that
were considered representative of residential living Options in terms of the diversity of
the student residents. The letter sent to potential subjects brieﬂy explaining the project
and requesting they call in to schedule an interview. Additionally, there was a monetary
incentive of $40 USD. While the initial mailed invitations went to randomly selected
students, students were selected for the study using a purposeful sampling strategy
(Patton 1990). Speciﬁcally, students were recruited so that the ﬁnal sample contained an

equal number of males and females that represented a wide diversity of degree programs.

 

6 University staff were also recognized as important participants in any campus-wide recycling initiative.
This group was involved in a parallel, focus group based study of programmatic needs (Hansen, McMellen
et al. 2008).

49

The student sample was recruited to be diverse based on degree program because
students living within the selected residential halls could reasonably represent the variety
of degree programs available. Additionally, students were screened for any prior
experience in the recycling industry or related organizational memberships either on- or
off-campus. In all, the sample consisted of 20 males and 20 females who were selected
from four different student residential halls (n=40)..

The faculty sample (n=18) was recruited from a randomized phone list, and
consisted of 14 male and 4 female subjects7. With the assistance of the Department of
Recycling and Solid Waste Management, which is responsible for the campus-wide
recycling program, we identiﬁed buildings on campus that were thought to be “recycling-
friendly” (e. g., buildings where there was adequate space and infrastructure available to
carry out MSU’s proposed recycling activities) as well as buildings where recycling is
typically more difﬁcult, called “recycling unfriendly” because these resources are not
present. All of the faculty members with ofﬁces in each building type were in the initial
list, and were recruited in order of a randomized priority to participate. Nine faculty
members were recruited and interviewed from each type of building. The faculty
members were not purposefully recruited on the basis of department or college, as some
of these units’ faculty worked in buildings that could not be identiﬁed as recycling

friendly or unfriendly, and in turn were not included in the study.

4.2.3 Design

 

7 For the faculty sample, the goal was to have the number of males and females be closely representative of
the actual gender distribution on campus. In 2007, females accounted for 37% of all faculty members; in
this study, 22% of the faculty subjects were female.

50

An expert model (Figure 4.1) was ﬁrst developed based on an extensive review of
the associated literature and with a series of open-ended interviews of recognized experts
familiar with campus recycling efforts at MSU and elsewhere. The experts involved in
the creation of this model were the administrator of MSU recycling program, the MSU’s
Sustainability Director, the two surrounding cities’ Recycling Directors (from East
Lansing and Lansing, MI), and the representatives for the contracted commercial
recycling hauler for MSU. This model was structured around six general concept areas:

(1) What: a comprehensive listing of the items or materials available for recycling
at MSU, but not necessarily collected from every area on campus.

(2) Where: an up to date account of places that have recycling infrastructure (e. g.,
pick-up or drop-off points, processing facilities, etc.) on the main campus of
MSU.

(3) How: a depiction of the processes an individual carries out to recycle,
including: separation, preparation, and general reasons to use a proper
method.

(4) Why: the incentives or otherwise beneﬁts of recycling in terms of
environmental, economical, and social elements.

(5) Why Not: factors that impede pe0ple from recycling and are considered the
disincentives to participating.

(6) Alternatives: recognizes that recycling is not the only way to dispose or
eliminate a product that is no longer needed for its original purpose and

includes garbage, reuse, and reduce.

51

An interview protocol was next derived from the expert model. The protocol was
purposefully open—ended to allow subjects to express both correct and incorrect
knowledge and beliefs. Each interview began with a broad question and was followed by
subsequent questions that sought to exhaust a subject’s knowledge. The ﬁrst question
was intentionally designed to elicit all latent knowledge, starting with: What can you tell
me about recycling at MSU? Following this, more speciﬁc questions were asked
branching from each main concept node depicted in the expert model. For example, a
series of questions aimed at eliciting a subject’s knowledge about paper recyclables might
include the following prompts: “Now that you ’ve talked about recyclable containers, are
there other materials that may be recycled on campus? ”; “What about paper
products? ”; and “A re there any other kinds of paper or things made of paper that can be
recycled on campus?” Each interview was conducted by a single interviewer (the ﬁrst
author) and was digitally recorded for accuracy. When a subject indicated that their
knowledge surrounding a given subject was exhausted, the interviewer moved on to the
next question or concept. Each interview lasted between 30 to 80 minutes for both

students and faculty (3? = 45 minutes).

4.2.4 Analysis

Subjects’ responses were transcribed to develop a mental model for each
individual that took part in the research. These models were used to indicate both where
their knowledge overlapped with the expert mental model and where there were gaps.

Other concepts—both valid ones and misconceptions—were included in the analysis.

52

Using these models, it was possible to compute frequencies with which subjects
understood concepts from the expert model.

In addition to developing a model for each subject, interview responses were also
coded according to a ﬁve-point scheme, with higher scores reﬂecting more accurate

comprehension of the concept; the codes used were:

4 = concept discussed without prompting and understood by subject;

3 = concept discussed when prompted and understood by subject;

2 = concept discussed without prompting but misunderstood by subject;
1 = concept discussed when prompted but misunderstood by subject;

0 = subject had no knowledge of the subject and was unable to answer

interview question (Zaksek and Arvai 2004).
Scores of 3 and 4 indicated correct knowledge, the difference being the ability of the
subject to be able to either discuss the concept on their own (a score of 4) or after gentle
prompting (a score of 3). For this reason, a concept was part of an individual’s mental
model when the subject response was correct (a score of 3 or 4).

These codes were then used to estimate mean levels of knowledge for each
concept present in the expert model. Chi-square tests were used to compare these means
according to a series of independent variables (i.e. by gender, residential hall or building,
between students and faculty). Because the sample sizes were relatively small, results
had to be ampliﬁed using the Monte Carlo (Agresti and Finlay 1997) approach (see

Tables 1 through 6).

4.3 Results

53

4.3.1 General Trends

A comprehensive model, depicting the relative frequency with which speciﬁc
concepts were understood by subjects, was developed for both MSU students (Figure 4.2)
and faculty (Figure 4.3) by combining all the individual mental models elicited from each
subject group (e.g. see Figure 4.4 and Figure 4.5). These models revealed important
gaps—on part of both MSU students and faculty—in understanding key recycling
concepts that are relevant to established campus-based waste reduction practices.

For example, both MSU students and faculty displayed an incomplete
understanding Of where they could recycle on campus. While students knew that
recycling Opportunities were present in academic buildings and campus dining areas, few
knew Of speciﬁc details regarding where else recycling took place on the MSU campus
(Figure 2). For example, only a small percentage Of students knew of speciﬁc collection
points for recyclables in on-campus sports venues (22.5%), MSU’s popular International
Center Food Court (10%), residential dining areas (40%), and cafes (12.5%).

The same was true Of MSU faculty members interviewed (Figure 4.3); relatively
few faculty members (33.3%) were aware Of collection points for recyclables in dining
and concession areas. Neither students (5%) nor faculty (11.1%) were well aware of
recycling opportunities at the general campus recycling facility.

There were also distinct gaps in knowledge Of students and faculty about a range
Of what speciﬁc items can be recycled on campus. For example, while all students and
faculty generally knew that paper could be recycled on campus, relatively few subjects
were aware of many common paper products that were recyclable; these included low

levels of understanding regarding the fact that soft-bound books and telephone directories

54

that were recyclable on-campus by students (25% and 2.5%, respectively) and faculty
(27.8% and 38.9%, respectively). Faculty members lack knowledge Of the recycling Of
mail compared tO students (1 1.1% faculty, 40% students), and students need more
information about paperboard8 (12.5% faculty, 0% students). On the positive side, both
students (97.5%) and faculty (94.4%) were aware of newspaper recycling Opportunities
on campus.

Beyond the general knowledge of categories of containers (plastics, glass, and
metals), both students and faculty were not well aware of speciﬁc items. For example,
only 55.6% of students and 42.5% of faculty mentioned recyclability Of clear glass items.
Similarly, another common container metal fOOd cans was mentioned as a recyclable item
by 55% of students and 44.4% of faculty. A few speciﬁc kinds of containers were known
to be recyclable, including #1 PETE plastic found in most soda and water bottles (82.5%
Of students and 66.7% of faculty mentioned this material).

In addition tO the categories of ﬁbers/papers and containers, was the category Of
items called “other” with items that did not ﬁt into either category. Of major signiﬁcance
to waste reduction in an academic setting, was the Observation that students and faculty
interviewed were not well aware Of many common forms Of electronic waste (e.g.
computers, mp3 players, mobile phones), and how they needed to be recycled properly.
Recyclability of items such as batteries, ink jet cartridges, ﬂuorescent light bulbs, and
toner cartridges—were not well known. Speciﬁcally, relatively few faculty members

(22.2%) or students (30%) knew about battery recycling. All Of these items mentioned

 

8 Both groups had a low understanding of paperboard, but students are considered a larger producer of the
material due to their personal food purchases. Should students be allowed to recycle this material more
readily in the future, they need information regarding the material characteristics and collection points.

55

contain materials (e. g. mercury, lead, and cadmium) that bear potentially harmful
consequences to the health of people and the environment if sent to the landﬁll.

Subjects also struggled with questions about how recycling must be carried out on
the MSU campus. Students and faculty were aware of the importance of strict separation
rules for a successful recycling program (82.5% of students and 94.4% of faculty
mentioned prOper sorting); they were, however, less knowledgeable regarding the
speciﬁcs of this process. The knowledge of separation followed the pattern of knowledge
of general items (i.e. paper, glass, metals, and plastic) with much less knowledge of the
speciﬁc items (e.g. separation Of different types of plastic). Further, few subjects knew to
rinse recyclable containers (cleaning recyclables was identiﬁed by 65%9 of students
interviewed and 38.9% of faculty interviewed), as well as removing caps or lids (with this
concept understood by 32.5% Of students and 27.8% Of faculty).

Faculty and students were knowledgeable about the beneﬁts that help explain why
to recycle in terms of the general association Of recycling with economic, social, and
enviromnental beneﬁts. But relatively few students and faculty could name any speciﬁc
beneﬁts. For example, the speciﬁc concepts Of energy conservation, air quality, water
protection, and land-use issues were not mentioned by a majority of students (37.5%,
47.5%, 32.5%, respectively) or faculty (44.4%, 22.8%, 16.7%, respectively). An
exception to this trend was that of land-use beneﬁts, which were understood by a
majority of students (50%) and faculty (77.8%).

As far as disincentives are concerned, these were relatively well understood by

both students and faculty; the only signiﬁcant exception here was a relatively low

 

9 Although 65% of students identifying the need to clean recyclables may seem high. this number was low
compared to the needs of recycling administrators at MSU.

56

understanding displayed by both students (22.5%) and faculty (22.2%) regarding space
constraints for recycling services in many campus buildings. Lack Of understanding for
how to recycle, described as the concept of know-how, was reported as a reason why
people do not recycle by 72.2% faculty and 95.0% students interviewed.

Besides recycling, faculty and students were aware that they have a few
alternatives, the most well known of these being (1) the placement Of recyclables in the
garbage (known by 100% of students and faculty) and (2) reusing items for the same or
related purpose (known by 92.5% Of students and 83.3% Of faculty). The exception here
was waste reduction practices (i.e. conscious decisions to use products that yield less
waste), which were not well known with 47.5% Of students and 38.9% Of faculty

included this item in their mental models.

4.3.2 Gender Differences

In addition to looking at general trends, we compared (using chi—square tests)
levels of student and faculty understanding according to a series of independent variables.
One of these independent variables was gender. Among students overall, very few—only
four—signiﬁcant differences were Observed (Table 1). These included a greater

understanding on the part of male students regarding recycling of biological wastes

(Emales=1.90, se= 0.44 and Y females =0.95, se= 0.34; p=0.02), pick-up options for
recyclables (7c ma11,532.95, se= 0.27 and 7t females=1 .95, se= 0.35; p=0.05), the availability
of a central processing facility (f ma1653.00, se=0.3l and 1? females] .30, se= 0.37;
p=0.007), and the recycling of left-over food waste (If lm$1651.55, se= 0.24 and

rfema.,,=0.95, se= 0.34; p=0.04l).

57

Gender differences among faculty (Table 2) included, for example, a greater
understanding by men Of white paper (2? males =4.00, se= 0.21 and 3 females = 3.50, se=
0.00; p= 0.004), and mixed paper (3' males =3.95, se= 0.34 and f fema|cs = 3.50, se= 0.00;
0.043) as recyclable items. Male faculty members, by contrast, had a better

understanding Of how to separate containers from papers for recycling (".r'mk,s =3.50, se=

0.14 and fpemales =2.00, S se= 1.15; p=0.008).

4.3.3 Faculty/Student Differences
Another independent variable was the role of the subject as a student or faculty
member on campus (Table 3). This analysis included differences based on where tO

recycle, such as students had a greater understanding Of recycling collection points in the
residential halls (f students=3.53, se=0.50 and 36 faculty: 0.50, se=0.23; p=0.0001) whereas
faculty had a greater grasp of recycling collection points in academic buildings
(7r stuck,,,ts=3.48, se=0.l3 and? faculty=4.00, se=0.00; p=0.006).

In terms of what can be recycled, students were more knowledgeable than faculty

about recycling cardboard (7c smdentsz3.25, se =0.20 and 7c faculty-:21]: se=0.46; p=0.019).
Faculty, by contrast, had a greater knowledge of recycling magazines (7c students=0.98,
se=0.26 and 7: faculty: 2.89, se =0.39; p=0.001) and mixed paper (7r students] .93, se=0.30
and 7c facultyz3.83, se=0.09; p=0.003).

Students had a greater understanding of certain disincentives to recycling

(f smdems=2.99, se= 0.46 and f faculty=2.56, se= 0.46; p= 0.006), including lack of time

(Estudems=3.03, se=0.23 and 7t faculty=1 .94, se=0.47; p=0.018) and lack of knowledge

58

about where to recycle items, what items are recyclable, and how to prepare items for

recycling (Y stuc1,,,m..,=3.55, se=0.15 and J? facu|ty=2.78, se= 0.42; p= 0.036).

4.3.4 Campus Facilities Differences
The student subjects’ residential hall (Tables 4 and 5) resulted in very few

differences. For the most part, these differences were based on the comprehensive

knowledge of a general concept, such as how to recycle (YAkers=1-94, se= 0.25 and
f Bunerﬁeld =1 .64, se= 0.22 and 35 Holmes =1 .97, se= 0.23 and 7t Hubbard =2.01, se=0.23;
p=0.049), and alternatives to recycling ()7 Akers =2.15, se= 0.94 and f Bunerfield =2.23, se=

0.80 and f Holmes=2.35, se=0.90 and 2? Hubbard =2.45, se=0.92; p=0.043).

The building type (recycling “friendly” or “unfriendly”) of a faculty subjects’
Ofﬁce associated was associated with a few differences (Table 6). For example, faculty

members in recycling “unfriendly” buildings had a greater awareness of used (post-

consumer) newspapers (f friendlyzo.44, se= 0.44 and Y unfriendlyzz-zza se=0.70; p= 0.046),
and removing impurities from items to prepare them for recycling (3? friendly=l.33,

SC=O.55 and f unfriendly=3-] 13, SC: 0.31; p: 0.032).

4.4 Discussion

The mental models approach provides communicators the Opportunity to provide
information that meets people’s information needs, rather than relying on their own
anecdotal perceptions. Without an informed understanding of the current level of

knowledge held by the communities that communicators are trying to reach, they run the

59

risk Of wasting valuable resources and time scattering uncoordinated messages with the
hOpe Of inﬂuencing behavior. In the end, research which develops a view of the the
audience’s information needs are—rather than what the communicators want to
communicate—is a step in the right direction for thoughtfully designed communication
efforts. Mental models research, as used in this study, is the ﬁrst step in a long-term
process to educate the Michigan State University community about recycling, and to a
lesser extent, inform the Operation and design of recycling collection on-campus.

There are two complementary ways of drawing conclusions from these results.
First, one can lOOk to the results from the various statistical analyses to determine if there
is a specialized need to address a particular issue (e.g., paper recycling) or to target a
speciﬁc group of consumers (e. g., faculty members). Second, analysts can look for so-
called “big picture” issues that highlight important gaps, misconceptions, or needs that
are largely shared by the groups that were the focus Of this research (students and

faculty).

4.4.1 Statistical Results Implications

Relatively few speciﬁc comparisons (which were made using chi-square tests)
revealed signiﬁcant differences either between individuals (e.g., between faculty and
students, or by gender) or across groups (e. g., by residential hall, academic building). Of
580 total comparisons, only 36 (or 6.55% of the comparisons) revealed signiﬁcant
differences. There are three implications from these statistical analysis, (1) practical and

signiﬁcant results, (2) signiﬁcant but not practical results, and (3) non-results.

6O

4. 4. 1.] Practical and significant results

Of these signiﬁcant comparisons, however, there were some notable results. For
example, there were distinct differences between the knowledge levels of students and
faculty regarding the different kinds of papers that may be recycled on campus (Table 3).

Faculty members appear to need additional information regarding the recycling Of

cardboard (Estudems=3.25, se =0.20 and 3? facuhy': 2.1 1, se =0.46; p=0.019) while students
need information regarding the recycling Of mixed paper (36 Smdems=l.93, se=0.30 and

Ifaculty=3.83, se=0.09; p=.003), magazines (Ystudems=0.98, se=0.26 and f faculty:2.89,

se=0.39; p=0.001). On the other hand, students outperformed faculty in terms Of their

level Of knowledge about the recyclability Of post-consumer paper materials such as

newspaper (J? stucicms=2.88, se=0.22 and f facum,=2.33, se=0.46; p=.039) and white paper

(3 students=2.85, se=0.25 and 3c” faculty=1 .33, se=0.46; p=0.001). These kinds of results

point to the possibility of identifying different areas Of emphasis—between students and
faculty, for example—in subject-speciﬁc outreach plans.
Along similar lines, students were more likely than their faculty counterparts to

provide detailed reasons behind why people do not recycle. Among the reasons why is

their view that recycling activities are a time consuming (if students: 3.03, se= 0.23 and
J? faculty: 1.94, se =0.47; p= 0.018) and difﬁcult activity (3 students: 2.99, se= 0.46 and

I faculty: 2.56, se = 0.46; p= 0.006). This important difference between students and

faculty warrants perhaps the most attention in future outreach—and importantly,

planning—efforts. Students are by a wide margin the largest population on campus and

61

their participation in sustainability activities such as recycling is critical to improving the
university’s performance in this area.

Thus, it is our view that administrators Of the MSU recycling program must
approach the design Of campus recycling from the standpoint of removing logistical
barriers that may make students relate recycling to these negative connotations. Related,
outreach efforts must work to counteract the idea that recycling is too difﬁcult (or worse,
a waste of time) and, therefore, an activity that is to be avoided. This latter Objective
could be accomplished through social marketing efforts that provide reasons for students
to take the extra steps to recycle (such as the speciﬁc beneﬁts of recycling to the

environment or revenue Of recycling positively affecting student programs).

4. 4. 1.2 Signiﬁcant, but not practical results

It is worth pointing out, however, that alongside these differences that were both
statistically and practically signiﬁcant, several other statistically signiﬁcant differences
were of limited value vis-a-vis informing the development of a recycling-speciﬁc

outreach plan. For example, mean scores Of faculty male and female knowledge of
white paper recycling were signiﬁcantly different ()7 males =4.00, se= 0.21 and Y females =

3.50, se= 0.00; p= 0.004). But the similarity in the mean scores (4.00 vs. 3.50) suggests
that both groups clearly understand this issue and, therefore, addressing it further in
outreach plans may not be necessary.

Along these lines, when considering both faculty and students, the differences
across the genders were largely minor, which mirrors results from other studies (e. g.

Berger 1997) that are typically unable to correlate recycling behavior with gender.

62

Although some differences exist (see above, and Tables 1 and 2), focusing on these in the
design of a recycling-speciﬁc outreach plan for MSU is—in our view—unlikely to be
productive. Both genders need accurate information speciﬁc to their student or faculty
role in recycling on campus. In sum, the role of a particular subject on campus seems to
be a greater determinant of what a subject would know and need to know, rather than
their gender.

To further illustrate this point, many of the faculty and student differences that
were evident from the chi-square tests (Table 3) can also be explained by considering
their roles at the university. For example, one would expect undergraduates living in
residential halls to know more about recycling opportunities in these areas. Likewise,
one could expect students to be more cost-conscious and therefore, aware of a wider
range of products that could be returned to a recycling depot in exchange for a fee (e.g.,
in the form of a returned deposit). In contrast, faculty are expected to be better aware of
specialized Ofﬁce products—cg, toner cartridges, certain kinds of paper—because they

are more likely to use these on a regular basis.

4.4.1.3 Non-results

Equally important in these kinds of analyses are what are commonly referred to as
“non-results”; these are areas where statistically signiﬁcant difference are expected (and
hence, inform the design of the mental models study) were absent. But the absence of
statistical signiﬁcance does not necessarily mean that these “non-results” lack outreach

implications. For example, student understanding of how to separate cloudy plastic from

other plastics was not a signiﬁcantly different between genders (Tcmalcﬁ 1.15, se= 0.36

63

and 3 females: 1.15, se =0.34; p>0.05), nor was knowledge of recycling ﬂorescent light

bulbs (imam: 0.15, se= 0.15 and 1? females: 0.00, se =0.00; p>0.05). Both Of these items

were not well known as recycable and deserve attention in outreach plans. For future
communication plans, it is less important that the results yielded few signiﬁcant
differences between genders, but more important that generally subjects were
unknowledgeable about certain tOpics that could be necessary for MSU community
members to successfully recycle.

The general lack of signiﬁcant and systematic differences in this study between
the four residential halls where students live and the buildings labeled “recycling
friendly” and “recycling unfriendly” where faculty members work is another example of
the important role of “non-results.” In all, residential hall difference resulted in two
signiﬁcant comparisons among the 116 comparisons conducted (see Table 4.6); building
differences resulted in only two differences as well. This is good news from the
standpoint of designing a recycling-speciﬁc outreach effort because it means that
sustainability planners at MSU need not develop building speciﬁc communication plans
(which would be time consuming and costly).

One notable exception in this regard relates to differences observed across
students from the four residential halls in terms of how to recycle; these levels of

knowledge were different depending upon which residence hall a student calls home
(3.4km: 1.94, se= 0.25 and Egunerﬁeld = 1.64, se= 0.22 and 7Holmes= 1.97, se= 0.23 and
Ygubbard = 2.01, se= 0.23; p= 0.049). These differences can be explained by the variation

in the operations Of the recycling program, some areas have consistent collection points

and signage allowing the student to easily recycle, while others do not. This emphasizes

64

the need to couple education with environmental alteration—the technique to increase the
rate of recycling by making it logistically easier to do. Access to a structured,
institutionalized program allowing for easy and convenient recycling is considered by
many to be a very effective technique in terms of leading to durable behavior change and
increasing recycling rates (DeYoung 1986; Derksen and Gartrell 1993; Howenstine 1993;
Boldero 1995; Lee, DeYoung et al. 1995; Oskamp, Zelezny et al. 1996; Ludwig, Gray et

al. 1998; Barr 2004).

4.4.2 Recommendations

The second and ﬁnal level of analysis draws conclusions based on trends that
were evident from the mental models (Figures 4.2 and 4.3). Based on the these results,
four general observations stand out as universally important; each of these deals with
issues that were either misunderstood or largely unknown by subjects based on the
frequency with which concepts were mentioned.

First, recycling locations were not well known outside of the typically used areas
of the academic building and residential hall, including dining and food venues, campus
owned apartments, and at the recycling processing facility on campus. This lack of
knowledge shows, perhaps, a slight indifference for recycling Opportunities when they
exist outside of one’s usual working or socializing areas. Besides education about
locations to recycle outside the dorm room and classroom, the idea that recycling should
and can be done almost anywhere and anytime on campus, needs reinforcement.

A second salient ﬁnding of this research was the lack of knowledge about how

speciﬁc types of paper, plastics, glass, and metals must be separated for proper recycling.

65

An example of this is that all student subjects knew the generally plastics were recycled
on campus, but knowledge of the different types of plastic were less known; #1 PETE
was mentioned by 82.5% of student subjects. #2 HDPE cloudy (55%), and #2 HDPE
colored (15%). Students and faculty need to know what types of these materials can be
recycled and how to identify the material (e.g. by looking for a speciﬁc plastic recycling
number) to better understand how each material needs to be separated. The good news is
that subjects were usually aware of these general concepts regarding material types and
separation (i.e. paper, plastics, and metals categories). We recommend outreach plans
build upon student and faculty general knowledge for a more complete understanding.

Overall, the importance of knowing what items can and can’t be recycled should
be stressed to achieve a successful recycling program. Lack Of knowledge not only
results in lower recycling intensity, lower diversion rates, and lower collection efﬁciency,
but the resulting trial-and-error approach increases contamination levels in collected
recyclables (Scott, 1999). In the end, the good intentions of an uncertain public can
reduce the efﬁciency and efﬁcacy of recycling programs.

Third, questions related to why one should recycle also revealed some important
knowledge gaps. How much students and faculty should know about the reasons why
they ought to recycle relates to the philosophy of higher education. Based on the long-
term vision and purpose Of MSU, which is to educate citizens—including students,
faculty, and staff—about community and enviromnental stewardship, it stands to reason
that gaps regarding the lesser-known concepts of the environmental and the socio-

economic beneﬁts Of recycling should be addressed.

66

These results reveal that MSU community members are not made aware of the
concepts related to outreaching beneﬁts of sustainable practices, in this case recycling.
Students and faculty. were well aware of deposit of soda cans and bottles as a monetary
incentive (due to Michigan having the largest bottle deposit of any state, at ten cents), but
not other sociO-economic beneﬁts (e.g., jobs in the recycling industry and the sale of
materials collected for recycling). All students and faculty interviewed understood the
general connection between recycling and environmental beneﬁts. However, relatively
few student and faculty subjects understood the speciﬁc nature of these beneﬁts (e.g.,
beneﬁts in terms of energy efﬁciency (37.5% and 44.4%, respectfully), cleaner air
(47.5% and 22.8%, respectfully), and cleaner water (32.5% andl6.7%, respectfully). In
accordance with MSU’s mission to empower people and communities, I recommend that
all community members be helped to understand (via metrics, for example) how their
resource use affects the both local and global environmental footprint of MSU.

Fourth, faculty and students were typically aware of reuse as an alternative to
waste generation and recycling, but were generally unaware of reduction practices.
Female faculty members and female students had a good understanding in general of
reduction, but the majority of the students and faculty did not. Reduction is perhaps the
hardest lesson to teach and practice, but it can be done. These measures can be
accomplished in many ways: through contracts with manufacturers who design products
with less packaging; through institution purchases of more durable products and internal
reuse of products; and through institutional changes in practices (i.e. default printing
paper on two sides); and through institutional purchases of less toxic products (F ishbein

and Gelb 1992). Without the MSU community’s full COOperation, reduction practices

67

will be difficult to coordinate and implement. The temporary inconvenience of critically
thinking about purchases may require more work for purchasing departments and
administrators; however, in the long-term, the reduction Of waste is perhaps the best way

to divert he most waste from the landﬁll.

4.5 Conclusion

The mental models approach carried out in this study allows the level of
stakeholder knowledge to be visible to those who are designing outreach plans. The
results can be used to allocate resources to ﬁll gaps in knowledge, and vice-versa, areas
of high understanding would need less communication. But mental models do not show
how important a concept is compared to all others on the model in terms of priority. The
models do not reveal how knowledgeable subjects should be about each concept or item;
all subjects being knowledgeable about every item maybe unnecessary for the success of
the program.

When considering residential hall recycling, for example, 87.5% of students knew
the location of at least one recycling collection point. Students’ seemingly well-
developed knowledge of the location is perhaps an argument for not highlighting the
topic in outreach effort. But an argument could also be made for further education on the
basis of informing students in residential halls about different recycling options available.
This may lead to informing students about recycling locations closer to their room and
thus, increasing the convenience and recycling rates of students. On the other hand, tire

recycling was known by none (0%) of the faculty subjects. But this lack Of knowledge is

68

not particularly a concern of outreach plans, because their duties do not require them to
service University vehicles.

In the end, the mental models approach reveals how well speciﬁc concepts are
known, but it does not make the decisions about how well the concepts should be known
(or if they are worth knowing at all). The method is meant to help people make decisions
about outreach plans, but it is not meant be the decision-maker. These decisions should
be' equitable and made by a larger group Of people including the recycling department
administrators, building/residential hall managers, people affected by the program, and
building maintenance employees who deal with recycling.

One approach to understand what items are necessary to communicate, could be a
future study comparing knowledge of recycling to actual recycling amounts. Although
this study shows subjects lack knowledge of certain areas, it does not have a component
that shows how this knowledge directly affects the amount of recycling and the levels of
contamination. This study could further develop the mental models research to design
recycling communications, and to a lesser extent, the physical characteristics of
collection.

Another approach to solve these unresolved questions could take the form of a
future comparative study using the same mental models interviews, to understand the
effectiveness of outreach plans to change and enhance knowledge. In this way, the
outreach messages could be measured by the change in the level of knowledge for each

item.

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Table 4.1: The frequency of, and mean level of understanding for, 116 concept areas
across male (M) and female (F) undergraduate students on the MSU campus. Means
were compared using chi-square tests at the p=0.05 signiﬁcance level.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Concept % Correct Males Females P
M F J? SE 35 SE

Where 24.67 23.67 1.84 1.75 0.37 0.39 n/s
Residence Hall 47.50 45.00 3.50 3.55 0.25 0.22 n/s
Location on Campus 45.00 45.00 3.35 3.40 0.25 0.24 n/s
Location in Residence Hall 45.00 45.00 3.35 3.45 0.25 0.25 n/s
Dining/Concessions 35.00 35.00 2.45 2.30 0.34 0.36 n/s
Sports Venues 15.00 10.00 1.20 0.65 0.34 0.30 n/s
Residential Dining 25.00 15.00 1.85 1.25 0.37 0.33 n/s
International Center Food Court 2.50 7.50 0.25 0.55 0.18 0.28 NS
Sparty‘s Cafes 7.50 7.50 0.60 0.40 0.30 0.28 NS
Campus and Academic Buildings 50.00 47.50 3.50 3.45 0.21 0.17 n/s
Location on Campus 47.50 47.50 3.30 3.35 0.27 0.17 n/s
Location in the Building 45.00 45.00 3.25 3.25 0.30 0.18 n/s
Campus-owned Apartments 2.50 2.50 0.25 0.25 0.16 0.6 n/s
Location in Complex 0.00 0.00 0.05 0.00 0.05 0.00 n/s
Location on Campus 0.00 0.00 0.05 0.05 0.05 0.05 n/s
Processing Facility 2.50 2.50 0.60 0.35 0.18 0.18 n/s
What 21.69 19.46 1.51 1.43 0.20 0.20 n/s
Fibers 0.00 5.00 0.00 0.35 0.00 0.24 n/s
Fabric 2.50 12.50 0.15 0.85 0.15 0.34 n/s
Paper 52.5 50.00 3.95 3.95 0.05 0.05 n/s
Magazines 15.00 17.50 0.9 1.05 0.36 0.37 n/s
White Paper 52.50 45.00 3.75 3.35 0.10 0.24 n/s
Pre-Consumer 2.50 5.00 0.20 0.20 0.20 0.20 n/s
Post-Consumer 47.50 35.00 3.10 2.65 0.26 0.36 n/s
Paperboard 7.50 7.50 0.50 0.40 0.28 0.22 n/s
Mixed Paper 30.00 20.00 2.25 1.60 0.43 0.41 n/s
Junk Mail 22.50 22.50 1.45 1.30 0.41 0.37 n/s
Newspaper 52.50 47.50 3.85 3.80 0.08 0.20 NS
Pre-Consumer 2.50 0.00 0.15 0.00 0.15 0.00 n/s
Post-Consumer 40.00 40.00 2.95 2.75 0.35 0.38 n/s
Softbound Books 12.50 15.00 0.65 0.90 0.30 0.32 n/s
Cardboard 45.00 42.50 3.30 3.20 0.23 0.32 n/s
Non-Fibers “Containers" 50.00 45.00 3.50 3.25 0.17 0.24 n/s
Metals (Tin, Aluminum) 50.00 50.00 3.60 3.80 0.21 0.09 NS
Scrap Metal 27.50 22.50 1.70 1.60 0.40 0.41 n/s
Food Cans 30.00 22.50 2.20 1.70 0.42 0.40 n/s
Pop Cans 47.50 47.50 3.45 3.65 0.28 0.21 n/s
Foil 12.50 7.50 0.90 0.30 0.36 0.21 n/s
Glass 52.50 42.50 3.60 3.20 0.11 0.27 n/s
Clear 22.50 20.00 1.50 1.25 0.36 0.35 n/s
Brown 10.00 5.00 0.60 0.40 0.28 0.22 n/s
Plastics 52.50 55.00 3.90 3.95 0.07 0.05 n/s
#1 PETE 40.00 42.50 2.60 3.40 0.36 0.28 n/s
#2 HDPE (Cloudy) 25.00 30.00 1.55 2.20 0.40 0.38 n/s
#2 HDPE (Colored) 7.50 7.50 0.50 0.50 0.28 0.28 n/s

 

 

 

 

70

 

Table 4.] (continued): The frequency of, and mean level of understanding for, 116
concept areas across male (M) and female (F) undergraduate students on the MSU
campus. Means were compared using chi-square tests at the p=0.05 signiﬁcance level.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Concept % Correct Males Females P
M F 3? SE 7c SE
Other 42.50 30.00 2.95 0.35 2.25 0.39 n/s
Fly Ash 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Construction Waste 7.50 5.00 0.50 0.28 0.35 0.24 NS
Tires 7.50 7.50 0.55 0.30 0.55 0.30 n/s
Toner 0.00 0.00 0.00 0.00 0.15 0.15 n/s
Tennis Shoes 2.50 2.50 0.15 0.15 0.20 0.20 n/s
Electronic Waste 7.50 12.50 0.55 0.30 1.05 0.37 n/s
Biological 25 .00 15.00 1.90 0.44 0.95 0.34 0.022
Leaves 10.00 2.50 0.60 0.28 0.15 0.15 n/s
Grass 7.50 10.00 0.45 0.25 0.60 0.28 n/s
Branches 7.50 2.50 0.45 0.25 0.15 0.15 n/s
Food Waste 20.00 5.00 1.55 0.44 0.35 0.24 0.041
Manure 7.50 5.00 0.55 0.30 0.03 0.21 n/s
Hazardous Waste 12.50 10.00 0.80 0.32 0.70 0.33 NS
Pallets 0.00 2.50 0.00 0.00 0.20 0.20 n/s
Batteries 20.00 10.00 1.30 0.41 0.80 0.32 NS
Ink-Jet Cartridges 5.00 17.50 0.30 0.21 1.40 0.40 n/s
F lorescent Light Bulbs 2.50 0.00 0.15 0.15 0.00 0.00 n/s
How 28.66 25.77 1.97 0.02 1.81 0.22 n/s
Quality Control 47.50 45.00 3.60 0.22 3.35 0.27 n/s
Proper Sorting 42.50 42.50 3.20 0.33 3.10 0.32 n/s
Signs 22.50 22.50 1.70 0.44 1.55 0.40 n/s
Removing Impurities 40.00 37.50 2.55 0.33 2.50 0.32 n/s
Clean 37.50 30.00 2.30 0.36 2.05 0.37 n/s
Remove Caps 12.50 20.00 0.75 0.32 1.45 0.37 n/s
Processing Facility 45.00 20.00 3.00 0.31 1.30 0.37 0.007
Pick-up 40.00 30.00 2.95 0.27 1.95 0.3 5 0.048
Self-Sorting 47.50 42.50 3.30 0.27 3.15 0.29 n/s
Non-Fibers "Containers” 42.50 40.00 2.85 0.32 2.65 0.36 n/s
Plastics 52.50 50.00 3.60 0.11 3.55 0.11 n/s
#1 PETE 20.00 17.50 1.35 0.39 1.35 0.36 n/s
#2 HDPE (Cloudy) 17.50 15.00 1.15 0.36 1.15 0.34 n/s
#2 HDPE (Colored) 7.50 5.00 0.50 0.28 0.45 0.25 n/s
Metals (Tin. Aluminum) 40.00 37.50 3.00 0.36 3.00 0.36 n/s
Glass 45.00 40.00 3.05 0.31 2.70 0.37 n/s
Brown 10.00 7.50 0.75 0.32 0.45 0.25 n/s
Clear 12.50 7.50 0.95 0.36 0.45 0.25 n/s
Paper 52.50 50.00 3.55 0.11 3.65 0.11 n/s
Magazines 2.50 5.00 0.20 0.20 0.35 0.24 n/s
Softbound Books 0.00 5.00 0.00 0.00 0.30 0.21 n/s
White Pemer 22.50 27.50 1.55 0.40 1.80 0.38 n/s
Paperboard 0.00 2.50 0.00 0.00 0.15 0.15 n/s
Mixed Paper 27.50 17.50 1.85 0.39 1.15 0.36 n/s
Newspaper 30.00 32.50 1.90 0.40 2.25 0.35 n/s
Cardboard 27.50 20.00 1.6 0.35 1.25 0.34 n/s

 

71

 

Table 4.] (continued): The frequency of, and mean level of understanding for, 116
concept areas across male (M) and female (F) undergraduate students on the MSU
campus. Means were compared using chi-square tests at the p=0.05 signiﬁcance level.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

% Correct Mean Score Standard Error

Knowledge Concept M F E SE Y SE P
Why 32.31 29.23 2.22 0.34 2.07 0.22 n/s
Environmental Beneﬁts 52.50 50.00 3.80 0.22 3.85 0.08 n/s
Water 17.50 12.50 1.15 0.08 0.95 .034 n/s
Land 32.50 17.50 2.20 .034 1.25 0.40 n/s
Air 20.00 27.50 1.40 0.40 1.90 0.40 n/s
Energy 17.50 20.00 1.15 0.40 1.45 0.41 n/s
Economic Beneﬁts 52.50 50.00 3.60 0.41 3.55 0.11 NS
Monetary Incentive 47.50 47.50 3.35 0.11 3.40 0.21 n/s
Deposit 47.50 45.00 3.35 0.21 3.30 0.27 n/s
Garbage Fees 10.00 5.00 0.70 0.27 0.40 0.24 n/s
Jobs 20.00 12.50 1.25 0.24 0.80 0.30 n/s
Tippingjees 15.00 10.00 0.90 0.30 0.60 0.28 n/s
Social Beneﬁts 45.00 42.50 3.05 0.28 2.85 0.26 NS
Awareness 42.50 40.00 2.90 0.26 2.65 0.32 n/s
Why Not 40.83 40.00 2.94 0.32 3.04 0.45 n/s
Disincentives 50.00 50.00 3.95 0.45 4.00 0.00 n/s
Time 42.50 40.00 3.05 0.00 3.00 0.36 n/s
Ease 45.00 42.50 3.15 0.36 3.10 0.32 n/s
Convenience 47.50 47.50 3.30 0.32 3.70 0.21 n/s
Space 10.00 12.50 ' 0.65 0.21 0.90 0.36 n/s
Know-how 50.00 47.50 3.55 0.36 3.55 0.21 n/s
Alternatives 31.88 29.38 2.33 0.21 2.26 0.92 n/s
Reduce 25.00 20.00 1.55 0.92 1.55 0.36 n/s
Reuse 47.50 47.50 3.55 0.36 3.55 0.17 NS
Surplus Store 2.50 0.00 0.00 0.17 0.00 0.00 n/s
Garbage 52.50 50.00 3.95 0.00 3.95 0.05 n/s

 

 

 

 

 

 

 

72

Table 4.2: The frequency of, and mean level of understanding for, 116 concept areas
across male (M) and female (F) faculty members on the MSU campus. Means were
compared using chi-square tests at the p=0.05 signiﬁcance level.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Concept % Correct Males Females P
M F 7c SE )7 SE

Where 22.59 25.00 1.13 0.50 1.03 0.41 n/s
Residence Hall 11.11 0.00 0.50 0.29 0.50 0.29 n/s
Location on Campus 5.56 0.00 0.21 0.21 0.00 0.00 n/s
Location in Residence Hall 5.56 0.00 0.21 0.21 0.00 0.00 n/s
Dining/Concessions 27.78 25.00 1.29 0.45 1.00 0.71 n/s
Sports Venues 11.11 0.00 0.50 0.34 0.25 0.25 n/s
Residential Dining 11.11 25.00 0.50 0.34 1.00 0.71 n/s
International Center Food Court 16.67 25.00 0.71 0.34 0.75 0.75 n/s
Sparty's Cafes 16.67 0.00 0.86 0.38 0.00 0.00 n/s
Campus and Academic Buildings 77.78 100.00 4.00 0.00 4.00 0.00 n/s
Location on Campus 72.22 100.00 3.79 0.21 4.00 0.00 n/s
Location in the Building 72.22 100.00 3.79 0.21 4.00 0.00 n/s
Campus-owned Apartments 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Location in Complex 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Location on Campus 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Processing Facility 11.1 1 0.00 0.57 0.34 0.00 0.00 n/s
What 30.07 36.41 1.42 0.26 1.36 0.22 n/s
Fibers 0.00 0.00 0.00 0.00 0.00 0.75 n/s
Fabric 0.00 25.0 0.00 0.00 0.75 0.00 n/s
Paper 77.78 100.00 4.00 0.49 4.00 0.29 n/s
Magazines 55.56 100.00 2.71 0.00 3.50 0.29 n/s

White Paper 77.78 100.00 4.00 0.21 3.50 0.00 0.004

Pre-Consumer 5.56 0.00 0.21 0.54 0.00 0.25 0.005
Post-Consumer 38.89 100.00 1.93 0.00 3.75 0.00 n/s
Paperboard 0.00 0.00 0.00 0.07 0.00 0.29 n/s

Mixed Paper 77.78 100.00 3.93 0.34 3.50 0.00 0.043
Junk Mail 11.11 0.00 0.50 0.29 0.00 0.00 n/s
Newspaper 72.22 100.00 3 .64 0.00 4.00 0.00 n/s
Pre-Consumer 0.00 0.00 0.00 0.50 0.00 1.15 n/s
Post-Consumer 22.22 50.00 1.14 0.38 2.00 1.00 n/s
Softbound Books 22.22 25.00 0.86 0.51 1.00 1.00 n/s
Cardboard 50.00 25.00 2.43 0.35 1.00 0.00 n/s
Non-Fibers “Containers” 61.11 100.00 2.93 0.29 4.00 0.00 n/s
Metals (Tin, Aluminum) 72.22 100.00 3.36 0.42 4.00 0.00 n/s
Scrap Metal 50.00 0.00 2.00 0.44 0.00 0.75 n/s
Food Cans 38.89 25.00 1.57 0.29 0.75 0.00 n/s
Pop Cans 72.22 100.00 3.50 0.38 4.00 1.00 n/s
Foil 22.22 25.00 0.86 0.27 1.00 0.87 n/s
Glass 72.22 50.00 3.17 0.43 1.50 0.87 n/s
Clear 44.44 50.00 1.79 0.41 1.50 0.75 n/s
Brown 33.33 25.00 1.29 0.38 0.75 0.25 NS
Plastics 66.67 100.00 3.21 0.48 3.75 1.00 n/s
#1 PETE 50.00 75.00 2.43 0.40 3.00 1.03 n/s
#2 HDPE (Cloudy) 38.89 50.00 1.57 0.29 1.75 0.00 NS
#2 HDPE (Colored) 11.11 0.00 0.57 0.38 0.00 1.00 n/s

 

 

 

 

 

73

 

Table 4.2 (continued): The frequency of, and mean level of understanding for, 116
concept areas across male (M) and female (F) faculty members on the MSU campus.
Means were compared using chi-square tests at the p=0.05 signiﬁcance level.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Concept % Correct Males Females
M F 1? SE 3 SE
Other 66.67 75.00 3.29 0.00 3.00 0.00 n/s
Fly Ash 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Construction Waste 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Tires 0.00 0.00 0.00 0.39 0.00 1.00 n/s
Toner 11.11 25.00 0.57 0.29 1.00 0.7 5 n/s
Tennis Shoes 5.56 25.00 0.29 0.49 0.75 1.03 n/s
Electronic Waste 38.89 50.00 1.71 0.47 1.75 1.00 n/s
Biological 22.22 25.00 1.07 0.29 1.00 0.00 n/s
Leaves 5.56 0.00 0.29 0.29 0.00 0.00 n/s
Grass 5.56 0.00 0.36 0.07 0.00 0.00 NS
Branches 0.00 0.00 0.07 0.29 0.00 0.00 NS
Food Waste 5.56 0.00 0.29 0.39 0.00 0.00 n/s
Manure 1 1.11 0.00 0.57 0.29 0.00 0.00 n/s
Hazardous Waste 5.56 0.00 0.29 0.00 0.00 0.00 n/s
Pallets 0.00 0.00 0.00 0.47 0.00 0.00 n/s
Batteries 22.22 0.00 1.07 0.49 0.00 1.15 n/s
Ink-Jet Cartridges 27.7 8 50.00 1.29 0.34 2.00 0.00 n/s
Florescent Light Bulbs 11.11 0.00 0.50 0.29 0.00 0.29 NS
How 43.80 52.89 2.03 0.35 1.99 0.29 n/s
Quality Control 72.22 100.00 3.57 0.23 3.75 0.25 n/s
Proper Sorting 72.22 100.00 3.57 0.23 3.75 0.25 n/s
Signs 44.44 25.00 2.14 0.52 1.00 1.00 n/s
Removing Impurities 38.89 100.00 1.93 0.45 3.25 0.25 n/s
Clean 22.22 75.00 1.29 0.42 2.50 0.87 n/s
Remove Caps 27.78 0.00 1.36 0.44 0.00 0.00 n/s
Processing Facility 11.11 0.00 0.50 0.34 0.00 0.00 n/s
Pick-up 33.33 75.00 1.57 0.48 2.50 0.87 n/s
Self-Sorting 78.78 100.00 3.86 0.10 4.00 0.00 NS
Non-Fibers “Containers” 78.78 50.00 3.50 0.14 2.00 1.15 0.008
Plastics 66.67 75.00 3 .00 0.36 3.00 1.00 NS
#1 PETE 38.89 75.00 1.71 0.49 2.75 0.95 n/s
#2 HDPE (Cloudy) 27.78 25.00 1.14 0.43 0.75 0.75 NS
#2 HDPE (Colored) 11.11 0.00 0.43 0.29 0.00 0.00 n/s
Metals (Tin, Aluminum) 61.11 7 5.00 2.79 0.42 3.00 1.00 n/s
Glass 55.56 50.00 2.80 0.45 2.00 1.15 n/s
Brown 22.22 25.00 0.86 0.38 0.75 0.75 n/s
Clear 33.33 25.00 1.36 0.44 0.75 0.75 n/s
Paper 77.78 100.00 3.64 0.13 4.00 0.00 n/s
Magazines 33.33 50.00 1.50 0.49 2.00 1.15 n/s
Softbound Books 5.56 25.00 0.21 0.21 1.00 1.00 n/s
White Paper 72.22 100.00 3.36 0.29 4.00 0.00 n/s
Paperboard 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Mixed Paper 72.22 100.00 3.21 0.28 4.00 0.00 n/s
Newspaper 55.56 0.00 2.50 0.45 0.00 0.00 n/s
Cardboard 27.78 25.00 1.29 0.49 1.00 1.00 NS

 

74

 

Table 4.2 (continued): The frequency of, and mean level of understanding for, 116
concept areas across male (M) and female (F) faculty members on the MSU campus.
Means were compared using chi-square tests at the p=0.05 signiﬁcance level.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Concept % Correct Males Females P
M F )7 SE 3' SE
Why 43.16 59.62 2.03 0.49 2.23 0.44 n/s
Environmental Beneﬁts 77.78 100.00 3.86 0.56 4.00 0.00 n/s
Water 11.11 25.00 0.43 0.10 0.70 0.75 n/s
Land 55.56 100.00 2.64 0.29 3.50 0.29 n/s
Air 16.67 25.00 0.71 0.48 0.75 0.75 n/s
Energy 38.89 25.00 1.64 0.38 1.00 1.00 n/s
Economic Beneﬁts 66.67 100.00 3.29 0.46 4.00 0.00 n/s
Monetary Incentive 66.67 100.00 3.21 0.34 4.00 0.00 n/s
Deposit 61.1 1 100.00 2.86 0.38 4.00 0.00 n/s
Garbage Fees 27.78 0.00 1.21 0.43 0.00 0.00 n/s
Jobs 27.78 25.00 1.36 0.46 0.75 0.75 n/s
Tipping Fees 11.11 25.00 0.43 0.51 0.75 0.75 n/s
Social Beneﬁts 50.00 7 5.00 2.43 0.29 2.75 0.95 n/s
Awareness 50.00 75.00 2.36 0.44 2.75 0.95 n/s
Why Not 50.93 70.83 2.51 0.46 2.7 5 0.65 n/s
Disincentives 77.78 100.00 4.00 0.81 4.00 0.00 n/s
Time 33.33 7 5.00 1.64 0.00 3.00 1.00 n/s
Ease 55.56 50.00 2.64 0.53 1.75 1.03 n/s
Convenience 66.67 100.00 3.36 0.48 3.7 5 0.25 n/s
Space 22.22 0.00 1.00 0.39 0.00 0.00 n/s
Know-how 50.00 100.00 2.43 0.44 4.00 0.00 n/s
Alternatives 48.89 87.5 2.39 0.51 3.50 0.40 n/s
Reduce 16.67 100.00 0.86 0.20 4.00 0.00 0.004
Reuse 61.1 1 100.00 2.79 0.46 4.00 0.00 n/s
Surplus Store 38.89 50.00 1.93 0.42 2.00 1.15 n/s
Garbagg 77.78 100.00 4.00 0.54 4.00 0.00 NS

 

 

 

 

 

75

Table 4.3: The frequency of, and mean level of understanding for, 116 concept areas
comparing faculty members and undergraduate students on the MSU campus. Means
were compared using chi-square tests at the p=0.05 signiﬁcance level.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

% Correct Students F acrrLy
concept Stu Fac 35 SE 35 SE P
Where 46.83 28.15 1.79 0.38 1.11 0.38 0.001
Residence Hall 90.00 11.11 3.53 0.16 0.50 0.23 0.0001
Location on Campus 87.50 5.56 3.38 0.17 0.17 0.17 NS
Location in Residence Hall 87.50 5.56 3.40 0.17 0.17 0.17 0.0001
Dining/Concessions 67.50 33.33 2.38 0.24 1.22 0.38 n/s
Sports Venues 22.50 11.11 0.93 0.23 0.44 0.27 n/s
Residential Dining 40.00 16.67 1.55 0.25 0.61 0.30 n/s
International Center Food Court 10.00 22.22 0.40 0.16 0.72 0.30 NS
Sparty’s Cafes 12.50 16.67 0.50 0.20 0.67 0.30 n/s
Camﬂrs and Academic Buildings 95.00 100.00 3.48 0.13 4.00 0.00 0.006
Location on Campus 92.50 94.44 3.33 0.16 3.83 0.17 0.003
Location in the Building 87.50 94.44 3.25 0.17 3.83 0.17 0.014
Campus-owned Apartments 5.00 0.00 0.25 0.11 0.00 0.00 n/s
Location in Complex 0.00 0.00 0.03 0.03 0.00 0.00 n/s
Location on Campus 0.00 0.00 0.05 0.03 0.00 0.00 n/s
Processing Facility 5.00 11.11 0.48 0.13 0.44 0.27 n/s
What 40.33 38.16 1.42 0.30 1.40 0.20 n/s
Fibers 5.00 0.00 0.18 0.12 0.00 0.00 n/s
Fabric 15.00 5.56 0.50 0.19 0.17 0.17 n/s
Paper 100.00 100.00 3.95 0.03 4.00 0.00 n/s
Magazines 27.50 77.78 0.98 0.26 2.89 0.39 0.001
White Paper 95.00 100.00 3.55 0.13 3.89 0.08 NS
Pre-Consumer 5.00 5.56 0.20 0.14 0.17 0.17 n/s
Post-Consumer 82.50 61 .11 2.88 0.22 2.33 0.46 0.039
Paperboard 12.50 0.00 0.45 0.18 0.00 0.00 n/s
Mixed Paper 50.00 100.00 1.93 0.30 3.83 0.09 0.003
Junk Mail 40.00 11.11 1.38 0.27 0.39 0.27 n/s
Newspaper 97.50 94.44 3.83 0.1 l 3.72 0.23 n/s
Pre-Consumer 2.50 0.00 0.08 0.08 0.00 0.00 n/s
Post-Consumer 77.50 33.33 2.85 0.25 1.33 0.46 0.001
Softbound Books 25.00 27 .78 0.78 0.22 0.89 0.35 n/s
Cardboard 85.00 55.56 3.25 0.20 2.11 0.46 0.019
Non-Fibers "Containers” 92.50 83.33 3.38 0.15 3.17 0.29 n/s
Metals (Tin, Aluminum) 97.50 94.44 3.70 0.11 3.50 0.23 n/s
Scrap Metal 47.50 50.00 1.65 0.28 1.56 0.38 n/s
Food Cans 55.00 44.44 1.95 0.29 1.39 0.38 n/s
Pop Cans 92.50 94.44 3.55 0.17 3.61 0.23 n/s
Foil 17.50 27.78 0.60 0.21 0.89 0.35 n/s
Glass 92.50 83.33 3.40 0.15 2.78 0.32 0.039
Clear 42.50 55.56 1.38 0.25 1.72 0.38 Ms
Brown 15.00 38.89 0.50 0.18 1.17 0.35 n/s
Plastics 100.00 88.89 3.93 0.04 3.33 0.30 0.018
#1 PETE 82.50 66.67 3.00 0.23 2.56 0.42 n/s
#2 HDPE (Cloudy) 55.00 50.00 1.88 0.28 1.61 0.37 n/s

 

 

 

 

76

 

Table 4.3 (continued): The frequency of, and mean level of understanding for, 116
concept areas comparing faculty members and undergraduate students on the MSU
campus. Means were compared using chi-square tests at the p=0.05 signiﬁcance level.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

% Correct Students FacultL
C°“°ept Stu Fac E SE 3 SE P
#2 HDPE (Colored) 15.00 11.11 0.50 0.19 0.44 0.23 n/s
Other 72.50 83.33 2.60 0.27 3.22 0.36 n/s
Fly Ash 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Construction Waste 12.50 0.00 0.43 0.18 0.00 0.00 n/s
Tires 15.00 0.00 0.55 0.21 0.00 0.00 n/s
Toner 2.50 16.67 0.08 0.08 0.67 0.36 0.027
Tennis Shoes 5.00 11.11 0.18 ' 0.12 0.39 0.27 n/s
Electronic Waste 22.50 50.00 0.80 0.24 1.72 0.43 n/s
Biological 40.00 27.78 1.43 0.28 1.06 0.42 n/s
Leaves 12.50 5.56 0.38 0.16 0.22 0.22 n/s
Grass 17.50 5.56 0.53 0.18 0.28 0.23 0.029
Branches 10.00 0.00 0.30 0.23 0.06 0.06 n/s
Food Waste 25.00 5.56 0.95 0.10 0.22 0.22 n/s
Manure 12.50 11.1 1 0.43 0.26 0.44 0.30 n/s
Hazardous Waste 22.50 5.56 0.75 0.23 0.22 0.22 NS
Pallets 2.50 0.00 0.10 0.10 0.00 0.00 n/s
Batteries 30.00 22.22 1.05 0.26 0.83 0.38 n/s
Ink-Jet Cartridges 25.00 38.89 0.85 0.24 1.44 0.44 n/s
Florescent Light Bulbs 2.50 l 1.11 0.08 0.08 0.39 0.23 n/s
How 53.37 55.56 1.89 0.22 2.02 0.29 NS
Quality Control 90.00 94.44 3.48 0.18 3.61 0.18 n/s
Proper Sorting 82.50 94.44 3.15 0.23 3.61 0.18 n/s
Signs 45.00 50.00 1.63 0.29 1.89 0.46 n/s
Removing Impurities 75.00 61 .11 2.53 0.23 2.22 0.38 n/s
Clean 65.00 38.89 2.18 0.26 1.56 0.39 n/s
Remove Caps 32.50 27.78 1.10 0.25 1.06 0.37 n/s
Processing Facility 62.50 11.11 2.15 0.27 0.39 0.27 0.002
Pick-up 70.00 50.00 2.45 0.23 1.78 0.42 NS
Self-Sorting 87.50 100.00 3.23 0.20 3.89 0.08 n/s
Non-Fibers “Containers” 77.50 88.89 2.75 0.24 3.17 0.29 n/s
Plastics 100.00 83.33 3.58 0.08 3.00 0.34 0.042
#1 PETE 40.00 55.56 1.35 0.26 1.94 0.43 NS
#2 HDPE (Cloudy) 35.00 33.33 1.15 0.25 1.06 0.37 n/s
#2 HDPE (ColoredL 15.00 11.11 0.48 0.18 0.33 0.23 n/s
Metals (Tin, Aluminum) 80.00 77 .78 3.00 0.25 2.83 0.38 n/s
Glass 80.00 66.67 2.88 0.24 2.39 0.42 n/s
Brown 17.50 27.78 0.60 0.20 0.83 0.33 n/s
Clear 20.00 38.89 0.70 0.22 1.22 0.38 n/s
Paper 100.00 100.00 3.60 0.08 3.72 0.11 n/s
Magazines 7.50 44.44 0.28 0.16 1.61 0.44 0.004
Softbound Books 5.00 11.11 0.15 0.10 039 0.27 n/s
White Paper 5000 94.44 1.68 0.27 3.50 0.23 0.000
Paperboard 2.50 0.00 0.08 0.08 0.00 0.00 n/s
Mixed Paper 45.00 94.44 1.50 0.27 3.39 0.23 0.001

 

77

 

Table 4.3 (continued): The frequency of, and mean level of understanding for, 116
concept areas comparing faculty members and undergraduate students on the MSU
campus. Means were compared using chi-square tests at the p=0.05 signiﬁcance level.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

% Correct Students Faculty
concept Stu Fac )7 SE )7 SE P
Newspaper 62.50 55.56 2.08 1.94 0.26 0.43 n/s
Cardboard 42.50 33.33 1.43 1.22 0.27 0.42 n/s
Why 60.58 56.41 2.14 2.08 0.33 0.29 n/s
Environmental Beneﬁts 100.00 100.00 3.83 3.89 0.06 0.08 n/s
Water 32.50 16.67 1.05 0.50 0.25 0.27 n/s
Land 50.00 77.78 1.73 2.83 0.28 0.38 n/s
Air 47.50 22.22 1.65 0.72 0.28 0.33 n/s
Energy 37.50 44.44 1.30 1.50 0.27 0.41 n/s
Economic Beneﬁts 100.00 88.89 3.58 3.44 0.08 0.27 0.033
Monetary Incentive 92.50 88.89 3.38 3.39 0.17 0.30 n/s
Deposit 90.00 83.33 3.33 3.11 0.19 0.35 n/s
Garbage Fees 15.00 27.78 0.55 0.94 0.19 0.37 n/s
Jobs 32.50 33.33 1.03 1.20 0.23 0.42 0.029
Tipping Fees 25.00 16.67 0.75 0.50 0.21 0.27 n/s
Social Beneﬁts 85.00 66.67 2.95 2.50 0.20 0.39 n/s
Awareness 80.00 66.67 2.78 2.44 0.23 0.41 n/s
Why Not 79.17 66.67 2.99 2.56 0.46 0.46 0.006
Disincentives 100.00 100.00 3 .98 4.00 0.03 0.00 n/s
Time 82.50 50.00 3.03 1.94 0.23 0.47 0.018
Ease 85.00 66.67 3.13 2.44 0.22 0.43 n/s
Convenience 92.50 88.89 3.50 3.44 0.17 0.30 NS
Space 22.50 22.22 0.78 0.78 0.23 0.36 n/s
Know-how 95.00 72.22 3.55 2.78 0.15 0.42 0.036
Alternatives 60.63 68.06 2.29 2.64 0.88 0.55 n/s
Reduce 47.50 38.89 1.73 1.56 0.26 0.47 0.010
Reuse 92.50 83.33 3.43 3.06 0.16 0.35 n/s
Surplus Store 2.50 50.00 0.08 1.94 0.08 0.47 0.0001
Garbage 100.00 100.00 0.50 4.00 0.03 0.00 n/s

 

78

 

Table 4.4: The frequency of understanding for 116 concept areas comparing
undergraduate students living in four MSU residential areas, namely Akers (Ak),
Butterﬁeld (Bu), Holmes (Ho), and Hubbard (Hu) Halls.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Concept % Correct

Ak Bu Ho Hu
Where 47.33 45.33 51.33 43.33
Residence Hall 100.00 80.00 90.00 90.00
Location on Campus 100.00 70.00 90.00 90.00
Location in Residence Hall 100.00 70.00 90.00 90.00
Dining/Concessions 70.00 70.00 70.00 60.00
Sports Venues 30.00 10.00 30.00 20.00
Residential Dining 40.00 50.00 60.00 10.00
International Center Food Court 10.00 10.00 10.00 10.00
Sparty's Cafes 0.00 20.00 10.00 20.00
Campus and Academic Buildings 90.00 100.00 100.00 90.00
Location on Campus 80.00 100.00 100.00 90.00
Location in the Building 80.00 100.00 100.00 70.00
Camﬂs-ovmed Apartments 0.00 0.00 10.00 10.00

Location in Complex 0.00 0.00 0.00 0.00

Location on Campus 0.00 0.00 0.00 0.00

Processing Facility 10.00 0.00 10.00 0.00
What 43.70 36.09 42.83 38.70

Fibers 10.00 10.00 0.00 0.00
Fabric 20.00 20.00 10.00 10.00
Paper 100.00 100.00 100.00 100.00
Magazines 10.00 10.00 30.00 60.00
White Paper 90.00 90.00 100.00 100.00
Pre-Consumer 10.00 0.00 0.00 10.00
Post-Consumer 80.00 80.00 90.00 80.00
Paperboard 0.00 20.00 20.00 10.00
Mixed Paper 60.00 30.00 60.00 50.00
Junk Mail 50.00 40.00 30.00 40.00
Newspaper 100.00 100.00 100.00 90.00

Pre-Consumer 10.00 0.00 0.00 0.00
Post-Consumer 70.00 80.00 80.00 80.00
Softbound Books 20.00 10.00 40.00 30.00
Cardboard 80.00 100.00 80.00 80.00
Non-Fibers “Containers” 100.00 80.00 90.00 100.00
Metals (Tin, Aluminum) 90.00 100.00 100.00 100.00
Scrap Metal 60.00 40.00 40.00 50.00
Food Cans 60.00 60.00 60.00 40.00
Pop Cans 90.00 90.00 100.00 90.00
Foil 10.00 10.00 0.00 50.00
Glass 100.00 90.00 100.00 80.00
Clear 60.00 30.00 50.00 30.00
Brown 20.00 10.00 10.00 20.00
Plastics 100.00 100.00 100.00 100.00
#1 PETE 100.00 80.00 90.00 60.00
#2 HDPE (Cloudy) 70.00 30.00 60.00 60.00
#2 HDPE (Colored) 20.00 0.00 20.00 20.00

 

 

 

 

 

79

 

Table 4.4 (continued): The frequency of understanding for 116 concept areas comparing
undergraduate students living in four MSU residential areas, namely Akers (Ak),
Butterﬁeld (Bu), Holmes (HO), and Hubbard (Hu) Halls.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Concept % Correct
Ak Bu HO Hu
Other 90.00 70.00 80.00 50.00
Fly Ash 0.00 0.00 0.00 0.00
Construction Waste 0.00 20.00 10.00 20.00
Tires 10.00 10.00 10.00 30.00
Toner 0.00 0.00 10.00 0.00
Tennis Shoes 20.00 0.00 0.00 0.00
Electronic Waste 30.00 20.00 30.00 10.00
Biological 60.00 20.00 50.00 30.00
Leaves 30.00 10.00 0.00 10.00
Grass 30.00 0.00 20.00 20.00
Branches 20.00 10.00 10.00 0.00
Food Waste 50.00 30.00 10.00 10.00
Manure 30.00 0.00 20.00 0.00
Hazardous Waste 10.00 10.00 50.00 20.00
Pallets 0.00 10.00 0.00 0.00
Batteries 20.00 30.00 40.00 30.00
Ink-Jet Cartridges 20.00 10.00 70.00 0.00
Florescent Light Bulbs 0.00 0.00 0.00 10.00
How 56.67 47.69 54.23 56.16
Quality Control 90.00 90.00 90.00 90.00
Proper Sorting 80.00 90.00 80.00 80.00
Signs 50.00 40.00 50.00 40.00
Removing Impurities 80.00 60.00 80.00 80.00
Clean 70.00 50.00 70.00 70.00
Remove Caps 50.00 30.00 30.00 20.00
Processing Facility 60.00 60.00 60.00 70.00
Pick-up 70.00 60.00 70.00 70.00
Self-Sorting 100.00 90.00 80.00 80.00
Non-Fibers “Containers” 100.00 60.00 70.00 80.00
Plastics 100.00 100.00 100.00 100.00
#1 PETE 60.00 10.00 40.00 50.00
#2 HDPE (Cloudy) 40.00 10.00 40.00 50.00
#2 HDPE (Colored) 10.00 10.00 20.00 20.00
Metals (Tin, Aluminum) 90.00 60.00 90.00 80.00
Glass 90.00 80.00 70.00 80.00
Brown 10.00 30.00 10.00 20.00
Clear 10.00 30.00 10.00 30.00
Paper 100.00 100.00 100.00 100.00
Magazines 0.00 0.00 10.00 20.00
Softbound Books 10.00 0.00 10.00 0.00
White Paper 40.00 50.00 50.00 60.00
Paperboard 0.00 0.00 10.00 0.00
Mixed Paper 40.00 30.00 50.00 60.00
Newspaper 60.00 60.00 70.00 60.00
Cardboard 30.00 40.00 50.00 50.00

 

 

 

 

 

80

 

Table 4.4 (continued): The frequency of understanding for 116 concept areas comparing
undergraduate students living in four MSU residential areas, namely Akers (Ak),
Butterﬁeld (Bu), Holmes (Ho), and Hubbard (Hu) Halls.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Concept % Correct

Ak Bu Ho Hu

Why 56.16 54.17 67.69 57.50
Environmental Beneﬁts 100.00 100.00 100.00 100.00
Water 20.00 30.00 50.00 30.00
Land 30.00 50.00 60.00 60.00

Air 30.00 50.00 70.00 40.00
Energy 40.00 20.00 50.00 40.00
Economic Beneﬁts 100.00 100.00 100.00 100.00
Monetary Incentive 90.00 90.00 100.00 90.00
Deposit 80.00 90.00 100.00 90.00
Garbage Fees 0.00 10.00 30.00 20.00

Jobs 40.00 40.00 30.00 20.00
Tipping Fees 40.00 30.00 10.00 20.00
Social Beneﬁts 80.00 30.00 90.00 90.00
Awareness 80.00 60.00 90.00 90.00
Why Not 81.67 78.33 85.00 71.67
Disincentives 100.00 100.00 100.00 90.00
Time 70.00 90.00 100.00 70.00
Ease 100.00 80.00 90.00 70.00
Convenience 100.00 100.00 90.00 80.00
Space 20.00 20.00 30.00 20.00
Know-how 100.00 80.00 100.00 100.00
Alternatives 55.00 60.00 65.00 62.50
Reduce 30.00 50.00 60.00 50.00
Reuse 90.00 80.00 100.00 100.00

Surplus Store 0.00 10.00 0.00 0.00
Garbage 100.00 100.00 100.00 100.00

 

 

 

 

81

 

Table 4.5: Mean levels of understanding for 116 concept areas comparing undergraduate
students living in four MSU residential areas (Akers, Butterﬁeld, Holmes, and Hubbard
Halls). Means were compared using chi-square tests at the p=0.05 signiﬁcance level.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Concept Akers Butterﬁeld Holmes Hubbard P
3' SE SE SE )7 SE 2 SE

Where 1.73 0.40 1.75 0.39 1.89 0.38 1.80 0.38 n/s
Residence Hall 3.90 0.10 3.00 0.45 3.50 0.40 3.70 0.21 n/s
Location on Campus 3.90 0.10 2.80 0.44 3.30 0.40 3.50 0.31 n/s
Location in Residence Hall 3.90 0.10 2.80 0.44 3.40 0.40 3.50 0.31 n/s
Dinirpg/Concessions 2.30 0.52 2.40 0.54 2.40 0.48 2.40 0.48 n/s
Sports Venues 0.90 0.46 0.60 0.40 1.30 0.54 0.90 0.46 n/s
Residential Dining 1.60 0.48 1.70 0.58 2.20 0.49 0.70 0.40 n/s
International Center Food Court 0.30 0.30 0.40 0.40 0.30 0.30 0.60 0.34 n/s
Sparty‘s Cafes 0.10 0.10 0.80 0.53 0.30 0.30 0.80 0.53 n/s
Campus and Academic Buildings 3.00 0.26 3.90 0.10 3.60 0.16 3.40 0.40 n/s
Location on Campus 2.70 0.40 3.80 0.13 3.40 0.16 3.40 0.40 n/s
Location in the Buildirg 2.70 0.40 3.80 0.13 3.50 0.17 3.00 0.47 NS
Campus-owned Apartments 0.00 0.00 0.10 0.10 0.40 0.31 0.50 0.31 n/s
Location in Complex 0.00 0.00 0.00 0.00 0.10 0.10 0.00 0.00 n/s
Location on Campus 0.00 0.00 0.00 0.00 0.10 0.10 0.10 0.10 n/s
Processing Facility 0.70 0.33 0.20 0.20 0.50 0.31 0.50 0.17 n/s
What 1.58 0.20 1.31 0.20 1.56 0.21 1.43 0.19 n/s
Fibers 0.30 0.30 0.40 0.40 0.00 0.00 0.00 0.00 n/s
Fabric 0.60 0.40 0.70 0.47 0.30 0.30 0.40 0.40 n/s
Paper 3.90 0.10 3.90 0.10 4.00 0.00 4.00 0.00 n/s
Magazines 0.30 0.30 0.40 0.40 1.00 0.52 2.20 0.61 n/s

White Paper 3.30 0.40 3.40 0.31 3.80 0.13 3.70 0.15 n/s
Pre—Consumer 0.40 0.40 0.00 0.00 0.00 0.00 0.40 0.40 n/s
Post-Consumer 2.70 0.47 2.50 0.43 3.40 0.40 2.90 0.50 n/s
Paperboard 0.00 0.00 0.80 0.42 0.70 0.47 0.30 0.30 n/s
Mixed Paper 2.20 0.61 1.00 0.52 2.40 0.60 2.10 0.64 n/s
Junk Mail 1.70 0.58 1.30 0.54 1.10 0.57 1.40 0.58 n/s
Newspaper 3 .90 0.10 4.00 0.00 4.00 0.00 3.40 0.40 n/s
Pre-Consumer 0.30 0.30 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Post-Consumer 2.40 0.54 3.00 0.52 3.10 0.53 2.90 0.50 n/s
Softbound Books 0.60 0.40 0.30 0.30 1.20 0.49 1.00 0.52 n/s
Cardboard 3.40 0.27 3.60 0.16 3.10 0.53 3.60 0.50 n/s
Non-Fibers “Containers” 3.80 0.13 2.90 0.35 3.20 0.39 3.60 0.16 n/s
Metals (Tin, Aluminum) 3.50 0.40 3.60 0.16 3.80 0.13 3.90 0.10 n/s
Scrap Metal 2.00 0.56 1.20 0.49 1.60 0.65 1.80 0.61 n/s
Food Cans 2.10 0.59 2.00 0.56 2.10 0.59 1.60 0.65 n/s
Pop Cans 3.60 0.40 3.40 0.40 3.90 0.10 3.30 0.40 n/s
Foil 0.30 0.30 0.30 0.30 0.00 0.00 1.80 0.61 n/s
Glass 3.60 0.16 3.40 0.31 3.50 0.17 3.10 0.46 n/s
Clear 1.90 0.53 0.90 0.46 1.60 0.54 1.10 0.50 n/s
Brown 0.60 0.40 0.50 0.34 0.30 0.30 0.60 0.40 n/s
Plastics 3.90 0.10 3.90 0.10 4.00 0.00 3.90 0.10 n/s
#1 PETE 3.70 0.15 2.80 0.49 3.20 0.39 2.30 0/63 n/s

#2 HDPE (Cloudy) 2.30 0.52 1.00 0.52 2.10 0.59 2.10 0.59 n/s

#2 HDPE (Colored) 0.60 0.40 0.00 0.00 0.70 0.47 0.70 0.47 n/s

 

 

 

 

 

82

Table 4.5 (continued): Mean levels of understanding for 116 concept areas comparing
undergraduate students living in four MSU residential areas (Akers, Butterﬁeld, Holmes,
and Hubbard Halls). Means were compared using chi-square tests at the p=0.05

signiﬁcance level.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Concept Akers Butterﬁeld Holmes Hubbard P
)7 SE I SE E SE J? SE

Other 3.40 0.40 2.50 0.56 2.70 0.47 1.80 0.61 n/s
Fly Ash 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Construction Waste 0.00 0.00 0.70 0.47 0.30 0.30 0.70 0.47 n/s
Tires 0.40 0.40 0.40 0.40 0.30 0.30 1.10 0.57 n/s
Toner 0.00 0.00 0.00 0.00 0.30 0.30 0.00 0.00 n/s
Tennis Shoes 0.70 0.47 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Electronic Waste 1.10 0.57 0.7 0.47 1.00 0.52 0.40 0.40 n/s
Biological 2.20 0.61 0.80 0.53 1.60 0.54 1.10 0.57 n/s
Leaves 0.90 0.46 0.30 0.30 0.00 0.00 0.30 0.30 n/s
Grass 0.90 0.46 0.00 0.00 0.60 0.40 0.60 0.40 n/s
Branches 0.60 0.40 0.30 0.30 0.30 0.30 0.00 0.00 n/s
Food Waste 1.80 0.61 1.20 0.61 0.40 0.40 0.40 0.40 n/s
Manure 1.10 0.57 0.00 0.00 0.60 0.40 0.00 0.00 n/s
Hazardous Waste 0.40 0.40 0.30 0.30 1.60 0.54 0.70 0.47 n/s
Pallets 0.00 0.00 0.40 0.40 0.00 0.00 0.00 0.00 n/s
Batteries 0.60 0.40 1.10 0.57 1.40 0.58 1.10 0.57 n/s
Ink-Jet Cartridges 0.70 0.47 0.30 0.30 2.40 0.54 0.00 0.00 n/s
Florescent Light Bulbs 0.00 0.00 0.00 0.00 0.00 0.00 0.30 0.30 n/s
How 1.94 0.25 1.64 0.22 1.97 0.23 2.01 0.21 0.05
Quality Control 3.60 0.22 3.40 0.40 3.60 0.40 3.30 0.40 n/s
Proper Sorting 3.20 0.42 3.40 0.40 3.20 0.53 2.80 0.49 n/s
Signs 1.60 0.54 1.50 0.62 1.90 0.64 1.50 0.62 n/s
Removing Impurities 2.70 0.47 2.10 0.53 2.80 0.42 2.50 0.43 n/s
Clean 2.40 0.54 1.60 0.54 2.40 0.48 2.30 0.52 n/s
Remove Caps 1.60 0.54 1.10 0.57 1.00 0.45 0.70 0.47 n/s
Processing Facility 2.10 0.59 1.90 0.53 2.00 0.56 2.60 0.58 n/s
Pick-up 2.40 0.43 2.30 0.45 2.50 0.45 2.60 0.58 n/s
Self-Sorting 3.40 0.6 3.20 0.29 3.20 0.53 3.10 0.53 n/s
Non-Fibers “Containers” 3.60 0.16 2.00 0.56 2.60 0.58 2.80 0.42 n/s
Plastics 3.70 0.15 3.30 0.15 3.70 0.15 3.60 0.16 n/s
#1 PETE 1.90 0.53 0.40 0.31 1.20 0.49 1.90 0.64 n/s
#2 HDPE (Cloudy) 1.20 0.49 0.40 0.31 1.20 0.49 1.80 0.61 n/s
#2 HDPELColored) 0.30 0.30 0.30 0.30 0.60 0.40 0.70 0.47 n/s
Metals (Tin, Aluminum) 3.40 0.40 2.10 0.59 3.60 0.40 2.90 0.50 n/s
Glass 3.30 0.40 2.70 0.47 2.60 0.58 2.90 0.50 n/s
Brown 0.50 0.40 1.00 0.52 0.30 0.30 0.60 0.40 n/s
Clear 0.50 0.40 1.00 0.52 0.30 0.30 1.00 0.52 n/s
Paper 3.60 0.16 3.40 0.16 3.90 0.10 3.50 0.17 n/s
Magazines 0.00 0.00 0.00 0.00 0.30 0.30 0.80 0.53 n/s
Softbound Books 0.30 0.30 0.00 0.00 03.0 0.30 0.00 0.00 n/s
White Paper 1.20 0.49 1.50 0.50 1.70 0.58 2.30 0.63 n/s
Paperboard 0.00 0.00 0.00 0.00 0.30 0.30 0.00 0.00 n/s
Mixed Paper 1.20 0.49 0.90 0.46 1.70 0.58 2.20 0.61 n/s
Newspaper 1.80 0.49 2.00 0.56 2.40 0.45 2.10 0.59 n/s
Cardboard 0.90 0.46 1.20 0.49 1.80 0.16 1.80 0.61 n/s

 

83

 

Table 4.5 (continued): Mean levels of understanding for 116 concept areas comparing
undergraduate students living in four MSU residential areas (Akers, Butterﬁeld, Holmes,
and Hubbard Halls). Means were compared using chi-square tests at the p=0.05

signiﬁcance level.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Concept Akers Butterﬁeld Holmes Hubbard P
3' SE 3 SE Tc SE 3 SE

Why 1.94 2.05 2.40 2.19 0.34 0.35 0.32 0.34 n/s
Environmental Beneﬁts 3.80 3.90 3.80 3.80 0.13 0.10 0.13 0.57 n/s
Water 0.60 0.90 1.60 1.10 0.40 0.46 0.54 0.59 n/s
Land 1.00 1.60 2.20 2.10 0.52 0.54 0.61 0.59 n/s
Air 1.10 1.60 1.30 1.60 0.57 0.54 0.52 0.65 n/s
Energg 1.40 0.60 1.80 1.40 0.58 0.40 0.61 0.58 n/s
Economic Beneﬁts 3.50 3.60 3.60 3.60 0.17 0.16 0.16 0.16 n/s
Monetary Incentive 3.20 3.30 3.70 3.30 0.39 0.40 0.15 0.40 n/s
Deposit 2.90 3.40 3.70 3.30 0.50 0.40 0.15 0.40 n/s
Garbage Fees 0.00 0.40 1.10 0.70 0.00 0.31 0.57 0.40 n/s

Jobs 1.20 1.20 1.10 0.60 0.49 0.49 0.50 0.40 n/s
Tipping Fees 1.20 0.0 0.30 0.60 0.40 0.46 0.30 0.40 n/s
Social Beneﬁts 2.70 2.90 3.00 3.20 0.40 0.50 0.37 0.39 n/s
Awareness 2.60 2.30 3.00 3.20 0.45 0.63 0.37 0.39 n/s
Why Not 3.22 2.83 3.09 2.80 0.52 0.50 0.43 0.48 NS
Disincentives 4.00 4.00 3.90 4.00 0.00 0.00 0.10 0.00 n/s
Time 2.80 3.00 3.60 2.70 0.61 0.37 0.16 0.60 n/s
Ease 3.90 2.60 3.40 2.60 0.10 0.45 0.40 0.58 n/s
Convenience 4.00 3.80 3.20 3.00 0.00 0.13 0.39 0.53 n/s
Space 0.80 0.60 1.00 0.70 0.53 0.40 0.52 0.47 n/s
Know-how 3.80 3.00 3.60 3.80 0.13 0.52 0.16 0.13 n/s
Alternatives 2.15 2.23 2.35 2.45 0.94 0.80 0.90 0.92 0.04
Reduce 1.20 1.70 1.90 2.10 0.44 0.45 0.53 0.64 n/s
Reuse 3.40 2.90 3.50 3.90 0.40 0.43 0.17 0.10 n/s
Surplus Store 0.00 0.30 0.00 0.00 0.00 0.30 0.00 0.00 n/s
Garbage_ 4.00 4.00 4.00 3.80 0.00 0.00 0.00 0.13 n/s

 

84

 

Table 4.6: Mean levels of understanding for 116 concept areas comparing faculty
working in academic buildings judged to be “recycling friendly” (F) and “recycling
unfriendly” (U). Means were compared using chi-square tests at the p=0.05 signiﬁcance
leveL

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

% Correct Friendly Unfriendly
concept F UF 3 srs E SE P
Where 20.74 35.56 0.90 0.39 1.32 0.38 NS
Residence Hall 0.00 22.22 0.22 0.15 0.78 0.43 n/s
Location on Campus 0.00 11.11 0.00 0.00 0.33 0.33 n/s
Location in Residence Hall 0.00 11.11 0.00 0.00 0.33 0.33 n/s
Dining/Concessions 11.10 55.56 0.56 0.34 1.89 0.61 n/s
Sports Venues 0.00 22.22 0.11 0.11 0.78 0.52 n/s
Residential Dining 0.00 33.33 0.11 0.11 1.11 0.56 n/s
International Center Food Court 11.11 33.33 0.44 0.34 1.00 0.50 n/s
Sparty's Cafes 11.11 22.22 0.56 0.44 0.78 0.43 n/s
Campus and Academic Buildings 100.00 100.00 4.00 0.00 4.00 0.00 n/s
Location on Campus 88.89 100.00 3.67 0.33 4.00 0.00 n/s
Location in the Building 88.89 100.00 3.67 0.33 4.00 0.00 n/s
C ampus-owned Apartments 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Location in Complex 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Location on Campus 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Processing Facility 0.00 22.22 0.11 0.11 0.78 0.52 n/s
What 36.23 40.10 1.32 0.21 1.49 0.19 n/s
Fibers 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Fabric 0.00 11.11 0.00 0.00 0.33 0.33 n/s
Paper 100.00 100.00 4.00 0.00 4.00 0.00 NS
Magazines 66.67 88.89 2.67 0.67 3.11 0.42 n/s
White Paper 100.00 100.00 4.00 0.00 3.78 0.15 n/s
Pre-Consumer 0.00 1 1.11 0.00 0.00 0.33 0.33 NS
Post-Consumer 44.44 77.78 1.78 0.70 2.89 0.56 n/s
Paperboard 0.00 0.00 0.00 0.00 0.00 0.00 NS
Mixed Paper 100.00 100.00 4.00 0.00 3.67 0.17 n/s
Junk Mail 22.22 0.00 0.78 0.52 0.00 0.00 n/s
Newspaper 100.00 88.89 3.89 0.11 3.56 0.44 n/s
Pre-Consumer 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Post-Consumer 11.1 1 55.56 0.44 0.44 2.22 0.70 0.046
Softbound Books 33.33 22.22 1.00 0.50 0.78 0.52 n/s
Cardboard 55.56 55.56 2.11 0.68 2.11 0.68 n/s
Non-Fibers "Containers” 88.89 77.78 3.11 0.42 3.22 0.43 n/s
Metals (Tin, Aluminum) 88.89 100.00 3.22 0.43 3.78 0.15 ms
Scrap Metal 55.56 44.44 1.78 0.57 1.33 0.53 n/s
Food Cans 33.33 55.56 1.00 0.50 1.78 0.57 n/s
Pop Cans 100.00 88.89 3.78 0.15 3.44 0.44 n/s
Foil 11.11 44.44 0.33 0.33 1.44 0.58 n/s
Glass 88.89 77.78 2.89 0.39 2.67 0.53 n/s
Clear 66.67 44.44 2.11 0.54 1.33 0.53 n/s
Brown 44.44 33.33 1.33 0.53 1.00 0.50 n/s
Plastics 88.89 88.89 3.22 0.43 3.44 0.44 NS
#1 PETE 66.67 66.67 2.56 0.65 2.56 0.58 n/s
#2 HDPE (Cloudy) 44.44 55.56 1.33 0.53 1.89 0.54 n/s
#2 HDPE (Colored) 0.00 22.22 0.11 0.11 0.78 0.43 n/s

 

 

 

 

 

85

 

Table 4.6 (continued): Mean levels of understanding for 116 concept areas comparing
faculty working in academic buildings judged to be “recycling friendly” (F) and
“recycling unfriendly” (U). Means were compared using chi-square tests at the p=0.05
signiﬁcance level.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

% Correct Friendly Unfriendly
concept F UF 7; SE 3' SE P
Other 88.89 77.78 3.44 0.44 3.00 0.58 n/s
Fly Ash 0.00 0.00 0.00 0.00 0.00 0.00 NS
Construction Waste 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Tires 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Toner 11.11 22.22 0.44 0.44 0.89 0.59 n/s
Tennis Shoes 0.00 22.22 0.00 0.00 0.78 0.52 n/s
Electronic Waste 44.4 55.56 1.56 0.63 1.89 0.61 n/s
Biological 0.00 55.56 0.00 0.00 2.11 0.68 n/s
Leaves 0.00 1 1.11 0.00 0.00 0.44 0.44 n/s
Grass 0.00 11.1 1 0.00 0.00 0.56 0.44 n/s
Branches 0.00 0.00 0.00 0.00 0.11 0.11 NS
Food Waste 0.00 11.11 0.00 0.00 0.44 0.44 n/s
Manure 0.00 22.22 0.00 0.00 0.89 0.59 n/s
Hazardous Waste 11.11 0.00 0.44 0.44 0.00 0.00 n/s
Pallets 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Batteries 33.33 1 1.1 1 1.22 0.62 0.44 0.44 n/s
Ink-Jet Cartridges 44.44 33.33 1.56 0.63 1.33 0.67 n/s
F lorescent Light Bulbs 22.22 0.00 0.78 0.52 0.00 0.00 n/s
How 52.14 58.97 1.95 0.27 2.09 0.21 n/s
Quality Control 100.00 88.89 3.89 0.11 3.33 0.33 n/s
Proper Sorting 100.00 88.89 3.89 0.11 3.33 0.33 n/s
Signs 55.56 44.44 2.22 0.70 1.56 0.63 NS
Removinglmpurities 33.33 88.89 1.33 0.55 3.11 0.31 n/s
Clean 33.33 44.44 1.33 0.55 1.78 0.57 n/s
Remove Caps 22.22 33.33 0.78 0.52 1.33 0.53 n/s
Processing Facility 0.00 22.22 0.00 0.00 0.78 0.52 n/s
Pick-up 55.56 44.44 2.11 0.61 1.44 0.58 NS
Self-Sorting 100.00 100.00 4.00 0.00 3.78 0.15 n/s
Non-Fibers “Containers” 88.89 88.89 3.11 0.42 3.22 0.43 n/s
Plastics 88.89 77.78 3.11 0.42 2.89 0.56 0.046
#1 PETE 44.44 66.67 1.67 0.67 2.22 0.57 n/s
#2 HDPE (Cloudy) 22.22 44.44 0.78 0.52 1.33 0.53 n/s
#2 HDPE (Colored) 0.00 22.22 0.00 0.00 0.67 0.44 n/s
Metals (Tin, Aluminum) 77.78 77.78 2.78 0.55 2.89 0.56 NS
Glass 66.67 66.67 2.44 0.63 2.33 0.60 n/s
Brown 22.22 33.33 0.67 0.44 1.00 0.50 n/s
Clear 33.33 44.44 1.11 0.56 1.33 0.53 n/s
Paper 100.00 100.00 3.78 0.15 3.67 0.17 NS
Magazines 44.44 44.44 1.67 0.67 1.56 0.63 n/s
Softbound Books 0.00 22.22 0.00 0.00 0.78 0.52 n/s
White Paper 100.00 88.89 3.78 0.15 3.22 0.43 n/s
Paperboard 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Mixed Paper 88.89 100.00 3.33 0.44 3.44 0.18 n/s
Newspaper 55.56 55.56 2.11 0.68 1.78 0.57 n/s
Cardboard 22.22 44.44 0.89 0.59 1.56 0.63 n/s

 

86

 

Table 4.6 (continued): Mean levels of understanding for 116 concept areas comparing
faculty working in academic buildings judged to be “recycling friendly” (F) and
“recycling unfriendly” (U). Means were compared using chi-square tests at the p=0.05

signiﬁcance level.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

% Correct Friendly Unfriendly

Concept F UF )7 SE 3 SE P

Why 51.28 61.54 1.89 0.33 2.26 0.37 n/s
Environmental Beneﬁts 100.00 100.00 3.78 0.15 4.00 0.00 n/s
Water 0.00 33.33 0.00 0.00 1.00 0.50 n/s
Land 66.67 88.89 2.56 0.65 3.11 0.42 n/s

Air 11.11 33.33 0.44 0.44 1.00 0.50 n/s
Energy 44.44 44.44 1.56 0.63 1.44 0.58 NS
Economic Beneﬁts 77.78 100.00 3.00 0.50 3.89 0.11 n/s
Monetary Incentive 77 .78 100.00 2.89 0.56 3.89 0.11 n/s
Deposit 77.78 88.89 2.78 0.55 3.44 0.44 n/s
Garbage Fees 44.44 11.11 1.56 0.63 0.33 0.33 n/s
Jobs 22.22 44.44 0.89 0.59 1.56 0.63 n/s
Tippingﬁs 11.11 22.22 0.33 0.33 0.67 0.44 n/s
Social Beneﬁts 66.67 66.67 2.44 0.56 2.56 0.58 n/s
Awareness 66.67 66.67 2.33 0.60 2.56 0.58 n/s
Why Not 61.11 72.22 2.39 0.52 2.74 0.43 n/s
Disincentives 100.00 100.00 4.00 0.00 4.00 0.00 NS
Time 44.44 55.56 1.67 0.67 2.22 0.70 n/s
Ease 66.67 66.67 2.56 0.65 2.33 0.60 n/s
Convenience 88.89 88.89 3.44 0.44 3.44 0.44 n/s
Space 11.1 1 33.33 0.44 0.44 1.11 0.56 n/s
Know-how 55.56 88.89 2.22 0.70 3.33 0.44 n/s
Alternatives 61.1 1 7 5.00 2.39 0.64 2.89 0.44 n/s
Reduce 22.22 55.56 0.89 0.59 2.22 0.70 n/s
Reuse 66.67 100.00 2.44 0.63 3.67 0.17 n/s
Surplus Store 55.56 44.44 2.22 0.70 4.00 0.67 n/s
Garbage 100.00 100.00 4.00 0.00 0.33 0.00 n/s

 

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5. Mental Models Research to Inform the Design of a Campus Recycling Program:

Report to the Ofﬁce of the Vice President of Finance and Operations

5.1 Executive Summary
The Michigan State University (MSU) initiative for comprehensive and campus-
wide recycling program has the goal of increasing campus-wide participation in the
university’s recycling. The two-part strategy to achieve this goal will require (1) more
knowledgeable students, faculty, and staff, as well as (2) a thoughtfully designed, well
structured, and institutionalized program. The research reported here speaks mainly to
Item 1 and the charge of educating the MSU community; speciﬁcally, the mental models
approach was used to inform the design of outreach efforts to address speciﬁc knowledge
gaps necessary for people to engage in the recycling program.
Based on the responses of student and faculty subjects, the conclusions reached. from
this research include the following:
- Students and faculty on campus have differing information needs about recycling;
communication efforts should be designed to address their speciﬁc knowledge gaps.
- Students have a better understanding than faculty regarding various disincentives to
recycling, such as the time involved, the difﬁculty of the activity, the inconvenience
of recycling compared to throwing things away.
- Both students and faculty were generally knowledgeable about different recyclable
materials (i.e. paper, plastics, glass, metals) but lacked speciﬁc knowledge about the
different types (e.g. characteristics of the types of plastic) and how to separate the

different types Of materials (e. g. materials considered mixed paper).

93

Opportunities for recycling hazardous items such as batteries, electronic waste, ink jet
cartridges, and ﬂorescent light bulbs, was not well understood.

Few subjects knew about the details of preparing items for recycling (e. g., removing
caps and rinsing items before recycling them); recycling education efforts are needed
to address this gap in knowledge

Waste reduction practices (e.g., avoidance of disposable tableware, duplex printing)
were not well known, however subjects generally understand the concept of reuse.
Students and faculty had limited knowledge of other recycling Opportunities outside
the residential hall and classroom; recycling education efforts should inform the MSU
community about recycling Options at outside this area (i.e. sports venues, The Union,

International Center).

These ﬁndings led to the following recommendations:

Outreach efforts should build upon existing, general knowledge to enhance the
understanding of speciﬁc materials recycled and how to separate them. Lack of
knowledge can result in increased contamination through the trial-and-error approach.
Outreach efforts should educate where recycling sites are located across campus, and
clearly marked with visible signage.

Both populations should learn about the speciﬁc environmental, social and economic
beneﬁts of recycling to create a lasting value change aligned with MSU’s mission of
environmental stewardship.

In accordance with MSU’s mission to empower people and communities, I

recommend that all community members be helped to understand (via regular

94

reporting of metrics, for example) how their resource use affects the both local and

global environmental footprint of MSU.

5.2 Introduction

Michigan State University (MSU) is comprised of students, faculty, and staff
whose activities yield a large volume of waste. In 2006-2007, MSU sent 36,092 cubic
yards of garbage to the landﬁll; this is enough to line a two-lane road, about 6.5 feet deep
that is nearly 3.3 miles long (roughly the distance from the MSU Main Library to the
Meridian Mall in Okemos, MI).

Often, institutions reduce their waste through initiatives like recycling. Of the Big
Ten schools, MSU is the only institution without a campus-wide and multi-material
recycling program. While MSU regularly recycles paper products on campus-wide scale,
the recycling of other materials is somewhat ad-hoc and scattered.

The Boldness by Design Environmean Stewardship initiative set forth by the
Ofﬁce of the Vice President of Finance and Operations of MSU recognized the creation
of a more comprehensive recycling program as an opportunity to improve environmental
stewardship. The recycling program’s expansion into more materials and more parts of
campus motivated of this study: to inform the design of outreach plans aimed at
increasing the MSU community’s participation in recycling. Because the recycling
program’s success is highly dependent on the involvement if the campus community,
programs designed to increase personal engagement in recycling activities warrant study
to devise how to best to inform potential subjects of the beneﬁts Of recycling, and engage

them in recycling the correct materials at the correct locations.

95

To help MSU achieve the goal of increasing recycling participation, I conducted a
review of the current literature to understand the state of practices surrounding the
development of recycling behavior. This work formed the bulk of my thesis research
towards a Master’s degree, with guidance from Dr. Joe Arvai. Based on my initial
research, literature tends to point to two practices that allow for, and increase, recycling
participation: (1) making the act Of recycling logistically easier for people to carry out,
and (2) educating people to know what, where, and how to recycle.

MSU is working to make recycling easier for people to do, as shown by the
approval for a new recycling processing facility in January 2008 by President Lou Anna
Simon and the MSU Board of Trustees. This demonstrated commitment to expanding
recycling Opportunities, coupled with making recycling on-campus easier and more
convenient addresses the logistical needs of the program.

Knowledge Of the recycling program’s characteristics is considered to be
necessary for participation. The more knowledgeable an individual is about what items
are recyclable, how to prepare items for recycling, and where to go to recycle, the more
likely the individual is to correctly take part in the activity. Outreach efforts can
appropriately help overcome lack of knowledge and successfully attract peOple to
recycling programs by fostering better recycling habits. This is particularly important for
those who are new to the campus and to the activity so that they may gain the knowledge
necessary to begin and continue recycling.

Outreach plans about recycling often make assumptions, rather than relying on
research-based ﬁndings, of the audience’s latent knowledge about topics of interest.

Given time and resource constraints, outreach efforts should focus on information peOple

96

actually need to complete the desired activity (because irrelevant information can waste
valuable time and resources). An approach for empirically identifying knowledge gaps is
mental models research. The mental models approach used in this study can be used to
inform the design of outreach plans to better meet the needs of MSU community
members, by way of effectively addressing deﬁciencies of knowledge without needlessly

repeating clearly understood information.

5.3 Methodology

This study seeks to inform the design of outreach plans, using the mental models
approach to quantify the level of knowledge about recycling concepts. The mental model
methodology outlined by Morgan, F ischhoff et al. (2002), helps communicators to better
understand their audience by way Of (1) a systematic analysis of the information they
currently possess and (2) an assessment of their information needs. Communicators can
then design and implement more effective outreach plans to address knowledge gaps
revealed through mental models research, while devoting relatively fewer resources to
enforce what the audience already understands. With a more targeted and systematic
outreach program, the audience will (theoretically) have improved knowledge upon
which they can base decisions to recycle more items, and—importantly—to recycle these
items correctly.

The process of mental model interviews begins with an expert mental model,
which depicts the current state of knowledge surrounding a particular issue—recycling in
this case. The expert model used in this study (Figure 5.1) was ﬁrst developed based on

an extensive review of the associated literature followed by a series of open-ended

97

interviews with recognized experts familiar with campus recycling efforts at MSU and

elsewhere. This model was structured around six general concept areas:

1.

What may be recycled on campus: Includes a comprehensive listing of the items
or materials available for recycling at MSU, but not necessarily collected from
every area on campus.

Where do recycling opportunities exist on campus: Includes an up to date account
of places that have recycling infrastructure (e.g., pick-up or drop-off points,
processing facilities, etc.) on the main campus of MSU.

How must recycling be carried out on campus: Includes a depiction of the
processes an individual carries out to recycle, including: separation, preparation,
and general reasons to use a prOper method.

Why is recycling on the MS U campus important: Includes a characterization of the
incentives or beneﬁts of recycling in terms of enviromnental, economical, and
social elements.

Why don ’t people recycle more frequently on campus: Includes a list of factors
that impede peOple from recycling and are considered the disincentives to
participating.

What are alternatives on the MS U campus: Based on the recognition that
recycling is not the only way to dispose or eliminate a product that is no longer
needed for its original purpose and includes garbage, reuse, and reduce.

An interview protocol for use with faculty and student subjects (see below) was

next derived from the expert model. The protocol is purposefully open-ended to allow

subjects to express both correct and incorrect responses. Each interview starts with a

98

broad question and is followed by subsequent questions that seek to exhaust a subject’s
knowledge. Subjects’ responses are then analyzed to develop a mental model for each

participant, indicating where their knowledge overlaps with expert knowledge.

5.3.1 Subjects

Because of their size and inﬂuence on the amount of waste generated (and
potentially recycled), students and faculty10 were identiﬁed by the university
administration as the ﬁrst target populations for the new campus recycling initiative.
Undergraduates living on campus were one focus of this study, due to their experience
with a wide-range of campus recycling options. In collaboration with residential hall
administration, four residential halls that were considered representative of residential
living Options in terms of the diversity of the student residents were then selected.
Student subjects were recruited via mail, 250 letters were sent to each residential hall.
The letter sent to potential subjects brieﬂy explaining the project and requesting they call
in to schedule an interview. Additionally, there was a monetary incentive of $40 USD
offered for the one hour interview. While the initial mailed invitations went to randomly
selected students, students were selected for the study using a purposeful sampling
strategy (Patton 1990). Speciﬁcally, students were recruited so that the ﬁnal sample
contained an equal number of males and females that represented a wide diversity of
degree programs. Additionally, students were screened for any prior experience in the

recycling industry or related organizational memberships either on- or Off-campus. In all,

 

'0 University staff were also recognized as important participants in any campus-wide recycling initiative.
This group was involved in a parallel, focus group based study of programmatic needs (Hansen, McMellen
et al. 2008).

99

the sample consisted of 20 males and 20 females who were selected from four different
student residential halls (n=40).

The faculty sample (n=18) was recruited from a randomized phone list, and
consisted of 14 male and 4 female subjects1 1. With the assistance of the Department of
Recycling and Solid Waste Management, which is responsible for the campus-wide
recycling program, we identiﬁed buildings on campus that were thought to be “recycling-
friendly” (e.g., buildings where there was adequate space and infrastructure available to
carry out MSU’s prOposed recycling activities) as well as buildings where recycling is
typically more difﬁcult, called “recycling unfriendly” because these resources are not
present. All Of the faculty members with ofﬁces in each building type were in the initial
list, and were recruited in order Of a randomized priority to participate. Nine faculty
members were recruited and interviewed from each type of building. The faculty
members were not purposefully recruited on the basis of department or college, as some
Of these units’ faculty worked in buildings that could not be identiﬁed as recycling
friendly or unfriendly, and in turn were not included in the study. Faculty and student

interviews lasted between 30 and 80 minutes, an average of 45 minutes.

5.3.2 Analysis

Student and faculty responses were analyzed to develop an individual mental
model for each subject, indicating where their knowledge overlapped with the expert
mental model. Both valid concepts and misconceptions suggested by the subjects, but

not reﬂected in the expert model, were included in the individual diagrams.

 

” For the faculty sample, the goal was to have the number of males and females be closely representative
of the actual gender distribution on campus. In 2007, females accounted for 37% of all faculty members; in
this study, 22% of the faculty subjects were female.

100

A comprehensive model, depicting the relative frequency with which speciﬁc
concepts were understood by subjects, was developed for both MSU students (Figure 5.2)
and faculty (Figure 5.3) by combining all the individual mental models elicited from each
subject group (e. g. see Figure 5.4 and Figure 5.5). Additionally, the comprehensive
models use a color-coding scheme to differentiate the levels of knowledge into three
categories; the color red signiﬁes 0-33% interviewed understood the subject, yellow
signiﬁes 34-66% understood the subject, and green signiﬁes 67-100% understood the
subject. This coloring is meant for quick observation of the models. These models
revealed important gaps—on part Of both MSU students and faculty—in understanding
key recycling concepts that are relevant to established campus-based waste reduction
practices.

In addition to these comprehensive mental models, each subject’s response was
coded according to a ﬁve-point scheme, with higher scores reﬂecting more accurate
comprehension of the concept, the codes used were:

4 = concept discussed without prompting and understood by subject;

3 = concept discussed when prompted and understood by subject;

2 = concept discussed without prompting but misunderstood by subject;

1 = concept discussed when prompted but misunderstood by subject;

0 = subject was unable to answer interview question.

Mean scores were then computed for each concept on the comprehensive mental model
and were compared using chi-square analysis according to a series of independent
variables (i.e. by gender, residential hall or building, between students and faculty),

allowing for the detection of differences in knowledge.

10]

5.4 Findings

A comprehensive model, depicting the relative frequency with which speciﬁc
concepts were understood by subjects, was developed for both MSU students (Figure 5.2)
and faculty (Figure 3) by combining all the individual mental models elicited from each
subject group (e.g. see Figure 5.4 and Figure 5.5). These models revealed important
gaps—on part of both MSU students and faculty—in understanding key recycling

concepts that are relevant to established campus-based waste reduction practices.

5.4.1 Where to Recycle at MSU?

Both MSU students and faculty displayed an incomplete understanding of where
they could recycle on campus. While students knew that recycling Opportunities were
present in academic buildings and campus dining areas, few knew of speciﬁc details
regarding where else recycling took place on the MSU campus (Figure 5.2). Only a
small percentage of students knew of speciﬁc collection points for recyclables in on-
carnpus sports venues (22.5%), MSU’s International Center Food Court (10%),
residential dining areas (40%), and Sparty’s Cafe locations (12.5%).

The same was true of MSU faculty members surveyed (Figure 5.3); relatively few
faculty members (33.3%) were aware of collection points for recyclables in dining and
concession areas. Neither students (5%) nor faculty (11.1%) were well aware of

recycling opportunities at the general campus recycling facility.

5.4.2 What is Recycled at MSU?

102

There were also distinct gaps in knowledge of students and faculty about a range
of what speciﬁc items can be recycled on campus. While all students and faculty
generally knew that paper could be recycled on campus, relatively few subjects were
aware of many common paper products that were recyclable; these included low levels of
understanding regarding the fact that soft-bound books and telephone directories that
were recyclable on—campus by students (25% and 2.5%, respectively) and faculty (27.8%
and 38.9%, respectively). Faculty members lack knowledge of the recycling of mail
compared to students (1 l. 1% faculty, 40% students), and both faculty and students need
more information about paperboard (12.5% faculty, 0% students) 12. On the positive
side, both students (97 .5%) and faculty (94.4%) were aware of newspaper recycling
opportunities on campus.

Beyond the general knowledge of categories of containers (plastics, glass, and
metals), both students and faculty were not well aware of speciﬁc items as it relates to
container type. For example, only 55.6% of students and 42.5% of faculty mentioned
recyclability of clear glass items. Similarly, metal food cans was mentioned as a
recyclable item by 55% of students and 44.4% of faculty. A few speciﬁc kinds of
containers were known to be recyclable, including #1 PETE plastic found in most soda
and water bottles (82.5% of students and 66.7% of faculty mentioned this material).

In addition to the categories “ﬁbers” and “containers”, was the category of items
called “other” with items that did not ﬁt into either category. Of major signiﬁcance to
waste reduction in an academic setting, was the Observation that students and faculty

interviewed were not well aware of many common forms of electronic waste (e.g.

 

'2 Both groups had a low understanding of paperboard, but students are considered a larger producer of the
material due to their personal food purchases. Should students be allowed to recycle this material more
readily in the future, they need information regarding the material characteristics and collection points.

103

computers, personal digital assistants, mp3 players, mobile phones, etc.), and how they
needed to be recycled properly. Recyclability of items such as batteries, ink jet
cartridges, ﬂuorescent light bulbs, and toner cartridges were also not well known.
Speciﬁcally, relatively few faculty members (22.2%) or students (30%) knew about
battery recycling. All of these items mentioned contain materials (e. g. mercury, lead, and
cadmium) that bear potentially harmful consequences to the health of people and the
environment if sent to the landﬁll.

The category of “other” items also contained items involving the recyclability
wood, a potential future focus material. Subjects brought up two types of wood, wood
pallets from shipping and loft wood from student housing. Recycling loft wood on-
campus was not well understood by students, 7.5% of the subjects knew about its
recyclability. Similarly, only one student and no faculty members mentioned wood pallet
recycling. However, it is not likely that students or faculty would dispose of wood pallets
themselves.

During discussions of “other” materials recycled on campus, Styrofoam was
mentioned as a potential addition to MSU’s recycling program. Of the faculty
interviewed, seven subjects—all of whom worked in laboratory environments on
campus—mentioned styrofoam recycling. Some lab materials manufacturers, as one
faculty subject explained, send prepaid mailing labels to the lab to return the styrofoam
packaging. According to communications with the Ofﬁce of the Vice President Of
Finance and Operations as well as the Materials Committee, the future recycling program
may collect this material, however the costs and beneﬁts of styrofoam recycling will need

to be assessed.

104

5.4.3 How to Recycle at MSU?

Subjects also struggled with questions about how recycling must be carried out on
the MSU campus. Students and faculty were aware of the importance of strict separation
rules for a successful recycling program (82.5% of students and 94.4% of faculty
mentioned proper sorting); they were, however, less knowledgeable regarding the
speciﬁcs of this process. The knowledge of separation followed the pattern of knowledge
Of general items (i.e. paper, glass, metals, and plastic) with much less knowledge of the
speciﬁc items (e. g. separation of different types of plastic). Further, not all subjects knew
to rinse recyclable containers (cleaning recyclables was identiﬁed by 65%13 of students
interviewed and 38.9% of faculty interviewed), as well as removing caps or lids (with this

concept understood by 32.5% of students and 27.8% of faculty).

5.4.4 Why Recycle at MSU?

Faculty and students were knowledgeable about the economic, social, and
environmental beneﬁts associated with recycling. But relatively few students and faculty
could name any speciﬁc beneﬁts. For example, the speciﬁc concepts of energy
conservation, air quality, water protection, and land-use issues were not mentioned by a
majority of students (37.5%, 47.5%, 32.5%, respectively) or faculty (44.4%, 22.8%,
16.7%, respectively). An exception to this trend was that of land-use beneﬁts, which

were understood by a larger percentage of students (50%) and faculty (77 .8%).

 

'3 Although 65% of students identifying the need to clean recyclables may seem high, this number was low
compared to the needs of recycling administrators at MSU.

105

 

5.4.5 Why Don’t People Recycle at MSU?

Disincentives to recycling were relatively well understood by both students and
faculty; the only signiﬁcant exception here was a limited understanding displayed by
both students (22.5%) and faculty (22.2%) regarding space constraints for recycling
services in many campus buildings. Lack of understanding for how to recycle, described
as the concept of know-how, was reported as a reason why peOple do not recycle by

72.2% faculty and 95.0% students interviewed.

5.4.6 Alternatives to Recycling at MSU?

Besides recycling, faculty and students were aware that they have alternatives to
handling waste, the most well known of these being (1) the placement of waste in the
garbage (known by 100% of students and faculty) and (2) reusing items for the same or
related purpose (known by 92.5% of students and 83.3% of faculty). The exception here
was waste reduction practices (i.e. conscious decisions to use less or products that yield
less waste), which were not well known with 47.5% of students and 38.9% of faculty

included this item in their mental models.

5.4.7 Gender Differences

In addition to looking at general trends, the means of the scored responses were
compared (using chi-square tests) levels of student and faculty understanding according
to a series of independent variables. One of these independent variables was gender.
Among students overall, only four signiﬁcant differences were observed (Table 1).

These included a greater understanding on the part of male students regarding recycling

106

of biological wastes (p=0.02), pick-up options for recyclables (p=0.05), the availability of
a central processing facility (p=0.007), and the recycling Of leftover food as food waste
(p=0.041).

Gender differences among faculty (Table 2) included, for example, a greater
understanding by men of white paper (p= 0.004), and mixed paper (0.043) as recyclable
items. Male faculty members, by contrast, had a better understanding of how to separate

containers from papers for recycling (p=0.008).

5.4.8 Student/Faculty Differences

Another independent variable was the role of the subject as a student or faculty
member on campus (Table 3). This analysis included differences based on where to
recycle, such as students had a greater understanding of recycling collection points in the
residential halls (p=0.0001) whereas faculty had a greater grasp of recycling collection
points in academic buildings (p=0.006).

In terms Of what can be recycled, students were more knowledgeable than faculty
about recycling cardboard (p=0.019). Faculty, by contrast, had a greater knowledge of
recycling magazines (p=.001) and mixed paper (p=.003).

Students had a greater understanding of disincentives to recycling (p= 0.006),
including a lack of time (p= 0.018) and lack Of knowledge about where to recycle items,

what items are recyclable, and how to prepare items for recycling (p= 0.036).

5.4.9 Differences Across Campus Facilities

107

The student subjects’ residential hall (Tables 4 and 5) resulted in very few
differences. For the most part, these differences were based on the comprehensive
knowledge of a general concept, such as how to recycle (p= 0.049), and alternatives to
recycling (p=0.043).

The building type (recycling “friendly” or “unfriendly”) Of a faculty subjects’
ofﬁce was associated with a few differences (Table 6). For example, faculty members in
recycling “unfriendly” buildings had a greater knowledge of used (post-consumer)
newspapers (p= 0.046), and removing impurities from items to prepare them for recycling

(p= 0.032).

5.5 Recommendations

The results of this mental models research are the ﬁrst step in a long-term process
to educate the Michigan State University community about recycling. The results should
be used to inform the Operation and design of recycling collection on-campus.

There are two complementary ways of drawing conclusions from these results.
First, one can look to the results from the various statistical analyses to determine if there
is a specialized need to address a particular issue (e.g., paper recycling) or to target a
speciﬁc group of consumers (e.g., faculty members). Second, analysts can look for so-
called “big picture” issues that highlight important gaps, misconceptions, or needs that
are largely shared by the groups that were the focus of this research (students and

faculty).

5.5.] Statistical Analyses

108

Relatively few speciﬁc comparisons (made using chi-square tests) revealed
signiﬁcant differences either between individuals (i.e. between faculty and students),
genders, and campus facilities (i.e. academic hall of faculty or residential hall Of
students). Of 580 total comparisons, only 36 revealed signiﬁcant differences (6.55%).

Of these, there were however, some notable ﬁndings.

5. 5. 1.] Student and Faculty Specific Recommendations

Speciﬁcally, there were several areas where students and faculty differed in terms
of their knowledge (Table 3). There were distinct differences, for example, between the
knowledge levels of students and faculty regarding the different kinds of papers that may
be recycled on campus. Faculty members appear to need additional information
regarding the recycling of cardboard (p=0.019) while students need information
regarding the recycling of mixed paper (p=.003), magazines (p=.001). On the other hand,
students outperformed faculty in terms of their level of knowledge about the recyclability
of post-consumer paper materials such as newspaper (p=.039) and white paper (p=.001).
These kinds of results point to the possibility of identifying different areas of emphasis—
between students and faculty, for example—in subject speciﬁc outreach plans.

Students were more likely than their faculty counterparts to provide detailed
reasons behind why people do not recycle. Among their reasons were that recycling is a
time consuming (p= 0.018) and difﬁcult activity (p= 0.006). This important difference
between students and faculty warrants perhaps the most attention in future outreach and

planning efforts. Students are the largest population on campus and their participation in

109

sustainability activities such as recycling is critical to improving the University’s
performance in this area.

Thus, it is my view that administrators of the MSU recycling program must
approach the design of campus recycling from the standpoint of removing logistical
barriers that may contribute to students negative perception of recycling. Related
outreach efforts must work to counteract the idea that recycling is too difﬁcult or a waste
of time and, therefore, an activity that is to be avoided. This latter objective could be
accomplished through social marketing efforts that provide reasons for students to take
the extra steps to recycle (such as the speciﬁc beneﬁts of recycling to the environment or

revenue generated by recycling positively affecting student programs).

5. 5. I . 2 Gender Differences
Very few differences were found between male and female students or faculty.
Most Of these differences were Of limited value vis-a-vis informing the development of a

recycling-speciﬁc outreach plan. For example, mean scores of faculty male and female

knowledge of white paper recycling were signiﬁcantly different ( 7c Males =4.00, se= 0.21

and fFemales = 3.50, se= 0.00; p= 0.004). But the similarity in the mean scores (4.00 vs.

3.50) suggests that both groups clearly understand this issue and, therefore, addressing it
further in outreach plans may not be necessary.

Along these lines, when considering both faculty and students, the differences
across the genders were largely minor, which mirrors results from other studies (e. g.
Berger 1997) that are typically unable to correlate recycling behavior with gender.

Although some differences exist (see above, and Tables 1 and 2), focusing on these in the

110

design of a recycling-speciﬁc outreach plan for MSU is——in our view—unlikely to be
productive. Both genders need accurate information speciﬁc to their student or faculty
role in recycling on campus. In sum, the role of a particular subject on campus seems to
be a greater determinant of what a subject would know and need to know, rather than
their gender.

Equally important in these kinds of analyses are what are commonly referred to as
“non-results”; these are areas where statistically signiﬁcant difference are expected (and
hence, inform the design of the mental models study) were absent. But the absence of
statistical signiﬁcance does not necessarily mean that these “non-results” lack outreach

implications. For example, student understanding of how to separate cloudy plastic from

other plastics was not a signiﬁcantly different between genders (7: males 1.15, se= 0.36
and 2? females: 1.15, se =0.34; p>0.05), nor was knowledge of recycling ﬂorescent light

bulbs (imam: 0.15, se= 0.15 and 7c females: 0.00, se =0.00; p>0.05). Both of these items

were not well known as recyclable and deserve attention in outreach plans. For future
communication plans, it is less important that the results yielded few signiﬁcant
differences between genders, but more important that generally subjects were
unknowledgeable about certain topics that could be necessary for MSU community

members to successfully recycle.

5.5. 1.3 Dtﬂerences Across Campus Facilities
The general lack of signiﬁcant and systematic differences in this study between
the four residential halls where students live, and the buildings labeled “recycling

friendly” and “recycling unfriendly” where faculty members work, is another example Of

111

 

the important role of “non-results.” In all, residential hall difference resulted in two
signiﬁcant differences among the 116 comparisons conducted (see Table 5.6); building
differences resulted in only two differences as well. This is good news from the
standpoint of designing a recycling-speciﬁc outreach effort because it means that
sustainability planners at MSU need not develop building speciﬁc communication plans
(which would be time consuming and costly).

One notable exception in this regard relates to differences observed across
students from the four residential halls in terms of how to recycle; these levels Of

knowledge were different depending upon which residence hall a student calls home

(1' Akers= 1.94, se= 0.25 and Ygunerﬁeld= 1.64, se= 0.22 and Egolmcﬁ 1.97, se= 0.23 and

I Hubbard = 2.01, se= 0.23; p= 0.049). These differences can be explained by the

building-speciﬁc variations in how MSU’s recycling program is Operated. For example,
some areas have consistent collection points and signage allowing the student to easily
recycle, while others do not. This emphasizes the need to couple education with
environmental alteration—the technique to increase the rate of recycling by making it
logistically easier to do. Access to a structured, institutionalized program allowing for
easy and convenient recycling is considered by many to be a very effective technique in
terms of leading to durable behavior change and increasing recycling rates (DeYoung
1986; Derksen and Gartrell 1993; Howenstine 1993; Boldero 1995; Lee, DeYoung et al.

1995; Oskamp, Zelezny et al. 1996; Ludwig, Gray et al. 1998; Barr 2004).

5.5.2 General Trends

112

 

The second and ﬁnal level of analysis draws conclusions based on trends that
were evident from the mental models (Figures 5.2 and 5.3). Based on the these results,
four general observations stand out as rmiversally important; each of these deals with
issues that were either misunderstood or largely unknown by subjects based on the
frequency with which concepts were mentioned.

First, recycling locations were not well known outside of the typically used areas
of the academic building and residential hall, including dining and food venues, campus
owned apartments, and at the recycling processing facility on campus. This lack of
knowledge shows, perhaps, a slight indifference for recycling opportunities when they
exist outside of one’s usual working or socializing areas. Besides education about
locations to recycle outside the dorm room and classroom. the idea that recycling should
and can be done almost anywhere and anytime on campus, needs reinforcement.

A second salient ﬁnding of this research was the lack of knowledge about how
speciﬁc types of paper, plastics, glass, and metals must be separated for proper recycling.
An example of this is that all student subjects knew the generally plastics were recycled
on campus, but knowledge Of the different types of plastic were less known; #1 PETE
was mentioned by 82.5% Of student subjects. #2 HDPE cloudy (55%), and #2 HDPE
colored (15%). Students and faculty need to know what types of these materials can be
recycled and how to identify the material (e.g. by looking for a speciﬁc plastic recycling
number) to better understand how each material needs to be separated. The good news is
that subjects were usually aware of these general concepts regarding material types and

separation (i.e. paper, plastics, and metals categories). We recommend outreach plans

113

 

build upon the general knowledge possessed by students and faculty to help facilitate a
more complete understanding about campus recycling Opportunities.

Overall, the importance of knowing what items can and can’t be recycled should
be stressed to achieve a successful recycling program. Lack of knowledge not only
results in lower recycling intensity, lower diversion rates, and lower collection efﬁciency,
but the resulting trial-and-error approach increases contamination levels in collected
recyclables (Scott, 1999). In the end, the good intentions Of an uncertain public can
reduce the efﬁciency and efﬁcacy of recycling programs.

Third, questions related to why one should recycle also revealed some important
knowledge gaps. How much students and faculty should know about the reasons why
they ought to recycle relates to the philosophy of higher education. Based on the long-
term vision and purpose of MSU, which is to educate citizens—including students,
faculty, and staff—about community and environmental stewardship, it stands to reason
that gaps regarding the lesser-known concepts of the environmental and the socio-
economic beneﬁts of recycling should be addressed.

These results reveal that MSU community members are not well aware of the
concepts related to the broader overarching beneﬁts of sustainable practices, in this case
recycling. Students and faculty were well aware of deposit of soda cans and bottles as a
monetary incentive (due to Michigan having the largest bottle deposit of any state, at ten
cents), but not other socio-economic beneﬁts (e.g., jobs in the recycling industry and the
sale of materials collected for recycling). All students and faculty interviewed understood
the general connection between recycling and environmental beneﬁts. However,

relatively few student and faculty subjects understood the speciﬁc nature of these beneﬁts

114

 

(e.g., beneﬁts in terms of energy efﬁciency (37.5% and 44.4%, respectfully), cleaner air
(47.5% and 22.8%, respectfully), and cleaner water (32.5% andl6.7%, respectfully). In
accordance with MSU’s mission to empower people and communities, I recommend that
all community members be helped to understand (via regular reporting of metrics, for
example) how their resource use affects the both local and global environmental footprint
Of MSU.

Fourth, faculty and students were typically aware of reuse as an alternative to
waste generation and recycling, but were generally unaware of reduction practices.
Female faculty members and female students had a good understanding in general about
reduction practices, but the majority of the students and faculty did not. Reduction is
perhaps the hardest lesson to teach and concept to practice, but it can be done. These
measures can be accomplished in many ways: through contracts with manufacturers who
design products with less packaging, through institution purchases of more durable
products and internal reuse of products; and through institutional changes in practices
(i.e. default printing paper on two sides); and through institutional purchases of less toxic
products (Fishbein and Gelb 1992). Without the MSU community’s full COOperation,
reduction practices will be difﬁcult to coordinate and implement. The temporary
inconvenience of critically thinking about purchases may require more work for
purchasing departments and administrators; however, in the long-term, the reduction of

waste is perhaps the best way to divert the most waste from the landﬁll.

5.6 Conclusion

115

 

The mental models approach carried out in this study allows the level Of
stakeholder knowledge to be visible to those who are designing outreach plans. The
results can be used to allocate resources to ﬁll gaps in knowledge, and vice-versa areas of
high understanding would need less communication. But mental models do not show
how important a concept is compared to all others on the model in terms of priority. The
models do not reveal how knowledgeable subjects should be about each concept or item.
All subjects being knowledgeable about every item may be unnecessary for the success
of the recycling program.

When considering residential hall recycling, for example, 87.5% of students knew
the location of at least one recycling collection point. Students’ seemingly well-
developed knowledge of the location is perhaps an argument for not highlighting the
topic in outreach effort. But an argrnnent could also be made for further education on the
basis of informing students in residential halls about different recycling Options available.
This may lead to informing students about recycling locations closer to their room and
thus, increasing the convenience and recycling rates of students. On the other hand, tire
recycling was known by none (0%) of the faculty subjects. But this lack of knowledge is
not particularly a concern of outreach plans, because their duties do not require them to
service University vehicles.

In the end, it is important to note the mental models approach reveals how well
speciﬁc concepts are known by a range of subjects, but it does not make the decisions
about how well the concepts should be known (or if they are worth knowing at all). The
method is meant to help managers make decisions about outreach plans, but it is not

meant be the decision-maker. These decisions should be thoughtful vis-a-vis what

116

program managers judge to be important concepts that should be the focus of outreach
plans, and to a lesser extent, the design of recycling programs. To the extent possible,
these decisions should also be equitable; they should be made by a diverse group of
people including the recycling department administrators, building/residential hall
managers, people affected by the program, and building maintenance employees who
deal with recycling.

One approach to understand what items are necessary to communicate could be a
future study comparing knowledge of recycling to actual recycling amounts. Although
this study shows subjects lack knowledge of certain areas, it does not have a component
that shows how this knowledge directly affects the amount of recycling and the levels of
contamination. This study could further develop the mental models research to design
recycling communications, and to a lesser extent, the physical characteristics of
collection.

Another approach to solve these unresolved questions could take the form of a
future comparative study using the same mental models interviews, to understand the
effectiveness of outreach plans to change and enhance knowledge. In this way, the
outreach messages could be measured by the change in the level Of knowledge for each

item.

117

 

Table 5.1: The frequency of, and mean level of understanding for, 116 concept areas
across male (M) and female (F) undergraduate students on the MSU campus. Means
were compared using chi-square tests at the p=0.05 signiﬁcance level.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Concept % Correct Males Females

M F E SE 3 SE
Where 24.67 23.67 1.84 1.75 0.37 0.39 n/s
Residence Hall 47.50 45.00 3.50 3.55 0.25 0.22 n/s
Location on Campus 45.00 45.00 3.35 3.40 0.25 0.24 n/s
Location in Residence Hall 45.00 45.00 3.35 3.45 0.25 0.25 NS
Dining/Concessions 35.00 35.00 2.45 2.30 0.34 0.36 n/s
Sports Venues 15.00 10.00 1.20 0.65 0.34 0.30 NS
Residential Dining 25.00 15.00 1.85 1.25 0.37 0.33 n/s
International Center Food Court 2.50 7.50 0.25 0.55 0.18 0.28 NS
Sparty‘s Cafes 7.50 7.50 0.60 0.40 0.30 0.28 n/s
Campus and Academic Buildings 50.00 47.50 3.50 3.45 0.21 0.17 n/s
Location on Campus 47.50 47.50 3.30 3.35 0.27 0.17 n/s
Location in the Building 45.00 45.00 3.25 3.25 0.30 0.18 n/s
Campus-owned Apartments 2.50 2.50 0.25 0.25 0.16 0.6 NS
Location in Complex 0.00 0.00 0.05 0.00 0.05 0.00 n/s
Location on Campus 0.00 0.00 0.05 0.05 0.05 0.05 NS
Processing Facility 2.50 2.50 0.60 0.35 0.18 0.18 n/s
What 21.69 19.46 1.51 1.43 0.20 0.20 NS
Fibers 0.00 5.00 0.00 0.35 0.00 0.24 n/s
Fabric 2.50 12.50 0.15 0.85 0.15 0.34 n/s
Paper 52.5 50.00 3.95 3.95 0.05 0.05 n/s
Magazines 15.00 17.50 0.9 1.05 0.36 0.37 n/s
White Paper 52.50 45.00 3.75 3.35 0.10 0.24 n/s
Pre-Consumer 2.50 5.00 0.20 0.20 0.20 0.20 NS
Post-Consumer 47.50 35.00 3.10 2.65 0.26 0.36 n/s
Paperboard 7.50 7.50 0.50 0.40 0.28 0.22 n/s
Mixed Paper 30.00 20.00 2.25 1.60 0.43 0.41 n/s
Junk Mail 22.50 22.50 1.45 1.30 0.41 0.37 NS
Newspaper 52.50 47.50 3.85 3.80 0.08 0.20 n/s
Pre-Consumer 2.50 0.00 0.15 0.00 0.15 0.00 n/s
Post-Consumer 40.00 40.00 2.95 2.75 0.35 0.38 n/s
Softbound Books 12.50 15.00 0.65 0.90 0.30 0.32 n/s
Cardboard 45.00 42.50 3.30 3.20 0.23 0.32 n/s
Non-Fibers “Containers” 50.00 45.00 3.50 3.25 0.17 0.24 n/s
Metals (Tin, Aluminum) 50.00 50.00 3.60 3.80 0.21 0.09 n/s
Scrap Metal 27.50 22.50 1.70 1.60 0.40 0.41 n/s
Food Cans 30.00 22.50 2.20 1.70 0.42 0.40 n/s
Pop Cans 47.50 47.50 3.45 3.65 0.28 0.21 n/s
Foil 12.50 7.50 0.90 0.30 0.36 0.21 n/s
Glass 52.50 42.50 3.60 3.20 0.11 0.27 n/s
Clear 22.50 20.00 1.50 1.25 0.36 0.35 n/s
Brown 10.00 5.00 0.60 0.40 0.28 0.22 n/s
Plastics 52.50 55.00 3.90 3.95 0.07 0.05 NS
#1 PETE 40.00 42.50 2.60 3.40 0.36 0.28 n/s
#2 HDPE (Cloudy) 25.00 30.00 1.55 2.20 0.40 0.38 n/s
#2 HDPE (Colored) 7.50 7.50 0.50 0.50 0.28 0.28 n/s

 

 

 

 

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Table 5.1 (continued): The frequency of, and mean level of understanding for, 116
concept areas across male (M) and female (F) undergraduate students on the MSU
campus. Means were compared using chi-square tests at the p=0.05 signiﬁcance level.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Concept % Correct Males Females P
M F 3? SE If SE
Other 42.50 30.00 2.95 0.35 2.25 0.39 n/s
Fly Ash 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Construction Waste 7 .50 5.00 0.50 0.28 0.35 0.24 NS
Tires 7.50 7.50 0.55 0.30 0.55 0.30 n/s
Toner 0.00 0.00 0.00 0.00 0.15 0.15 n/s
Tennis Shoes 2.50 2.50 0.15 0.15 0.20 0.20 n/s
Electronic Waste 7.50 12.50 0.55 0.30 1.05 0.37 n/s
Biological 25.00 15 .00 1.90 0.44 0.95 0.34 0.022
Leaves 10.00 2.50 0.60 0.28 0.15 0.15 n/s
Grass 7.50 10.00 0.45 0.25 0.60 0.28 n/s
Branches 7.50 2.50 0.45 0.25 0.15 0.15 n/s
Food Waste 20.00 5.00 1.55 0.44 0.35 0.24 0.041
Manure 7.50 5.00 0.55 0.30 0.03 0.21 n/s
Hazardous Waste 12.50 10.00 0.80 0.32 0.70 0.33 NS
Pallets 0.00 2.50 0.00 0.00 0.20 0.20 n/s
Batteries 20.00 10.00 1.30 0.41 0.80 0.32 n/s
Ink-Jet Cartridges 5.00 17 .50 0.30 0.21 1.40 0.40 n/s
F lorescent Light Bulbs 2.50 0.00 0.15 0.15 0.00 0.00 n/s
How 28.66 25.77 1.97 0.02 1.81 0.22 n/s
Quality Control 47.50 45.00 3.60 0.22 3.35 0.27 n/s
Proper Sorting 42.50 42.50 3.20 0.33 3.10 0.32 n/s
Signs 22.50 22.50 1.70 0.44 1.55 0.40 NS
Removing Impurities 40.00 37.50 2.55 0.33 2.50 0.32 n/s
Clean 37 .50 30.00 2.30 0.36 2.05 0.37 n/s
Remove Caps 12.50 20.00 0.75 0.32 1.45 0.37 n/s
Processing Facility 45.00 20.00 3.00 0.31 1.30 0.37 0.007
Pick-up 40.00 30.00 2.95 0.27 1.95 0.35 0.048
Self-Sorting 47.50 42.50 3.30 0.27 3.15 0.29 n/s
Non-Fibers “Containers” 42.50 40.00 2.85 0.32 2.65 0.36 n/s
Plastics 52.50 50.00 3.60 0.11 3.55 0.11 n/s
#1 PETE 20.00 17.50 1.35 0.39 1.35 0.36 n/s
#2 HDPE (Cloudy) 17.50 15.00 1.15 0.36 1.15 0.34 n/s
#2 HDPE (Colored) 7.50 5.00 0.50 0.28 0.45 0.25 n/s
Metals (I in, Aluminum) 40.00 37.50 3.00 0.36 3.00 0.36 n/s
Glass 45.00 40.00 3.05 0.31 2.70 0.37 n/s
Brown 10.00 7.50 0.75 0.32 0.45 0.25 n/s
Clear 12.50 7.50 0.95 0.36 0.45 0.25 n/s
Paper 52.50 50.00 3.55 0.11 3.65 0.11 n/s
Magazines 2.50 5.00 0.20 0.20 0.35 0.24 n/s
Softbound Books 0.00 5.00 0.00 0.00 0.30 0.21 n/s
White Paper 22.50 27.50 1.55 0.40 1.80 0.38 n/s
Paperboard 0.00 2.50 0.00 0.00 0.15 0.15 n/s
Mixed Paper 27.50 17.50 1.85 0.39 1.15 0.36 n/s
Newspaper 30.00 32.50 1.90 0.40 2.25 0.35 n/s
Cardboard 27.50 20.00 1.6 0.35 1.25 0.34 n/s

 

119

 

 

 

Table 5.1 (continued): The frequency of, and mean level of understanding for, 116
concept areas across male (M) and female (F) undergraduate students on the MSU
campus. Means were compared using chi-square tests at the p=0.05 signiﬁcance level.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

% Correct Mean Score Standard Error

Knowledge Concept M F 35 SE 3' SE P
Why 32.31 29.23 2.22 0.34 2.07 0.22 n/s
Environmental Beneﬁts 52.50 50.00 3.80 0.22 3.85 0.08 n/s
Water 17 .50 12.50 1.15 0.08 0.95 .034 n/s
Land 32.50 17.50 2.20 .034 1.25 0.40 n/s
Air 20.00 27.50 1.40 0.40 1.90 0.40 NS
Energy 17.50 20.00 1.15 0.40 1.45 0.41 n/s
Economic Beneﬁts 52.50 50.00 3.60 0.41 3.55 0.11 n/s
Monetary Incentive 47.50 47.50 3.35 0.11 3.40 0.21 NS
Deposit 47.50 45.00 3.35 0.21 3.30 0.27 n/s
Garﬁge Fees 10.00 5.00 0.70 0.27 0.40 0.24 n/s
Jobs 20.00 12.50 1.25 0.24 0.80 0.30 n/s
Tipping Fees 15.00 10.00 0.90 0.30 0.60 0.28 n/s
Social Beneﬁts 45.00 42.50 3.05 0.28 2.85 0.26 n/s
Awareness 42.50 40.00 2.90 0.26 2.65 0.32 n/s
Why Not 40.83 40.00 2.94 0.32 3.04 0.45 n/s
Disincentives 50.00 50.00 3.95 0.45 4.00 0.00 n/s
Time 42.50 40.00 3.05 0.00 3.00 0.36 n/s
Ease 45.00 42.50 3.15 0.36 3.10 0.32 n/s
Convenience 47.50 47.50 3.30 0.32 3.70 0.21 n/s
Space 10.00 12.50 0.65 0.21 0.90 0.36 n/s
Know-how 50.00 47 .50 3.55 0.36 3.55 0.21 n/s
Alternatives 31.88 29.38 2.33 0.21 2.26 0.92 n/s
Reduce 25.00 20.00 1.55 0.92 1.55 0.36 n/s
Reuse 47.50 47.50 3.55 0.36 3.55 0.17 NS
Surplus Store 2.50 0.00 0.00 0.17 0.00 0.00 n/s
Garbage 52.50 50.00 3.95 0.00 3.95 0.05 n/s

 

 

 

 

 

120

 

Table 5.2: The frequency of, and mean level of understanding for, 116 concept areas
across male (M) and female (F) faculty members on the MSU campus. Means were
compared using chi-square tests at the p=0.05 signiﬁcance level.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Concept % Correct Males Females P
M F E SE )7 SE

Where 22.59 25.00 1.13 0.50 1.03 0.41 n/s
Residence Hall 11.11 0.00 0.50 0.29 0.50 0.29 NS
Location on Campus 5.56 0.00 0.21 0.21 0.00 0.00 n/s
Location in Residence Hall 5.56 0.00 0.21 0.21 0.00 0.00 n/s
Dining/Concessions 27.7 8 25.00 1.29 0.45 1.00 0.71 n/s
Sports Venues 11.11 0.00 0.50 0.34 0.25 0.25 NS
Residential Dining 11.11 25.00 0.50 0.34 1.00 0.71 n/s
International Center Food Court 16.67 25.00 0.71 0.34 0.75 0.75 n/s
Sparty’s Cafes 16.67 0.00 0.86 0.38 0.00 0.00 n/s
Campus and Academic Buildings 77.78 100.00 4.00 0.00 4.00 0.00 n/s
Location on Campus 72.22 100.00 3.79 0.21 4.00 0.00 n/s
Location in the Building 72.22 100.00 3.79 0.21 4.00 0.00 n/s
Campus-owned Apartments 0.00 0.00 0.00 0.00 0.00 0.00 NS
Location in Complex 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Location on Campus 0.00 0.00 0.00 0.00 0.00 0.00 NS
Processing Facility 11.11 0.00 0.57 0.34 0.00 0.00 n/s
What 30.07 36.41 1.42 0.26 1.36 0.22 n/s
Fibers 0.00 0.00 0.00 0.00 0.00 0.75 NS
Fabric 0.00 25.0 0.00 0.00 0.75 0.00 n/s
Paper 77.78 100.00 4.00 0.49 4.00 0.29 n/s
Magazines 55.56 100.00 2.71 0.00 3.50 0.29 n/s

White Paper 77.78 100.00 4.00 0.21 3.50 0.00 0.004

Pre-Consumer 5.56 0.00 0.21 0.54 0.00 0.25 0.005
Post-Consumer 38.89 100.00 1.93 0.00 3.75 0.00 n/s
Paperboard 0.00 0.00 0.00 0.07 0.00 0.29 n/s

Mixed Paper 77.78 100.00 3.93 0.34 3.50 0.00 0.043
Junk Mail 11.11 0.00 0.50 0.29 0.00 0.00 n/s
Newspaper 72.22 100.00 3.64 0.00 4.00 0.00 n/s
Pre-Consumer 0.00 0.00 0.00 0.50 0.00 1.15 n/s
Post-Consumer 22.22 50.00 1.14 0.38 2.00 1.00 n/s
Softbound Books 22.22 25.00 0.86 0.51 1.00 1.00 NS
Cardboard 50.00 25.00 2.43 0.35 1.00 0.00 n/s
Non-Fibers “Containers” 61.11 100.00 2.93 0.29 4.00 0.00 n/s
Metals (Tin, Aluminum) 72.22 100.00 3.36 0.42 4.00 0.00 NS
Scrap Metal 50.00 0.00 2.00 0.44 0.00 0.75 n/s
Food Cans 38.89 25.00 1.57 0.29 0.75 0.00 n/s
Pop Cans 72.22 100.00 3.50 0.38 4.00 1.00 n/s
Foil 22.22 25.00 0.86 0.27 1.00 0.87 n/s
Glass 72.22 50.00 3.17 0.43 1.50 0.87 n/s
Clear 44.44 50.00 1.79 0.41 1.50 0.75 n/s
Brown 33.33 25.00 1.29 0.38 0.75 0.25 n/s
Plastics 66.67 100.00 3.21 0.48 3.75 1.00 n/s
#1 PETE 50.00 75.00 2.43 0.40 3.00 1.03 n/s
#2 HDPE (Cloudy) 38.89 50.00 1.57 0.29 1.75 0.00 n/s
#2 HDPE (Colored) 11.11 0.00 0.57 0.38 0.00 1.00 n/s

 

 

 

 

121

 

Table 5.2 (continued): The frequency of, and mean level of understanding for, 116
concept areas across male (M) and female (F) faculty members on the MSU campus.
Means were compared using chi-square tests at the p=0.05 signiﬁcance level.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Concept % Correct Males Females
M F E SE )7 SE
Other 66.67 75.00 3.29 0.00 3.00 0.00 n/s
Fly Ash 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Construction Waste 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Tires 0.00 0.00 0.00 0.39 0.00 1.00 n/s
Toner 1 1.11 25.00 0.57 0.29 1.00 0.75 n/s
Tennis Shoes 5.56 25.00 0.29 0.49 0.75 1.03 n/s
Electronic Waste 38.89 50.00 1.71 0.47 1.75 1.00 n/s
Biological 22.22 25.00 1.07 0.29 1.00 0.00 n/s
Leaves 5.56 0.00 0.29 0.29 0.00 0.00 NS
Grass 5.56 0.00 0.36 0.07 0.00 0.00 n/s
Branches 0.00 0.00 0.07 0.29 0.00 0.00 n/s
Food Waste 5.56 0.00 0.29 0.39 0.00 0.00 n/s
Manure 1 1.1 1 0.00 0.57 0.29 0.00 0.00 n/s
Hazardous Waste 5.56 0.00 0.29 0.00 0.00 0.00 n/s
Pallets 0.00 0.00 0.00 0.47 0.00 0.00 n/s
Batteries 22.22 0.00 1.07 0.49 0.00 1.15 NS
Ink-Jet Cartridges 27.78 50.00 1.29 0.34 2.00 0.00 n/s
Florescent Light Bulbs 1 1.1 l 0.00 0.50 0.29 0.00 0.29 n/s
How 43.80 52.89 2.03 0.35 1.99 0.29 n/s
Quality Control 72.22 100.00 3.57 0.23 3.75 0.25 n/s
Proper Sorting 72.22 100.00 3.57 0.23 3.75 0.25 n/s
Signs 44.44 25.00 2.14 0.52 1.00 1.00 NS
Removing Impurities 38.89 100.00 1.93 0.45 3.25 0.25 n/s
Clean 22.22 75.00 1.29 0.42 2.50 0.87 n/s
Remove Caps 27.78 0.00 1.36 0.44 0.00 0.00 n/s
Processing Facility 11.11 0.00 0.50 0.34 0.00 0.00 n/s
Pick-up 33.33 75.00 1.57 0.48 2.50 0.87 n/s
Self-Sorting 78.78 100.00 3.86 0.10 4.00 0.00 n/s
Non-Fibers “Containers” 78.78 50.00 3.50 0.14 2.00 1.15 0.008
Plastics 66.67 7 5.00 3.00 0.36 3.00 1.00 n/s
#1 PETE 38.89 75.00 1.71 0.49 2.75 0.95 n/s
#2 HDPE (Cloudy) 27.78 25.00 1.14 0.43 0.75 0.75 n/s
#2 HDPE (Colored) 11.11 0.00 0.43 0.29 0.00 0.00 n/s
Metals (Tin, Aluminum) 61 .11 7 5.00 2.79 0.42 3.00 1.00 n/s
Glass 55.56 50.00 2.80 0.45 2.00 1.15 n/s
Brown 22.22 25.00 0.86 0.38 0.75 0.75 n/s
Clear 33.33 25.00 1.36 0.44 0.75 0.75 n/s
Paper 77.78 100.00 3.64 0.13 4.00 0.00 n/s
Magazines 33.33 50.00 1.50 0.49 2.00 1.15 n/s
Softbound Books 5.56 25.00 0.21 0.21 1.00 1.00 n/s
White Paper 72.22 100.00 3.36 0.29 4.00 0.00 n/s
Paperboard 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Mixed Paper 72.22 100.00 3.21 0.28 4.00 0.00 n/s
Newspaper 55.56 0.00 2.50 0.45 0.00 0.00 n/s
Cardboard 27.78 25.00 1.29 0.49 1.00 1.00 n/s

 

122

 

Table 5.2 (continued): The frequency of, and mean level Of understanding for, 116
concept areas across male (M) and female (F) faculty members on the MSU campus.
Means were compared using chi—square tests at the p=0.05 signiﬁcance level.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Concept % Correct Males Females P
M F )7 SE 36 SE
Why 43.16 59.62 2.03 0.49 2.23 0.44 n/s
Environmental Beneﬁts 77.78 100.00 3.86 0.56 4.00 0.00 n/s
Water 11.11 25.00 0.43 0.10 0.70 0.75 n/s
Land 55.56 100.00 2.64 0.29 3.50 0.29 n/s
Air 16.67 25.00 0.71 0.48 0.75 0.75 n/s
Enqu 38.89 25.00 1.64 0.38 1.00 1.00 n/s
Economic Beneﬁts 66.67 100.00 3.29 0.46 4.00 0.00 n/s
Monetary Incentive 66.67 100.00 3.21 0.34 4.00 0.00 n/s
Deposit 61.1 1 100.00 2.86 0.38 4.00 0.00 n/s
Garbage Fees 27.78 0.00 1.21 0.43 0.00 0.00 n/s
Jobs 27.78 25.00 1.36 0.46 0.75 0.75 n/s
TippinLFees 11.11 25.00 0.43 0.51 0.75 0.75 n/s
Social Beneﬁts 50.00 75.00 2.43 0.29 2.75 0.95 NS
Awareness 50.00 75.00 2.36 0.44 2.75 0.95 n/s
Why Not 50.93 70.83 2.51 0.46 2.75 0.65 n/s
Disincentives 77.78 100.00 4.00 0.81 4.00 0.00 n/s
Time 33.33 75.00 1.64 0.00 3.00 1.00 n/s
Ease 55.56 50.00 2.64 0.53 1.75 1.03 n/s
Convenience 66.67 100.00 3.36 0.48 3.75 0.25 n/s
Space 22.22 0.00 1.00 0.39 0.00 0.00 n/s
Know-how 50.00 100.00 2.43 0.44 4.00 0.00 n/s
Alternatives 48.89 87.5 2.39 0.51 3.50 0.40 n/s
Reduce 16.67 100.00 0.86 0.20 4.00 0.00 0.004
Reuse 61.1 1 100.00 2.79 0.46 4.00 0.00 NS
Surplus Store 38.89 50.00 1.93 0.42 2.00 1.15 n/s
Garbage 77.78 100.00 4.00 0.54 4.00 0.00 n/s

 

 

 

 

123

 

Table 5.3: The frequency of, and mean level of understanding for, 1 16 concept areas
comparing faculty members and undergraduate students on the MSU campus. Means
were compared using chi-square tests at the p=0.05 signiﬁcance level.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

% Correct Students Faculty
concept Stu Fac )7 SE 3; SE P
Where 46.83 28.15 1.79 0.38 1.11 0.38 0.001
Residence Hall 90.00 11.11 3.53 0.16 0.50 0.23 0.0001
Location on Campus 87.50 5.56 3.38 0.17 0.17 0.17 NS
Location in Residence Hall 87.50 5.56 3.40 0.17 0.17 0.17 0.0001
Dining/Concessions 67.50 33.33 2.38 0.24 1.22 0.38 n/s
Sports Venues 22.50 11.11 0.93 0.23 0.44 0.27 n/s
Residential Dining 40.00 16.67 1.55 0.25 0.61 0.30 NS
International Center Food Court 10.00 22.22 0.40 0.16 0.72 0.30 n/s
Sparty‘s Cafes 12.50 16.67 0.50 0.20 0.67 0.30 n/s
Campus and Academic Buildings 95.00 100.00 3.48 0.13 4.00 0.00 0.006
Location on Campus 92.50 94.44 3.33 0.16 3.83 0.17 0.003
Location in the Building 87.50 94.44 3.25 0.17 3.83 0.17 0.014
Campus-owned Apartments 5.00 0.00 0.25 0.11 0.00 0.00 n/s
Location in Corglex 0.00 0.00 0.03 0.03 0.00 0.00 n/s
Location on Campus 0.00 0.00 0.05 0.03 0.00 0.00 n/s
Process'g Facility 5.00 11.11 0.48 0.13 0.44 0.27 NS
What 40.33 38.16 1.42 0.30 1.40 0.20 n/s
Fibers 5.00 0.00 0.18 0.12 0.00 0.00 n/s
Fabric 15.00 5.56 0.50 0.19 0.17 0.17 n/s
Paper 100.00 100.00 3.95 0.03 4.00 0.00 n/s
Magazines 27.50 77.78 0.98 0.26 2.89 0.39 0.001
White Paper 95.00 100.00 3.55 0.13 3.89 0.08 n/s
Pre-Consumer 5.00 5.56 0.20 0.14 0.17 0.17 n/s
Post-Consumer 82.50 61.1 1 2.88 0.22 2.33 0.46 0.039
Paperboard 12.50 0.00 0.45 0.18 0.00 0.00 n/s
Mixed Paper 50.00 100.00 1.93 0.30 3.83 0.09 0.003
Junk Mail 40.00 11.11 1.38 0.27 0.39 0.27 n/s
Newspaper 97 .50 94.44 3.83 0.1 1 3.72 0.23 n/s
Pre-Consumer 2.50 0.00 0.08 0.08 0.00 0.00 n/s
Post-Consumer 77 .50 33.33 2.85 0.25 1.33 0.46 0.001
Softbound Books 25.00 27.78 0.78 0.22 0.89 0.35 n/s
Cardboard 85.00 55.56 3.25 0.20 2.11 0.46 0.019
Non-Fibers “Containers” 92.50 83.33 3.38 0.15 3.17 0.29 n/s
Metals (Tin, Aluminum) 97.50 94.44 3.70 0.11 3.50 0.23 n/s
Scrap Metal 47.50 50.00 1.65 0.28 1.56 0.38 n/s
Food Cans 55.00 44.44 1.95 0.29 1.39 0.38 n/s
Pop Cans 92.50 94.44 3.55 0.17 3.61 0.23 NS
Foil 17.50 27.78 0.60 0.21 0.89 0.35 NS
Glass 92.50 83.33 3.40 0.15 2.78 0.32 0.039
Clear 42.50 55.56 1.38 0.25 1.72 0.38 n/s
Brown 15.00 38.89 0.50 0.18 1.17 0.35 n/s
Plastics 100.00 88.89 3.93 0.04 3.33 0.30 0.018
#1 PETE 82.50 66.67 3.00 0.23 2.56 0.42 n/s
#2 HDPE (Cloudy) 55.00 50.00 1.88 0.28 1.61 0.37 n/s

 

124

 

 

Table 5.3 (continued): The frequency of, and mean level of understanding for, 116
concept areas comparing faculty members and undergraduate students on the MSU
campus. Means were compared using chi-square tests at the p=0.05 signiﬁcance level.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

% Correct Students Faculﬂ
camel” Stu Fac 3 SE 7: SE P
#2 HDPE (Colored) 15.00 11.11 0.50 0.19 0.44 0.23 n/s
Other 72.50 83.33 2.60 0.27 3.22 0.36 n/s
Fly Ash 0.00 0.00 0.00 0.00 0.00 0.00 NS
Construction Waste 12.50 0.00 0.43 0.18 0.00 0.00 n/s
Tires 15.00 0.00 0.55 0.21 0.00 0.00 n/s
Toner 2.50 16.67 0.08 0.08 0.67 0.36 0.027
Tennis Shoes 5.00 11.11 0.18 0.12 0.39 0.27 n/s
Electronic Waste 22.50 50.00 0.80 0.24 1.72 0.43 n/s
Biological 40.00 27 .7 8 1.43 0.28 1.06 0.42 n/s
Leaves 12.50 5.56 0.38 0.16 0.22 0.22 n/s
Grass 17.50 5.56 0.53 0.18 0.28 0.23 0.029
Branches 10.00 0.00 0.30 0.23 0.06 0.06 NS
Food Waste 25.00 5.56 0.95 0.10 0.22 0.22 n/s
Manure 12.50 11.11 0.43 0.26 0.44 0.30 n/s
Hazardous Waste 22.50 5.56 0.75 0.23 0.22 0.22 n/s
Pallets 2.50 0.00 0.10 0.10 0.00 0.00 n/s
Batteries 30.00 22.22 1.05 0.26 0.83 0.38 n/s
Ink-Jet Cartridges 25.00 38.89 0.85 0.24 1.44 0.44 n/s
Florescent Light Bulbs 2.50 11.11 0.08 0.08 0.39 0.23 n/s
How 53.37 55.56 1.89 0.22 2.02 0.29 NS
Quality Control 90.00 94.44 3.48 0.18 3.61 0.18 n/s
Proper Sorting 82.50 94.44 3.15 0.23 3.61 0.18 n/s
Signs 45.00 50.00 1.63 0.29 1.89 0.46 n/s
Removing Impurities 75.00 61 .11 2.53 0.23 2.22 0.38 n/s
Clean 65.00 38.89 2.18 0.26 1.56 0.39 n/s
Remove Caps 32.50 27.78 1.10 0.25 1.06 0.37 n/s
Processing Facility 62.50 11.11 2.15 0.27 0.39 0.27 0.002
Pick-ug 70.00 50.00 2.45 0.23 1.78 0.42 n/s
Self-Sorting 87 .50 100.00 3.23 0.20 3.89 0.08 n/s
Non-Fibers “Containers” 77.50 88.89 2.75 0.24 3.17 0.29 n/s
Plastics 100.00 83.33 3.58 0.08 3.00 0.34 0.042
#1 PETE 40.00 55.56 1.35 0.26 1.94 0.43 n/s
#2 HDPE (Cloudy) 35.00 33.33 1.15 0.25 1.06 0.37 n/s
#2 HDPE (Colored) 15.00 11.11 0.48 0.18 0.33 0.23 n/s
Metals (Tin. Aluminum) 80.00 77.78 3.00 0.25 2.83 0.38 n/s
Glass 80.00 66.67 2.88 0.24 2.39 0.42 n/s
Brown 17.50 27.78 0.60 0.20 0.83 0.33 n/s
Clear 20.00 38.89 0.70 0.22 1.22 0.38 NS
Paper 100.00 100.00 3.60 0.08 3.7 2 0.11 n/s
Magazines 7.50 44.44 0.28 , 0.16 1.61 0.44 0.004
Softbound Books 5.00 11.11 0.15 0.10 039 0.27 n/s
White Pgrer 50.00 94.44 1.68 0.27 3.50 0.23 0.000
Paperboard 2.50 0.00 0.08 0.08 0.00 0.00 n/s
Mixed Paper 45.00 94.44 1.50 0.27 3.39 0.23 0.001

 

125

 

Table 5.3 (continued): The frequency of, and mean level of understanding for, 116
concept areas comparing faculty members and undergraduate students on the MSU
campus. Means were compared using chi-square tests at the p=0.05 signiﬁcance level.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

% Correct Students Faculty
COMB“ Stu Fac 3 SE 35 SE P
Newspaper 62.50 55 .56 2.08 1.94 0.26 0.43 n/s
Cardboard 42.50 33.33 1.43 1.22 0.27 0.42 n/s
Why 60.58 56.41 2.14 2.08 0.33 0.29 n/s
Environmental Beneﬁts 100.00 100.00 3.83 3.89 0.06 0.08 n/s
Water 32.50 16.67 1.05 0.50 0.25 0.27 NS
Land 50.00 77.78 1.73 2.83 0.28 0.38 n/s
Air 47.50 22.22 1.65 0.72 0.28 0.33 n/s
Energy 37.50 44.44 1.30 1.50 0.27 0.41 n/s
Economic Beneﬁts 100.00 88.89 3.58 3.44 0.08 0.27 0.033
Monetary Incentive 92.50 88.89 3.38 3.39 0.17 0.30 n/s
Deposit 90.00 83.33 3.33 3.11 0.19 0.35 n/s
Garbage Fees 15.00 27.78 0.55 0.94 0.19 0.37 n/s
Jobs 32.50 33.33 1.03 1.20 0.23 0.42 0.029
Tipping Fees 25.00 16.67 0.75 0.50 0.21 0.27 n/s
Social Beneﬁts 85.00 66.67 2.95 2.50 0.20 0.39 n/s
Awareness 80.00 66.67 2.78 2.44 0.23 0.41 n/s
Why Not 79.17 66.67 2.99 2.56 0.46 0.46 0.006
Disincentives 100.00 100.00 3.98 4.00 0.03 0.00 n/s
Time 82.50 50.00 3.03 1.94 0.23 0.47 0.018
Ease 85.00 66.67 3.13 2.44 0.22 0.43 n/s
Convenience 92.50 88.89 3.50 3.44 0.17 0.30 n/s
Space 22.50 22.22 0.78 0.78 0.23 0.36 n/s
Know-how 95.00 72.22 3.55 2.78 0.15 0.42 0.036
Alternatives 60.63 68.06 2.29 2.64 0.88 0.55 n/s
Reduce 47 .50 38.89 1.73 1.56 0.26 0.47 0.010
Reuse 92.50 83.33 3.43 3.06 0.16 0.35 n/s
Surplus Store 2.50 50.00 0.08 1.94 0.08 0.47 0.0001
Garbage 100.00 100.00 0.50 4.00 0.03 0.00 n/s

 

126

 

 

Table 5.4: The frequency of understanding for 116 concept areas comparing
undergraduate students living in four MSU residential areas, namely Akers (Ak),
Butterﬁeld (Bu), Holmes (HO), and Hubbard (Hu) Halls.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Concept % Correct

Ak Bu Ho Hu
Where 47.33 45.33 51.33 43.33
Residence Hall 100.00 80.00 90.00 90.00
Location on Campus 100.00 70.00 90.00 90.00
Location in Residence Hall 100.00 70.00 90.00 90.00
Dininﬂoncessions 70.00 70.00 70.00 60.00
Sports Venues 30.00 10.00 30.00 20.00
Residential Dining 40.00 50.00 60.00 10.00
International Center Food Court 10.00 10.00 10.00 10.00
Sparty’s Cafes 0.00 20.00 10.00 20.00
Campus and Academic Buildings 90.00 100.00 100.00 90.00
Location on Campus 80.00 100.00 100.00 90.00
Location in the Building 80.00 100.00 100.00 70.00
Campus-owned Apartments 0.00 0.00 10.00 10.00

Location in Complex 0.00 0.00 0.00 0.00

Location on Campus 0.00 0.00 0.00 0.00

Processing Facility 10.00 0.00 10.00 0.00
What 43.70 36.09 42.83 38.70

Fibers 10.00 10.00 0.00 0.00
Fabric 20.00 20.00 10.00 10.00
Paper 100.00 100.00 100.00 100.00
Magaziines 10.00 10.00 30.00 60.00
White Paper 90.00 90.00 100.00 100.00
Pre-Consumer 10.00 0.00 0.00 10.00
Post-Consumer 80.00 80.00 90.00 80.00
Paperboard 0.00 20.00 20.00 10.00
Mixed Paper 60.00 30.00 60.00 50.00
Junk Mail 50.00 40.00 30.00 40.00
Newspaper 100.00 100.00 100.00 90.00

Pre-Consumer 10.00 0.00 0.00 0.00
Post-Consumer 70.00 80.00 80.00 80.00
Softbound Books 20.00 10.00 40.00 30.00
Cardboard 80.00 100.00 80.00 80.00
Non-Fibers “Containers” 100.00 80.00 90.00 100.00
Metals (Tin, Aluminum) 90.00 100.00 100.00 100.00
Scrap Metal 60.00 40.00 40.00 50.00
Food Cans 60.00 60.00 60.00 40.00
Pop Cans 90.00 90.00 100.00 90.00
Foil 10.00 10.00 0.00 50.00
Glass 100.00 90.00 100.00 80.00
Clear 60.00 30.00 50.00 30.00
Brown 20.00 10.00 10.00 20.00
Plastics 100.00 100.00 100.00 100.00
#1 PETE 100.00 80.00 90.00 60.00
#2 HDPE (Cloudy) 70.00 30.00 60.00 60.00
#2 HDPE (Colored) 20.00 0.00 20.00 20.00

 

 

 

 

 

127

 

Table 5.4 (continued): The frequency of understanding for 116 concept areas comparing
undergraduate students living in four MSU residential areas, namely Akers (Ak),
Butterﬁeld (Bu), Holmes (HO), and Hubbard (Hu) Halls.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Concept % Correct
Ak Bu Ho Hu
Other 90.00 70.00 80.00 50.00
Fly Ash 0.00 0.00 0.00 0.00
Construction Waste 0.00 20.00 10.00 20.00
Tires 10.00 10.00 10.00 30.00
Toner 0.00 0.00 10.00 0.00
Tennis Shoes 20.00 0.00 0.00 0.00
Electronic Waste 30.00 20.00 30.00 10.00
Biolomal 60.00 20.00 50.00 30.00
Leaves 30.00 10.00 0.00 10.00
Grass 30.00 0.00 20.00 20.00
Branches 20.00 10.00 10.00 0.00
Food Waste 50.00 30.00 10.00 10.00
Manure 30.00 0.00 20.00 0.00
Hazardous Waste 10.00 10.00 50.00 20.00
Pallets 0.00 10.00 0.00 0.00
Batteries 20.00 30.00 40.00 30.00
Ink-Jet Cartridges 20.00 10.00 70.00 0.00
F lorescent Light Bulbs 0.00 0.00 0.00 10.00
How 56.67 47.69 54.23 56.16
Quality Control 90.00 90.00 90.00 90.00
Proper Sorting 80.00 90.00 80.00 80.00
Signs 50.00 40.00 50.00 40.00
Removm Impurities 80.00 60.00 80.00 80.00
Clean 70.00 50.00 70.00 70.00
Remove Caps 50.00 30.00 30.00 20.00
ProcessiriFacility 60.00 60.00 60.00 70.00
Pick-up 70.00 60.00 70.00 70.00
Self-Sorting 100.00 90.00 80.00 80.00
Non-Fibers "Containers” 100.00 60.00 70.00 80.00
Plastics 100.00 100.00 100.00 100.00
#1 PETE 60.00 10.00 40.00 50.00
#2 HDPE (Cloudy) 40.00 10.00 40.00 50.00
#2 HDPE (Colored) 10.00 10.00 20.00 20.00
Metals (Tin, Aluminum) 90.00 60.00 90.00 80.00
Glass 90.00 80.00 70.00 80.00
Brown 10.00 30.00 10.00 20.00
Clear 10.00 30.00 10.00 30.00
Paper 100.00 100.00 100.00 100.00
Magazines 0.00 0.00 10.00 20.00
Softbound Books 10.00 0.00 10.00 0.00
White Paper 40.00 50.00 50.00 60.00
Paperboard 0.00 0.00 10.00 0.00
Mixed Paper 40.00 30.00 50.00 60.00
Newspaper 60.00 60.00 70.00 60.00
Cardboard 30.00 40.00 50.00 50.00

 

128

 

Table 5.4 (continued): The frequency of understanding for 116 concept areas comparing
undergraduate students living in four MSU residential areas, namely Akers (Ak),
Butterﬁeld (Bu), Holmes (Ho), and Hubbard (Hu) Halls.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Concept % Correct

Ak Bu Ho Hu

Why 56.16 54.17 67.69 57.50
Environmental Beneﬁts 100.00 100.00 100.00 100.00
Water 20.00 30.00 50.00 30.00
Land 30.00 50.00 60.00 60.00

Air 30.00 50.00 70.00 40.00
Enepgy 40.00 20.00 50.00 40.00
Economic Beneﬁts 100.00 100.00 100.00 100.00
Monetary Incentive 90.00 90.00 100.00 90.00
Deposit 80.00 90.00 100.00 90.00
Garbage Fees 0.00 10.00 30.00 20.00

Jobs 40.00 40.00 30.00 20.00
Tipping Fees 40.00 30.00 10.00 20.00
Social Beneﬁts 80.00 30.00 90.00 90.00
Awareness 80.00 60.00 90.00 90.00
Why Not 81.67 78.33 85.00 71.67
Disincentives 100.00 100.00 100.00 90.00
Time 70.00 90.00 100.00 70.00
Ease 100.00 80.00 90.00 70.00
Convenience 100.00 100.00 90.00 80.00
Space 20.00 20.00 30.00 20.00
Know-how 100.00 80.00 100.00 100.00
Alternatives 55.00 60.00 65.00 62.50
Reduce 30.00 50.00 60.00 50.00
Reuse 90.00 80.00 100.00 100.00

Surplus Store 0.00 10.00 0.00 0.00
Garbage 100.00 100.00 100.00 100.00

 

129

 

 

Table 5.5: Mean levels of understanding for 116 concept areas comparing undergraduate
students living in four MSU residential areas (Akers, Butterﬁeld, Holmes, and Hubbard
Halls). Means were compared using chi-square tests at the p=0.05 signiﬁcance level.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Concept Akers Butterﬁeld Holmes Hubbard P

E SE 2 SE 1? SE )7 SE
Where 1.73 0.40 1.75 0.39 1.89 0.38 1.80 0.38 n/s
Residence Hall 3.90 0.10 3.00 0.45 3.50 0.40 3.70 0.21 n/s
Location on Campus 3.90 0.10 2.80 0.44 3.30 0.40 3.50 0.31 NS
Location in Residence Hall 3.90 0.10 2.80 0.44 3.40 0.40 3.50 0.31 n/s
Dining/Concessions 2.30 0.52 2.40 0.54 2.40 0.48 2.40 0.48 n/s
Sports Venues 0.90 0.46 0.60 0.40 1.30 0.54 0.90 0.46 n/s
Residential Dining 1.60 0.48 1.70 0.58 2.20 0.49 0.70 0.40 n/s
International Center Food Court 0.30 0.30 0.40 0.40 0.30 0.30 0.60 0.34 n/s
Sparty’s Cafes 0.10 0.10 0.80 0.53 0.30 0.30 0.80 0.53 n/s
Campus and Academic Buildings 3.00 0.26 3.90 0.10 3.60 0.16 3.40 0.40 n/s
Location on Campus 2.70 0.40 3.80 0.13 3.40 0.16 3.40 0.40 n/s
Location in the Building 2.70 0.40 3.80 0.13 3.50 0.17 3.00 0.47 n/s
Campus-owned Apartments 0.00 0.00 0.10 0.10 0.40 0.31 0.50 0.31 NS
Location in Complex 0.00 0.00 0.00 0.00 0.10 0.10 0.00 0.00 n/s
Location on Campus 0.00 0.00 0.00 0.00 0.10 0.10 0.10 0.10 n/s
Processing Facility 0.70 0.33 0.20 0.20 0.50 0.31 0.50 0.17 n/s
What 1.58 0.20 1.31 0.20 1.56 0.21 1.43 0.19 n/s
Fibers 0.30 0.30 0.40 0.40 0.00 0.00 0.00 0.00 n/s
Fabric 0.60 0.40 0.70 0.47 0.30 0.30 0.40 0.40 n/s
Paper 3.90 0.10 3.90 0.10 4.00 0.00 4.00 0.00 n/s
Magazines 0.30 0.30 0.40 0.40 1.00 0.52 2.20 0.61 n/s
White Paper 3.30 0.40 3.40 0.31 3.80 0.13 3.70 0.15 n/s
Pre-Consumer 0.40 0.40 0.00 0.00 0.00 0.00 0.40 0.40 n/s
Post-Consumer 2.70 0.47 2.50 0.43 3.40 0.40 2.90 0.50 n/s
Paperboard 0.00 0.00 0.80 0.42 0.70 0.47 0.30 0.30 n/s
Mixed Paper 2.20 0.61 1.00 0.52 2.40 0.60 2.10 0.64 n/s
Junk Mail 1.70 0.58 1.30 0.54 1.10 0.57 1.40 0.58 n/s
Newspaper 3.90 0.10 4.00 0.00 4.00 0.00 3.40 0.40 n/s
Pre-Consumer 0.30 0.30 0.00 0.00 0.00 0.00 0.00 0.00 NS
Post-Consumer 2.40 0.54 3.00 0.52 3.10 0.53 2.90 0.50 n/s
Softbound Books 0.60 0.40 0.30 0.30 1.20 0.49 1.00 0.52 n/s
Cardboard 3.40 0.27 3.60 0.16 3.10 0.53 3.60 0.50 n/s
Non-Fibers “Containers” 3.80 0.13 2.90 0.35 3.20 0.39 3.60 0.16 n/s
Metals (Tin, Aluminum) 3.50 0.40 3.60 0.16 3.80 0.13 3.90 0.10 n/s
ScLap Metal 2.00 0.56 1.20 0.49 1.60 0.65 1.80 0.61 n/s
Food Cans 2.10 0.59 2.00 0.56 2.10 0.59 1.60 0.65 n/s
Pop Cans 3.60 0.40 3.40 0.40 3.90 0.10 3.30 0.40 n/s
Foil 0.30 0.30 0.30 0.30 0.00 0.00 1.80 0.61 n/s
Glass 3.60 0.16 3.40 0.31 3.50 0.17 3.10 0.46 n/s
Clear 1.90 0.53 0.90 0.46 1.60 0.54 1.10 0.50 n/s
Brown 0.60 0.40 0.50 0.34 0.30 0.30 0.60 0.40 n/s
Plastics 3.90 0.10 3.90 0.10 4.00 0.00 3.90 0.10 n/s
#1 PETE 3.70 0.15 2.80 0.49 3.20 0.39 2.30 0/63 NS
#2 HDPE (Cloudy) 2.30 0.52 1.00 0.52 2.10 0.59 2.10 0.59 n/s
#2 HDPE (Colored) 0.60 0.40 0.00 0.00 0.70 0.47 0.70 0.47 n/s

 

 

 

 

 

130

Table 5.5 (continued): Mean levels Of understanding for 116 concept areas comparing
undergraduate students living in four MSU residential areas (Akers, Butterﬁeld, Holmes,
and Hubbard Halls). Means were compared using chi-square tests at the p=0.05

signiﬁcance level.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Concept Akers Butterﬁeld Holmes Hubbard P
If SE 3? SE E SE 3 SE

Other 3.40 0.40 2.50 0.56 2.70 0.47 1.80 0.61 n/s
Fly Ash 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Construction Waste 0.00 0.00 0.70 0.47 0.30 0.30 0.70 0.47 n/s
Tires 0.40 0.40 0.40 0.40 0.30 0.30 1.10 0.57 n/s
Toner 0.00 0.00 0.00 0.00 0.30 0.30 0.00 0.00 n/s
Tennis Shoes 0.70 0.47 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Electronic Waste 1.10 0.57 0.7 0.47 1.00 0.52 0.40 0.40 n/s
Biolgjcal 2.20 0.61 0.80 0.53 1.60 0.54 1.10 0.57 n/s
Leaves 0.90 0.46 0.30 0.30 0.00 0.00 0.30 0.30 n/s
Grass 0.90 0.46 0.00 0.00 0.60 0.40 0.60 0.40 n/s
Branches 0.60 0.40 0.30 0.30 0.30 0.30 0.00 0.00 n/s
Food Waste 1.80 0.61 1.20 0.61 0.40 0.40 0.40 0.40 n/s
Manure 1.10 0.57 0.00 0.00 0.60 0.40 0.00 0.00 n/s
Hazardous Waste 0.40 0.40 0.30 0.30 1.60 0.54 0.70 0.47 n/s
Pallets 0.00 0.00 0.40 0.40 0.00 0.00 0.00 0.00 n/s
Batteries 0.60 0.40 1.10 0.57 1.40 0.58 1.10 0.57 n/s
Ink-Jet Cartridges 0.70 0.47 0.30 0.30 2.40 0.54 0.00 0.00 n/s

F lorescent Light Bulbs 0.00 0.00 0.00 0.00 0.00 0.00 0.30 0.30 n/s
How 1.94 0.25 1.64 0.22 1.97 0.23 2.01 0.21 0.05
Quality Control 3.60 0.22 3.40 0.40 3.60 0.40 3.30 0.40 n/s
Proper Sorting 3.20 0.42 3.40 0.40 3.20 0.53 2.80 0.49 n/s
Signs 1.60 0.54 1.50 0.62 1.90 0.64 1.50 0.62 n/s
Remov'mg Impurities 2.70 0.47 2.10 0.53 2.80 0.42 2.50 0.43 n/s
Clean 2.40 0.54 1.60 0.54 2.40 0.48 2.30 0.52 n/s
Remove Caps 1.60 0.54 1.10 0.57 1.00 0.45 0.70 0.47 n/s
Processing Facility 2.10 0.59 1.90 0.53 2.00 0.56 2.60 0.58 n/s
Pick-up 2.40 0.43 2.30 0.45 2.50 0.45 2.60 0.58 n/s
Self-Sorting 3.40 0.6 3.20 0.29 3.20 0.53 3.10 0.53 n/s
Non-Fibers “Containers” 3.60 0.16 2.00 0.56 2.60 0.58 2.80 0.42 n/s
Plastics 3.70 0.15 3.30 0.15 3.70 0.15 3.60 0.16 NS
#1 PETE 1.90 0.53 0.40 0.31 1.20 0.49 1.90 0.64 NS
#2 HDPE (Cloudy) 1.20 0.49 0.40 0.31 1.20 0.49 1.80 0.61 n/s
#2 HDPE (Colored) 0.30 0.30 0.30 0.30 0.60 0.40 0.70 0.47 n/s
Metals (Tin, Aluminum) 3.40 0.40 2.10 0.59 3.60 0.40 2.90 0.50 n/s
Glass 3.30 0.40 2.70 0.47 2.60 0.58 2.90 0.50 n/s
Brown 0.50 0.40 1.00 0.52 0.30 0.30 0.60 0.40 n/s
Clear 0.50 0.40 1.00 0.52 0.30 0.30 1.00 0.52 n/s
Paper 3.60 0.16 3.40 0.16 3.90 0.10 3.50 0.17 n/s
Magazines 0.00 0.00 0.00 0.00 0.30 0.30 0.80 0.53 NS
Softbound Books 0.30 0.30 0.00 0.00 03.0 0.30 0.00 0.00 n/s
White Paper 1.20 0.49 1.50 0.50 1.70 0.58 2.30 0.63 NS
Paperboard 0.00 0.00 0.00 0.00 0.30 0.30 0.00 0.00 n/s
Mixed Pﬂrer 1.20 0.49 0.90 0.46 1.70 0.58 2.20 0.61 n/s
Newspaper 1.80 0.49 2.00 0.56 2.40 0.45 2.10 0.59 n/s
Cardboard 0.90 0.46 1.20 0.49 1.80 0.16 1.80 0.61 n/s

 

I31

 

Table 5.5 (continued): Mean levels Of understanding for 116 concept areas comparing
undergraduate students living in four MSU residential areas (Akers, Butterﬁeld, Holmes,
and Hubbard Halls). Means were compared using chi-square tests at the p=0.05

signiﬁcance level.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Concept Akers Butterﬁeld Hohnes Hubbard P
76 SE )7 SE 2 SE )7 SE

Why 1.94 2.05 2.40 2.19 0.34 0.35 0.32 0.34 n/s
Environmental Beneﬁts 3.80 3.90 3.80 3.80 0.13 0.10 0.13 0.57 n/s
Water 0.60 0.90 1.60 1.10 0.40 0.46 0.54 0.59 n/s
Land 1.00 1.60 2.20 2.10 0.52 0.54 0.61 0.59 n/s

Air 1.10 1.60 1.30 1.60 0.57 0.54 0.52 0.65 n/s
Energy 1.40 0.60 1.80 1.40 0.58 0.40 0.61 0.58 n/s
Economic Beneﬁts 3.50 3.60 3.60 3.60 0.17 0.16 0.16 0.16 n/s
Monetary Incentive 3.20 3.30 3.70 3.30 0.39 0.40 0.15 0.40 n/s
Deposit 2.90 3.40 3.70 3.30 0.50 0.40 0.15 0.40 n/s
Gaﬁrge Fees 0.00 0.40 1.10 0.70 0.00 0.31 0.57 0.40 n/s

Jobs 1.20 1.20 1.10 0.60 0.49 0.49 0.50 0.40 n/s
Tipging Fees 1.20 0.0 0.30 0.60 0.40 0.46 0.30 0.40 NS
Social Beneﬁts 2.70 2.90 3.00 3.20 0.40 0.50 0.37 0.39 n/s
Awareness 2.60 2.30 3.00 3.20 0.45 0.63 0.37 0.39 n/s
Why Not 3.22 2.83 3.09 2.80 0.52 0.50 0.43 0.48 n/s
Disincentives 4.00 4.00 3.90 4.00 0.00 0.00 0.10 0.00 n/s
Time 2.80 3.00 3.60 2.70 0.61 0.37 0.16 0.60 n/s
Ease 3.90 2.60 3.40 2.60 0.10 0.45 0.40 0.58 n/s
Convenience 4.00 3.80 3.20 3.00 0.00 0.13 0.39 0.53 n/s
Space 0.80 0.60 1.00 0.70 0.53 0.40 0.52 0.47 n/s
Know-how 3.80 3.00 3.60 3.80 0.13 0.52 0.16 0.13 n/s
Alternatives 2.15 2.23 2.35 2.45 0.94 0.80 0.90 0.92 0.04
Reduce 1.20 1.70 1.90 2.10 0.44 0.45 0.53 0.64 n/s
Reuse 3.40 2.90 3.50 3.90 0.40 0.43 0.17 0.10 n/s
Surplus Store 0.00 0.30 0.00 0.00 0.00 0.30 0.00 0.00 n/s
Garbage 4.00 4.00 4.00 3.80 0.00 0.00 0.00 0.13 NS

 

132

 

Table 5.6: Mean levels of understanding for 116 concept areas comparing faculty
working in academic buildings judged to be “recycling friendly” (F) and “recycling
unfriendly” (U). Means were compared using chi-square tests at the p=0.05 signiﬁcance
level.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

% Correct Friendly Unfriendly
concept F UF 7; SE 3? SE P
Where 20.74 35.56 0.90 0.39 1.32 0.38 n/s
Residence Hall 0.00 22.22 0.22 0.15 0.78 0.43 n/s
Location on Campus 0.00 11.11 0.00 0.00 0.33 0.33 n/s
Location in Residence Hall 0.00 11.11 0.00 0.00 0.33 0.33 n/s
Dining/Concessions 11.10 55.56 0.56 0.34 1.89 0.61 n/s
Sports Venues 0.00 22.22 0.11 0.11 0.78 0.52 n/s
Residential Dining 0.00 33.33 0.11 0.11 1.11 0.56 n/s
International Center Food Court 11.11 33.33 0.44 0.34 1.00 0.50 n/s
Sparty’s Cafes 11.11 22.22 0.56 0.44 0.78 0.43 n/s
Campus and Academic Buildings 100.00 100.00 4.00 0.00 4.00 0.00 n/s
Location on Campus 88.89 100.00 3.67 0.33 4.00 0.00 n/s
Location in the Building 88.89 100.00 3.67 0.33 4.00 0.00 n/s
Campus-owned Apartments 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Location in Complex 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Location on Campus 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Processing Facility 0.00 22.22 0.11 0.11 0.78 0.52 n/s
What 36.23 40.10 1.32 0.21 1.49 0.19 n/s
Fibers 0.00 0.00 0.00 0.00 0.00 0.00 NS
Fabric 0.00 1 1.1 1 0.00 0.00 0.33 0.33 n/s
Paper 100.00 100.00 4.00 0.00 4.00 0.00 NS
Magazines 66.67 88.89 2.67 0.67 3.11 0.42 n/s
White Paper 100.00 100.00 4.00 0.00 3.78 0.15 n/s
Pre-Consumer 0.00 1 1.1 l 0.00 0.00 0.33 0.33 n/s
Post-Consumer 44.44 77.78 1.78 0.70 2.89 0.56 n/s
Paperboard 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Mixed Paper 100.00 100.00 4.00 0.00 3.67 0.17 NS
Junk Mail 22.22 0.00 0.78 0.52 0.00 0.00 n/s
Newspaper 100.00 88.89 3.89 0.11 3.56 0.44 NS
Pre-Consumer 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Post-Consumer 11.11 55.56 0.44 0.44 2.22 0.70 0.046
Softbound Books 33.33 22.22 1.00 0.50 0.78 0.52 n/s
Cardboard 55.56 55.56 2.11 0.68 2.11 0.68 n/s
Non-Fibers “Containers” 88.89 77.78 3.11 0.42 3.22 0.43 n/s
Metals (Tin, Aluminum) 88.89 100.00 3.22 0.43 3.78 0.15 n/s
Scrap Metal 55.56 44.44 1.78 0.57 1.33 0.53 n/s
Food Cans 33.33 55.56 1.00 0.50 1.78 0.57 n/s
Pop Cans 100.00 88.89 3.78 0.15 3.44 0.44 n/s
Foil 11.11 44.44 0.33 0.33 1.44 0.58 n/s
Glass 88.89 77.78 2.89 0.39 2.67 0.53 n/s
Clear 66.67 44.44 2.11 0.54 1.33 0.53 n/s
Brown 44.44 33.33 1.33 0.53 1.00 0.50 n/s
Plastics 88.89 88.89 3.22 0.43 3.44 0.44 n/s
#1 PETE 66.67 66.67 2.56 0.65 2.56 0.58 n/s
#2 HDPE (Cloudy) 44.44 55.56 1.33 0.53 1.89 0.54 n/s
#2 HDPE (Colored) 0.00 22.22 0.11 0.11 0.78 0.43 n/s

 

 

 

 

 

133

 

Table 5.6 (continued): Mean levels of understanding for 116 concept areas comparing
faculty working in academic buildings judged to be “recycling friendly” (F) and
“recycling unfriendly” (U). Means were compared using chi-square tests at the p=0.05
signiﬁcance level.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

% Correct Friendly Unfriendly
concept F UF 7; SE 3 SE P
Other 88.89 77.78 3.44 0.44 3.00 0.58 n/s
Fly Ash 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Construction Waste 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Tires 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Toner 11.11 22.22 0.44 0.44 0.89 0.59 n/s
Tennis Shoes 0.00 22.22 0.00 0.00 0.78 0.52 n/s
Electronic Waste 44.4 55.56 1.56 0.63 1.89 0.61 n/s
Biological 0.00 55.56 0.00 0.00 2.11 0.68 n/s
Leaves 0.00 1 1.1 1 0.00 0.00 0.44 0.44 NS
Grass 0.00 11.11 0.00 0.00 0.56 0.44 n/s
Branches 0.00 0.00 0.00 0.00 0.11 0.11 n/s
Food Waste 0.00 11.11 0.00 0.00 0.44 0.44 n/s
Manure 0.00 22.22 0.00 0.00 0.89 0.59 n/s
Hazardous Waste 11.11 0.00 0.44 0.44 0.00 0.00 n/s
Pallets 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Batteries 33.33 1 1.1 1 1.22 0.62 0.44 0.44 n/s
Ink-Jet Cartridges 44.44 33.33 1.56 0.63 1.33 0.67 n/s
F lorescent Light Bulbs 22.22 0.00 0.78 0.52 0.00 0.00 n/s
How 52.14 58.97 1.95 0.27 2.09 0.21 n/s
Quality Control 100.00 88.89 3.89 0.11 3.33 0.33 n/s
Proper Sorting 100.00 88.89 3.89 0.11 3.33 0.33 n/s
Signs 55.56 44.44 2.22 0.70 1.56 0.63 n/s
Removinglmpurities 33.33 88.89 1.33 0.55 3.11 0.31 n/s
Clean 33.33 44.44 1.33 0.55 1.78 0.57 n/s
Remove Caps 22.22 33.33 0.78 0.52 1.33 0.53 n/s
Processing Facility 0.00 22.22 0.00 0.00 0.78 0.52 n/s
Pick-up 55.56 44.44 2.11 0.61 1.44 0.58 n/s
Self-Sorting 100.00 100.00 4.00 0.00 3 .78 0.15 n/s
Non-Fibers “Containers” 88.89 88.89 3.11 0.42 3.22 0.43 n/s
Plastics 88.89 77.78 3.11 0.42 2.89 0.56 0.046
#1 PETE 44.44 66.67 1.67 0.67 2.22 0.57 n/s
#2 HDPE (Cloudy) 22.22 44.44 0.78 0.52 1.33 0.53 n/s
#2 HDPE LColored) 0.00 22.22 0.00 0.00 0.67 0.44 n/s
Metals (Tin. Aluminum) 77.78 77.78 2.78 0.55 2.89 0.56 n/s
Glass 66.67 66.67 2.44 0.63 2.33 0.60 n/s
Brown 22.22 33.33 0.67 0.44 1.00 0.50 n/s
Clear 33.33 44.44 1.11 0.56 1.33 0.53 n/s
Paper 100.00 100.00 3.78 0.15 3.67 0.17 n/s
Magazines 44.44 44.44 1.67 0.67 1.56 0.63 n/s
Softbound Books 0.00 22.22 0.00 0.00 0.78 0.52 n/s
White Paper 100.00 88.89 3.78 0.15 3.22 0.43 n/s
Paperboard 0.00 0.00 0.00 0.00 0.00 0.00 n/s
Mixed Paper 88.89 100.00 3.33 0.44 3.44 0.18 n/s
Newspaper 55.56 55.56 2.11 0.68 1.78 0.57 n/s
Cardboard 22.22 44.44 0.89 0.59 1.56 0.63 n/s

 

134

 

Table 5.6 (continued): Mean levels of understanding for 116 concept areas comparing
faculty working in academic buildings judged to be “recycling friendly” (F) and
“recycling unfriendly” (U). Means were compared using chi-square tests at the p=0.05

signiﬁcance level.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

% Correct Friendly Unfriendly

concept F UF 3' SE r SE P
Why 51.28 61.54 1.89 0.33 2.26 0.37 n/s
Environmental Beneﬁts 100.00 100.00 3 .78 0.15 4.00 0.00 n/s
Water 0.00 33.33 0.00 0.00 1.00 0.50 n/s
Land 66.67 88.89 2.56 0.65 3.11 0.42 n/s
Air 11.11 33.33 0.44 0.44 1.00 0.50 n/s
Energy 44.44 44.44 1.56 0.63 1.44 0.58 n/s
Economic Beneﬁts 77.78 100.00 3.00 0.50 3.89 0.11 n/s
Monetary Incentive 77.78 100.00 2.89 0.56 3.89 0.11 n/s
Deposit 77.78 88.89 2.78 0.55 3.44 0.44 n/s
Garﬁrge Fees 44.44 11.11 1.56 0.63 0.33 0.33 n/s
Jobs 22.22 44.44 0.89 0.59 1.56 0.63 n/s
Tipping Fees 11.11 22.22 0.33 0.33 0.67 0.44 n/s
Social Beneﬁts 66.67 66.67 2.44 0.56 2.56 0.58 n/s
Awareness 66.67 66.67 2.33 0.60 2.56 0.58 n/s
Why Not 61 .11 72.22 2.39 0.52 2.74 0.43 n/s
Disincentives 100.00 100.00 4.00 0.00 4.00 0.00 n/s
Time 44.44 55.56 1.67 0.67 2.22 0.70 n/s
Ease 66.67 66.67 2.56 0.65 2.33 0.60 n/s
Convenience 88.89 88.89 3.44 0.44 3.44 0.44 n/s
Space 11.11 33.33 0.44 0.44 1.11 0.56 NS
Know-how 55.56 88.89 2.22 0.70 3.33 0.44 n/s
Alternatives 61.1 1 75.00 2.39 0.64 2.89 0.44 n/s
Reduce 22.22 55.56 0.89 0.59 2.22 0.70 n/s
Reuse 66.67 100.00 2.44 0.63 3.67 0.17 n/s
Surplus Store 55 .56 44.44 2.22 0.70 4.00 0.67 n/s
Garbage_ 100.00 100.00 4.00 0.00 0.33 0.00 n/s

 

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146

MICHIGAN STATE UNIVERSlT IIIIIIIIII

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