Confident, but Confounded: Consumer Comprehension of Telecommunications Agreements

Abstract

Consumer wellbeing in relation to contractual agreements is founded on a basic principal of informed consent. Informed consent is usually ascertained by directly asking consumers, by means of a contract or agreement, whether they understand their obligations and rights under a contract. While this is testing the reflective capacity of consumers in relation to their belief that they have understood something, it is arguable that it is not actually measuring whether the consumer has actually understood the agreement. In other words, a person may claim to understand the implications of their signing a contract, but may fail to appreciate the possible consequences until they are presented with a particular challenge arising from or related to the terms of the contract.
Understanding the terms and conditions of a contract has also been repeatedly argued to be beneficial to the consumer. Policy makers, business, legal bodies, and economists contend that comprehending terms and conditions before embarking upon a service is necessary to avoid "unfair surprise, fraud, and collusion", especially in new "high-tech markets" such as telecommunications.
Having access to an appropriate amount of information, that consumers understand and are able to process in the context of the agreement protects consumers from suboptimal outcomes where they may be forced to make purchase decisions based on peripheral information, like a company's reputation or brand and price signals. However, there is significant evidence to suggest that consumers often do not adequately understand the contents of contracts, should they read them at all.
Amongst other things, we already know that consumers do not, or barely, read contracts upon purchase (ex ante). The reasons for this are many and varied and include that consumers have no choice but to accept if they wanted the underlying product, that they are too long and time consuming, that they are full of legal jargon, that they are all the same, that the level of detail in agreements are mostly irrelevant, and that vendors are usually reputable.
In the context of telecommunications, the industry has indicated long standing desire for more flexibility with fewer restrictions in the information provided on a mandatory basis to consumers, arguing that information should be provided as consumers require it. It has been contended that current mandatory consumer information requirements, particularly in terms of the amount of information that must be provided at point-of-sale, are not necessarily useful to consumers and result in substantial cost to the industry.
In this research, we sought to investigate consumer comprehension of telecommunications agreements, not by asking consumers whether they understood their agreements, which is the standard practice for achieving informed consent, but by testing whether consumers did understand their agreements, through a knowledge test. We found that while, in general, consumers displayed confidence in their self-assessed ability to understand telecommunications agreements, only a very small proportion were able to demonstrate adequate understanding of the standard operations and potential problems arising from telecommunications agreements.
Indeed, through our empirical research and knowledge test, we found that there was a negative relationship between self-assessed understanding and correct answer, that there was a positive relationship between those who rated the information as relevant to their needs and correct answers, that people with vocational qualifications did worse than all other educational levels, and that people whose first language is not English did worse than others.
We also found that different key stakeholders underestimated and overestimated the capacity of consumers to solve every day telecommunications problems. We found that, on average, regulators had the most realistic expectations of consumers’ understanding of their agreements, while consumer advocates underestimated, and telecommunications representatives overestimated consumer capacity to understand their agreements.

Full text

Confident, but
confounded
Consumer Comprehension of
Telecommunications Agreements

Confident,
but confounded
Consumer Comprehension of
Telecommunications Agreements
Dr Paul Harrison, Laura Hill and Charles Gray
September, 2016

“Confident, but Confounded: Consumer Comprehension of Telecommunications
Agreements”
Authored by Dr Paul Harrison, Laura Hill and Charles Gray.
Published in 2016
The operation of the Australian Communications Consumer Action Network is made
possible by funding provided by the Commonwealth of Australia under section 593 of
the Telecommunications Act 1997. This funding is recovered from charges on
telecommunications carriers.
Centre for Organisational Health and Consumer Wellbeing
Deakin University
Website: www.deakin.edu.au
Email: paul.harrison@deakin.edu.au
Telephone: +61 3 9244 6100
Australian Communications Consumer Action Network
Website: www.accan.org.au
Email: research@accan.org.au
Telephone: +61 2 9288 4000
If you are deaf, or have a hearing or speech impairment, contact us through the
National Relay Service: www.relayservice.gov.au
ISBN: 978-1-921974-44-1
Cover image: Design by Richard Van Der Male with images from Shutterstock, 2016.
This work is copyright, licensed under the Creative Commons Attribution 4.0
International License. You are free to cite, copy, communicate and adapt this work, so
long as you attribute “Paul Harrison, Laura Hill, Charles Gray, Deakin University and
the Australian Communications Consumer Action Network (ACCAN)”. To view a copy
of this licence, visit http://creativecommons.org/licenses/by/4.0/
This work can be cited as: Harrison, Paul, Hill, Laura, and Gray, Charles, 2016,
Confident, but Confounded: Consumer Comprehension of Telecommunications
Agreements, Australian Communications Consumer Action Network, Sydney.

1
Table of Contents
Table of Contents ......................................................................................................... 1
Figures and Tables ....................................................................................................... 4
Foreword ...................................................................................................................... 7
Executive summary ...................................................................................................... 8
Introduction ................................................................................................................. 10
Overview of previous research .................................................................................... 11
The importance of comprehending telecommunications contracts terms and
conditions ................................................................................................................ 11
Consumers tend not to read contracts initially (ex ante) .......................................... 11
Reasons most consumers do not read contracts ..................................................... 13
Consumers are more likely to read a contract when an issue arises (ex post) ......... 13
The complexity of telecommunications contracts ..................................................... 14
Low switching behaviour suggests complexity ......................................................... 15
The effect of information characteristics on written information comprehension ...... 15
Amount of information .......................................................................................... 16
Type of information .............................................................................................. 16
Language............................................................................................................. 16
Format and structure ........................................................................................... 17
The effect of reader characteristics on written information comprehension .............. 18
Demography ........................................................................................................ 18
Self-efficacy ......................................................................................................... 19
Prior knowledge ................................................................................................... 20
Financial Literacy ................................................................................................. 21
Motivation ............................................................................................................ 21
Research Method ....................................................................................................... 22
Online questionnaire ............................................................................................... 24
Delayed testing – 24 hours and 14 – 21 days after initial receipt ............................. 25
Development of questions ....................................................................................... 25
Self-assessed items ............................................................................................. 25
Knowledge test .................................................................................................... 26
Results ....................................................................................................................... 28

2
Approach ................................................................................................................. 28
Questionnaire One: demographics and self-assessed understanding of the contract
................................................................................................................................ 29
Demographics and manipulation of the data ........................................................ 29
Self-assessed factors/key constructs ................................................................... 31
Questionnaire Two and Three: Comparing the knowledge test across the two time
points ...................................................................................................................... 32
Questionnaire Three: analysis of all questions asked after a few weeks.................. 33
Relationship between different forms of information and performance on
knowledge test ..................................................................................................... 36
Some specific findings ......................................................................................... 37
Expectations of stakeholders ............................................................................... 38
Recommendations ...................................................................................................... 42
Conclusion and Future Research ................................................................................ 43
References ................................................................................................................. 45
Technical appendix ..................................................................................................... 55
Results .................................................................................................................... 56
Descriptive statistics: demographic factors .......................................................... 56
Descriptive statistics: key concepts ...................................................................... 65
Analysis of average number of common questions answered correctly in Stages 2
and 3 ................................................................................................................... 74
Analysis of average number of questions answered correctly in Stage 3 ............. 75
Questionnaire ............................................................................................................. 82
Stage One ............................................................................................................... 82
General Self-Efficacy (Schwarzer and Jerusalem, 1995) ..................................... 82
Satisfaction (adapted from Harris and Harrison, 2014)......................................... 82
Believability (adapted from Harris and Harrison, 2014) ........................................ 82
Relevance (adapted from McQuilken, Robertson, Polonsky and Harrison, 2015) 82
Understanding (adapted from McQuilken, Robertson, Polonsky and Harrison,
2015) ................................................................................................................... 83
Financial Literacy (OECD measures) - (1 = Strongly Disagree – 5 = Strongly
Agree) .................................................................................................................. 83
Demographic information ..................................................................................... 83
Stages Two and Three ............................................................................................ 85
Sales Summary Only (10 questions): ................................................................... 85

3
Sales Summary and CIS (7 questions): ............................................................... 86
All Three Documents (6 questions): ..................................................................... 87
Information provided to participants ......................................................................... 89
Sales Summary (SS) ........................................................................................... 89
Critical Information Summary (CIS) ...................................................................... 89
Terms and Conditions (T&Cs) .............................................................................. 93
Authors ..................................................................................................................... 113
Dr Paul Harrison .................................................................................................... 113
Laura Hill ............................................................................................................... 113
Charles Gray ......................................................................................................... 113

4
Figures and Tables
Table 1: Counts for all countries ................................................................................. 30
Table 2: Composite measures for self-assessed categories ....................................... 31
Table 3: Summary statistics for each key concept ...................................................... 32
Table 4: Significant factors from the difference of common questions model .............. 33
Table 5: Summary statistics on all questions asked at Stage 3 ................................... 34
Table 6: Significant factors of optimised regression model for all questions asked at
Stage 3 ....................................................................................................................... 35
Table 7: Summary statistics for questions by difficulty ................................................ 36
Table 8: Significant factors of the regression model for intermediate questions .......... 37
Figure 1: Comparison of key stakeholder expectations with knowledge test ............... 40
Figure 2: Comparison of key stakeholder expectations with knowledge test ............... 41
Table 9: Gender counts by group ................................................................................ 56
Table 10: Gender proportions by group ...................................................................... 56
Table 11: Counts for country of origin by group........................................................... 56
Table 12: Proportions for country of origin by group .................................................... 57
Table 13: Counts for all countries................................................................................ 57
Table 14: Counts for primary language spoken by group ............................................ 57
Table 15: Proportions for primary language spoken by group ..................................... 58
Aboriginal or Torres Strait Islander .............................................................................. 58
Table 16: Aboriginal or Torres Strait Islander Status by group .................................... 58
Table 17: Counts of age by group ............................................................................... 59
Table 18: Proportions of age by group ........................................................................ 59
Highest education level attained ............................................................................. 60
Table 19: Counts of highest education level attained by group ................................... 60
Table 20: Proportions of highest education level attained by group ............................. 60
Table 21: Counts for employment status by group ...................................................... 61
Table 22: Proportions for employment status by group ............................................... 62
Table 23: Counts for income by group ........................................................................ 63
Table 24: Proportions for income by group ................................................................. 63
Table 25: Summary statistics for each key concept .................................................... 65
Table 26: Summary statistics for each key concept for the SS knowledge group ........ 65

5
Table 27: Summary statistics for each key concept for the SS_CIS knowledge group 65
Table 28: Summary statistics for each key concept for the SS_CIS_TC knowledge
group .......................................................................................................................... 66
Table 29: Summary statistics of self-efficacy .............................................................. 66
Table 30: ANOVA for difference between mean self-efficacy scores by knowledge
groups ........................................................................................................................ 66
Figure 3: Boxplot of self-efficacy scores for each knowledge group ............................ 67
Table 31: Summary statistics for satisfaction score by groups .................................... 67
Table 32: ANOVA for difference in mean satisfaction between groups ....................... 68
Figure 4: Boxplot of satisfaction scores for knowledge groups .................................... 68
Table 33: Summary statistics for believability score by groups .................................... 69
Table 34: ANOVA for difference between mean believability scores by knowledge
group .......................................................................................................................... 69
Figure 5: Boxplot of believability scores for knowledge groups ................................... 70
Table 35: Summary statistics for relevance score by groups....................................... 70
Table 36: ANOVA for difference between mean relevance scores by knowledge groups
................................................................................................................................... 71
Figure 6: Boxplots of relevance score for knowledge groups ...................................... 71
Table 37: Summary statistics for understanding score by groups ............................... 72
Table 38: ANOVA for difference between mean understanding scores by knowledge
groups ........................................................................................................................ 72
Figure 7: Boxplots of understanding score by knowledge groups ................................ 73
Table 39: Summary statistics for financial literacy scores by groups ........................... 73
Table 40: ANOVA for difference between mean financial literacy score between groups
................................................................................................................................... 74
Figure 8: Boxplots for financial literacy by group ......................................................... 74
Table 41: Summary statistics on the difference between correctly answered questions
at Stage 3 and at Stage 2 ........................................................................................... 74
Table 42: Significant factors from the difference of common questions model ............ 75
Table 43: Summary statistics on all questions asked at Stage 3 ................................. 75
Table 44: Significant factors of regression model for all questions asked at Stage 3 ... 76
Table 45: Regression model for all questions asked at Stage 3 .................................. 77
Table 46: Summary statistics for elementary questions asked at Stage 3 ................... 77
Table 47: Significant factors from the regression model for elementary questions asked
at Stage 3 ................................................................................................................... 78
Table 48: Regression model for elementary questions asked at Stage 3 .................... 79

6
Table 49: Summary statistics for intermediate questions asked at Stage 3 ................. 79
Table 50: Significant factors of the regression model for intermediate questions ........ 79
Table 51: Regression model for intermediate questions .............................................. 80
Table 52: Summary statistics for advanced questions asked at Stage 3 ..................... 80
Table 53: Significant factors from the regression model for advanced questions asked
at Stage 3 ................................................................................................................... 80
Table 54: Regression model for advanced questions .................................................. 81

7
Foreword
ACCAN has identified the need for independent empirical evidence to reveal the extent
to which consumers understand the information provided to them by
telecommunications providers and factors that influence comprehension both at the
time of entering a contract and further down the track.
ACCAN originally approached Dr Paul Harrison and Deakin University in 2014 to
conduct research to inform our engagement with the telecommunications industry’s
Customer Information Obligations Framework
1
, designed as a roadmap for reviewing
industry regulatory obligations to provide specific information to customers. We were
aware that important consumer protections could be lost in the absence of an
independent evidence based approach, to the detriment of both industry and its
customers.
Consumer information is fundamentally important, but must be designed so that
customers can understand what they are buying, how to use their service and
constructively solve future issues that may arise. This is particularly the case in the
essential but complex area of telecommunications.
This research will guide ACCAN’s constructive contribution to future reviews of
telecommunications industry customer information obligations, at a time when
significant structural changes in the telecommunications market mean that consumers
will be offered greater choice of retail providers and services. It is hoped this research
will also be of value to regulators and policy makers, and lead to better outcomes
overall.
Una Lawrence, ACCAN
1
http://www.commsalliance.com.au/__data/assets/pdf_file/0007/44539/Customer-Information-
Obligations-Framework-Final.pdf

8
Executive summary
Consumer wellbeing in relation to contractual agreements is founded on a basic principal of
informed consent. Informed consent is usually ascertained by directly asking consumers, by
means of a contract or agreement, whether they understand their obligations and rights
under a contract. While this is testing the reflective capacity of consumers in relation to their
belief that they have understood something, it is arguable that it is not actually measuring
whether the consumer has actually understood the agreement. In other words, a person may
claim to understand the implications of their signing a contract, but may fail to appreciate the
possible consequences until they are presented with a particular challenge arising from or
related to the terms of the contract.
Understanding the terms and conditions of a contract has also been repeatedly argued to be
beneficial to the consumer. Policy makers, business, legal bodies, and economists contend
that comprehending terms and conditions before embarking upon a service is necessary to
avoid "unfair surprise, fraud, and collusion", especially in new "high-tech markets" such as
telecommunications.
Having access to an appropriate amount of information, that consumers understand and are
able to process in the context of the agreement protects consumers from suboptimal
outcomes where they may be forced to make purchase decisions based on peripheral
information, like a company's reputation or brand and price signals. However, there is
significant evidence to suggest that consumers often do not adequately understand the
contents of contracts, should they read them at all.
Amongst other things, we already know that consumers do not, or barely, read contracts
upon purchase (ex ante). The reasons for this are many and varied and include that
consumers have no choice but to accept if they wanted the underlying product, that they are
too long and time consuming, that they are full of legal jargon, that they are all the same, that
the level of detail in agreements are mostly irrelevant, and that vendors are usually
reputable.
In the context of telecommunications, the industry has indicated long standing desire for
more flexibility with fewer restrictions in the information provided on a mandatory basis to
consumers, arguing that information should be provided as consumers require it. It has been
contended that current mandatory consumer information requirements, particularly in terms
of the amount of information that must be provided at point-of-sale, are not necessarily
useful to consumers and result in substantial cost to the industry.
In this research, we sought to investigate consumer comprehension of telecommunications
agreements, not by asking consumers whether they understood their agreements, which is
the standard practice for achieving informed consent, but by testing whether consumers did
understand their agreements, through a knowledge test. We found that while, in general,
consumers displayed confidence in their self-assessed ability to understand
telecommunications agreements, only a very small proportion were able to demonstrate
adequate understanding of the standard operations and potential problems arising from
telecommunications agreements.

9
Indeed, through our empirical research and knowledge test, we found that there was a
negative relationship between self-assessed understanding and correct answer, that there
was a positive relationship between those who rated the information as relevant to their
needs and correct answers, that people with vocational qualifications did worse than all other
educational levels, and that people whose first language is not English did worse than
others.
We also found that different key stakeholders underestimated and overestimated the
capacity of consumers to solve every day telecommunications problems. We found that, on
average, regulators had the most realistic expectations of consumers’ understanding of their
agreements, while consumer advocates underestimated, and telecommunications
representatives overestimated consumer capacity to understand their agreements.
All of these findings and more are noted in the following pages.
Arising out of this research, we recommend the following:
Recommendation 1: As part of its current research on the operation of the TCP Code
since the Reconnecting the Customer Inquiry
2
, the ACMA should include an evaluation of
the CIS to determine the extent to which they assist consumers to understand the key
features of their agreement.
Recommendation 2: Telecommunications retailers should ensure that plans and market
offers are kept as simple as possible with clear elementary features that their customers
can easily understand.
Recommendation 3: In order to promote better understanding of telecommunications
contracts, more work is required by the industry to understand consumer needs during
the sale transaction and lifecycle of a contract, and to tailor the time delivery of core
information for maximum comprehensibility.
Recommendation 4: It is recommended that telecommunications retailers adopt a
proactive strategy by conducting follow up courtesy contact with new customers after
three billing cycles to see if the customer needs further assistance in understanding their
obligations.
Recommendation 5: Despite the small sample size, this research finds a need for expert
independent research to provide an evidence base when introducing or reviewing
customer information obligations, to reduce the risk of inaccurate presumptions about
consumer behavior informing regulatory obligations.
2
http://www.acma.gov.au/Industry/Telco/Reconnecting-the-customer/Public-inquiry/final-report-
reconnecting-the-customer-acma

10
Introduction
The Australian telecommunications industry has indicated a long standing desire for more
flexibility with fewer restrictions in the information provided on a mandatory basis to
consumers. It has been contended that current mandatory consumer information
requirements, particularly in terms of the amount of information that must be provided at
point-of-sale, are not necessarily useful to consumers and result in substantial cost to the
industry.
Constantiou (2009, p. 3) argues that consumers of telecommunications services have
"matured in terms of experiences and knowledge about available technologies, due to their
exposure to many technological solutions during the last decade". Consequently, it may be
that consumers have sufficient general knowledge pertaining to their rights and the average
terms and conditions of their contract so as to negate their need to receive specific
information up front. Moreover, many studies have shown that presenting too much
information to consumers at once can lead to 'information overload' and reduce
comprehension and retention (Amoriggi 2007; Hillman 2006a; Hillman & Rachlinski 2002;
Leong, Ewing & Pitt 2002; Xavier 2008).
However, before implementing any significant changes to the way in which consumers
receive their telecommunications contract information it is necessary to ensure that any
changes to consumer protections are supported by contemporary and relevant empirical
research. Although various studies have been undertaken on consumer comprehension of
non-telecommunications purchase contracts, other types of standard form contracts, product
instructions, health information and other consumer-related written material; no study has
specifically examined the degree to which Australian consumers comprehend the
information contained in their telecommunications contracts, nor the degree to which their
general knowledge may be a sufficient substitute. Such a study is therefore necessary to
fully substantiate the industry's assertions regarding the Australian consumer and to properly
inform the revision and application of the Communications Alliance Customer Information
Obligations Framework.
Moreover, studies to date have mainly tested participants’ comprehension immediately after
reading the information, neglecting to study the information’s retention in long-term memory
and how these memories might influence future behaviour. The purpose of this study,
therefore, is to empirically examine the accuracy with which consumers comprehend and
retain information provided in telecommunications contracts.

11
Overview of previous research
This review will examine the existing research on the comprehension of contracts and other
written material, as well as consumer behaviour within the telecommunications industry. It
will then discuss various methodologies employed by a number of similar studies in order to
develop an appropriate methodology for this study.
The importance of comprehending telecommunications
contracts terms and conditions
Legal literature indicates that standard form contracts, such as those used in
telecommunications, provide clear benefits to businesses. Through the existence of judicially
enforceable contract terms, businesses can reduce costs that might otherwise be associated
with bargaining (Hillman & Rachlinski 2002) and pass certain risks onto the consumer
(Broome & Hayes 1997).
Understanding the terms and conditions of a contract has also been repeatedly argued to be
beneficial to the consumer. Policy makers, businesses, legal bodies, and economists
contend that as well as enabling consumers to pass on certain risks to the seller, contracts
prevent businesses from exploiting consumers (Broome & Hayes 1997; Hillman & Rachlinski
2002; Slawson 1971). Epstein (2006, p. 208) argues that comprehending terms and
conditions before embarking upon a service is necessary to avoid "unfair surprise, fraud, and
collusion", especially in new "high-tech markets" such as telecommunications. Xavier (2008)
suggested that having too little or inconsistent quality can lead to consumers paying too
much, buying the wrong product or service, being disappointed with the product or service or
failing to participate in the market at all due to limited awareness of offerings. Other
researchers similarly explain that having access to an appropriate amount of information
protects consumers from suboptimal outcomes where they may be forced to make purchase
decisions based on peripheral information, like a company's reputation and price signals
(Hillman & Rachlinski 2002; Stark & Joplin 2010).
Yet these presumed consumer benefits rely on consumers’ comprehension of the contents
and operation of a contract. However, there is significant evidence to suggest that
consumers often do not adequately understand the contents of contracts, should they read
them at all.
Consumers tend not to read contracts initially (ex ante)
Studies have shown that consumers predominantly do not read standard form contracts
presented at the time of purchase, but sign them regardless. This is despite warnings to the
contrary, and potential legal sanctions. For example, in Becher’s and Unger-Avirams (2010)
study, only eight per cent, 19 per cent and 25 per cent of participants respectively read a

12
bank account, car rental or laundry contract prior to commencing the service – ex ante
3
.
Significantly, the smallest percentage of participants in this study read the bank account
contract, which may be similar in style and consequence to a telecommunications
agreement.
Studies involving online contracts that require website terms and conditions to be accepted
have yielded similar results. In Plaut & Bartlett's (2012, p. 297) study, over 80 per cent of
participants reported "not reading at all" or not "really reading" click through agreements
(CTAs). Bakows, Marotta-Wurgler and Trossen (2009) similarly found that only one or two in
1000 potential software buyers accessed the online licence agreement for at least one
second; and only 4 per cent of legal students in Hillman's (2006b) study claimed to read e-
purchase contracts as a matter of course (44 per cent did not read them under any
circumstances).
A traditionally upheld theory is that despite the majority of consumers not reading contracts,
the existence of an informed minority who do read them will ensure sellers cannot offer one-
sided terms or risk losing out in a competitive market (Salop & Stiglitz 1977; Schwartz &
Wilde 1979). Yet Gillette (2004, p. 32) explains that a reading minority is insufficient to
represent the non-reading majority due to possibly having "very different preferences" and
dealings with vendors.
Moreover, the very small number of participants who read the contracts in Bakows, Marotta-
Wurgler's and Trossen's (2009) study, for example, appears to debunk this notion. As
Hillman & Rachlinksi (2002) explain, if the number of savvy consumers is too small it may
not be worthwhile to compete for their custom.
Pertinently, the problem presented by the low number of consumers actually reading
contracts is well-known within the judicial system. Consequently, Llewellyn (1960) introduced
the notion of 'blanket assent' that currently dominates contract law (including the 2011
Australian Consumer Law), i.e., if a contract is presented formally and has reasonable
substance, consumers are assumed to agree to be bound by the broad type of transaction,
as well as any reasonable and decent terms included on the form that do not alter the
transaction's main meaning.
Accordingly, unread fine print cannot undercut the reasonable meaning of the main contract
terms (Green 2013; Meiklejohn 1994). Gillette (2004, p. 680) explains: "because the
recipient of terms cannot reasonably be expected to negotiate, review, or fully comprehend
[standard form contracts] that are drafted by more sophisticated and self-interested sellers,
the effectiveness of alleged contract terms becomes a matter for judicial scrutiny". This
same concept appears in the Australian Consumer Law (Competition and Consumer Act
2010 (Cth) sch 2), under which a term of a standard form consumer contract is deemed to
be void if it is “unfair”, regardless of the consumer’s acceptance of the contract. An unfair
3
76 per cent of consumers fully read a contract upon placing their child or children in a
nursery school, indicating the greater consideration consumers place upon a purchase
arrangement involving the care of their children.

13
term may include one that is not “expressed in reasonably plain language” and “presented
clearly”, but may also include a term that unfairly disadvantages one party in some way.
Reasons most consumers do not read contracts
Research consistently reveals several reasons consumers do not, or barely, read contracts
upon purchase (ex ante). These include that they are too long and time consuming (Becher
& Zarsky 2008; Plaut & Bartlett 2012), that they are full of legal jargon (Becher and Zarsky
2008; Masson & Waldron 1994; Wright 1971), that they are all the same (Epstein 2006), that
they are irrelevant (Plaut & Bartlett 2012), that consumers have no choice but to accept if
they wanted the underlying product (Hillman & Rachlinski 2002; Rakoff 1983), and that
vendors are usually reputable (Gillette 2004; Katz 1998; Stark & Joplin 2010).
The latter two reasons in particular pertain to a rational tendency to equate low probability
risks with zero probability risks; as well as to use heuristics, or processes and hunches that
simplify decision making, such as positive confirmation bias (Hillman 2006a; Hillman &
Rachlinski 2002; Goldstein & Gigerenzer 2002; Stark & Joplin 2010). There are also often
social norms and signals not to read the contract, such as the expectation to sign the form
and “keep moving” (Hillman & Rachlinski 2002; Stark & Joplin 2010). Ultimately, as Gillette
(2004, p. 680) explains, "failure to read may be perfectly rational, especially given the
inability to negotiate around terms, if the buyer accurately predicts that the costs of review
exceed its benefits".
Consumers have, however, been shown to be more inclined to read contracts ex ante when
a few conditions are met:
 The product or service cost is significant,
 The contract is perceived as short,
 There is a perceived likelihood of changing or influencing contract terms, and,
 The contract contains terms that are different from expected (Bechter & Unger-
Aviram 2010).
Accordingly, Plaut & Bartlett (2012) found that consumers could be enticed to read a CTA if
it were presented in a manner that suggested these conditions were met.
Consumers are more likely to read a contract when an issue
arises (ex post)
In contrast to the low readership of standard form contracts prior to signing them, a much
larger number of consumers have been found to read them once an issue arises that needs
to be addressed (ex post). Becher & Unger-Aviram (2010) found that the number of
participants who read the contract ex post, but not ex ante, more than doubled for the car
rental contract, almost tripled for the laundry contract and rose nearly seven times for the
bank account contract. The top three reasons consumers were motivated to read the
contract ex poste were:
1. The cost of the transaction,
2. The opportunity to learn, and,
3. The opportunity to change or improve contract terms (Becher & Unger-Aviram 2010).

14
Becher and Zarsky (2008) similarly found that consumers often review, and indeed read
properly for the first time, contracts ex post to acquaint themselves with their rights and
obligations. The relevant situations are where the product or service did not meet the
consumer’s expectations; for example, the product or service was not as the vendor
represented it to be; or the product arrived late or damaged, or malfunctioned. Often
consumers do not directly associate their complaint with a breach of a standardised term
they “supposedly agreed to” when originally signing the contract (p. 315), but equate it with
other matters they were not made aware of at the time of agreement.
Pertinently, Becher and Zarsky explain that comprehension is likely to be greater for
consumers reading ex post. Consumers reading ex post are not “prone to many of the
cognitive errors and biases” that normally plague the ex ante consumer (2008, p. 316). As
the breach or dispute has already occurred, consumers are better able to attend to the
originally “non-salient” but now relevant terms of the contract in dealing with the situation,
free from the initial vendor pressure to read and sign quickly.
Despite ex post reading resulting in greater consumer comprehension, the consumer must
deal with an arisen situation without the benefit of preventative measures or of being able to
negotiate terms. As Becher and Zarsky explain, “late recognition of flaws in contracts they
previously entered into will not change the terms of the contract between the parties” (2008,
p. 316).
Becher and Zarsky (2008, p. 320) consequently advocate for a greater flow of information
from those who have already purchased the product or service (ex post) to those who have
not yet signed the contract (ex ante) via online reviewing and social media, for example;
contending that the reading costs of such a process for the ex ante consumer are
“substantially lower than directly confronting the standard form contract terms”. However,
consumers often base such reviews on product attributes rather than on the specifics of
contract terms, limiting the efficacy of such media for this purpose (Becher & Tarsky 2008;
Chari 2010). Moreover, the rapidity with which new telecommunications contract plans and
schemes are introduced would further limit the effectiveness of relying on this kind of review-
based information scheme (Xavier 2008).
The complexity of telecommunications contracts
The complexity of standard form contracts is a particular barrier to readership. Cogan (2010)
examined in depth the complexity of health insurance contracts and determined that they
were constructed in language that was too difficult to understand by consumers and were
written for the benefit of the insurer rather than the consumer. Rameezdeen & Rodrigo
(2013) similarly found that university level reading skills or better were needed to
comprehend half the clauses in a standard form contract used in the construction industry.
These studies support previous research that has come to similar conclusions (Davis et al.
1996; Stark & Choplin, 2010; Wright 1981). Moreover, Stark and Choplin (2010, p. 89)
contend that consumers lack "contractual schemas or knowledge structures", and often have
"inaccurate default assumptions of how contractual provisions are likely to be structured and
whether the terms can be negotiated"; exacerbating their complexity.
Furthermore, telecommunications contracts have particular complexities that render them
especially difficult for consumers to read, understand and make good choices regarding their

15
content. The complex range of tariff structures and discount schemes; an intangible and
unpredictable range of communicants; the pace of technological advancement; the continual
changing of immediate telecommunication experiences and the multiplying effect of these
four in combination force the consumer to make complex, multidimensional value
judgements which are difficult, if not impossible (Lunn 2013; Xavier 2 008). Further,
consumers often fail to accurately anticipate their telecommunications service usage due to
overconfidence bias and procrastination inertia (Bar-Grill & Stone 2009; DellaVigna 2009;
Lambrecht & Skiera 2006; Lunn 2013).
Low switching behaviour suggests complexity
The relatively low switching behaviour demonstrated by telecommunications consumers
compared with other similar industries, despite relatively low financial switching costs
(Ofcom 2010; Xavier & Ypsilanti 2008), indicates the particular complexities involved in the
telecommunications agreement (Klemperer 1987; Lunn 2013). Lunn (2013) found that the
majority of consumers of telecommunications services do not even consider switching
provider over a one year period; and that the most important factors were transaction costs,
or the time and effort required to complete the initial administrative process; and learning
costs, which entailed the time and effort required to research other products and to learn to
exploit brand-specific attributes. These findings are unsurprising considering the difficulty
consumers have in making decisions that require balancing many different factors (Arkes,
Dawes and Christensen 1986; Harrison, Robertson and McQuilken 2012).
Other psychological factors, including the 'endowment effect', the ‘status quo bias' and
'ambiguity aversion' are also likely to play a part in this low switching behaviour (Ellsberg
1961; Samuelson & Zeckhauser 1988; Stark & Joplin 2010). However, the endowment effect
– whereby consumers are likely to more highly value a good they already own than one they
do not – has been shown to be exacerbated when consumers are relatively more uncertain
about the relative value of offerings than in other industries – as in the telecommunications
industry, due especially to the aforementioned complexities (Chatterjee, Inder & O'Brien
2003; Irmak & Rose 2013; Lunn 2013).
The low switching behaviour demonstrated by telecommunications consumers due to the
inherent complexities and value judgements required, as well as the frequency with which
consumers make bad decisions in this market, highlights the importance of consumers fully
understanding the terms and conditions surrounding their telecommunications contracts. It
also elucidates the particular complexities inherent in this particular type of contract.
The effect of information characteristics on written information
comprehension
Multiple characteristics of written information have been shown through various studies to
influence consumers' ability to comprehend it. The amount and type, language, format and
structure of the information have all been shown to significantly impact reader
comprehension.

16
Amount of information
Studies have indicated that presenting too much information to consumers at once can
cause 'information overload', preventing them from properly comprehending it (Amoriggi
2007; Hillman 2006a; Leong, Ewing & Pitt 2002). Consumer attention is limited, such that an
excess of information diminishes the attention consumers are able to pay to extra
information (Xavier 2008). Similarly, a 2007 study by The UK Better Regulation Executive
and National Council examined comprehension of real examples of regulated consumer
information; including safety warnings, information on extended warranties and credit card
agreements; and found that consumers often rejected much of the information because
there was too much of it. Conversely, in a contrasting but narrower study, the amount of
information provided was not a significant factor in comprehension of consumers choosing a
hospital (Zwijnenberg et al. 2012).
Several theorists have additionally discovered that having too much prior knowledge can
actually hamper comprehension. This will be discussed in the below section entitled ‘prior
knowledge’.
Type of information
The type of information presented has been shown to influence comprehension. For
example, explanative information – or facts connected by explanation of the relationships
between them – is more comprehensible than isolated facts presented with no such
explanation (Lim & Benbasat 2002). In Moorman's (1990) study, information on possible
negative consequences was utilised more effectively than information on positive
consequences; and consequence-related information was generally processed more easily
than non-consequence information - due to arousing greater motivation in the consumer to
comprehend it - but with no effect on comprehension accuracy (Moorman 1990). Further,
Kaphingst's, Rudd's, Dejong's and Daltroy's (2005) study on the comprehension of
medication advertisements saw 76 per cent of participants correctly answer questions about
medication benefits but only 26 per cent correctly answer a question about side effects.
Language
Various studies have shown that contracts that had been written in plain language are more
comprehensible to consumers than those written in legal jargon (Barnes 2010; Chapanis
1965; Houghton 1968; Masson & Waldron 1994; Stolle 1998). However, other theorists have
argued that the specific, legal narrative that needs to be conveyed in a standard form
contract necessitates specific language such that completely 'plain language' may remove
appropriate nuance and not suit this purpose (Barnes 2010; Broome & Hayes 1997; Kimble
1994; Rameezdeen & Rodrigo 2013). Additionally, Fish (1989) argues that advocating the
use of plain language depends on the assumption that language is capable of achieving
objective meaning, whereas it is not, as even language rules are documents that are subject
to interpretation. Nonetheless, Butt (2002) explains that skilful 'plain' language can
communicate directly and effectively with its intended audience without being complex, even
when it is used to describe legal issues.
Various word and sentence-based factors have been shown to influence the readability and
therefore comprehension of written information (Bailin & Grafstein 2001; Leong et al 2002).

17
Vocabulary difficulty (Anderson & Davison 1988; Kucera & Francis 1967; Thorndike 1921);
word complexity, including number of syllables (Harrison 1980; Leong et al 2002); syntactic
complexity, whether achieved through short or long sentences; grammar; style (Bailin &
Grafstein 2001); and textual coherence, or the presence or absence of explicitly-stated
logical connections are all significant (Harrison 1986).
The readability of a contract can be tested on a number of scales that have been developed
for this purpose; including the Flesch Reading Ease Formula, Flesch–Kincaid Grade Level,
Simple Measure of Gobbledygook, Automated Readability Index, Spache Readability
Formula, Coleman Liau Index, Gunning Fog Index, Raygor Estimate Graph, and Fry
Readability Graph and Vogel and Washvurne Formula (Anderson & Davison 1988;
Rameezdeen & Rodrigo 2013). Readability formulas generally elicit a reading age or grade
level, with one notable exception being Nicoll and Harrison's (1984) study, which multiplied
the reading difficulty level of a newspaper by its readership in order to develop a reading
level measure.
However, these readability formulas can be flawed. For example, measuring readability by
sentence length does not take into account that longer sentences can be more
comprehensible due to the addition of explanatory information (Pichert & Elam 1985; Wright
1971). Moreover, readability formulas may give an exaggerated impression of the
contribution of linguistic factors, whereas other text and reader properties that cannot be
measured by formulas probably have a far greater influence on comprehension (Anderson &
Davison 1988).
Using 'oral language', or personal pronouns in place of official terms like 'mortgagee', has
been shown to improve comprehension (Masson & Waldon 1994). According to Chafe and
Tannen (1987) this is due to the emphasis oral language places on interpersonal
involvement, as opposed to the emphasis written language places on information
transmission. Moreover, those with only basic literacy skill have been found to rely on oral
strategies in writing. Using oral language causes a reader to pay more attention to the
author's intention than the specific words used, improving comprehension (Olson 1980).
Format and structure
Various issues pertaining to format and structure have also been shown to affect the
comprehensibility of written information. For example, text accompanied by audio resulted in
more correct answers about its content than text alone (Kaphingst et al. 2005). Similarly,
including video and audio has been shown to facilitate the retention and subsequent recall of
explanative but not descriptive information (Lim & Benbasat 2002).
The use of graphics and imagery has been shown to aid comprehension due to adding
situational dynamics and explanation (Lowe & Pramono 2006). Waddill & McDaniel (1992)
found that pictures depicting details increased recall of those details and pictures depicting
relationships increased recall of that relational information (relative to a no-picture control
condition) due to presumably enabling additional processing to occur. Animations have also
been shown to have similar advantageous effects but with the advantage over static
graphics that they can "present dynamic aspects without the need for additional markings to
be incorporated within the display" (Lowe & Pramono 2006).

18
In Moore and Zabrucky's (1995) study, textual information presented online was
comprehended better than information presented in printed form – despite Hillman's (2006a)
unsubstantiated assertion that reading large amounts of text on a screen can be off-putting.
However, Moore and Zabrucky's study presented information in printed text form in a solid
block and the online text one sentence at a time, as enabled by the interactive online
medium.
Verbal information has been shown to be comprehended better than numeric information,
despite a greater avowed preference for information in numeric format (Vahabi 2010;
Zwijnenberg et al. 2012). In Vahabi's study greater comprehension occurred regardless of
whether numeric or verbal format was avowedly preferred by the participant.
Tables have also been shown to be useful for facilitating comprehension. Wright's (1971)
research showed that action sequences are better comprehended in tabular arrays than in
prose, especially if the reader knows what to look up; but a list of short sentences were
shown to be easier to remember than either table form or logical tree, which causes
consumers to visualise the information.
Further, consumers tend not to read information from the beginning to end, indicating the
need for adequate signposting and cross-referencing (Wright 1981). Converse to common
writer assumption, readers will not necessarily start from the beginning and read through in
order but rather jump about, looking for information which catches their eye and seems
relevant (Brake 1980). Further, indexes and contents lists assist comprehension by helping
readers find answers to questions. Similarly, logical trees can be useful if the consumer
needs help in finding the relevant part of the information (Better Regulation Executive 2007;
Wright 1971).
The effect of reader characteristics on written information
comprehension
Demography
The significance of demographic factors in comprehension has been examined in several
studies. For example, Zwijnenberg et al (2012) found that demographic characteristics
including age, socio-economic status, and literacy were the most significant factors affecting
the comprehension of written information regarding choosing a hospital. Age has been found
to be a significant factor in the ability to comprehend written information (Miller al. 2009;
Moore & Zabrucky 1995; Moorman 1990; Zwijnenberg et al. 2012). Controlling for other
factors, comprehension of written material decreased with age for adults (Miller et al. 2009).
In Moore and Zabrucky's (1995) study younger adults spent less time reading texts and
recalled more information than older adults, regardless of the format of the information.
Zwijnenberg and colleagues (2012) explained that a decline in our deliberative, analytical
and conscious thinking occurs with age, resulting in difficulty controlling attention and
monitoring the accuracy of information in memory, particularly in unfamiliar or less
meaningful situations. Pertinently, Moorman (1990) found that despite the clear worsening of
comprehension abilities with age, it increased readers' perceptions of their abilities.
Mixed results have been found on the effect of income, with Donelle, Hoffman-Goetz,
Gatobu and Arocha (2009) linking higher income to greater comprehension ability but Davis

19
and colleagues (1996) finding no correlation. Stark and Joplin's (2010) findings adhere to the
former: consumers of lower socioeconomic status were more likely to trust the contract
vendor, reducing critical comprehension. This is in line with Richter and Rapp's (2014)
research that revealed trust in the vendor influenced reader's expectations about the
plausibility of expectations.
Literacy, strongly correlated with education level, was found by multiple studies to be
positively associated with the ability to comprehend written information (Donelle et al. 2009;
Kaphingst et al. 2005; Vahabi 2010; Zwijnenverg et al. 2012). Wittwer and Ihme (2014)
discovered that less skilled readers were more likely to be influenced in their judgment of the
coherency of prose by semantic similarity between sentences, whereas more skilled readers
are more likely to be influenced by the presence of verbs that indicate causation between
sentences.
In a similar vein, Mason, Meaden-Kaplansky, Hedin and Taft (2013, p. 69) explain that
"students who struggle with learning may not have the metacognition needed to support the
multiple processes required to understand what is read in informational text". Moreover,
Waddill and McDaniel (1992) ascertained that pictures did not appear to compensate for
poorer comprehension resulting from lower literacy levels.
Self-efficacy
Self-efficacy, or beliefs about one’s capabilities to learn or perform behaviours at specified
levels (Bandura 1986, 1987), has been shown to predict people’s performance on written
tasks and the learning of written information (Bouffard-Bouchard 1989; Bouffard-Bouchard,
Parent and Larivee 1991; Pajares 1996; Schunk 1995, 1996, 2003). People acquire
perceptions of their own efficacy through comparing their performances with those of others,
in particular with those similar to themselves (Schunk 1987); and by receiving feedback and
reinforcement, including via performance accomplishments (Schunk 2003). Greater self-
efficacy promotes performance through increasing motivation, which in turn raises effort and
persistence (Bandura 1986; Schunk 2003); especially for text-based learning (Salomon
1984).
For example, those with high efficacy are more likely to view tasks, including
comprehensions tasks, as challenges that will result in learning and work diligently to master
them despite barriers and setbacks; while those with low efficacy may attempt to avoid tasks
due to perceiving that they will have difficulty and may not learn anything, becoming “more
self-diagnostic than task diagnostic” (Bandura & Dweck 1987; Bandura 1997; Schunk 1991,
2003; Wood & Bandura 1989 p. 408). This adheres to the expectancy-value theory of
motivation espoused by Wigfield and Eccles (2000), which highlights the importance of belief
in one’s own ability in promoting cognitive performance.
Wood and Bandura (1989) demonstrated the impact of self-efficacy on cognitive
performance, including through its effect on analytic strategies, by manipulating self-efficacy
in participants. In this experiment, participants who were instilled with the belief that the skill
required to complete a cognitive task was acquirable displayed strong and resilient self-
efficacy and therefore used analytic strategies in efficient ways, ultimately succeeding at the
task. Conversely, participants who were told that the skill was inherent and fixed rather than
acquirable demonstrated reducing self-efficacy as they encountered problems. This resulted

20
in increasingly erratic analytic thinking, lowering motivation and progressively deteriorating
performance. Consequently, it is appropriate to consider self-efficacy in our study, as it may
influence consumers’ comprehension of telecommunications information or their ability to
convey that comprehension via a knowledge test.
Prior knowledge
The effect of prior knowledge on comprehension has been found variously to have both
positive and negative effects on comprehension. Prior related knowledge – including
technical competence – was shown by several researchers to positively influence a reader's
ability to comprehend written information (Bailin & Grafstein 2001; Celsi & Olson 1988;
Miller, Gibson & Applegate 2009; Pearson, Hansen & Gordon 1979). Cook and Brien (2014)
found that relevant prior knowledge became activated upon reading narrative text and
affected validation of the information, positively influencing comprehension. Moreover, Celsi
and Olson (1988, p. 213; 222) found that "domain knowledge" influenced the "types of
meanings produced by the comprehension processes" and was "increasingly influential" as
comprehension became more controlled and focused, as per reading contracts to
understand the contents.
Pearson, Hansen and Gordon (1979) similarly found that prior knowledge influenced
children’s comprehension. Unsurprisingly, they found that the effect was more pronounced
when textually implicit questions that required inferable prior knowledge were asked than
when explicit questions were asked. Implicit questions were those that required the reader to
refer to prior knowledge, such as “what part of Webby’s body is nearly the same as part of a
snake’s body?” where there was no reference to a snake in the text. By contrast, explicit
questions asked for information that could be found in the text, such as “what does Webby
bite insects with?” (p. 204).
Conversely, Moorman’s (1990) study found that prior knowledge of the subject matter
actually reduced information elaboration and processing due to the resulting “illusion of
being more informed than one really is” - akin to other studies that have revealed the
detrimental effect of knowledge-based overconfidence (Camerer, Loewenstein & Weber
1989; Hall, Ariss and Todorov 2005). However, Moorman’s study focused only on high levels
of or no prior knowledge. In contrast, studies that have focussed on participants with a
moderate level of prior knowledge have found it to result in greater processing and therefore
comprehension (Bettman and Park 1980; Johnson & Russo 1984).
Other theorists have similarly found that too much information can have a negative effect on
decision-making ability, even if the consumer perceives that they have comprehended the
information. This has been shown to be due to the inability to ignore unhelpful prior
knowledge (Camerer, Loewenstein & Weber 1989), specific decision-making biases that
arise through familiarity and an assumption of being knowledgeable (Hall, Ariss & Todorov
2005) and a tendency to engage in less information processing due to the assumption of
knowledgeability (Moorman 1990).
Similarly, Andersen and colleagues (Lewis & Anderson 1976; Reder & Anderson 1980) have
found that utilising prior knowledge to judge or comprehend new material can actually
hamper comprehension due to an ‘interference effect’, whereby the more one knows about a
topic, the harder it is to retrieve any specific facts about it. Their finding is underpinned by

21
their contention that humans make judgments based on themes rather than facts, and
experience more interference according to the more themes that are known about a
particular concept. However, these theorists contend that if only an overall consistency
judgment is required without the retrieval of a specific fact, the ‘interference effect’ can be
mitigated.
Financial Literacy
Financial literacy may be an important concept to consider in the context of
telecommunications contracts in light of the effects of prior knowledge, including relevant
technical knowledge, on the comprehension of written information. Financial literacy, or an
individual’s knowledge and skills required to efficiently manage their financial resources
(Huston 2010; Remund 2010), has been shown to be positively correlated with achievement
of optimal financial decisions (Huang, Nam & Sherraden 2013), even after controlling for
socioeconomic characteristics (Lusardi & Mitchell 2011).
For example, as well as resulting in less retirement planning, saving (Behrman et al. 2010;
Lusardi and Mitchell 2006, 2008, 2011), wealth accumulation and stock investment
(Christelis, Jappelli, and Padula 2010; van Rooij, Lusardi, and Alessie 2007); low financial
literacy is associated with paying higher fees and interest rates (Lusardi & Tufano 2009).
This raises the question of whether those with lower financial literacy have more difficulty
comprehending financial information from service providers. Moreover, it indicates the need
to consider financial literacy – with lower rates more prevalent among younger people,
women, the unemployed and the less educated (Agnew 2013) – in our study of consumers’
comprehension of telecommunications contracts.
Motivation
Studies have generally shown that motivation to comprehend – particularly in terms of
interest – plays a more significant role in actual comprehension than prior knowledge (Celsi
& Olson 1988; Klare 1975; Miller et al. 2009; Moorman 1990; Wright 1981). Celsi and Olson
(1988) found that the influence of prior knowledge in increasing comprehension became
more pronounced as motivation to understand the contents increased. Additionally, Miller,
Gibson & Applegate (2009) found that although motivation to understand the information did
not directly affect comprehension accuracy, it influenced attention, which in turned
influenced accuracy.
In a similar vein, Celsi & Olson (1988) described the phenomenon of 'felt involvement', or an
individual's level of perceived personal relevance to the information contents. They defined
'felt involvement' as being influenced by physical and social aspects of the immediate
environment and intrinsic characteristics of the individual. Their study revealed that 'felt
involvement' positively influenced attention to and comprehension of information, as well as
the "number and types of meanings produced by the comprehension processes" (p. 221).
Moreover, they found that this 'felt involvement' was a more significant factor than the prior
knowledge of the consumer. However, this research also underscores the difficulty of
separating consumer motivation and prior knowledge and highlights their interactive effect.

22
Research Method
Previous studies that have tested for comprehension of written information vary in
methodological aspects of their procedures but tend to converge on many aspects. Most
undertook written pre-testing for various participant characteristics; including prior knowledge
pertaining to the information being presented, such as knowledge of nutrition or familiarity
with CTAs (Donelle et al. 2009; Miller et al. 2009; Pearson et al.1979; Plaut & Bartlett 2012);
motivational factors (Batra & Ray 1986; Celsi & Olson 1988; Miller et al. 2009; Plaut &
Bartlett 2012); prose and numeric literacy skills (Donelle et al. 2009; Kaphingst et al. 2005)
and demographics; including age, sex, preferred language, ethnicity, years of formal
education, income and location of birth (Donelle et al. 2009; Kaphingst et al. 2005).
Thomson & Hoffman-Goetz (2011) conversely conducted this pre-testing in a verbal one
hour interview.
These studies then either separated participants into groups based on the participant
characteristics they had tested for; such as prior knowledge, literacy skills and demographics
(Donelle et al. 2009); or simply noted the variables for statistical analysis later (Davis et al.
1996; Kaphingst et al. 2005). By contrast, Waddill & McDaniel (1992) asked participants to
rate their degree of prior knowledge after they had completed the free-recall comprehension
task.
Studies then commonly presented information to the participant in written form (Davis et al.
1996; Donelle et al 2009; Pearson et al. 1979; Thomson & Hoffman-Goetz 2011). Studies
testing for the effect on comprehension of different formats also presented participants with
the same information in different formats (Donelle et al 2009; Miller et al. 2009; Moore &
Zabrucky 1995; Vahabi 2010; Waddill & McDaniel 1992) and using different types of
language (Masson & Waldron 1994). The length of time it took to read the written information
was often recorded (Davis et al. 1996; Masson & Waldron 1994; Moore & Zabrucky 1995).
In many studies participants were then asked to self-rate their comprehension of the material
using either a multi-point scale (Waddill & McDaniel 1992) or multiple choice questions such
as ‘did you understand the passage’: ‘no’, ‘sort of’ or ‘yes’ (Moore & Zabrucky 1995). Some
studies also asked participants to qualitatively discuss the perceived usefulness and clarity
of the information (Zwijnenberg et al. 2012; Better Regulation Executive 2007; Kaphingst et
al. 2005) and their attitude towards the contract (Broome and Hayes 1997). The latter
questioning may be important due to findings that the perceived usefulness of information
influences its likelihood of being comprehended (Zwijnenberg et al. 2012), as does the
perceived credibility of the source (Richter & Rapp 2014; Plaut & Bartlett 2012).
The majority of studies also directly tested for their comprehension of the written information:
a more accurate approach than self-reporting due to the latter’s known limitations,
particularly in terms of validity being affected by deliberate or unconscious deception,
misunderstanding of the queries or a lack of accurate knowledge responses (Crawley 2010;
Stapleford 2012). This was done through a variety of methods, including asking participants
for a written long answer description of what was understood (Celsi & Olson 1988; Waddill &
McDaniel 1992), a verbal long answer description (Moore & Zabrucky 1995), verbal short

23
answer questions (Pearson et al. 1979), a written paraphrasing of segments (Masson &
Waldron 1994), answers to true-false questions (Kaphingst et al. 2005), written short answer
questions (Davis et al. 1996; Kaphingst et al. 2005; Vahabi 2010; Zwijnenberg et al. 2012),
written multiple choice content questions (Miller et al. 2009), and a ‘fill in the blank’ Cloze
test (Rameezdeen & Rodrigo 2013; Thomson & Hoffman-Goetz 2011).
Health-related studies also used specific comprehension tests that have been developed for
health information, such as the S-TOFHLA, and REALM (Thomson & Hoffman-Goetz 2011).
It is possible that some of these tests could be modified for use in a non-health context. The
REALM is a word recognition test that requires participants to read health-related words like
‘osteoporosis’ and ‘allergic’ aloud: it is consequently unlikely to be useful for this study due to
the lower likelihood of specific unfamiliar terms being included in a telecommunications
contract (Bass, Wilson & Griffith 2003). However, the S-TOFHLA utilises a modified Cloze
procedure wherein every fifth to seventh word is omitted and participants need to ‘fill in the
gaps’ with the missing information, and so could be easily adapted and utilised in a non-
health context, as per the Cloze procedure utilised by Rameezdeen and Rodrigo (2013) and
Thomson and Hoffman-Goetz (2011).
Some contract-specific studies also directed participants to respond to certain hypothetical
scenarios – such as a bank debit card being stolen and used for unapproved purchases and
a rental car not working – in order to determine participants’ ability to apply the terms and
conditions of the contracts to real world contract-related problems and to make judgments
about the rights of parties, thereby indicating their true comprehension of the material
(Becher’s & Unger-Aviram's 2010; Masson & Waldron 1994). Considering the importance of
understanding the degree to which consumers are able to actually utilise and enact the
terms of their telecommunications contracts, rather than simply be superficially cognisant of
them; such a real-world scenario-based testing method is appropriate for this study.
In this study, participants were allocated into three randomised groups, and provided with
three different levels of information, viz., Sales Summary Only (SS) = Elementary
information, Sales Summary and Critical Information Summary (SS_CIS) = Intermediate
information, and Sales Summary, Critical Information Summary, and Terms and Conditions
(SS_CIS_T&C) = Advanced information. Each of these sources of information were
developed from “real-world” telecommunications documents, except for the sales summary,
which was developed after the researchers visited a series of different telecommunications
retailers and asked them to talk them through a standard purchase for the type of agreement
contained in the CIS and T&C. This sales summary was then tested with an expert panel for
authenticity, and was also validated with participants in the study (see results in the technical
appendix).
After allocation into the three groups, participants were provided with the appropriate
information, and asked to read it thoroughly. After they had read the information, they were
then directed to an online questionnaire that measured their response to the information
provided, as well as demographic questions (Stage One).
Participants were contacted 24 – 48 hours (Stage Two) after first reading the material
provided and the completion of the Stage One questionnaire, and provided with a series of
scenarios common to people using telecommunications products (for details in relation to the
questions asked and their development, see Knowledge Test section). Participants were

24
provided with access to the original information via a web-link, to assist them to complete
their component of the scenarios.
As a final test of both comprehension and also of retention of the information, respondents
were contacted 14 – 21 days after the first contact, and asked to provide the answers to
problems from Stage Two, as well as to additional problems related to the information
provided in the SS, CIS and/or the T&C.
Justification for both the online distribution and for the method used in Stages Two and
Three is provided over the next three pages.
Online questionnaire
As utilised by many studies discussed in this review (Celsi & Olsen 1988; Donelle et al.
2009; Miller et al. 2009; Moore & Zabrucky 1995; Waddill & McDaniel 1992; Vahabi 2010), a
written questionnaire, including a combination of multiple choice and open-ended questions
is the most appropriate methodology for this study.
Compared with verbal interviewing, such as that undertaken by Pearson, Hansen and
Gordon (1979), written questionnaires are relatively easy to construct and administer; enable
a large amount of information to be obtained; can have a relatively high reliability when fixed-
response questions are used; and are relatively simple to code, analyse and interpret (Brace
2013).
Yet, despite the majority of researchers electing to utilise hard copy written form for their
questionnaires - with one notable exception being Miller, Gibson and Applegate (2009) -
questionnaires presented in an online format have been shown to have numerous
advantages over those presented in other formats. The advantages of online questionnaires
include greater flexibility of and control over format; reduced response time; lower cost;
interactivity; convenience; ease and accuracy of data entry and analysis; control of answer
order; required completion of answers; recipient acceptance of the format and the ability to
obtain additional response-set information for more relevant results (Bryman 2012; Evans &
Mathur 2005; Granello & Wheaton 2004; Malhotra 2012; Terhanian & Bremer 2012; Ward et
al. 2014). Online presentation has also been shown to enhance comprehension (Moore &
Zabrucky 1995), particularly through graphics and other electronic media, which can provide
variety, stimulate choice, and enhance or clarify questions (Granello & Wheaton 2004; Lowe
& Pramono 2006; Waddill & McDaniel 1992). Moreover, participants perceive their
anonymity is better protected when completing online questionnaires, reducing the risk of
social desirability bias (Brace 2013; Randall & Fernandes 1991; Ward et al. 2014).
Consequently, this study utilises an online questionnaire.
Further, utilising a combination of multiple-choice and open-ended questions enables the
benefits of these different kinds of questions to be reaped in combination. For example,
multiple-choice questions render it easy to code and compare the resulting data, while open-
ended questions enable richer qualitative data to be obtained (Bryman 2012; Malhotra
2014).

25
Delayed testing – 24 hours and 14 – 21 days after initial receipt
In most studies featured in this review participants were only tested immediately after
reviewing the written information. It is conversely important to determine the extent to which
consumers retain telecommunications information in their long-term memory, as consumers
who need to refer to their contracts' terms are likely to need to do so after weeks or months
have passed. Consequently, this study adds two stages of re-testing of comprehension, both
24-48 hours and 3–4 weeks after the point-of-sale to determine retention.
Development of questions
In the following paragraphs, we outline our approach in relation to the development of the
two question types, viz., self-assessed items and the knowledge test.
Self-assessed items
General self-efficacy
As discussed earlier, self-efficacy, or beliefs about one’s capabilities to learn or perform
behaviours at specified levels (Bandura 1986, 1987), has been shown to predict people’s
performance on written tasks and the learning of written information (Bouffard-Bouchard
1989; Bouffard-Bouchard, Parent and Larivee 1991; Pajares 1996; Schunk 1995, 1996,
2003). Multiple scales exist that measure self-efficacy in a range of different contexts,
however, the General Self-Efficacy scale developed by Schwarzer and Jerusalem (1995) is
commonly used to assess a general sense of perceived self-efficacy in relation to how
people cope with daily hassles. In particular, our scale used ten items that measure
elements of perceived self-efficacy such as goal-setting, effort investment, persistence in
facing barriers and recovery from setbacks. The scale has been validated in multiple
contexts including health provision (Schwarzer and Jerusalem 1995), parenting (Sanders
and Woolley 2005), workplace stress (Semmer 2003), smoking cessation (Harris and
Harrison 2014), use of the internet (Schwarzer et al 1999) and education (Gregoire 2003).
The ten items in the scale are noted in in the technical appendix.
Financial literacy
Financial literacy was measured using items taken from the OECD Measuring Financial
Literacy (2011) guidelines and questionnaire, and adapted to this study. As other elements
of this study assessed demographic characteristics, along with self-assessment measures
(see self-efficacy, understanding, and relevance measures), nine questions were taken from
the overall study that were considered to have face validity in measuring financial literacy.
These items were tested later in the study for convergent and discriminant validity (from
other concepts measured in the self-assessed measures).
Satisfaction, believability, understanding and relevance of the
information provided
Measures of satisfaction, believability, understanding and relevance of the information
provided in the sales summary, critical information summary and terms and conditions were
used to ascertain participants’ assessment of the contractual information presented to them.
Each of these concepts were measured using validated scales developed by Harris and

26
Harrison (2014) and McQuilken et al (2015) and adapted to this context, to measure
consumer responses to advertising and information communications in consumer
experiments.
Satisfaction with the information provided was included to measure respondents’ belief that
the information they were provided, viz., Sales Summary, Critical Information Summary,
Terms and Conditions, was satisfactory for them to make decisions related to purchasing
telecommunications products, and consisted of the following questions: I was very satisfied
with the information about the agreement; The information that I received in the preceding
paragraphs was helpful; I am happy with the amount of information I have received in
relation to the telco agreement; The information I received in the preceding paragraphs
would be enough information for me to consider agreeing to sign-up for a SIM card.
Responses were measured using a five-point Likert scale with 1 = Strongly Disagree and 5 =
Strongly Agree.
Believability of the information provided was included to measure consumer attitudes in
relation to whether the SS, CIS and/or T&C were believable in the context of their
experience of these types of information sources. The measurement of believability in
hypothetical scenarios is a common approach to gain participants attitude toward the
scenarios (McQuilken et al 2015). Similar to the satisfaction measure, and other metrics in
the self-assessment category, we used a five-point Likert scale, with 1 = Not at all, 5 =
Completely, and consisted of three questions, viz., How close to the reality of a telco
agreement is the information that has been provided to you; How authentic is the information
provided; How likely is this information to be the kind of information a telecommunications
provider would give you if you were considering using their services?
We measured self-assessed understanding of the information provided in the documentation
using the following items taken from McQuilken et al (2015) which has been validated in the
telecommunications context; I have understood the information contained in the [insert
documentation received]; I believe that I could solve basic problems with my phone plan with
the information provided; I did not understand the agreement with the telecommunications
company; The agreement was too complex, and, I was not sure what my rights were under
the agreement.
We also tested respondents’ belief that the information provided would be relevant to them
making decisions in a telecommunications context. The questions were adapted from
McQuilken et al (2015), viz., The information provided would be relevant in my consideration
of the SuperMobile Smartphone Plan; The information provided would be useful in my
consideration of a SuperMobile Smartphone Plan; The amount of information provided would
be appropriate in my consideration of a SuperMobile Smartphone Plan.
Knowledge test
In the following section, we describe the process of development for the knowledge test.
Development of the knowledge test
By using material from the Sales Summary (SS), Critical Information Summary (CIS), and
Terms and Conditions (T&C), we developed a series of questions or scenarios that could be
solved or understood by reference to the key documents. As such, questions (n = 10)

27
classified in the “elementary” category were taken from information provided in the SS,
questions (n = 7) in the “intermediate” category were taken from information in the CIS, and
questions (n = 6) in the “advanced” category were taken from information contained in the
T&C. Details of these questions are provided in the Technical Appendix.
An example from the elementary category is, “How much data per month is included in the
agreement?”, from the intermediate category, “After three days of being with SuperMobile,
you realise that the plan does not suit your needs. You decide to cancel the service. What is
the total amount you will pay for your three days with SuperMobile?”, and from the advanced
category, “You have been told that your data has now reached 85 per cent which means that
you have (DROP DOWN BOX: 0.15, 0.23 (Y), 0.5, 1.0) GB left until the end of the month.”
For each of the questions, in all categories, four options were provided (multiple choice), and
participants were required to choose one of these as the correct answer.
It is important to emphasise that the information for each of the questions in each category
was explicitly available in each of the documents provided, i.e., no information was withheld
from the appropriate groups, nor was any deception involved in the experiment.
Prior to distribution of the knowledge test, the questions were given to an expert panel of
telecommunications stakeholders, marketing specialists, psychology and sociology experts,
to assess the efficacy of the questions in the context of the information that was provided to
participants. All questions were considered reasonable, and that the information provided
would suffice for people to be able to answer the questions pertaining to each category. In
addition, we distributed the knowledge test to complete amongst another expert panel, with
the results provided in the following pages.

28
Results
A detailed description of the data analysis for the experimental phase is included as a
technical appendix to this report.
Approach
In order to understand which forms of information provide consumers with the best
understanding of their telecommunications agreement, we need to consider other factors
that might influence consumers’ understanding. For example, do older or highly educated
consumers understand the terms of their agreement better?
To control for these factors, we conducted several regression analyses wherein these
potentially confounding factors were treated as variables in addition to variables representing
the three information groups of primary interest. It is worth observing that although structural
equation modelling is common in studies such as this, this method cannot be employed here
due to the dichotomous nature of the response variables (Blunch 2008, p. 224), i.e., since
the knowledge tests in this study mostly comprise questions that are either right or wrong,
we cannot assume normality in these responses, which is an underlying assumption of
structural equation modeling.
The focus of this analysis is how many correct knowledge-test questions each of the three
information groups (SS, SS_CIS, SS_CIS_TC) achieved, and whether there was a
significant difference between the groups. Thus, we need to ensure that the subdivision of
the sample into the three information groups are broadly similar in terms of representing
Australian consumers, in addition to the broad biases of the sample discussed above. For
example, if the majority of one group does not speak English as a primary language but the
other two knowledge groups do, then this could influence how the group performs on the
knowledge test, regardless of the information provided.
Before describing the results of the regression analyses, we first provide an overview of the
demographic make-up of the sample. The reason for this is two-fold. Firstly, the sample may
not reflect the Australian population, but some subset of it. Indeed, as is explained in the
next section, our sample has a high representation of Australians over 50. This is not
problematic, per se, as the sampling was random, however, this should be considered when
reading the results of the study, particularly in relation to statistical significance of the
findings. The second reason is that the demographic analysis informs how variables are built
into the regression model. Since regression analyses are studies of variation, it is not useful
to include variables whose sample sizes are particularly small. Consider that there is no
variation with a sample size of one (Montogomery, et al. 2012); Sheather 2009). The
demographic analysis allows us to make decisions to alleviate problems such as these, by
combining factors or creating composite measures.

29
Questionnaire One: demographics and self-assessed
understanding of the contract
A sample of 362 participants were randomly selected from a database of 350,000
Australians in the three questionnaires, divided evenly across the three knowledge groups
with: 121 in the sales summary (SS) group; 120 provided with the sales summary and the
critical information summary (SS_CIS); and 121 provided with the sales summary, the critical
information summary, and the terms and conditions (SS_CIS_TC). As the modelling will
show, these sample sizes are sufficient for providing practical 95 per cent confidence
intervals for covariate estimates, which is standard in statistical analysis.
Questionnaire One was conducted immediately after participants had undergone one of the
sales processes (i.e., SS, CIS and/or T&C) and included two types of questions:
 Demographic questions
o Gender
o Country of origin
o Language
o Aboriginal or Torres Strait Islander
o Age
o Highest education level attained
o Employment status
o Income
 Self-assessed understanding of the telecommunications agreement using validated
scales to examine each construct, viz.,
o General self-efficacy
o Satisfaction
o Believability
o Relevance
o Understanding
o Financial literacy
The demographic questions allow us to build questions into the statistical analysis, such as,
Are highly educated people better at understanding their telecommunications agreements?
Using the results of these demographic questions we can control for other factors that might
influence consumers’ understanding of their telecommunications agreements.
The self-assessed questions allow us to control for whether there is a relationship between
how consumers rate their own understanding of the agreement and how well they perform,
as measured by the knowledge tests performed in Stages Two and Three.
Demographics and manipulation of the data
A standout feature of the sample is that 50 per cent of the participants were aged 55 and
over. 27 per cent of the sample were aged between 55 and 64, 21 per cent of the sample
were aged between 65 and 74, and 3 per cent of the sample were aged 75 or over.
According to the 2011 ABS census, 22 per cent of the Australian population was over 55.
We must therefore bear in mind throughout the analysis of the data that these results are
more representative of older Australians.

30
Another overall feature of the sample was that well over 50 per cent of the sample fell into
two educational categories (highest education level attained). Twenty seven per cent of the
participants held a Bachelor’s degree and 30 per cent of the sample had attained some kind
of trade, technical, or vocational training outside of a Bachelor’s degree. The sample is not
as representative of those with high school or lower as the highest educational level
attained, as well as those who have attained a postgraduate level of training. This is less of a
problem than the age issue discussed above, since this is close to representative of the
educational level of the Australian public.
In terms of country of origin, primary language spoken, and Aboriginal or Torres Strait
Islander status, we found subcategories with small numbers (< 10) disproportionate to other
far larger subcategories. In these cases, we collapsed several subcategories into one
subcategory, or simply excluded this category from the analysis. There are natural ways to
collapse subcategories into larger subcategories, which we now detail.
We first consider country of origin. Of our sample of 362 participants, 262 people nominated
Australia as their country of origin, and the rest were divided into many small groups detailed
in Table 1: Counts for all countriesError! Reference source not found.. Here we have a
number of small subcategories, including a number of subcategories (e.g., Hong Kong,
Romania, Latvia) with a sample size of 1.
Table 1: Counts for all countries
Country
Count
Australia
262
England
34
Germany
6
India
6
Malaysia
6
New Zealand
5
Philippines
4
South Africa
3
Ukraine
3
Austria
2
Canada
2
China
2
Finland
2
France
2
Scotland
2
Country
Count
Zimbabwe
2
Bosnia
1
Czech
1
Greece
1
Hong Kong
1
Indonesia
1
Italy
1
Latvia
1
Lebanon
1
Romania
1
Serbia
1
Sri Lanka
1
Trinidad
1
Vietnam
1
Prefer not to say
6

31
In order to perform regression analysis, we categorized people into three groups:
those who identified Australia as their country of origin (262); those who did not
(93); and those who preferred not to say (6). These three subdivisions were
represented across the three knowledge groups.
The sample displayed similar results for primary language spoken, so we
collapsed these subcategories into two subcategories, those that spoke English
as a primary language (343), and those who spoke some other language as their
primary language (19). There was little difference between the proportions of
these two categories across the three knowledge groups.
Only one person identified as Aboriginal or Torres Strait Islander, so it is not
possible to compare the performance of different knowledge groups with respect
to this category, in addition to the statistical limitations of small sample sizes
discussed above.
Self-assessed factors/key constructs
As well as small sample sizes, we also manipulated the self-assessed results to
better target research questions. Each of the self-assessed categories was
measured by several questions drawn from validated scales. Since each
question was answered on a scale of 1 to 5, we can create composite measures
by calculating the sum of the questions’ results for each self-assessed category.
In Table 2 we detail the number of questions asked and the associated
composite measure for that self-assessed category.
Table 2: Composite measures for self-assessed categories
Self-assessed category
Number of questions
Maximum composite
score
Self-efficacy
10
50
Satisfaction
4
20
Believability
3
15
Relevance
3
15
Understanding
5
25
Financial literacy
9
45
On average, the participants in the study rated themselves fairly highly for those
questions measuring general self-efficacy (mean score: 37.5 ±6.3 out of a
maximum of 50). Participants rated the believability of the different types of
information provided for the groups (SS, CIS and T&C) as close to the reality of
the type of information that would be provided in a typical scenario, with average
believability 11 ± 2.5 out of 15. For further detail regarding each of the self-

32
assessed constructs, please refer to Appendix 2 – Questionnaire. Table 3
(below) provides summary statistics for all self-assessed factors.
Table 3: Summary statistics for each key concept
All
Min.
1st
Qu.
Median
Mean
3rd
Qu.
Max.
SD
Self-efficacy
10
34
38
37.45
41
50
6.28
Satisfaction
4
12.25
15
14.8
16
20
3.18
Believability
3
9
11
10.92
12
15
2.54
Relevance
3
9
12
11.24
13
15
2.93
Understanding
6
14
15
15.44
17
25
2.58
Financial literacy
9
30
33
32.59
35
45
4.13
We did not find any significant difference in the average composite scores for
self-efficacy, financial literacy, and believability between the information groups.
That is, the participants in the three information groups rated themselves in a
similar fashion with respect to their financial literacy. The participants rated
equally the believability of the sales summary, the critical information summary,
and the terms and conditions.
For the self-assessed factors in which we found a significant difference (via
ANOVA) we performed a post-hoc analysis. Whilst there were statistically
significant differences between one group and the other two information groups
for satisfaction (with the information provided), relevance (of the information to
the decision-making), and understanding, these differences were small. For
example, there was a difference in satisfaction between the SS and SS_CIS
groups, but the predicted difference between these two groups, while statistically
significant, is only between 0.15 and 1.81. Given that these differences are
relatively small, we can proceed with the regression analysis.
Questionnaire Two and Three: Comparing the
knowledge test across the two time points
Since Questionnaire Three asked the same questions as Questionnaire Two with
additional questions, we split the data analysis into two sets of regression
models. In this section we compare the twelve common questions asked in
Questionnaire Two and Questionnaire Three, to see if there is a difference
between consumers’ understanding of their telecommunications agreement
between 24 hours and a few weeks after the sales process. In the next section
we include the extra questions asked in Questionnaire Three and focus only on
that time point, which is a few weeks after entering the agreement. Of particular
interest in the analysis in this section is whether there is a significant difference

33
between the results of the two time points. If there is not, then we may essentially
disregard the results of Questionnaire Two, which does not include the extra
questions asked in Questionnaire Three.
We calculated the difference between the number of correct questions
participants obtained in Questionnaire Two and Questionnaire Three and
performed a regression analysis on the difference, incorporating all demographic
factors as well as the self-assessed factors as covariates. We ran the model
through a step algorithm to identify the subset of covariates that would maximize
the strength (Akaike Information Criterion) of the model. The results of this
analysis are presented in Table 4.
Table 4: Significant factors from the difference of common questions model
Factors
Estimate
Std.
Error
t value
Pr(>|t|)
2.50%
97.5%
Group: SS
0.62
0.35
1.76
0.08
-0.07
1.31
Language:
other
-0.81
0.4
-2.04
0.04
-1.58
-0.03
Relevance
-0.05
0.03
-1.57
0.12
-0.11
0.01
This tells us that the model predicts that there is little difference between
consumers’ understanding of their telecommunications agreements after 24
hours and after a few weeks.
The model suggests that Australians for whom English is not their primary
spoken language would perform slightly worse on the common questions in
Questionnaire Three. Our model predicts, with 95 per cent confidence, that
consumers whose primary language is not English would answer between -1.58
and -0.03 questions correctly (out of 12 total questions) at the second time point.
Aside from this small difference, the model suggests there is not a significant
difference between consumers’ answers at the two time points. Thus, in the next
section we can disregard the responses to Questionnaire Two.
Questionnaire Three: analysis of all questions asked
after a few weeks
There is one feature of the data that is worth bearing in mind before exploring the
results of the regression analysis: participants answered less than half of the
knowledge test questions correctly regardless of which information group they
were assigned to. In Table 5 we see summary statistics of the number of correct
questions answered for each information group (SS, SS_CIS, SS_CIS_TC).
This preliminary analysis suggests there is little difference between the
information groups’ responses. Overall, the majority of participants answered

34
11.03 ± 3.31 questions correctly out of a total of 26 questions (between 30 and
55 per cent correct). On average: the group provided with the sales summary
(SS) answered 10.25 ± 2.59 questions correctly (between 29 and 49 per cent
correct); the group provided with the sales summary and the critical information
summary (SS_CIS) answered 11.64 ± 3.73 questions correctly (between 30 and
59 per cent correct); and the group provided with the sales summary, the critical
information summary, and the terms and conditions answered 11.20 ± 3.39
questions correctly (between 30 and 56 per cent correct). Each of these
differences were statistically significant, but small. Not only were the results
similar for each knowledge group, the mean scores were all below the 13
required to answer half the questions correctly.
Table 5: Summary statistics on all questions asked at Stage 3
Min.
1st
Qu.
Median
Mean
3rd
Qu.
Max.
SD
SS
4
8
10
10.25
12
15
2.59
SS_CIS
5
9
12
11.64
14.25
19
3.73
SS_CIS_TC
3
8
11
11.2
14
19
3.39
All
3
8
11
11.03
14
19
3.31
The similarity of the results for the three knowledge groups, and the poor results
overall, should temper our interpretation of the data in the sense that although
findings may be statistically significant, they may not be significant in any kind of
practicable way. That said, such a low number of correct answers, when
compared to the Expert Panel (see Expectations of Stakeholders), suggests that
the typical consumer struggles with the overall complexity of solving
telecommunications problems, regardless of the amount of information provided.
An optimised regression analysis (significant factors presented in Table 6) was
performed on the total number of correct questions attained in Questionnaire
Three with the same explanatory variables as those employed in the regression
analysis of Questionnaire Two.

35
Table 6: Significant factors of optimised regression model for all questions asked
at Stage 3
Factors
Estimate
Std.
Error
t value
Pr(>|t|)
2.50%
97.50%
Education:
Trade/technical/
vocational training
-1.49
0.45
-3.3
0
-2.39
-0.6
Language: other
-1.68
0.75
-2.24
0.03
-3.17
-0.2
Understanding
-0.16
0.07
-2.4
0.02
-0.29
-0.03
Group: SS
8.84
2.7
3.27
0
3.53
14.15
Group: SS_CIS
1.17
0.41
2.89
0
0.38
1.97
Group: SS_CIS_TC
1.03
0.4
2.57
0.01
0.24
1.82
Relevance
0.28
0.06
4.74
0
0.17
0.4
Overall, the model does suggest some difference between the groups. The
model predicts (with 95 per cent confidence
4
) that consumers provided with the
sales summary information (SS) would answer between 3.53 and 14.15
questions correctly. The model predicts that those provided with the sales
summary and critical information summary (SS_CIS) would answer between 0.38
and 1.97 more questions correctly than the SS group. Those provided with the
sales summary, the critical information summary, and the terms and conditions
are predicted to perform slightly worse than the SS_CIS group, answering
between 0.24 and 1.82 more questions correctly than the SS group.
Notice that these confidence intervals for the SS_CIS and SS_CIS_TC groups
have minimums that are less than one and maximums that are less than two,
which is to say, that consumers provided with the critical information summary
and/or the terms and conditions would only answer one or two more questions
correctly than those provided with only the sales summary. Therefore, the real
significance of these differences must be questioned despite the statistical
significance suggested by the model.
Further, the maximum of 14.15 of the confidence interval for the SS group is low
(out of 26 total questions), and the interval is very wide, suggesting some
variation in responses. It is therefore worthwhile exploring other mitigating factors
that affect consumers’ understanding of their telecommunications agreement
than the form of summary information provided.
4
All confidence intervals are provided with 95% confidence; i.e., we are 95% confident
the true parameter falls between these two values.

36
Relationship between different forms of information and
performance on knowledge test
We also performed regression analyses on the subsets of Questionnaire Three
that were targeted at the different knowledge groups. Some questions required
the sales summary, some required the critical information summary, and some
required the terms and conditions. Table 7 presents summary statistics for the
three levels of difficulty: elementary (11 questions), intermediate (8 questions),
and advanced (7 questions).
Table 7: Summary statistics for questions by difficulty
Min.
1st
Qu.
Median
Mean
3rd
Qu.
Max.
SD
Elementary
2
6
8
7.35
8
11
1.60
Intermediate
0
1
2
2.38
4
6
1.71
Advanced
0
1
1
1.31
2
4
0.98
The sample performed moderately well on the elementary questions, with an
average of 7.34 ± 1.60 out of 11, with some participants answering all questions
correctly. Participants did not perform well on the intermediate or advanced
questions, on average, answering 2.38 ± 1.71 intermediate questions out of 7,
and only 1.31 ± 0.98 advanced questions out of 7. The most advanced questions
any participant answered correctly were 4 out of 7. As a starting point, these
summary statistics suggest that it is unrealistic to expect a greater than moderate
understanding of the elementary features of consumers’ telecommunications
agreement, and highly unrealistic to expect consumers to have an understanding
of the intermediate and advanced features.
We performed regression analyses on the three different levels of questions to
see if these results suggest a difference between the knowledge groups, or
whether any other factors contribute. Curiously, the regression analysis suggests
the three knowledge groups perform the same on the elementary questions
(requiring only the sales summary) and the advanced questions (requiring the
terms and conditions). That is, consumers equipped with the terms and
conditions are not predicted to have a better understanding of their
telecommunications agreement than those only provided with the sales
summary. This possibly supports the phenomenon of `information overload’
discussed previously (Amoriggi 2007; Hillman 2006a; Leong, Ewing & Pitt 2002).
Or, this could be another case of complexity as a barrier to understanding
(Cogon 2010; Rameezdeen & Rodrigo 2013).
The difference between the groups found in the analysis of all questions is
explained entirely by the model for the intermediate questions, as there are no
significant differences between the information groups for the elementary

37
questions and the advanced questions. Table 8 presents the significant factors
from the optimized regression analysis for the intermediate questions.
Table 8: Significant factors of the regression model for intermediate questions
Factors
Estimate
Std.
Error
t value
Pr(>|t|)
2.50%
97.5%
SS_CIS
group
0.72
0.21
3.43
0
0.31
1.13
SS_CIS_TC
group
0.66
0.21
3.13
0
0.25
1.07
Relevance
0.14
0.03
4.57
0
0.08
0.2
Although relevance is listed as a significant factor here, consumers are only
predicted to answer between 0.08 and 0.20 more questions correctly for each
point in their relevance score.
The analysis on the subsets of questions explains what part of the model predicts
significant differences between the groups; however the differences between the
groups must be tempered by the context of the poor performance of all groups.
Although one information group might attain one or two more questions correctly
on average than another information group, if all groups are still achieving less
than 50 per cent on the knowledge test then we must arguably downgrade
expectations of consumers’ understanding of their telecommunications
agreement. Although consumers rate themselves as good problem solvers who
understand their telecommunications agreement fairly well (Table 8), the model
suggests their understanding of the agreement will not live up to their own
expectations. In other words, self-assessed understanding does not predict
actual comprehension, at least in the context of being able to solve problems.
Some specific findings
People whose first language is not English did worse than
others
According to the model, there are two demographic factors that reduce the
number of correct questions consumers attain in Questionnaire Three.
Consumers for whom English is not their primary language are predicted to
answer between -3.1 and -0.2 answers correctly than those whose primary
spoken language is English. Arguably, this is not surprising.

38
People with vocational qualifications did worse than all other
educational levels
What is interesting, however, is that consumers with a highest education level
attained of trade, technical, or vocational training are predicted to answer, on
average, between -2.4 and -0.6 answers correctly than those with Bachelor’s
degrees. Interestingly, no other education level is predicted to impact on a
consumers’ understanding of their telecommunications agreement. Those who
are educated to high school or below are not predicted to perform worse than
those with Bachelor’s degrees, and postgraduate qualifications are not predicted
to improve consumers’ understanding.
There was a negative relationship between self-assessed
understanding and correct answers
Another curious negative relationship that emerges from the model is that of
initial self-assessed understanding of the telecommunications agreement. There
is an inverse relationship between how well consumers rated their understanding
of the agreement straight after the sales process and how many questions they
answered correctly in Questionnaire Three. For each point in their understanding
score (maximum 25) consumers are predicted to answer between -0.29 and -
0.03 questions correctly. In other words, asking people if they have understood
their obligations does not predict understanding of their obligations.
There was a positive relationship between those who rated the
information as relevant to their needs and correct answers
There is a positive association between how highly consumers rated the
information they were provided with, measured by relevance (maximum 15), and
the number of questions answered correctly. For each point in relevance,
consumers are predicted to answer between 0.17 and 0.40 questions correctly.
This would suggest that the more a consumer believes the information provided
is relevant to their needs the better they are able to recall, and make use of, that
information.
Expectations of stakeholders
In addition to knowledge test distributed to a random population, we undertook a
comparison of key stakeholder expectations of consumers’ abilities to solve the
posited problems with the outcomes of the knowledge test. We asked ten
experts, from telecommunications companies, law firms, consumer advocacy
organisations, universities and regulators to complete the knowledge test noted
above. The findings from these are noted as “Knowledge Test – Experts”
alongside the average result from all participants in the knowledge test in Tables
9 and 10.
In addition, we asked representatives of telecommunications companies,
consumer advocates, and regulators a series of questions related to their

39
expectations of what consumers should be able to do in relation to understanding
their telecommunications contracts. We contacted 10 telecommunications
representatives, with three completions. We contacted 10 consumer advocates
with nine completions. We contacted 10 regulatory representatives, with five
completions.
We modified the language so that a problem to be solved became an expectation
question. So, for example, a problem such as “How much data per month is
included in the agreement?” is rephrased as “I believe that the typical consumer
would know how much data is included in their plan”. We asked the expert panel
to make their judgement on a Juster type (11 pt) scale, where 0 = There is no
probability, no chance, through to 100% = Highest probability, 99 - 100% chance.
● We then analysed the responses of the different members of the expert
panels, viz., Regulators, Consumer Advocates and Telecommunications
Representatives. Their average responses are displayed in Figure 1and
Figure 2 under the headings “Expectations – Regulators”, “Expectations –
Consumer Advocates”, and “Expectations – Telecommunications
Companies”.
We found that, on average, regulators had the most realistic expectations of
consumers’ understanding of their agreements, while consumer advocates
underestimated, and telecommunications representatives overestimated
consumer capacity to understand their agreements. Details for each of the
questions are provided in Figure 1 and Figure 2 (next page).

40
Figure 1: Comparison of key stakeholder expectations with knowledge test
* = Questions contained in SS questionnaire
** = Questions contained in SS_CIS questionnaire
*** = Questions contained in SS_CIS_T&C questionnaire
0% 20% 40% 60% 80% 100% 120%
Is likely to be able to calculate how many calls they
make each month on their smartphone plan*
Would know if premium services are part of their
included value on their plan*
Would know how long they are committed to a
telco provider under their agreement*
Would know if 1300 numbers are included in their
plan*
Would be able to calculate how much a telephone
call to another mobile or landline would cost**
Would know whether there are any penalties
involved in terminating their contract*
Would know what types of calls are excluded from
their plan**
Is likely to be able to calculate how much data they
have left when they receive a message telling them
that their data has now reached 85 per cent***
Knowledge Test - General Public
Knowledge Test - Experts
Expectations - Regulators
Expectations - Telecommunications Companies
Expectations - Consumer Advocacy Orgs

41
Figure 2: Comparison of key stakeholder expectations with knowledge test
0% 20% 40% 60% 80% 100% 120%
Would know if their agreement is a prepaid or post-
paid service*
Would know how much data is included in their
agreement*
Would understand the difference between 1.5MB
and 1.5GB*
Is likely to be able to calculate the cost of sending
an MMS (if this was not included in their plan)*
Would know who to contact if they were
experiencing financial difficulty
Is likely to be able to calculate the cost of a one-
minute video call (if this was not included in their
plan)**
Would know the costs associated with retrieving
voice mail (if there were any)*
Knowledge Test - General Public
Knowledge Test - Experts
Expectations - Regulators
Expectations - Telecommunications
Companies
Expectations - Consumer Advocacy Orgs

42
Recommendations
1. Recommendation 1: As part of its current research on the operation of the TCP Code since the
Reconnecting the Customer Inquiry
5
, the ACMA should include an evaluation of the CIS to determine
the extent to which they assist consumers to understand the key features of their agreement. The
research found that the information most retained by consumers is the sales summary, not the more
detailed information in the CIS or the more complex T&Cs, and concluded that it is unrealistic to expect
any more than a moderate understanding of the elementary features of telecommunications
agreements. This finding suggests that the CIS are not as useful for consumers as previously believed,
that they may be overly complex and in need of simplification.
2. Recommendation 2: Telecommunications retailers should ensure that plans and market offers are
kept as simple as possible with clear elementary features that their customers can easily understand.
3. Recommendation 3: In order to promote better understanding of telecommunications contracts, more
work is required by the industry to understand consumer needs during the sale transaction and lifecycle
of a contract, and to tailor and time delivery of core information for maximum comprehensibility. The
research identifies that a key enabler to understanding information is the consumer’s perception of its
relevance. If the consumer considers the information to be relevant, then it is more readily understood.
4. Recommendation 4: It is recommended that telecommunications retailers adopt a proactive strategy
by conducting follow up courtesy contact with new customers after three billing cycles to see if the
customer needs further assistance in understanding their obligations. Consumers overestimate their
ability to solve problems arising from their telecommunications contracts and their understanding of
their agreement. The research found that confidence is not a strong predictor of ability to apply the
information to solve problems, should they arise.
5. Recommendation 5: Despite the small sample size, this research finds a need for expert independent
research to provide an evidence base when introducing or reviewing customer information obligations,
to reduce the risk of inaccurate presumptions about consumer behavior informing regulatory
obligations. The study has found that regulators, consumer advocates and industry have different
perceptions of consumer understanding of features of their telecommunications agreement. Consumer
understanding is at times underestimated by consumer advocates, overestimated by the industry, with
regulators hovering in between.
5
http://www.acma.gov.au/Industry/Telco/Reconnecting-the-customer/Public-inquiry/final-report-reconnecting-the-
customer-acma

43
Conclusion and Future Research
Informed consumers are the cornerstone of a competitive and effective marketplace. Keeping consumers
informed in the 21st century is a rapidly shifting challenge, particularly in the telecommunications sector
where increased product complexity and choice dominate the consumer landscape.
Overall, the study shows that consumers generally struggle with the complexity involved when attempting
to solve problems with telecommunications contracts. The study also challenges long-held assumptions
about consumer behaviour, understanding and knowledge when entering into telecommunications
agreements. The results provide an opportunity for collaboration between regulators, industry and
consumer advocates to increase consumers’ understanding of their rights and obligations in
telecommunications contracts.
This study makes several important findings. First, the results show that consumers overestimate their
ability to solve problems with telecommunications contracts. In fact, the higher consumers rated their
understanding and ability to solve problems, the less likely they were to correctly answer questions about
their contract. The study also showed the difficulties consumers face in recalling information within their
contract. This result found that three weeks after being given information about a sample contract, none of
the respondents were able to answer more than half of the questions correctly. This result was the same
regardless of the level of detail that the consumer was provided with initially. In the study, some customers
were given a sales summary, a second group was given a Critical Information Summary and the sales
summary, and the third group was given both summaries plus the full legal terms and conditions.
Interestingly, the study found that consumers who were given terms and conditions understood less than
those who were given a sales summary, Critical Information Summary, or both. This is surprising as one
might expect that consumers who were given terms and conditions would at least understand the key
points of their rights and obligations even if they may not understand the finer legal points. This coincided
with the finding that consumers who rated information as more relevant to their needs answered more
questions correctly.
These results suggest several things. First, providing consumers with even moderate amounts of
information does not mean that they will retain this information, even over a relatively short time period (3
weeks). Second, the current approach of providing information about the contract only at the sign-up stage
hinders consumers’ understanding of their rights and obligations. Third, the findings suggest that customer
communications should only include information of relevance, as the inclusion of irrelevant information
negatively affects understanding.
The study also found that regulators, industry and consumer advocates hold varying assumptions about
consumers’ understanding of communication contracts. The study showed that regulators were the most
accurate in assessing consumers' actual understanding, while the industry significantly over estimated
consumer understanding. Interestingly, consumer advocates slightly underestimated consumer
understanding. The disparity between these views suggests that stakeholders should collaborate to ensure
that future information disclosure requirements better meet consumers’ level of understanding.
Future research should monitor consumer understanding of telecommunications agreements, particularly
as agreements become more complex and multi-product focused. One approach would be to test the
efficacy of a double opt-in opportunity for consumers to give them time away from the pressure of the sales
experience to process their agreement, before the finalisation of the sale. This may or may not address the

44
various situational and cognitive biases held by consumers as part of the current instantaneous sign up
process.
Future research could also explore whether providing consumers with information over the life cycle of the
contract, rather than just at sign-up, increases consumers’ understanding and problem solving abilities. In
addition, future work could also test the effectiveness of a shorter Critical Information Summary, as
recommended by Harrison et al (2012). The findings on the importance of providing relevant information
points to the potential benefits of initiatives such as Midata in the UK, which allow consumers to use their
own data and usage patterns to find products that suit their needs, when needed
6
. Such tools can be
empowering, if carefully designed with end user requirements in mind. By using tools that increase
engagement in the decision making process, consumers will be better informed about the products they are
purchasing.
As the telecommunications market continues to evolve, consumers must be properly informed about their
rights and obligations in telecommunications agreements. The study demonstrates the need for caution
around assumptions made about consumer ability to understand telecommunications contracts and solve
problems. Consumers need more than simply disclosure at sign-up, often involving large quantities of
information. Consumers need clear, relevant, repeated and timely communications in order to increase
their problem-solving skills and understanding of telecommunications contracts. Any changes to consumer
disclosure and contract requirements should reflect this increased understanding of consumer needs.
6
https://billmonitor.com/

45
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Zwijnenberg, NC, Hendriks, M, Damman, OC, Bloemendal, E, Wendel, S, de Jong, JD and Rademakers, J
2012, ' Understanding and using comparative healthcare information; the effect of the

54
amount of information and consumer characteristics and skills', BMC Medical Informatics
and Decision Making, vol. 12, no. 101, pp. 1-11.

55
Technical appendix
The analysis was conducted in the R programming language. Demographic, as well as self-assessed key
concepts, were controlled for. The demographic factors considered were:
 Gender
 Country of origin
 Language
 Aboriginal or Torres Strait Islander
 Age
 Highest education level attained
 Employment status and type of work
 Income
Self-assessed factors were also considered using validated scales:
 General self-efficacy
 Satisfaction
 Believability
 Relevance
 Understanding
 Financial literacy
Descriptive statistics for each of these factors were found. ANOVAs were performed for differences of
means, along with post-hoc analyses (Bartlett’s test and Bonferroni comparison of means), where
appropriate.
After 24 hours (Stage 2), a series of questions (Questionnaire Two) were asked to determine the
participants’ understanding of their telecommunications agreement.
After 3 weeks (Stage 3), the same questions (denoted common questions) were asked, as well as
additional questions.
Since the manifest response variables are dichotomous, the normality assumption required for structural
equation modeling (SEM) is not satisfied. Thus, a SEM model cannot be applied (Blunch 2008, p. 224). A
twofold regression model was employed, firstly on the common questions between the two time points, and
secondly on all questions asked at Stage 3.
To compare the responses to the common questions asked at Stage 2 and Stage 3, the mean difference of
total common questions asked was calculated. A regression analysis was performed controlling for the
demographic factors, as well as the self-assessed factors.
Similarly, a multiple linear regression analysis was performed on the mean number of questions answered
correctly at Stage 3 (including the additional questions that were not asked at Stage 2).
For both regression models, a step algorithm was applied via the step() function in R to maximize the
Akaike Information Criterion.
In regression analysis, the smaller the sample for a particular factor, the larger the standard error of the
estimated difference for that factor. Thus, for particularly small samples, the standard error can be so large
as to render any inference about the difference of that factor from the control meaningless (see, for

56
example, regression texts such as Montgomery, et al. (2012), or Sheather (2009)). Hence, where
explanatory variables were found to be largely in one category, with very small numbers in other
categories, these were collapsed into one dichotomous variable.
Results
Descriptive statistics: demographic factors
Gender
Table 9: Gender counts by group
Counts
SS
SS_CIS
SS_CIS_TC
Total
Female
72
67
55
194
Male
49
53
66
168
Total
121
120
121
362
Table 10: Gender proportions by group
Proportions
SS
SS_CIS
SS_CIS_TC
Total
Female
19.89
18.51
15.19
53.59
Male
13.54
14.64
18.23
46.41
Total
33.43
33.15
33.43
100
In Table 10 we see that 194 females and 168 males participated at both time points. By Table 10Error!
Reference source not found., we see that similar proportions of each gender were assigned to each
knowledge group.
Country of origin
Table 11: Counts for country of origin by group
Counts
SS
SS_CIS
SS_CIS_TC
Total
Australia
77
90
95
262
Other
41
28
25
94
Prefer not to
say
3
2
1
6
Total
121
120
121
362

57
Table 12: Proportions for country of origin by group
Proportions
SS
SS_CIS
SS_CIS_TC
Total
Australia
21.27
24.86
26.24
72.38
Other
11.33
7.73
6.91
25.97
Prefer not to
say
0.83
0.55
0.28
1.66
Total
33.43
33.15
33.43
100
We also considered the country of birth of the participants. In Table 11, we see that 262 participants told us
they were born in Australia, and 6 participants chose not to declare their country of birth. 34 participants
were born in England, and the rest of the participants came from different countries.
By Table 12, we see that approximately three quarters of participants (72.38 per cent) identify as
Australian. In Table 13, a breakdown of the number of participants from all countries is provided.
Table 13: Counts for all countries
Australia
262
Austria 2
Bosnia 1
Canada 2
China 2
Czech 1
England 34
Finland 2
France 2
Germany 6
Greece 1
Hong Kong
1
India 6
Indonesia 1
Italy 1
Latvia 1
Lebanon 1
Malaysia 6
New
Zealand 5
Philippines
4
Romania 1
Scotland 2
Serbia 1
South
Africa 3
Sri Lanka 1
Trinidad 1
Ukraine 3
Vietnam 1
Zimbabwe
2
Prefer not
to say 6
Here we have a similar situation to that of language spoken at home, in that there are many categories for
which we have only one participant. That is, there is only one participant from Vietnam and one from
Trinidad. In order to keep the model simple, we reduced this question to whether the participant was born in
Australia or not (or preferred not to say).
Language
Table 14: Counts for primary language spoken by group
Counts
SS
SS_CIS
SS_CIS_TC
Total
English
111
116
116
343
Other
10
4
5
19
Total
121
120
121
362

58
Table 15: Proportions for primary language spoken by group
Proportions
SS
SS_CIS
SS_CIS_TC
Total
English
30.66
32.04
32.04
94.75
Other
2.76
1.1
1.38
5.25
Total
33.43
33.15
33.43
100
As noted in Table 14, English was the main language spoken at home for 343 of the 362 participants, 19
participants spoke a language other than English at home, and 2 participants chose not to say. Table 15
shows that approximately 95 per cent of participants (94.75) speak English as a primary language.
There were three participants that spoke Cantonese, and two participants that spoke each of Vietnamese,
Mandarin, Russian, and Telugu. There were several languages that only one participant spoke, viz.,
Croatian, French, Greek, Hokkien, Tamil, and Zulu. As the study was interested in investigating general
comprehension of telecommunications agreements, amongst the broad population, we did not specifically
seek particular language groups.
Given the small numbers of each language spoken other than English, we chose to consider language
dichotomously. That is, we simplified the question of language to whether or not English was the primary
spoken language at home. This enables us to perform analysis of variance, and by extension regression
analysis, since we now have two samples of 343 primary English speakers and 19 others who primarily
speak a language other than English.
Roughly the same numbers of participants were assigned to each information group, based on language.
Aboriginal or Torres Strait Islander
Table 16: Aboriginal or Torres Strait Islander Status by group
SS
SS_CIS
SS_CIS_TC
Total
Aboriginal or Torres Strait Islander
0
0
1
1
No
121
120
120
361
Total
121
120
121
362
Only one participant out of 362 identified as Aboriginal, and no participants identified as Torres Strait
Islander
Since there is no variation over a single number, we cannot make inference about the standard deviation of
consumers who identify as Aboriginal or Torres Strait Islander. The standard deviation of the population is
then assumed to be equal to those who do not identify as Aboriginal or Torres Strait Islander, which may
not be true, introducing a flaw in the assumptions of the model. Furthermore, within a regression model, a
small sample size causes a large standard error for the estimated coefficient for that particular factor. So
much so, in fact, that it is not practicable to make inference about the effect of a consumers’ Aboriginal or
Torres Strait Islander status on the number of questions correctly answered. It must, therefore, be left for
future research to answer whether this factor affects consumers’ understanding of their telecommunications
contract.

59
Age
Table 17: Counts of age by group
Counts
SS
SS_CIS
SS_CIS_TC
Total
18-24
1
0
0
1
25-34
12
17
22
51
35-44
26
21
17
64
45-54
20
16
27
63
55-64
27
37
33
97
65-74
31
24
20
75
75+
4
4
2
10
Prefer not to
say
0
1
0
1
Total
121
120
121
362
Table 18: Proportions of age by group
Proportions
SS
SS_CIS
SS_CIS_TC
Total
18-24
0.28
0
0
0.28
25-34
3.31
4.7
6.08
14.09
35-44
7.18
5.8
4.7
17.68
45-54
5.52
4.42
7.46
17.4
55-64
7.46
10.22
9.12
26.8
65-74
8.56
6.63
5.52
20.72
75+
1.1
1.1
0.55
2.76
Prefer not to
say
0
0.28
0
0.28
Total
33.43
33.15
33.43
100
Of particular note is that 50 per cent of participants were aged 55 and over (Table 18). Only 1 participant
was under 25 (Table 17). Thus, our findings provide a better reflection of an older consumer’s
understanding of their telecommunications agreement, than that of a younger consumer.

60
Highest education level attained
Table 19: Counts of highest education level attained by group
Counts
SS
SS_CIS
SS_CIS_TC
Total
Bachelor's degree
27
32
37
96
Doctorate degree/PhD
2
1
2
5
High school graduate
23
20
16
59
Master's degree
9
5
8
22
Professional degree
4
1
2
7
Some high school
11
13
14
38
Trade/technical/vocational
training
29
41
37
107
University diploma
16
7
5
28
Total
121
120
121
362
Table 20: Proportions of highest education level attained by group
Proportions
SS
SS_CIS
SS_CIS_TC
Total
Bachelor's degree
7.46
8.84
10.22
26.52
Doctorate degree/PhD
0.55
0.28
0.55
1.38
High school graduate
6.35
5.52
4.42
16.3
Master's degree
2.49
1.38
2.21
6.08
Professional degree
1.1
0.28
0.55
1.93
Some high school
3.04
3.59
3.87
10.5
Trade/technical/vocational
training
8.01
11.33
10.22
29.56
University diploma
4.42
1.93
1.38
7.73
Total
33.43
33.15
33.43
100
Almost a third of participants were undertaking trade, technical, or vocational training. A similar number
were enrolled in a Bachelor’s degree (Table 19). Several participants had only completed education at high
school level or lower. No participants chose not to say what their education level was (Table 19).

61
Employment status and type of work
Table 21: Counts for employment status by group
Counts
SS
SS_CIS
SS_CIS_TC
Total
Full-time carer
0
1
1
2
Full-time employed for
wages
32
35
40
107
Homemaker
6
14
7
27
Out of work and looking for
work
3
3
5
11
Out of work but not
currently looking for work
1
2
0
3
Part-time employed for
wages
29
11
25
65
Prefer not to say
0
0
1
1
Retired
32
36
28
96
Self employed
8
12
4
24
Student
3
2
4
9
Unable to work
7
4
6
17
Total
121
120
121
362

62
Table 22: Proportions for employment status by group
Proportions
SS
SS_CIS
SS_CIS_TC
Total
Full-time carer
0
0.28
0.28
0.55
Full-time employed for
wages
8.84
9.67
11.05
29.56
Homemaker
1.66
3.87
1.93
7.46
Out of work and looking for
work
0.83
0.83
1.38
3.04
Out of work but not
currently looking for work
0.28
0.55
0
0.83
Part-time employed for
wages
8.01
3.04
6.91
17.96
Prefer not to say
0
0
0.28
0.28
Retired
8.84
9.94
7.73
26.52
Self employed
2.21
3.31
1.1
6.63
Student
0.83
0.55
1.1
2.49
Unable to work
1.93
1.1
1.66
4.7
Total
33.43
33.15
33.43
100
Almost a third of participants were employed full time (Table 22). A similar number of participants were
retired. The next largest group were part-time employees. Only one participant chose not to say what their
employment status was (Table 21).

63
Income
Table 23: Counts for income by group
Counts
SS
SS_CIS
SS_CIS_TC
total
<10K
9
8
8
25
>=10K<20K
3
8
5
16
>=20K<30K
17
12
24
53
>=30K<40K
3
0
4
7
>=40K<50K
1
2
0
3
>=50K<60K
0
1
0
1
>=60K<70K
3
1
2
6
>=70K<80K
21
23
14
58
>=80K<90K
14
9
9
32
>=90K<100K
11
8
11
30
>=100K<120K
5
6
6
17
>=120K<140K
5
10
4
19
>=140K<160K
4
4
5
13
>=160K<180K
4
7
7
18
>=180K<200K
3
3
6
12
200K+
1
0
2
3
Prefer not to say
17
18
14
49
Total
121
120
121
362
Table 24: Proportions for income by group
Proportions
SS
SS_CIS
SS_CIS_TC
total
<10K
2.49
2.21
2.21
6.91
>=10K<20K
0.83
2.21
1.38
4.42
>=20K<30K
4.7
3.31
6.63
14.64
>=30K<40K
0.83
0
1.1
1.93
>=40K<50K
0.28
0.55
0
0.83
>=50K<60K
0
0.28
0
0.28
>=60K<70K
0.83
0.28
0.55
1.66
>=70K<80K
5.8
6.35
3.87
16.02
>=80K<90K
3.87
2.49
2.49
8.84

64
Proportions
SS
SS_CIS
SS_CIS_TC
total
>=90K<100K
3.04
2.21
3.04
8.29
>=100K<120K
1.38
1.66
1.66
4.7
>=120K<140K
1.38
2.76
1.1
5.25
>=140K<160K
1.1
1.1
1.38
3.59
>=160K<180K
1.1
1.93
1.93
4.97
>=180K<200K
0.83
0.83
1.66
3.31
200K+
0.28
0
0.55
0.83
Prefer not to say
4.7
4.97
3.87
13.54
Total
33.43
33.15
33.43
100
A substantial proportion, 49 out of 362, of participants chose not to state their income category (Table 24).
Given there were so many retirees, unemployed, and students involved, it is unsurprising that a quarter of
participants reported incomes of less than $50,000 (Table 24).

65
Descriptive statistics: key concepts
Table 25: Summary statistics for each key concept
All
Min.
1st Qu.
Median
Mean
3rd Qu.
Max.
SD
Self-efficacy
10
34
38
37.45
41
50
6.28
Satisfaction
4
12.25
15
14.8
16
20
3.18
Believability
3
9
11
10.92
12
15
2.54
Relevance
3
9
12
11.24
13
15
2.93
Understanding
6
14
15
15.44
17
25
2.58
Financial
literacy
9
30
33
32.59
35
45
4.13
Table 25 provides overall numerical summaries of each of the self-assessed key concepts. Below, in Table
26, Table 27, and Table 28, a breakdown by group of numerical summaries for each of the knowledge
groups is provided.
Table 26: Summary statistics for each key concept for the SS knowledge group
SS group
Min.
1st
Qu.
Median
Mean
3rd
Qu.
Max.
SD
Self-efficacy
18
35
38
37.53
41
50
6.48
Satisfaction
7
13
15
15.01
16
20
3.14
Believability
3
9
11
10.93
12
15
2.48
Relevance
3
9
12
11.21
12
15
2.83
Understanding
9
14
15
15.36
17
22
2.59
Financial literacy
9
31
33
32.54
35
45
4.19
Table 27: Summary statistics for each key concept for the SS_CIS knowledge group
SS_CIS group
Min.
1st
Qu.
Median
Mean
3rd
Qu.
Max.
SD
Self-efficacy
21
34
38
37.35
40
50
6.16
Satisfaction
8
14
16
15.38
17
20
2.81
Believability
4
9
11
11
12
15
2.52
Relevance
3
11
12
11.9
14
15
2.74
Understanding
6
13
15
15.08
16
25
2.8
Financial literacy
18
30
32
32.33
35
45
4.39

66
Table 28: Summary statistics for each key concept for the SS_CIS_TC knowledge group
SS_CIS_TC group
Min.
1st
Qu.
Median
Mean
3rd
Qu.
Max.
SD
Self-efficacy
10
33
38
37.48
41
50
6.23
Satisfaction
4
12
15
14.02
16
20
3.42
Believability
3
9
11
10.82
12
15
2.64
Relevance
3
9
11
10.62
12
15
3.07
Understanding
11
14
16
15.88
17
25
2.28
Financial literacy
18
31
33
32.88
35
45
3.8
We now consider each of the key concepts in detail.
General self-efficacy
Table 29: Summary statistics of self-efficacy
Min.
1st Qu.
Median
Mean
3rd Qu.
Max.
SD
SS
18
35
38
37.53
41
50
6.48
SS_CIS
21
34
38
37.35
40
50
6.16
SS_CIS_TC
10
33
38
37.48
41
50
6.23
All
10
34
38
37.45
41
50
6.28
Since general self-efficacy was measured by 10 questions on a scale of 1 – 5, the highest score a
participant could achieve was 50. Across all knowledge groups, the median score for general self-efficacy
was 38 (Table 29). In all knowledge groups there was at least one participant who rated themselves at the
maximum (50) for self-efficacy. That is, they answered all 10 self-efficacy questions with a response of 5
(the maximum score for each question).
Table 30: ANOVA for difference between mean self-efficacy scores by knowledge groups
Df
Sum Sq
Mean Sq
F value
Pr(>F)
Group
2
2.1
1.027
0.0259
0.9744
Residuals
359
14223.6
39.620
In Table 30 we see the results of an ANOVA test for different means of self-efficacy composite scores by
group. In this case, we have 97.44 per cent probability of observing the F-value 0.0259 under the
assumption that all means are equal.

67
Figure 3: Boxplot of self-efficacy scores for each knowledge group
A visual inspection of a boxplot comparison (Figure 3) supports the results of the ANOVA test; there is little
difference between the self-efficacy scores between the knowledge groups.
Thus, we conclude that there is no significant difference in mean composite score for self-efficacy between
the groups.
Satisfaction
Table 31: Summary statistics for satisfaction score by groups
Min.
1st Qu.
Median
Mean
3rd Qu.
Max.
SD
SS
7
13
15
15.01
16
20
3.14
SS_CIS
8
14
16
15.38
17
20
2.81
SS_CIS_TC
4
12
15
14.02
16
20
3.42
All
4
12.25
15
14.8
16
20
3.18
The median values for satisfaction score (4 questions with a maximum score of 5 each) was slightly
different between the knowledge groups. Those in the SS_CIS group reported a median satisfaction score
of 15, whereas the other two knowledge groups reported a median satisfaction score of 16 (Table 31). On
average, participants reported a satisfaction score of 14.8 ± 3.18 points out of 20.
To see whether there was a significant difference between groups’ average satisfaction score, an ANOVA
test was performed.
● ●●● ● ●
●● ●●●● ●
●●
SS
SS_CIS
SS_CIS_TC
10 20 30 40 50
Self efficacy
Knowledge group
Composite Score for Self efficacy

68
Table 32: ANOVA for difference in mean satisfaction between groups
Df
Sum Sq
Mean Sq
F value
Pr(>F)
Group
2
119
59.40
6.05
0.0026
Residuals
359
3524
9.82
The ANOVA presented in Table 32 suggests there is a significant difference between the knowledge
groups’ satisfaction score. That is, the probability of observing an F-value of 6.05 is 0.0026 under the
assumption that the means for satisfaction are the same across the knowledge groups.
Figure 4: Boxplot of satisfaction scores for knowledge groups
A visual inspection of the boxplots of the different knowledge groups’ satisfaction scores provided in Figure
4 is in agreement with the results of the ANOVA.
Thus, a post-hoc analysis was performed. The variances of the three knowledge groups are assumed to
be equal (Bartlett’s test, p = 0.0969). There was no significant difference between the mean satisfaction
score of the SS and the SS_CIS groups (Bonferroni score = 0.49). However, there was a significant
difference between the SS and the SS_CIS_TC groups (Bonferroni score 0.03, 95 per cent CI = [0.15,
1.81]) and the SS_CIS and SS_CIS_TC groups (Bonferroni score < 0.00, 95 per cent CI = [0.56, 2.15]).
●●●●
●●●●
●●
SS
SS_CIS
SS_CIS_TC
510 15 20
Satisfaction
Knowledge group
Composite Score for Satisfaction

69
Believability
Table 33: Summary statistics for believability score by groups
Min.
1st Qu.
Median
Mean
3rd Qu.
Max.
SD
SS
3
9
11
10.93
12
15
2.48
SS_CIS
4
9
11
11
12
15
2.52
SS_CIS_TC
3
9
11
10.82
12
15
2.64
All
3
9
11
10.92
12
15
2.54
The median score for believability was the same (11) across all groups (Table 33). On average, the
believability score was 10.92 ± 2.5 points out of 15 for all groups. Three questions were asked, each with a
scale of 5. So, the maximum believability score possible is 15. Since this is in the range of Table 33, at
least one participant answered 5 for all believability questions.
An ANOVA was performed to see if there was a significant difference between the knowledge groups’
believability score (Table 34). In this case, we have 85.50 per cent probability of observing the F-value
0.157 under the assumption that all means are equal.
Table 34: ANOVA for difference between mean believability scores by knowledge group
Df
Sum Sq
Mean Sq
F value
Pr(>F)
Group
2
2
1.021
0.157
0.855
Residuals
359
2332
6.494
A visual inspection of a boxplot comparison (Figure 5) supports the results of the ANOVA test; there is little
difference between the believability scores between the knowledge groups.

70
Figure 5: Boxplot of believability scores for knowledge groups
Thus, we conclude that there is no significant difference between the knowledge groups’ believability
scores.
Relevance
Table 35: Summary statistics for relevance score by groups
Min.
1st Qu.
Median
Mean
3rd Qu.
Max.
SD
SS
3
9
12
11.21
12
15
2.83
SS_CIS
3
11
12
11.9
14
15
2.74
SS_CIS_TC
3
9
11
10.62
12
15
3.07
All
3
9
12
11.24
13
15
2.93
The median number of questions asked was different between the knowledge groups. The SS_CIS _TC
group had a median score of 11, whereas the other two knowledge groups reported a median score of 12
(Table 35). On average, the participants reported a relevance score of 11.24 ± 2.93 out of 15.
An ANOVA was performed to see if there was a significant difference between the knowledge groups’
believability score (Table 36).
●●
●
●●●
SS
SS_CIS
SS_CIS_TC
4 8 12
Believability
Knowledge group
Composite Score for Believability

71
Table 36: ANOVA for difference between mean relevance scores by knowledge groups
Df
Sum Sq
Mean Sq
F value
Pr(>F)
Group
2
99.9
49.47
5.938
0.0029
Residuals
359
2991.1
8.33
The ANOVA presented in Table 36 suggests there is a significant difference between the knowledge
groups’ relevance score. That is, the probability of observing an F-value of 5.938 is 0.0029 under the
assumption that the means for relevance are the same across the knowledge groups.
A visual inspection of the knowledge groups’ boxplots (Figure 6) for relevance confirms the result of the
ANOVA.
Figure 6: Boxplots of relevance score for knowledge groups
Thus, we performed a post-hoc analysis to see which groups differed by mean relevance score. The
variances of the three groups are assumed to be equal (Bartlett’s test, p = 0.43). There was no significant
difference between the mean relevance for the SS and SS_CIS groups (Bonferroni score = 0.08) or the SS
and SS_CIS_TC groups (Bonferroni score = 0.19). The significant difference of means was between the
SS_CIS and SS_CIS_TC groups (Bonferroni score = 0.001, 95 per cent CI = [0.54, 2.02]).
● ●●●
● ●● ●●●● ●
●●●●●●●
SS
SS_CIS
SS_CIS_TC
4 8 12
Relevance
Knowledge group
Composite Score for Relevance

72
Understanding
Table 37: Summary statistics for understanding score by groups
Min.
1st Qu.
Median
Mean
3rd Qu.
Max.
SD
SS
9
14
15
15.36
17
22
2.59
SS_CIS
6
13
15
15.08
16
25
2.8
SS_CIS_TC
11
14
16
15.88
17
25
2.28
All
6
14
15
15.44
17
25
2.58
The median score for understanding was 15 out of 25 points (5 questions with a maximum of 5 each).
There was a slight difference between the groups, with the group provided with the terms and conditions
(SS_CIS_TC) reporting a median understanding score of 16, with the other two knowledge groups
reporting a median of 15 (Table 37). On average the understanding score was 15.44 ± 2.58 points out of
25.
An ANOVA test (Table 38) was performed on the understanding score to see if there was a difference in
means between groups, which suggested that there was a significant difference between the knowledge
groups’ understanding score. That is, the probability of observing an F-value of 3.03 is 0.0496 under the
assumption that the means for understanding are the same across the knowledge groups.
Table 38: ANOVA for difference between mean understanding scores by knowledge groups
Df
Sum Sq
Mean Sq
F value
Pr(>F)
Group
2
39.9
19.927
3.03
0.0496
Residuals
359
2361.2
6.577
-
-
A visual inspection of the boxplots of understanding score by groups in Figure 7 shows agrees with the
results of the ANOVA.

73
Figure 7: Boxplots of understanding score by knowledge groups
Thus, a post-hoc analysis was performed. Equal variance of the knowledge groups’ understanding scores
is assumed (Bartlett’s test, p = 0.9). There was no difference between the SS and SS_CIS groups
(Bonferroni score = 0.63) and the SS and SS_CIS_TC groups (Bonferroni score = 0.15). However, there
was a difference between the SS_CIS and SS_CIS_TC groups (Bonferroni score = 0.02, 95 per cent CI = [-
1.14, 0.10]).
Financial literacy
Table 39: Summary statistics for financial literacy scores by groups
Min.
1st Qu.
Median
Mean
3rd Qu.
Max.
SD
SS
9
31
33
32.54
35
45
4.19
SS_CIS
18
30
32
32.33
35
45
4.39
SS_CIS_TC
18
31
33
32.88
35
45
3.8
All
9
30
33
32.59
35
45
4.13
The median score for self-reported financial literacy was 33 out of 45 points (9 questions with a maximum
of 5 each). There was a slight difference between the groups, with the group provided with the critical
information summary (SS_CIS) reporting a median financial literacy score of 32, with the other two
knowledge groups reporting a median of 33 (Table 39). On average the financial literacy score was 32.59 ±
4.13 points out of 45.
An ANOVA test was performed on the financial literacy score to see if there was a difference in means
between groups.
● ●● ●●
●●●● ● ●
●●
SS
SS_CIS
SS_CIS_TC
10 15 20 25
Understanding
Knowledge group
Composite Score for Understanding

74
Table 40: ANOVA for difference between mean financial literacy score between groups
Df
Sum Sq
Mean Sq
F value
Pr(>F)
Group
2
19
9.358
0.548
0.579
Residuals
359
6131
17.078
-
-
Since the probability of observing the F-value of 0.548 is 0.579 under the assumption of equal means, the
ANOVA suggests there is no difference between the means of the knowledge groups’ financial literacy
scores.
Figure 8: Boxplots for financial literacy by group
A visual inspection of the boxplots provided by Figure 8 supports the results of the ANOVA.
Thus, we conclude there is no significant difference between the financial literacy scores between
knowledge groups.
Analysis of average number of common questions answered correctly in
Stages 2 and 3
Table 41: Summary statistics on the difference between correctly answered questions
at Stage 3 and at Stage 2
Min.
1st Qu.
Median
Mean
3rd Qu.
Max.
SD
SS
-6
-1
0
0.16
1
4
1.52
SS_CIS
-5
-1
0
-0.11
1
5
1.75
SS_CIS_TC
-5
-1
0
0.07
1
5
1.78
All
-6
-1
0
0.04
1
5
1.69
● ●● ●
● ●●●●
●● ●●
SS
SS_CIS
SS_CIS_TC
10 20 30 40
Financial literacy
Knowledge group
Composite Score for Financial literacy

75
Participants answered 0.04 ± 1.69 common questions correctly at Stage 3 as opposed to correctly
answered questions at Stage 2 (Table 41). Common questions are the questions that were asked at both
Stage 2 and at Stage 3.
Table 42: Significant factors from the difference of common questions model
Factors
Estimate
Std.
Error
t value
Pr(>|t|)
2.50 per
cent
97.50 per
cent
(Intercept)
0.62
0.35
1.76
0.08
-0.07
1.31
Language
other
-0.81
0.4
-2.04
0.04
-1.58
-0.03
Relevance
-0.05
0.03
-1.57
0.12
-0.11
0.01
Table 42 presents the regression model for the difference of common questions asked across Stage 2 and
Stage 3. There was no significant difference between the three knowledge groups in the number of
common questions correctly answered in Stage 2 (24 hours) and Stage 3 (3 weeks). There was little
difference between the common questions answered correctly at the two time points. Table 42 provides a
regression model for the difference in common questions correctly answered between Stage 3 and Stage
2.
The model suggests, with 95 per cent confidence, that the consumers would answer somewhere between -
0.07 and 1.31 more questions correctly at Stage 3 than at Stage 2 (Table 42: 95 per cent CI (confidence
interval) = [-0.07, 1.31]). The model suggests that primary language and the self-assessed relevance total
affect the number of common questions answered over the two time points.
If a consumer spoke a primary language other than English, the model suggests they would answer
between -1.58 and -0.03 fewer questions correct at Stage 3, as compared to Stage 2 (Table 42: 95 per cent
CI = [-1.58, -0.03]). For each point in a consumers’ self-assessed relevance score, the model predicts that
consumers would answer between –0.11 and 0.01 questions correct as compared to Stage 2 (Table 42: 95
per cent CI = [-0.11, 0.01]).
It is important to note that each of these confidence intervals is small. That is, the 95 per cent confidence
prediction of the difference in number of correct common questions is only at most 2 more at Stage 3.
Furthermore, each confidence interval contains 0. Hence, we conclude that these differences are not very
significant. Indeed, there is no significant difference between the two time points. Thus, we may disregard
the questions asked at Stage 2, and consider all questions asked at Stage 3. The advantage of this is that
the extra questions, asked in addition to the common questions with Stage 2 can be built into the model.
Analysis of average number of questions answered correctly in Stage 3
All questions
Table 43: Summary statistics on all questions asked at Stage 3
Min.
1st Qu.
Median
Mean
3rd Qu.
Max.
SD
SS
4
8
10
10.25
12
15
2.59
SS_CIS
5
9
12
11.64
14.25
19
3.73
SS_CIS_TC
3
8
11
11.2
14
19
3.39
All
3
8
11
11.03
14
19
3.31
On average, participants answered 11 ± 3.31 questions correctly out of a total of 26 questions (Table 43).

76
Table 44: Significant factors of regression model for all questions asked at Stage 3
Factors
Estimate
Std.
Error
t
value
Pr(>|t|)
2.50%
97.50%
Education
Trade/technical/vocational
training
-1.49
0.45
-3.3
0
-2.39
-0.6
Language other
-1.68
0.75
-2.24
0.03
-3.17
-0.2
Understanding
-0.16
0.07
-2.4
0.02
-0.29
-0.03
(Intercept)
8.84
2.7
3.27
0
3.53
14.15
Group SS_CIS
1.17
0.41
2.89
0
0.38
1.97
Group SS_CIS_TC
1.03
0.4
2.57
0.01
0.24
1.82
Relevance
0.28
0.06
4.74
0
0.17
0.4
Table 44 presents the significant factors of the regression model for all questions (26 total) asked at Stage
3 (Table 45). On average, the model predicts that consumers will answer 8.84 questions correctly out of 26.
The model indicates that there is a minor difference between the three knowledge groups, with the group
provided with critical information summary, but not the terms and conditions (SS_CIS), performing best.
The model suggests that consumers provided with the critical information summary (SS_CIS) would
answer 1.17 more questions correctly than the group provided only with the sales summary (SS) (Table 44:
95 per cent CI = [0.38, 1.97], p < 0.1). Consumers provided with the terms and conditions (SS_CIS_TC)
would answer slightly fewer questions correctly, with 1.03 more questions answered correctly than those
provided with the sales summary (SS) (Table 44: 95 per cent CI = [0.24, 1.82], p = 0.01). Whilst there is
statistically significant differences between the three groups’ total number of correct questions answered,
no group answered more than 50 per cent of the questions asked correctly. Thus, it is questionable as to
whether there is a truly significant difference between the three knowledge groups, or indeed if these forms
of information are the best way of ensuring consumers’ understanding of their telecommunications
contracts.
Other factors that affected the number of correctly answered questions at Stage 3 were primary language,
education level, relevance, and understanding.
The model suggests that consumers whose primary language is not English would answer -1.68 questions
correctly than those whose primary language is English (Table 44: 95 per cent CI = [-3.17, -0.2], p = 0.03).
For education, those whose highest education level is trade, technical, or vocational training are predicted
to answer -1.49 questions correctly than those who are educated to Bachelor level (Table 44: 95 per cent
CI = [-2.39, -0.6], p < 0.01).
A negative relationship with understanding is predicted by the model, where for every point in
understanding a consumer would answer -0.16 questions correctly (Table 44: 95 per cent CI = [-0.29, -
0.03], p = 0.02).
Relevance, however, has a positive relationship with the number of questions correctly answered. For
every point in relevance score, consumers are predicted to answer 0.28 more questions correctly (Table
44: 95 per cent CI = [0.17, 0.4], p < 0.01).

77
Table 45: Regression model for all questions asked at Stage 3
Factors
Estimate
Std.
Error
t
value
Pr(>|t|)
2.50%
97.50%
(Intercept)
8.84
2.7
3.27
0
3.53
14.15
Education Doctorate degree/PhD
1.06
1.4
0.76
0.45
-1.69
3.82
Education High school graduate
-0.95
0.52
-1.82
0.07
-1.98
0.07
Education Master's degree
-0.63
0.72
-0.87
0.38
-2.06
0.79
Education Professional degree
-1.77
1.19
-1.48
0.14
-4.11
0.58
Education Some high school
-1.12
0.61
-1.84
0.07
-2.31
0.08
Education Trade/technical/vocational training
-1.49
0.45
-3.3
0
-2.39
-0.6
Education University diploma
-0.44
0.68
-0.66
0.51
-1.77
0.89
Employment Full-time employed for wages
2.24
2.18
1.03
0.31
-2.05
6.53
Employment Homemaker
1.99
2.22
0.89
0.37
-2.39
6.36
Employment Out of work and looking for work
2.54
2.35
1.08
0.28
-2.07
7.16
Employment Out of work but not currently looking
for work
-2.04
2.79
-0.73
0.47
-7.54
3.45
Employment Part-time employed for wages
1.86
2.18
0.85
0.4
-2.44
6.15
Employment Prefer not to say
3.14
3.74
0.84
0.4
-4.22
10.51
Employment Retired
0.63
2.18
0.29
0.77
-3.65
4.91
Employment Self employed
2.65
2.24
1.18
0.24
-1.75
7.05
Employment Student
0.66
2.41
0.27
0.78
-4.08
5.4
Employment Unable to work
2.1
2.27
0.92
0.36
-2.37
6.57
Financial literacy
0.07
0.05
1.51
0.13
-0.02
0.16
Group SS_CIS
1.17
0.41
2.89
0
0.38
1.97
Group SS_CIS_TC
1.03
0.4
2.57
0.01
0.24
1.82
Language other
-1.68
0.75
-2.24
0.03
-3.17
-0.2
Relevance
0.28
0.06
4.74
0
0.17
0.4
Self-efficacy
-0.06
0.03
-2.08
0.04
-0.12
0
Understanding
-0.16
0.07
-2.4
0.02
-0.29
-0.03
Elementary questions
Table 46: Summary statistics for elementary questions asked at Stage 3
Min.
1st Qu.
Median
Mean
3rd Qu.
Max.
SD
SS
3
6
7
7.21
8
10
1.44
SS_CIS
3
6
8
7.44
9
11
1.71
SS_CIS_TC
2
7
7
7.39
8
11
1.66
All
2
6
8
7.34
8
11
1.6
Out of 11 elementary questions, participants answered 7.34 ± 1.6 questions correctly, on average (Table
46).

78
Table 47: Significant factors from the regression model for elementary questions asked at Stage 3
Factors
Estimate
Std.
Error
t value
Pr(>|t|)
2.50%
97.50%
Income>=100K<120K
-1.05
0.48
-2.19
0.03
-2
-0.11
Language: other
-0.75
0.37
-2.05
0.04
-1.48
-0.03
Understanding
-0.08
0.03
-2.57
0.01
-0.15
-0.02
(Intercept)
7.86
0.75
10.49
0
6.39
9.34
Income>=30K<40K
1.51
0.66
2.3
0.02
0.22
2.8
Income>=60K<70K
1.52
0.7
2.16
0.03
0.13
2.9
Relevance
0.12
0.03
4.21
0
0.06
0.18
Table 47 presents the significant factors from the regression model for elementary questions (11 total), the
full output of which is provided in Table 48. Three factors indicate a decrease and two factors indicate an
increase in the average number of correct elementary questions. The model predicts with 95 per cent
confidence that a consumer would answer between 6.39 and 9.34 elementary questions correctly (
Table 47: 95 per cent CI = [6.39, 9.34], p < 0.1). Of particular importance is that the model suggests there is
no significant difference in the number of correct elementary questions between the three knowledge
groups.
On average, the model suggests that consumers whose primary spoken language is not English would
answer -0.75 elementary questions correctly (out of 11) than those who speak English primarily (
Table 47: 95 per cent CI = [-1.48, -0.03], p = 0.04).
There was a slight inverse relationship between the self-assessed factor of understanding. That is, for each
point in their understanding score (with a maximum of 25), the model suggests consumers would answer -
0.08 fewer questions correctly (
Table 47: 95 per cent CI = [-0.15, -0.02], p = 0.01).
There was a slight positive relationship between the self-assessed factor of relevance. The model suggests
that for each point in a consumer’s relevance score, they would answer, over average, 0.12 more questions
correctly (
Table 47: 95 per cent CI = [0.06, 0.18], p < 0.01).
The other factor that influenced the number of elementary questions answered correctly was income.
Interestingly, those in the 100K to 200K income bracket answered -1.05 elementary questions correctly
than those on 10K (
Table 47: 95 per cent CI = [-2, -0.11], p = 0.03). However, those who earned between 30K and 40K, as well
as 60K and 70K, answer 1.51 and 1.52, respectively, more questions correctly than those on 10K or less (
Table 47: 95 per cent CI = [0.22, 2.8], p = 0.02, 95 per cent CI = [0.13, 2.9], p = 0.03).

79
Table 48: Regression model for elementary questions asked at Stage 3
Factors
Estimate
Std. Error
t value
Pr(>|t|)
2.50%
97.50%
(Intercept)
7.86
0.75
10.49
0
6.39
9.34
Income>=100K<120K
-1.05
0.48
-2.19
0.03
-2
-0.11
Income>=10K<20K
0.26
0.49
0.54
0.59
-0.7
1.23
Income>=120K<140K
0.55
0.47
1.18
0.24
-0.37
1.47
Income>=140K<160K
0.2
0.52
0.39
0.7
-0.83
1.23
Income>=160K<180K
0.93
0.48
1.96
0.05
0
1.87
Income>=180K<200K
0.5
0.54
0.92
0.36
-0.57
1.56
Income>=20K<30K
0
0.37
0.01
0.99
-0.73
0.74
Income>=30K<40K
1.51
0.66
2.3
0.02
0.22
2.8
Income>=40K<50K
0.86
0.93
0.92
0.36
-0.97
2.69
Income>=50K<60K
2.36
1.56
1.52
0.13
-0.7
5.43
Income>=60K<70K
1.52
0.7
2.16
0.03
0.13
2.9
Income>=70K<80K
0.3
0.37
0.82
0.41
-0.42
1.02
Income>=80K<90K
0.19
0.41
0.45
0.65
-0.62
0.99
Income>=90K<100K
0.43
0.41
1.03
0.31
-0.39
1.24
Income200K+
-0.46
0.94
-0.49
0.62
-2.31
1.38
Income Prefer not to
say
0.48
0.38
1.26
0.21
-0.27
1.22
Language other
-0.75
0.37
-2.05
0.04
-1.48
-0.03
Relevance
0.12
0.03
4.21
0
0.06
0.18
Self-efficacy
-0.02
0.01
-1.68
0.09
-0.05
0
Understanding
-0.08
0.03
-2.57
0.01
-0.15
-0.02
Intermediate questions
Table 49: Summary statistics for intermediate questions asked at Stage 3
Min.
1st Qu.
Median
Mean
3rd Qu.
Max.
SD
SS
0
1
2
1.87
3
4
1.29
SS_CIS
0
1
3
2.77
4
6
1.89
SS_CIS_TC
0
1
2
2.5
4
6
1.78
All
0
1
2
2.38
4
6
1.71
Out of eight intermediate questions, participants answered 2.38 ± 1.71 questions correctly, on average
(Table 49).
Table 50: Significant factors of the regression model for intermediate questions
Factors
Estimate
Std. Error
t value
Pr(>|t|)
2.50%
97.50%
Group
SS_CIS
0.72
0.21
3.43
0
0.31
1.13
Group
SS_CIS_TC
0.66
0.21
3.13
0
0.25
1.07
Relevance
0.14
0.03
4.57
0
0.08
0.2
Table 50 provides the significant factors from the regression model (Table 51) for the average number of
intermediate questions (8 total) correctly answered at Stage 3.
Whilst there was no difference between the three knowledge groups for elementary and advanced, there
was a statistically significant difference between the knowledge groups for the intermediate questions.
Therefore, it was this subgroup of questions that accounts for the significant difference between knowledge

80
groups when considering all questions. However, since these differences are small, it is arguable as to
whether there is a genuinely significant difference between the knowledge groups.
Out of 8 intermediate questions, the model predicts that consumers would only answer 0.49 questions
correctly (Table 51: 95 per cent CI = [-3, 3.97]). Those provided with the critical information summary, but
not the terms and conditions (SS_CIS), are predicted to answer 0.72 more questions correctly than those
only provided with the sales summary (SS) (Table 50: 95 per cent CI = [0.31, 1.13], p < 0.01). Those also
provided with the terms and conditions (SS_CIS_TC) are predicted to perform better than the SS group, but
not as well as the SS_CIS group, answering 0.66 more questions correctly than the SS group (Table 50: 95
per cent CI = [0.25, 1.07], p < 0.01).
The self-assessed factor of relevance had a slight positive relationship with the correct number of
intermediate questions, with a consumer predicted to answer 0.14 more questions correctly for every point
in relevance score (Table 50: 95 per cent CI = [0.08, 0.2], p < 0.1).
Table 51: Regression model for intermediate questions
Factors
Estimate
Std. Error
t value
Pr(>|t|)
2.50%
97.50%
(Intercept)
0.49
1.77
0.27
0.78
-3
3.97
Age25-34
0.83
1.63
0.51
0.61
-2.36
4.03
Age35-44
1.02
1.62
0.63
0.53
-2.17
4.2
Age45-54
1.08
1.62
0.67
0.51
-2.11
4.27
Age55-64
1.28
1.62
0.79
0.43
-1.9
4.46
Age65-74
0.48
1.62
0.29
0.77
-2.71
3.66
Age75+
0.25
1.69
0.15
0.88
-3.06
3.57
Age Prefer not
to say
2.66
2.28
1.17
0.24
-1.82
7.13
Financial
literacy
0.03
0.02
1.39
0.17
-0.01
0.08
Group SS_CIS
0.72
0.21
3.43
0
0.31
1.13
Group
SS_CIS_TC
0.66
0.21
3.13
0
0.25
1.07
Relevance
0.14
0.03
4.57
0
0.08
0.2
Self-efficacy
-0.03
0.02
-2.1
0.04
-0.06
0
Understanding
-0.06
0.03
-1.82
0.07
-0.13
0
Advanced questions
Out of seven advanced questions, participants answered 1.31 ± 0.98 questions correctly, on average
(Table 52).
Table 52: Summary statistics for advanced questions asked at Stage 3
Min.
1st Qu.
Median
Mean
3rd Qu.
Max.
SD
SS
0
0
1
1.17
2
4
1
SS_CIS
0
1
1
1.43
2
4
0.99
SS_CIS_TC
0
1
1
1.31
2
3
0.94
All
0
1
1
1.31
2
4
0.98
Table 53 provides the significant factors from the regression model for the average number of advanced
questions (7) correctly answered at Stage 3 (Table 54). For the advanced questions, there was no
difference between the three knowledge groups.
Table 53: Significant factors from the regression model for advanced questions asked at Stage 3

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Factors
Estimate
Std. Error
t value
Pr(>|t|)
2.50%
97.50%
Education Trade/technical/vocational Training
-0.49
0.14
-3.58
0
-0.75
-0.22
(Intercept)
1.14
0.22
5.24
0
0.71
1.57
On average, respondents answered 1.14 questions correctly out of 7 (Table 53: 95 per cent CI = [0.71,
1.57], p < 0.01). Out of the factors that were controlled for, only those whose highest education level was
trade, technical, or vocational showed a significant difference in number of correctly answered questions.
The model suggests that consumers whose highest education level is trade, technical, or vocational would
answer 1.14 fewer advanced questions correctly (Table 53: 95 per cent CI = [-0.75, -0.22], p < 0.01).
Table 54: Regression model for advanced questions
Factors
Estimate
Std. Error
t value
Pr(>|t|)
2.50%
97.50%
(Intercept)
1.14
0.22
5.24
0
0.71
1.57
Doctorate degree/PhD
0.21
0.44
0.48
0.63
-0.66
1.08
High school graduate
-0.28
0.16
-1.76
0.08
-0.6
0.03
Master's degree
-0.24
0.23
-1.04
0.3
-0.69
0.21
Professional degree
-0.58
0.38
-1.54
0.12
-1.33
0.16
Some high school
-0.16
0.18
-0.85
0.4
-0.52
0.21
Trade/technical/vocational training
-0.49
0.14
-3.58
0
-0.75
-0.22
University diploma
-0.39
0.21
-1.88
0.06
-0.8
0.02
Relevance
0.04
0.02
2.17
0.03
0
0.07

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Questionnaire
All questions for the questionnaire were taken from validated scales.
Stage One
General Self-Efficacy (Schwarzer and Jerusalem, 1995)
(1 = Strongly Disagree – 5 = Strongly Agree)
1. I can always manage to solve difficult problems if I try hard enough
2. If someone opposes me, I can find the means and ways to get what I want
3. It is easy for me to stick to my aims and accomplish my goals
4. I am confident that I could deal efficiently with unexpected events
5. Thanks to my resourcefulness, I know how to handle unforeseen situations
6. I can solve most problems if I invest the necessary effort
7. I can remain calm when facing difficulties because I can rely on my coping abilities
8. When I am confronted with a problem, I can usually find several solutions
9. If I am in trouble, I can usually think of a solution
10. I can usually handle whatever comes my way
Satisfaction (adapted from Harris and Harrison, 2014)
(1 = Strongly Disagree – 5 = Strongly Agree)
1. I was very satisfied with the information about the agreement
2. The information that I received in the preceding paragraphs was helpful
3. I am happy with the amount of information I have received in relation to the telco agreement
4. The information I received in the preceding paragraphs would be enough information for me to
consider agreeing to sign-up for a SIM card
Believability (adapted from Harris and Harrison, 2014)
(1 = Not at all, 5 = Completely)
1. How close to the reality of a telco agreement is the information that has been provided to you?
2. How authentic is the information provided?
3. How likely is this information to be the kind of information a telecommunications provider would give
you if you were considering using their services?
Relevance (adapted from McQuilken, Robertson, Polonsky and Harrison, 2015)
(1 = Strongly Disagree – 5 = Strongly Agree)
1. The information provided would be relevant in my consideration of the SuperMobile smartphone
plan.
2. The information provided would be useful in my consideration of a SuperMobile Smartphone Plan.
3. The amount of information provided would be appropriate in my consideration of an AustTel
Smartphone Plan.

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Understanding (adapted from McQuilken, Robertson, Polonsky and Harrison,
2015)
(1 = Strongly Disagree – 5 = Strongly Agree)
1. I have understood the information contained in the [insert documentation received]
2. I believe that I could solve basic problems with my phone plan with the information provided.
3. I did not understand the agreement with the telecommunications company
4. The agreement was too complex
5. I was not sure what my rights were under the agreement
Financial Literacy (OECD measures) - (1 = Strongly Disagree – 5 = Strongly
Agree)
1. In general, I feel confident when making day-to-day financial calculations.
2. I would consider myself financially literate.
3. Before I buy something I carefully consider whether I can afford it
4. I tend to live for today and let tomorrow take care of itself
5. I find it more satisfying to spend money than to save it for the long term
6. I pay my bills on time
7. I keep a close personal watch on my financial affairs
8. I set long term financial goals and strive to achieve them
9. Money is there to be spent
Demographic information
What is your age?
 18-24 years old
 25-34 years old
 35-44 years old
 45-54 years old
 55-64 years old
 65-74 years old
 75 years or older
 Prefer not to answer
What is the highest degree or level of school you have completed? If currently enrolled, highest degree
received.
 No schooling completed
 Some high school
 High school graduate
 Trade/technical/vocational training
 University Diploma
 Bachelor’s degree
 Master’s degree
 Professional degree
 Doctorate degree/PhD
 Prefer not to answer
Are you currently...?

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 Full time employed for wages
 Part-time employed for wages
 Self-employed
 Out of work and looking for work
 Out of work but not currently looking for work
 A homemaker
 A full-time carer
 A student
 Retired
 Unable to work
 Prefer not to answer
Please describe your work.
 Employee of a for-profit company or business or of an individual, for wages, salary, or commissions
 Employee of a not-for-profit, tax-exempt, or charitable organization
 Local government employee (city, county, etc.)
 State government employee
 Federal government employee
 Self-employed in own not-incorporated business, professional practice, or farm
 Self-employed in own incorporated business, professional practice, or farm
 Working without pay in family business or farm
 Prefer not to answer
What is your sex?
 Female
 Male
 Prefer not to answer
What is your individual annual income (before tax)?
 Less than $10,000
 $10,000 to $19,999
 $20,000 to $29,999
 $30,000 to $39,999
 $40,000 to $49,999
 $50,000 to $59,999
 $60,000 to $69,999
 $70,000 to $79,999
 $80,000 to $89,999
 $90,000 to $99,999
 $100,000 to $119,999
 $120,000 to $139,999
 $140,000 to $159,999
 $160,000 to $179,999
 $180,000 to $199,999
 $200,000 or more
 Prefer not to answer
In which country were you born?
 Australia
 England

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 New Zealand
 Italy
 Vietnam
 India
 China
 Scotland
 Philippines
 Greece
 Germany
 Thailand
 Germany
 USA
 Other – please specify: __________________________________
 Prefer not to answer
What is the main language that you speak at home?
 English
 Other – please specify: _________________________________
 Prefer not to answer
Are you of Aboriginal or Torres Strait Islander Origin
 No
 Yes, Aboriginal
 Yes, Torres Strait Islander
 Prefer not to answer
Stages Two and Three
In stages Two (24 – 48 hours after Stage One) and Three (14 – 21 after Stage Two) we conducted a
knowledge test with questions or problems to solve that could be answered based on the information that
the participant would have received. For example, some participants received only the Sales Summary
(SS), and therefore, would have only had enough detailed information to answer those questions that rely
on information provided in the SS only.
Sales Summary Only (10 questions):
1. How many calls can you make per month to SUPERMOBILE Mobile and home phones?
 6000
 $500 worth of calls
 Unlimited (Y)
 1000
2. How much data per month is included in the agreement?
1.5gb (Y)
1.5mb
500mb
500gb
3. You subscribe to receive a daily horoscope via a premium service. Is this part of your included value?
Yes
No (Y)

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4. How much does it cost to get directory assistance?
0.99c
$1.39
$2.00 (Y)
It’s free
5. For how long are you tied to SuperMobile under this agreement?
I can get out at anytime with no penalties (Y)
I can get out at anytime with a small charge
I have to wait six months
I have to wait twelve months
6. Are calls to 1800 numbers (e.g., 1800 CENTRELINK) included in the plan?
Yes (Y)
No
7. How much does it cost for you to send a photo via MMS to a friend who uses a different
telecommunications provider?
$1.00
50c (Y)
25.3c
$1.50 + 40c Flagfall
8. Are calls to 1300 numbers included in the plan?
Yes
No (Y)
9. Does the plan include a new phone with SuperMobile?
Yes
No (Y)
10. Is 1.5GB more data than 1.5MB?
Yes (Y)
No
11. Is this a pre-paid or post-paid service?
Pre-paid
Post-paid
Both
Neither
Sales Summary and CIS (7 questions):
1. How much would a two minute call to a 1300 cost you on this plan?
$1.02
$1.53
$2.04
$2.44 (Y)
$1.93
2. How much would be deducted from your included value for a one minute, 48 second call to a mobile or
landline?
$0

87
$1.89
$2.38 (Y)
$1.98
$1.49
3. You have received four voicemail messages, how much will be deducted from your included value to
retrieve all of the messages in the one call to your message inbox?
99c
$1.39
$4.36 (Y)
$3.96
4. After three days of being with SuperMobile, you realise that the plan does not suit your needs. You
decide to cancel the service. What is the total amount you will pay for your three days with SuperMobile?
$3
$30
$50
$70 (Y)
5. How much will it cost you to make a one minute video call to an Australian number on your plan?
$1.40 (Y)
$1.00
$1.50
0.99c
6. If you don’t understand something in your agreement, where would you turn to for clarification
 Telecommunications Industry Ombudsman (TIO)
 Call SuperMobile
 Search the SuperMobile website
 A friend/family member
 A legal centre
 Whirpool
7. What does “Plan Inclusion” mean?
That any of my activity covered by “plan inclusion” is deducted from the $30 I pay each month
That any of my activity covered by “plan inclusion”” is deducted from my $500 included value (Y)
That any of my activity covered by the “plan inclusion” is unlimited
That any of my activity covered by the “plan inclusion” is in addition to the $30 I pay each month
8. Yesterday you received a text message to tell you that you have used 85 per cent of your data for the
month. Today you received another text to say that you have used 103 per cent of your data. Why?
There was a delay between my usage and the alert
Some of my apps have been using data in the background
I used 280mb since yesterday
(ALL ANSWERS IN THIS QUESTION COULD BE CORRECT, THIS IS TO TEST WHAT PEOPLE
UNDERSTAND ABOUT ALERTS)
All Three Documents (6 questions):
1. You have been told that that your data has now reached 85 per cent which means that you have (DROP
DOWN BOX: 0.15, 0.23 (Y), 0.5, 1.0) GB left until the end of the month.

88
2. For the past two days, you have been streaming your favourite TV program on your phone on your half
hour commute to and from work. About how much data do you think you will have used?
 0.5gb
 1.5gb
 2.0gb
 3.5gb (Y)
(Taken from http://www.vodafone.com.au/personal/mobile-internet/data-guide )
3. Your provider has told you that you have reached your data limit for the month, with three days to go
before it renews. How much will you be charged in addition to your $30 plan if you use on average 120mb
per day until the end of the month.
 $10.00
 $12.00
 $36.00 (Y)
 $40.00
4. You send, on average, 20 SMS text messages per day. When will you reach your included free SMS
limit?
 I won’t
 At around six months
 At around 10 months (Y)
 At around 12 months
5. This month you sent 2000 text messages, the previous you sent 4000 text messages, and the month
before you sent 500. How much will you be deducted from your included value for SMS messages in this
month’s bill?
 Nothing. I get 6000 free texts per month
 $1,644.50
 $126.50 (Y)
 Nothing. I have unlimited SMS.
6. You have exceeded your data limit for this month, and the excess data charges come to $30. How much
money will be deducted from your direct debit this month to cover this excess?
$10 (Y)
$20
$30
Nothing
6. How much will it cost you to port your number from SuperMobile to Telstra?
 Nothing, it’s free
 Just an administration fee
 $30
 It depends on my new provider
You have lost your job, and realise that you won’t be able to pay your bill by the due date. What could you
do to solve your difficulty?
(Open-ended response)

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Information provided to participants
Sales Summary (SS)
Imagine that you have spent some time searching for an appropriate plan for your smartphone. The
salesperson has listened to your needs, and has recommended the following:
“The plan that I recommend for you is the $30 SIM only plan with AusTel.
This means that all you need to do is bring your own phone to the plan, and you have no lock-in contract to
tie you to AusTel. You just pay for the plan month-to-month.
The great thing about this plan is that there are no exit fees and you can change plans or leave AusTel at
any time with no added fees.
As part of the plan, each month you will pay $30 to receive 1.5 GB of data and $500 worth of calls and
texts. The first 6000 texts are free, and after that you pay 25.3c for each text which is deducted from your
$500 included value. MMS texts are 50c.
Also deducted from your $500 included value are calls to mobile phone and landlines. Calls to landlines
and mobile phones are 99c per minute plus a 40c flagfall. 13/1300, 1800 numbers and premium SMS
services are not included in the plan and are charged at a separate rate to calls to mobile phones and
landlines. Calls to directory assistance are charged at a $2.00 flat rate.
If you exceed the 1.5GB of data usage, you’ll be charged 10c per MB and you’ll receive a text message
alert when you have reached 50%, 85% and 100% of the 1.5 GB limit. These texts may be delayed by up
to 48 hours.”
Critical Information Summary (CIS)
(Starts next page)

90
Information about the Service
Service Description
The Service is a prepaid SIM Only mobile service
with an automatic prepayment top-up when the payment
falls to a trigger point. The service includes the
following monthly Included Value for use within
Australia:
 $500 Included Value for Calls & MMS to
Standard Australian Numbers, Calls & Text
(SMS and MMS) to International Numbers
(Landlines & Mobiles)
 6000 Included SMS to Standard Australian
Numbers
 Unlimited Calls to AUSTEL Mobile & AUSTEL
Home Phone
 1.5GB Included Data
Minimum Term
AUSTEL Mobile Services are supplied on a rolling
month-to-month basis. Customers are permitted to
terminate the acquisition of the Service at any
time.
Call Rates
Excluded Value
AUSTEL Plans exclude Calls and SMS to 19 numbers, Premium SMS,
Third Party Content, Video Calls to International Numbers,
International Roaming, Diversion to International numbers,
Directory Assistance, Calls thru to connect services (e.g.
124YES) and other Enhance Services
Bundling Arrangements & Mandatory Goods
 Supply of the Service does not require Bundling
 It is not a requirement of AUSTEL Mobile Services that
customers acquire handsets or other equipment from
AUSTEL
Usage Types in Australia
Rate
Plan Inclusion
Calls to AUSTEL Mobile and Home Phones
Unlimited
✓
Calls to Standard Australian Numbers
99¢ + 40¢ flagfall
✓
Diversions within Australia
99¢ per minute
✓
International Calls (Mobiles and Landlines)
See website for rates
✓
Video Calls to Australian Numbers
$1 per minute + 40¢ flagfall
✓
Video Calls to International Numbers
$1.50 per minute + 40¢ flagfall
✕
SMS to Australian Numbers
First 6000 SMS included, 25.3¢ per message
thereafter
✓
SMS to International Numbers
50¢ per message (max 160 characters)
✓
MMS to Australian Numbers
50¢ per message
✓
MMS to International Numbers
75¢ per message
✓
Voicemail Deposit
Unlimited
✓
Voicemail Retrieval
99¢ per message + 40¢ flagfall
✓
Excess Data
10¢ per MB (charged per KB or part therof)
✕
Calls to AUSTEL Support 13 11 44
Unlimited
✓
13/1300 Numbers
$1.02 per minute + 40¢ flagfall
x
1800 Numbers
$1.24 per minute + 40¢ flagfall
✓
1900 Numbers
Surcharge of 44¢ per minute and rate of
holder of number
✕
Directory Assistance 1223
$2 per call
✕
Premium SMS
Variable, dependent on holder number
✕
Critical Information Summary
AUSTEL Mobile T3G Plan - Large

91
Information about the Pricing
Other Information
Usage Information
You can monitor you USTEL Mobile Usage by logging
into Your Account online at
www.austel.com.au/account.
International Roaming
While roaming, calls, SMS, MMS and data are
charged at higher rates than they are used in
Australia.
Charges for using International Roaming are not
part of your plan’s Monthly Included Value. We
highly recommend you disable Mobile Date (GPRS)
before going overseas to ensure that you doo not
incur unexpected and high data usage fees whilst
roaming.
Please be aware there is often a lag of up to 21
days before the roaming usage is shown in “Your
Account” due to the delay in receiving your
roaming usage records from overseas network
carriers.
Customer Support
Customer Service
Email: mobile.customer.service@austel.com.au
Phone: 13 14 23 (option 3, and then option 2)
Technical Support
Email: mobile.helpdesk@tpg.com.au
Phone: 13 14 23 (option 2, and then option 3)
Complaints Handling
If you have a dispute with AUSTEL and wish to make
a complaint, please contact Customer Relations, a
specialist complaint resolutions team, by:
Email: customer_relations@austel.com.au
Further Options
If you are not satisfied with our handling of your
complaint and you have escalated this within
AUSTEL, you may seek complaint mediation or
further assistance from the Telecommunications
Industry Ombudsman (telephone 1800 062 058).
Plan
T3G Plan - Large
Monthly
$30
Included Value
$500
Included Data
1.5GB
Upfront Fees
Once off SIM $20
Mobile Prepayment Outside Included Value $20
Minimum Monthly Charge – 1st Month
$70
Early Termination Charge
N/A
Cost of a 2 Min Standard National Call
$2.38 (incl. 40¢ flagfall)
Number of Standard National Calls you could make from your
Included Value if you restricted your use solely to Standard
national mobile Calls each of 2 minutes in duration
210 Calls
Cost of a Standard National SMS (up to 160 characters)
First 6000 SMS included, 25.3¢ per message there after
Cost of 1MB Excess Data
10¢
Critical Information Summary
AUSTEL Mobile T3G Plan - Large

92
This is a summary only – the full terms and conditions for this service are available at www.austel.com.au/terms_conditions

93
Terms and Conditions (T&Cs)
AusTel Standard Terms & Conditions
These Standard Terms and Conditions apply to services supplied to consumers by
Australian Telecommunication Company (AusTel) Pty Ltd.
1. The Agreement
1.1. An agreement is formed when you apply to acquire a service from
us and we accept your application. The application may be made over the
phone, or by completing an online ordering process or a physical order form.
You warrant that you are over 18 years of age and legally entitled to enter into
the agreement.
1.2. The agreement will be made up of:
(a) Your application;
(b) The service description;
(c) The plan brochure or other document provided to you relating
to the service during the application process; and
(d) These Standard Terms and Conditions.
1.3. If there is inconsistency between any part of the agreement, the
inconsistency will be resolved according to the following order of priority:
(a) The plan brochure;
(b) The service description;
(c) these Standard Terms and Conditions; and then
(d) your application.
2. Period of the Agreement
2.1. The agreement commences when your application is accepted by
us.
2.2. For contracts other than fixed period contracts, the agreement will
continue until it is terminated by either party on 30 days notice or otherwise in
accordance with the agreement.
2.3. For fixed period contracts, the agreement will continue:
(a) for the minimum contract period referred to in your application
or in the service description or plan brochure; or
(b) until it is terminated in accordance with clause 12.
2.4. If neither you nor we cancel the agreement at the end of the fixed
period contract, we will continue to supply the service to you on a month-to-
month basis.
2.5. If we will not continue to provide the service to you at the end of the
fixed-period contract or if we wish to change the terms of the agreement,
including charges, we will inform you of this at least 30 days before the end of
the fixed period contract.
3. Changes to the Agreement
3.1. We may change the agreement in the following circumstances:
(a) Where you agree to the change;
(b) Where the change will not adversely affect you and, before
the changes take effect, we have given you notice of the change;
(c) Where the change is in relation to charges for making
international telephone calls or roaming and, before the changes take

94
effect, we have given you notice of the change;
(d) Where the change is to introduce or vary a fee or charge to
pass on a tax or levy imposed by law and, before the changes take
effect, we have given you notice of the change;
(e) Where the change is to introduce or to vary a charge
associated with a content or premium service where we rely on a third
party for the service and the third party increases its price to us and,
before the changes take effect, we have given you reasonable notice of
the change;
(f) If the agreement is a fixed period contract and the change is
adverse to you, and we provide to you not less than 21 days notice of
the change.
3.2. We may withdraw any plans/packages at any time by giving you
notice but such withdrawals will only take effect from the end of your then
current fixed-period contract.
3.3. Notice of a change to the agreement may be given by us:
(a) by email to your nominated account email address,
(b) with or as part of a bill, or
(c) otherwise in writing, including by fax or mail.
3.4. Changes to these standard terms or a service description will be
made available online and you are encouraged to check our website regularly.
3.5. If we change the agreement under clause 3.1(f), you may cancel
the agreement within 42 days of the date of the notice without incurring
charges, other than usage or network access charges to the date the
agreement ends and outstanding amounts for installation or for equipment with
other suppliers' services.
3.6. Your ongoing use of the service after the date of a variation,
alteration, replacement or revocation or on the expiry of the 42 day period, is
deemed acceptance of the variation, alteration, replacement or revocation.
4. Applications
4.1. You warrant that information provided to us in the application is true
and correct in all material respects and you acknowledge that we will rely on it.
You agree that, if you give us incorrect information during an application which
is then relied upon and used by a third party carrier for the provision or
attempted provision of a service, you will be liable for a resubmission payment
to us.
4.2. An application for Service may be refused by us in the following
circumstances:
(a) Where there is a technical limitation to our ability to provide
you the service, including where there are network capacity constraints;
(b) Where you have not completed an application process
correctly or have been unwilling to provide us with a document or
information we require;
(c) Where you do not meet our credit assessment criteria.
4.3. By applying for a service, you authorise to communicate with credit
referencing bodies/associations about your credit history and in so doing to
provide them with the details that you have provided to us. We may do this from
time to time during the term of the agreement
4.4. We may apply restrictions to a service where you have not met our
credit assessment criteria. We will advise you of the general nature of the
reasons for these restrictions and, if applicable, how you may access services
which have been restricted.

95
4.5. We may pay commission to a dealer or agent acting on our behalf
who is involved in your application process.
5. Your Private Information
5.1. As part of your application and in connection with the provision of
service to you, we may obtain from you private information about you.
is required by law to collect certain Personal Information about you,
including your name, address and telephone service number to provide it to the
operator of the Independent Public Numbering Database (IPND). Information in
the IPND is used to develop directories and to assist emergency service
organisations.
5.2. We use our best endeavours to comply with a privacy policy which
is available on our website or by contacting us. This policy governs the
information we collect on you, how we use it and your rights to access it. You
consent to us to collect and disclose your personal information including any
unlisted telephone number and address from or to:
(a) any credit providers or credit reporting agencies to use the
information for all purposes permitted by the Privacy Act (1988)
including to obtain a credit report about you or your registered business,
maintaining a credit information file about you, or notifying a default by
you;
(b) any law enforcement agencies to use the information to
assist them in the prevention or prosecution of criminal activities;
(c) to conduct ongoing credit management of your account;
(d) any of our shareholders, related entities, suppliers, agents or
professional advisers for reporting, accounting, product supply and
service, marketing and audit purposes;
(e) any upstream supplier to us to use the information for any
purposes connected with the service or your use of the service; and
(f) any person who provides us with your username(s) or
password(s).
5.3. From time to time we will update you on our services, news,
promotions and offers including those from related or affiliated organisations.
You consent to us contacting you at any time (including after you have
terminated the agreement), for this purpose through any available contact
methods. You can withdraw your consent at any time by contacting us.
6. Minimum Contract Period
6.1. The minimum contract period is the minimum fixed period during
which you must acquire the service. The minimum contract period may be
specified in your application or in the plan. The minimum contract period
commences when the service is activated.
6.2. If, during the minimum contract period, you cancel the service or we
cancel the service because of your default, you may be liable to pay an early
termination charge which is either set out in the plan brochure or in the service
description.
6.3. Once the Minimum Contract Period is over, your service will
continue to renew automatically, and you will continue to be charged for the
service, until such time as you or we cancel the service by giving 30 days
notice.
7. Usage
7.1. You acknowledge that charges will be incurred when the service is

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used. It is therefore important that you take steps to ensure that such usage
does not occur without your authorisation. You should ensure that you are in
control of devices that might make use of your services, such as computers,
handsets, mobile phones, and wireless devices connected to your service and
that third parties cannot access or use such equipment without your authority.
You acknowledge that usage of some services can occur because of an
infection of your computer with a virus or due to other unauthorised third party
intrusions. You should ensure that you have appropriate protection systems
operating on your equipment to restrict or limit the possibility of unauthorised
usage.
7.2. As we are not able to control access or usage of your handsets and
other equipment, you are responsible for all usage charges in respect of the use
of the service, whether or not such usage was authorised by you, unless the
usage was caused by a mistake by us.
7.3. You are not permitted to authorise a third party to use your service
without direct supervision and/or written authorisation by us.
7.4. You acknowledge that we cannot be held responsible for any loss
incurred by you because of faults and/or failures within a third party carrier's
network infrastructure.
7.5. While we will use our best endeavours in providing the service, you
use it at your own risk. Even if you lose some equipment or permit another
person to use your service, you are solely responsible for its use including:
(a) the calls made and messages sent;
(b) the sites and content accessed;
(c) the content or software downloaded and the effect it may
have on your equipment or service;
(d) the products and services purchased;
(e) the information provided to others;
(f) the installation or use of any equipment or software whether
provided by us or not;
(g) the modification of any settings or data on your service or
related services or equipment whether instructed by us or not;
(h) the personal supervision of any users under the age of 18
who use the service; and
(i) the lawfulness of your activities when using the service and
accessing any sites and third party content.
7.6. The service is provided to you on the basis that it is used only for
approved purposes. In particular you must:
(a) not use the service in any manner involving illegal, malicious,
deceptive or misleading activity;
(b) not breach any standards, content requirements or codes set
out by any relevant authority or industry body;
(c) not use the service in any way which interferes with the
operations of the service network, anyone else's enjoyment of their
service or which upsets or offends any person;
(d) not use the service for commercial purposes or in any way
distribute or resell the service without our written permission;
(e) obey all laws, regulations, guidelines and our reasonable
instructions concerning your use of the service;
(f) give us all information and cooperation that we may need in
relation to the service; and
(g) advise us of changes in your personal information such as
account details, debit or credit card details and expiry dates and billing
and service addresses.

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7.7. You must not use the service in a way which contravenes any fair
use policy, acceptable use policy or fair go policy that applies to the service.
7.8. We may suspend or terminate, with or without notice, your service
if, in AusTel's reasonable opinion, the service has been directly or indirectly
involved in activities that are detrimental to our internet service or jeopardise the
use of our service or its performance for other customers or how the wider
community will perceive AusTel. Such activities include, but are not limited to:
(a) 'Spamming' e-mail or forwarding spammed e-mail to other
Internet user's e-mail addresses'
(b) being listed or causing the listing of us or our other customers
on any real-time blacklist;
(c) e-mail bombing and the use of bulk e-mail programs to
unsolicited recipients making commercial advertising, informational
announcements, charity requests, petitions for signatures, chain letters
and political or religious messages;
(d) attempting to obtain unauthorised access to other Internet
servers and systems; and
(e) making misrepresentations or abusive or offensive behaviour
in newsgroups and other online facilities.
In any of the above circumstances, if we elect to proceed without giving
notice, we will initially only suspend the service and will provide you notice of
the suspension having occurred and the grounds on which the suspension was
made. We will reasonably consider any evidence or submissions you may
provide to us to demonstrate that the service was not used for the activity. If we
are satisfied that the service was not used for the activity, we will reinstate the
service as soon as practicable. If we are not so satisfied, we will terminate the
service by giving notice.
7.9. You must not use the service in a way or post to or transmit to or via
the service any material which interferes with other users or defames, harasses,
threatens, menaces, offends or restricts any person or which inhibits any other
customer from using or enjoying the service. You must not use the service to
send unsolicited electronic mail messages to anyone. You must not attempt any
of these acts or permit another person to do any of these acts.
7.10. We may suspend without notice your account if it has been used in
offensive and/or illegal activities under State and/or Commonwealth laws. This
includes the dissemination of banned pornographic material and other illegal
content. In such cases, the relevant law enforcement agency(ies) will be
notified, and offending material(s) may be passed on to them.
7.11. If who use a website or web hosting service provided by us for the
public dissemination of violent or pornographic material, you must issue
appropriate content warnings and provide viewing guidelines on your website,
as per the Classification Act. This is especially important with respect to content
which is likely to be considered unsuitable for children according to the
Classification Guidelines provided in the Act. If it is brought to our attention that
these appropriate content warnings and/or viewing guidelines have not been
provided, then we reserve the right to suspend or terminate your account and
pass this information on to the relevant authorities.
7.12. What constitutes inappropriate use will be determined by us, at our
sole discretion provided that we act reasonably.
7.13. We may monitor the use of your service, however we do not
promise to do so. If we identify excessive use or unusual activity we may
temporarily restrict or suspend your service. If we do so we will endeavour to
contact you via your nominated primary contact details. We may require an

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advance payment before your service is restored. You should not rely on us to
contact you or to suspend your service in the event of excessive or unusual
activity.
7.14. We may investigate any misuse of the service by you, in
conjunction with relevant law enforcement agencies. If your use of the service
results in loss to other users or us, you may be liable to pay compensation.
8. Phone Numbers
This section applies if you acquire a telephone or other service number.
8.1. If you do not already have a phone number for your phone for use
with the service, we will issue you a phone number.
8.2. All phone numbers are selected, issued and used by us in
accordance with ACMA's Numbering Plan and Telecommunications Numbering
Plan Number Declarations (numbering regulations).
8.3. We may be required to recover or recover and replace a phone
number we have issued to you in order for us to comply with the numbering
regulations.
8.4. We will give you as much notice as is reasonably practicable if we
have to do this.
8.5. You may request a new phone number. If we agree to issue you a
new phone number, you may have to pay a charge.
8.6. If you need a new phone number because you have received calls
of a harassing nature and you reported the matter to the relevant law
enforcement agency, we will supply you with a new phone number free of
charge on the first two occasions. You will have to pay a charge for any further
phone number changes.
8.7. You do not own the phone number but your right to use the phone
number starts when we issue the phone number to you.
8.8. Your right to use the phone number ends if you no longer obtain the
service unless you port the phone number.
8.9. You may transfer your service number to another carrier or service
provider. If you do so you acknowledge and understand that:
(a) charges may apply as a consequence of a transfer from us to
another carrier or service provider;
(b) any outstanding fees and charges which remain are your
responsibility;
(c) the transfer may result in disconnection of any related
services such as Voicemail, paging and data services, silent numbers,
priority assistance or other enhanced services;
(d) it is your responsibility to ensure that any equipment or
software used by you in connection with your service works with your
new carrier or service provider; and
(e) if after the transfer of your service from us, you continue to
use our service (for example through the use of an override code), you
agree to pay us for any fees and charges incurred for those services.
8.10. In the event that you transfer from us prior to the expiration of the
minimum term of your plan you will be liable for any outstanding fees and
charges including plan payout and plan cancellation fees.
8.11. Where you transfer to us:
(a) you authorise us to sign on your behalf and in your name
forms of authority to your current supplier to transfer your service
number(s) to us and you authorise your current supplier to transfer to us

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all services relating to the service numbers transferred to us;
(b) if your current supplier charges or credits us with any amount
concerning services provided before the date of transfer, we will credit
or charge that amount to your account accordingly and as soon as
practicable; and
(c) you indemnify us against any claims made by your current
supplier to us in relation to any amounts owing by you to them.
8.12. If you stop obtaining the service and do not port the phone
number, we may issue the phone number to another customer in accordance
with the numbering regulations.
8.13. We are not liable to you for any expense or loss incurred by you
due to:
(a) any recovery or recovery and replacement of the phone
number under clause 8.4 above, or
(b) you ceasing to have the right to use the phone number under
clause 8.9 above.
8.14. If your service is disconnected or transferred from us you must pay
us all outstanding amounts under the agreement. Once we have received
payment, we will refund to you any amount(s), which we may still hold. If we are
unable to refund monies owed within 12 months of your disconnection we will
retain the funds, which you agree to forfeit to us.
9. IP Adresses
9.1. You agree that the IP Address(es) issued to you for use in
connection with a service are only issued to you for use during the term of your
acquisition of the service. On termination of the service, your right to use the IP
Address(es) ceases.
9.2. We are responsible for all DNS delegation and routing in connection
with the service.
10. Billing and account payment
10.1. The plan brochure or service description may provide that bills will
not be issued. If that is so, charges will be incurred notwithstanding that no bill
has been issued.
10.2. Where we have agreed to issue bills, we will send to you by mail
or email notification a tax invoice at the end of billing periods unless the plan
brochure stipulates otherwise. You must pay all outstanding amounts by the
due date as shown on your tax invoice.
10.3. Usage records and download times can vary from time to time.
Whilst we aim to do so, we are unable to guarantee that all usage records
during a billing period will appear on the corresponding bill. This is particularly
so for charges incurred whilst using international roaming but also applies for
other types of usage.
10.4. Payments may be made to us through our available payment
methods. Service fees and charges may apply for some available payment
methods. We will apply payments made by you against outstanding tax invoices
at our discretion.
10.5. If you have chosen to use our direct debit facilities, and we have
not received your payment by the due date, unless we agree with you
otherwise, we will debit your nominated account on or after the due date. We
may continue to do so at any time until all amounts due are paid. We will
provide SMS or email notification when debits are made.

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10.6. Where in our opinion you have a reasonable claim or dispute with
an invoice or a debit, we will suspend our collection or recovery processes until
a determination on your claim or dispute has been made. We will reimburse any
incorrectly debited amount as soon as reasonably practicable.
10.7. All administration, registration and set-up fees are non-refundable.
You may exchange or receive a refund for equipment which has not been
opened or used and has been returned to us within 30 days of purchase.
10.8. If you require us to send to you a printed copy of an invoice, this
may be subject to an administration fee of $10.00 inc GST will apply per
request.
10.9. Accepted credit cards: Visa, Mastercard, American Express,
Diners Club. Accounts paid with an American Express or Diners Club card will
incur a surcharge of 3.2% (incl. GST) of the debited amount when we debit the
card.
10.10. You are responsible for ensuring there is sufficient funds/credit
available in your nominated credit card or direct debit account at any time we
debit the account. You must pay dishonor fees and any other charges,
expenses or losses resulting from our attempting unsuccessfully to debit the
credit card or direct debit account unless the failure was due to a clear error on
our part. Dishonored cheques incur a $16.50 inc GST handling charge. Direct
Debit rejections incur a $10.00 inc GST charge.
10.11. You are required to inform us if your credit card is due to expire
at least two weeks prior to the expiry date and are required to provide us with
details of a current credit card. You must also advise us if your nominated direct
debit account is transferred or closed, or the account details have changed.
10.12. Where a customer provides a new credit card number or re-
advises a credit card number, AusTel will immediately debit the credit card for
any outstanding amount owing or an amount of $1 if there is no current amount
owing. This debit is to confirm with the Customer's financial institution that the
card number and CVC are correct. The CVC is not retained by AusTel. The
amount received is credited to the customer's account.
10.13. AusTel will not accept Prepaid Visa/Master credit cards or gift
cards.
10.14. If you have failed to pay to AusTel an amount which is due, we
may following appropriate notice to you refer the debt to a third party collections
agent for the purpose of collection activity. You must pay all costs, charges and
expenses that we may incur in relation to our attempts to recover all debts due
by you to us, including accounting, mercantile agents costs and interest.
11. Bank account direct debit terms
11.1. If you have arranged to pay us by providing a Direct Debit Request
("Your Direct Debt Request"), this clause sets out the terms on which we accept
and act to debit amounts from your account under the Direct Debit System.
11.2. We agree to be bound by this clause when we receive your Direct
Debit Request complete with the particulars we need to draw an amount under
it.
11.3. We may have requested from you an online or verbal declaration
giving us authority to deduct monies from your bank account. By agreeing to
this declaration you will be regarded as having 'signed' a Direct Debit Request
(DDR) Form. You also agree that we may reproduce this document from our
electronic records and that the reproduced document shall, in the absence of
error, be an accurate copy of this document signed by you.
11.4. If you are not authorised to operate this bank account by yourself

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then those person(s) whose authority is required must complete and sign a
DDR and return it to us.
11.5. As recipient of a Direct Debit Facility (DDF) from you, we will:
(a) provide you with a statement of the amounts we draw under
your Direct Debit Request every month;
(b) provide you at least 21 days notice in writing, if we propose
to:
(i) change our procedures in this agreement;
(ii) change the terms of your Direct Debit Request; or
(iii) cancel your Direct Debit Request.
(c) agree to deal with any dispute raised under your Direct Debit
Request as follows: We will investigate the dispute and if it is found that
the amount has been debited in error we will refund the disputed amount
within 5 business days. Where it is found that the disputed amount has
been debited correctly and in accordance to the terms of the Direct
Debit Agreement, we will notify you of that outcome in writing within 5
business days; and
(d) not disclose any personal information provided to us under
the Direct Debit Request, which is not generally available, unless: you
dispute any amount we draw under your Direct Debit Request and we
need to disclose any information relating to your Direct Debit Request or
to any amount we draw under it to the Financial Institution at which your
account is held or the Financial Institution which sponsors our use of the
Direct Debit System or both of them; you consent to that disclosure; or
we are required to disclose that information by law.
11.6. As the provider of DDF you:
(a) authorise us to draw money from your account in accordance
with the terms of your Direct Debit Request and the agreement;
(b) acknowledge that if the day on which you are due to make
payment to us is not a business day we draw under your Direct Debit
Request on the next business day following the normal payment date.
You will need to enquire directly with your Financial Institution if you are
uncertain when they will process an amount we draw under your Direct
Debit Request on a day that is not a business day;
(c) may ask us to:
(i) alter the terms of your Direct Debit Request;
(ii) defer a payment to be made under your Direct Debit
Request;
(iii) stop a drawing under your Direct Debit Request. In
such instances an alternative method of payment must be
arranged 3 days prior to the due date and payment received by
the due date; or
(iv) may cancel all your services including your Direct
Debit Request by sending a written request including your
customer number and telephone number to us;
(d) will advise us of any disputed amount drawn under your
Direct Debit Request as soon as practically possible by notifying us of
your dispute by letter or fax, (include your customer number and
telephone number to us) and provide us with details of the payments in
dispute and reasons for the dispute. We will endeavour to resolve any
dispute within 21 days. Disputes may also be directed to your own
Financial Institution;
(e) acknowledge it is your responsibility to ensure there are
sufficient clear funds available in your account by the due date, on which

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we will draw any amount under your Direct Debit Request, to enable us
to obtain payment in accordance with your Direct Debit Request;
(f) acknowledge that if your Financial Institution rejects any of our
attempts to draw an amount in accordance with your Direct Debit
Request, we will recharge any dishonour fees charged to us by the
Financial Institution, to your account. We will make two attempts to draw
outstanding amounts in accordance with your Direct Debit Request. If
these fail, we will contact you by telephone or in writing to seek
alternative methods of payment for the outstanding balance of your
account, and to agree a suitable payment method for future account
payments;
(g) acknowledge not all accounts held with a Financial Institute
are available to be drawn under the Direct Debit System and that prior to
providing your account details to us under the Direct Debit Request,
have verified those details against a recent statement from your
Financial Institution to ensure those details are correct.
12. Suspension/Disconnection of the service
12.1. If your fixed period contract has expired or you are on a month-to-
month contract, you or we may disconnect the service and cancel the
agreement at any time by giving 30 days notice.
12.2. If you fail to comply with what we consider to be an important term
or condition of this agreement or should you fail to comply with a number of less
important terms and conditions then we can suspend or disconnect your service
or reroute calls from your service. We will generally provide you with notice of
your failure and allow you a reasonable time to remedy it. However we may
suspend or disconnect your service without notice to you where:
(a) you exceed the amount of your air limit or credit limit;
(b) there has been, in our opinion, unusual activity on your
service such as:
(i) usage of the service which is extremely high compared
to your usage of the service in prior months and which will result
in you incurring high charges; or
(ii) activity that is consistent with your service or
equipment connected to your service having been infected with a
virus or other malicious software; or
(iii) other activity that AusTel reasonably believes is
evident that the service is being used for fraudulent or other
illegal purposes;
(c) you have not paid charges when due and have not remedied
that failure within what we consider to be a reasonable time;
(d) you do something which we believe may damage the service
network;
(e) you are no longer approved by us under our assessment
policies or otherwise to receive the service;
(f) an authority such as the ACMA or enforcement agency
instructs us to do so;
(g) we believe that you have used your service to commit
unauthorised, criminal or unlawful activity;
(h) you vacate the premises in which you are provided the
service without notifying us beforehand;
(i) there are technical problems with the service network or the
service network requires repairs or maintenance;

103
(j) we believe it is necessary to comply with our legal obligations;
(k) we are entitled to do so under the specific terms and
conditions of your plan or package;
(l) you verbally abuse, attempt, threaten or cause harm to any
staff, equipment or network infrastructure of ours or any of the service
networks.
12.3. In the following additional circumstances we may suspend or
disconnect your service(s) or reroute calls from your service(s) but we will
provide you with reasonable notice prior to doing so:
(a) you have a mobile service and you inform us that you have
lost your SIM card;
(b) you have a mobile service which does not toll in any three
month period;
(c) you do anything which we believe may damage the service
network;
(d) you have used the service, in our opinion, other than in
accordance with the agreement;
(e) you do not comply with the terms set out in a Plan Brochure
or a Service Description.
12.4. Where one or more services included in a bundled offer(s) are
disconnected, entitlement to any discounts under such offers may be forfeited.
12.5. While your service is suspended or disconnected we will continue
to charge you any applicable fees and charges. We will only do so where the
suspension or disconnection is due to your failure to comply with your
obligations under this agreement, or is performed at your request.
12.6. Where we disconnect your service prior to the expiration of the
minimum term of your plan you will be liable for any outstanding fees and
charges, including the remaining access fees on your plan plus a plan
cancellation fee if applicable. We will only charge a plan cancellation fee in
circumstances where you have failed to comply with an important term or
condition of our agreement.
12.7. We are not liable to you or any person(s) claiming through you for
any loss or damage arising from suspension or disconnection of your service in
accordance with this clause.
13. Force Majeure
13.1. We will not be liable for:
(a) any delay in installing any service.
(b) any delay in correcting any fault in any service.
(c) failure or incorrect operation of any service, or
(d) any other delay or default in performance under this
Agreement
if it is caused by any event or circumstance reasonably beyond our
control, including but not limited to; war, accident, civil commotion, riot, military
action, sabotage, act of terrorism, vandalism, embargo, judicial action, labour
dispute, an act of a government or a government authority, acts of God,
earthquake, fire, flood, plague or other natural calamity, computer viruses,
hacker attacks or failure of the internet or delay, or failure or default by any
other supplier.
14. Liability
14.1. You may have certain rights and remedies under:

104
(a) the Competition and Consumer Act 2010 (Cth) and other
laws, which may imply certain conditions and warranties into this
agreement; and
(b) the Customer Service Guarantee issued by the ACMA, which
established minimum connection and fault repair times, breach of which
entitles you to certain specified amounts of damage.
14.2. We do not exclude or restrict or modify those rights, remedies or
implied conditions and warranties.
14.3. Where we are liable for any loss or damage in connection with or
arising from the breach of any term, condition, warranty or remedy implied by
the Competition and Consumer Act 2010 (Cth) our liability is limited to
resupplying, repairing or replacing the relevant service or equipment where the
service or equipment is not of a kind ordinarily required for personal, domestic
or household use or consumption and where it is fair and reasonable to do so.
14.4. You must let us know as soon as you become aware or believe
that you have a claim against us.
14.5. We are not liable for any defamatory, offensive or illegal conduct
or material found in connection with our services, including such conduct or
material transmitted by any means by any other person
14.6. You indemnify us from and against all actions, claims, suits,
demands, liabilities, losses, costs and expenses arising out of or in any way
connected with your use of the service or the equipment in a manner contrary to
the terms of this agreement.
14.7. Where you are two or more persons your liability will be joint and
several.
15. Assignment
15.1. You may transfer your rights and obligations under this agreement
to other person(s) approved by us under our assessment policies.
15.2. Where we reasonably consider there will be no detriment to you,
we can without your permission and without notice:
(a) transfer our rights and obligations under this agreement to
our nominee;
(b) temporarily or permanently delegate our obligations under
this agreement to our nominee; or
(c) novate this agreement to our nominee by ending this
agreement and entering into a new agreement between you and our
nominee, on terms similar to this agreement.
15.3. If we do any of the above the transfer or delegation or novation will
take effect when the relevant document is signed. You irrevocably appoint us as
your attorney to sign any necessary documents to enable the transfer,
delegation or novation to take effect.
16. Governing law
16.1. This agreement is governed by the laws of the state or territory of
Australia in which you are normally resident. You and we agree to submit to the
jurisdiction of the courts of such state or territory.
17. Meaning of words
17.1. Terms used within this agreement have the following meaning
unless the context suggests otherwise.
(a) ACMA means the Australian Communications and Media

105
Authority.
(b) agreement means the agreement for the provision of the
services between us comprising the items outlined in clause 1.2 of these
standard terms.
(c) air limit means a usage threshold we may impose on use of
your mobile service.
(d) available service area means locations in which the service
network is capable of providing service. Information on coverage areas
is available by contacting us or visiting our website.
(e) billing period means the period in which you are billed by us
for service. You will have 12 billing periods per year unless we agree
otherwise.
(f) billing run means the process of producing a bill for you. Each
billing run corresponds to a billing period.
(g) carrier means a Telecommunications carrier licensed under
the Telecommunications Act 1997.
(h) contact method means mail, SMS, MMS, email or telephone.
(i) credit assessment policies means those rules we use to
determine whether we wish to accept or decline to provide or continue to
provide you with the service. These policies may change from time to
time without notice to you. Under these policies you must: be at least 18
years of age; be capable of entering into a legal contract; be alive; not
be insolvent or bankrupt or subject to any proceedings to make you
insolvent or bankrupt; where you are in a partnership, the partnership
must not have been dissolved; where you are a company neither you
nor any of your assets may have been assumed under the terms of a
debt security instrument or under court order or otherwise appointed.
(j) credit limit means a limit we may place on your use of a
service or on amounts you owe us at a point in time.
(k) current supplier means a carrier or telecommunications
service provider who supplied telecommunications to you at the time of
signing the agreement.
(l) customer care policies means the policies, procedures, terms
and conditions under which we provide services. Our customer care
policies are updated from time to time and are available on our website
or by contacting us.
(m) customer service guarantee means the current minimum
performance standard set by the ACMA under sections 115, 117 and
120 of the Telecommunications (Consumer Protection and Service
Standards) Act 1999.
(n) direct debit date means the date, on or after the due date, on
which we will automatically debit your direct debit facility for amounts
due.
(o) direct debit facility means the debit account or credit/charge
account nominated by you for the debiting of your fees and charges.
(p) due date means the date the amount shown on your tax
invoice is due to be paid to us. The due date is not less than 14 days
after the tax invoice date.
(q) enhanced services means the services we provide that are
designated by us as enhanced services. Our website and plan
brochures will detail which services we have designated as enhanced
services.
(r) equipment means the item(s) required or otherwise used in
conjunction with your service such as mobile phones, fixed lines

106
phones, personal computers, software and modems purchased from us
or otherwise.
(s) factsheets means detailed information made available on our
website or otherwise.
(t) fees and charges means fees and charges payable by you
under your plan and under this agreement including any amounts of
applicable GST.
(u) fixed line service means the standard telephone service
comprising connection to the public switched telephone network plus
any other service(s) offered by us including any enhanced services.
(v) fixed period contracts are entered into where you commit to a
minimum period for which you will acquire the service and may be set
out in the plan brochure but do not include month to month contracts.
(w) GST means the tax imposed by A New Tax System (Goods
and Services Tax Imposition General) Act 1999 and any regulations
thereto or such other Act and regulations of equivalent effect.
(x) GST Act means A New Tax System (Goods and Service Tax)
Act 1999.
(y) GST supply means a supply as defined in and which is
subject to liability for GST under the GST Act.
(z) Hardware means the Call Saver Unit or any equipment that
we may provide from time to time.
(aa) internet service means connection to the global network of
computers known as the internet using software protocols supported by
us, plus any other services offered by us including enhanced services.
(bb) mobile network means the mobile network over which we
supply the service.
(cc) mobile service means the connection to the Mobile Network
plus any other services offered by us including enhanced services.
(dd) package means a grouping of services and plans, which are
sold together under specific terms and conditions.
(ee) passwords means the personal information or security
codes such as your customer service account passcode or website
password used by us to confirm that an individual has authority to
enquire or transact on your account.
(ff) personal information has the same meaning as defined within
the Privacy Act 2000.
(gg) plan means your plan for each of the service(s), the terms
and conditions of which may include a minimum term, monthly fees and
call charges as amended from time to time.
(hh) plan brochure is any brochure or other document (including
a webpage) which sets out the terms and conditions of a plan.
(ii) premium services means content or information services,
charged at a flat or timed rate, such as picture, ringtone and game
downloads, and SMS messages to weather services, as well as psychic,
voting and competition lines. Premium Service phone numbers usually
begin with 190 or an international prefix, whilst SMS numbers usually
begin with 18 or 19.
(jj) primary contact means the mobile or fixed line service
number, email address or other specific contact designated by you and
accepted by us to use as our primary means of contacting you in
relation to your account.
(kk) priority assistance means services offered to persons who
are diagnosed with a life threatening medical condition with a high risk of

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rapid deterioration to a life threatening situation and where access to a
telephone would assist to remedy the life threatening situation.
(ll) service means any and all of the digital mobile phone
services, fixed line services and internet services that we provide to you
including any enhanced services and also includes our customer
support services. Information on our services is available on our
website.
(mm) service network means the carrier of the
telecommunications services sold to you by us and includes the mobile
network.
(nn) SIM card means the subscriber identity module card, which
the network owns, but is provided to you to be placed into your mobile
phone to enable you to access your mobile service.
(oo) tax invoice date means the date you are issued with a tax
invoice containing a fee or charge.
(pp) third party content means products and information provided
by third parties to you, which you can access through your service.
(qq) third party content supplier means a party that provides third
party content to you through your service.
(rr) toll means making a voice call or SMS from your mobile
service.
(ss) transfer means to port, move or swap your service number
from one carrier or service provider to another as defined by the
Telecommunications Numbering Plan 1997.
(tt) usage record means the record of a call or data transfer
provided to us by the service network.
(uu) user means someone who uses a service, which may or
may not be the account holder.
(vv) username means the username created by you when you
registered for a particular service.
(ww) We, our, us means the member of the SP Telemedia
Limited group which enters into the agreement with you.
Complaint Handling Policy
AusTel aims to provide our Customers with the best possible service. If you haven’t
received the service you expected or your would like to make a suggestion we always
appreciate your feedback. Customer Service is your main point of contact within
AusTel whether you wish to discuss an issue regarding your account or you want
information about our services.
Our Customer Service staff can be contacted by:
Email - customer_service@AusTel.com.au
Phone - 13 14 23
Fax - 02 9850 0813
Mail - PO Box 1844, Macquarie Centre, North Ryde, NSW 2113
You will find the majority of matters can be handled on the first call. If further
investigation is required we will give you a timeframe & keep you posted along the way.
Our Customer Service staff may escalate your case to a Technical Support Officer, our
Customer Relations Team or even their Supervisor. If you are not satisfied with the way

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in which the Customer Service staff is dealing with your issue, you can request to be
escalated to a Supervisor. Customer Relations can be contacted directly by emailing
customer_relations@AusTel.com.au. We aim to respond to all written correspondence
within one working day.
AusTel believes that its internal resolution process is the most effective and quickest
way to resolve complaints. However If you are not satisfied with our handling of your
issue and you have escalated this within AusTel, you may seek further assistance from
external avenues of recourse in your state or territory.
AusTel aims to provide our customers with the best possible service. If you haven't
received the service you expected or you would like to make a suggestion we always
appreciate your feedback.
Consumers and former customers have the right to make a complaint for escalation
within AusTel.
A complaint means an expression of dissatisfaction made to us in relation to our
products or the complaints handling process itself, where a response or resolution is
explicitly or implicitly expected by you. Contacting AusTel to request support or to
report a service difficulty is not necessarily a complaint.
Level 1 Customer Support
AusTel Technical Support is the level 1 customer support within AusTel who assist to
resolve issues of a technical nature.
AusTel Customer Service is the level 1 customer support and main point of contact
within AusTel for questions regarding your account or for information about our
services.
If you are having difficulties with your Customer Service or Technical Support
representative, a supervisor may be called upon to assist.
AusTel believes that our internal resolution process is the most effective and quickest
way to resolve issues.
AusTel Technical Support can be contacted by:
Email -
Phone - 13 14 23 at the cost of a local call from a landline or 02 9850 0800 (option 2)
Fax - 02 9850 0813
Mail - PO Box 1844, Macquarie Centre, North Ryde, NSW 2113
Our Customer Service staff can be contacted by:
Email -

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Phone - 13 14 23 at the cost of a local call from a landline or 02 9850 0800 (option 3)
Fax - 02 9850 0813
Mail - PO Box 1844, Macquarie Centre, North Ryde, NSW 2113
Making a Complaint
If our Customer Service or Technical Support teams have been unable to satisfy your
issue, you can request for your call to be considered a complaint.
You may also make a complaint directly to Customer Relations, a specialist complaint
resolutions team, by:
Email -
Fax - 02 9850 0813
Mail - PO Box 1844, Macquarie Centre, North Ryde, NSW 2113
You may nominate an authorised representative or advocate to liaise with us on your
behalf. If you need assistance with understanding this process or lodging a complaint,
please let us know. This includes consumers with a disability or those who are suffering
hardship or are from a non-English speaking background.
What We Will Do Next
We will acknowledge a complaint immediately on the phone or within 2 business days
of receiving it and provide you a reference number. Where possible, our level 1
Customer Service and Technical Support teams will resolve your complaint upon first
contact. Where they have been unable to do so, our Customer Relations team will take
over management of the complaint and resolve your complaint within 15 business days
of receiving it, depending on the complexity of your complaint.
We will let you know any reasons for delay and a specific timeframe for resolution. We
will keep you updated with the status of your complaint and you may contact us either
by phone or by email with your reference number to request a status update. Please
note that AusTel is unable to implement any resolution until you have accepted it.
Further options
You will find the majority of matters can be handled by AusTel’s internal processes and
we do ask that you first allow us the opportunity to exhaust all avenues in resolving
your complaint. However, if you are not satisfied with our handling of your complaint
and you have escalated this within AusTel, you may seek complaint mediation or

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further assistance from the Telecommunications Industry Ombudsman (telephone 1800
062 058) or the fair trading department in your state or territory.
Urgent Complaints
Please advise us if your complaint is urgent. Complaints will be considered as urgent if:
▪ You have applied for or have been accepted as being in Financial Hardship under
AusTel’s Financial Hardship policy (located at
▪ http://www.AusTel.com.au/terms_conditions/financialhardship.php) and where the
nature of the complaint can reasonably be presumed to directly contribute to or
aggravate your Financial Hardship, or
▪ Disconnection of a service is imminent or has already occurred and where due
process has not been followed
Please note AusTel does not offer the Priority Assistance scheme.
Urgent complaints will be acknowledged within one business day. We aim to resolve
the urgent aspects of such a complaint within 2 business days or let you know of any
reasons for delay and a specific timeframe for resolution.
Click here for a PDF version of this process document.
Summary of Financial Hardship Policy
Financial Hardship is a term used to describe a situation where a person is unable to
meet their financial commitments due to one or more factors contributing to their
financial position. Common contributing factors include:
 Loss of employment of you or a family member
 Illness, including physical incapacity, hospitalization, or mental illness of you or
a family member
 Family breakdown
 A death in the family
 Other factors resulting in an unforeseen change in your capacity to meet their
payment obligations, whether through a reduction in income or through an
increase in non-discretionary expenditure.
If you are having a problem paying your bill, or you wish to discuss options to minimize
your bill, call us today on 13 14 23.
The earlier you contact us, the better. Discussing your concerns gives us the
opportunity to help you manage your bills.
If you do require time to pay an outstanding amount, agreeing to a payment plan and
sticking to it can help prevent disconnection or restriction of your service.
Disconnection of your service is used only as a last resort, and we will endeavour to
work with you to ensure this does not happen.
To assist us in establishing the level of support you require, dependent on your
individual circumstance, we may request supporting evidence, including, but not limited
to:

111
 Documentation such as a statutory declaration from a person familiar with the
customer’s circumstances (family doctor, clergy, bank officer, etc);
 Or evidence of the customer having consulted with, and/or being accompanied
by a recognized financial counselor or a booking to see a financial counselor.
There are also a range of other financial support services available such as free
financial counseling services offered in each state and territory in Australia. For more
information on these & other options available please see the ACMA’s website:
http://www.acma.gov.au/WEB/STANDARD/pc=PC_2939
Please contact us on 13 14 23 if you are having difficulty paying your bill so that we
may discuss the options that are available to you.
Minimising your Debt
There are options available for minimizing your debts & to stay connected whilst
managing your spending. Examples include:
 Call barring
 Reconnection of a service with restricted access
 Plan change
 Cancel any content subscription or premium services (e.g. ring tones, jokes,
pictures, etc)
 You can access the “Your Account” system via our website, which offers
Account Management across all services such as checking your usage.
If you are having a problem paying your bill, or you wish to discuss options to minimize
your bill, call us today on 13 14 23
Financial Hardship is a term used to describe a situation where a person is unable to
meet their financial commitments due to one or more factors contributing to their
financial position. Common contributing factors include:
Loss of employment of you or a family member
Illness, including physical incapacity, hospitalisation or mental illness of
you or a family member
Family breakdown
A death in the family
Other factors resulting in an unforeseen change in your capacity to
meet their payment obligations, whether through a reduction in income or
through an increase in non-discretionary expenditure.
If you are having a problem paying your bill, or you wish to discuss options available to
you to minimise your bill, call Customer Service today on 13 14 23(option 3).
Monday - Friday
8am - 7:30pm AEDST
Saturday - Sunday
9am - 6pm AEDST
Public Holidays
9am - 6pm AEDST

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The earlier you contact us, the better. Discussing your concerns gives us the
opportunity to help you manage your bills.
If you do require time to pay an outstanding amount, agreeing to a payment plan and
sticking to it can help prevent disconnection or restriction of your service.
Disconnection of your service is used only as a last resort, and we will endeavor to
work with you to ensure this does not happen.
To assist us in establishing the level of support you require, dependent on your
individual circumstance, we may request supporting evidence, including, but not limited
to:
Documentation such as a statutory declaration from a person familiar
with the your circumstances (family doctor, clergy, bank officer, etc);
Evidence of you having consulted with, and/or being accompanied by a
recognised financial counselor or a booking to see a financial counselor.
Minimising your Debt
There are options available for minimising your debts and staying connected whilst
managing your spending. Examples include:
Call barring
Reconnection of a service with restricted access
Plan change
Cancel any content subscription or premium services (e.g. ring tones,
jokes, pictures, etc)
You can access the "My Account" system via our website, which offers
Account Management across all services such as checking your usage.
Further Options
There are also a range of other financial support services available such as free
financial counseling services offered in each state and territory in Australia. For more
information on these and other options available please see the ACMA's website.

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Authors
Dr Paul Harrison
Dr Paul Harrison is Director of the Centre for Organisational Health and Consumer
Wellbeing, a Senior Lecturer in Marketing, and Unit Chair of Consumer Behaviour in
the Department of Marketing at the Deakin Business School.
His research is focused on emotional and rational behaviour, and how our biology and
the environment interact to influence the way we make decisions. His work has been
published in a wide range of international journals and conference proceedings, and
has informed policy and business practice in Australia and internationally.
Paul is a director of the Telecommunications Industry Ombudsman (TIO), a former
chair of the Asylum Seeker Resource Centre (ASRC), a member of VicHealth's Social
Marketing Expert Panel and a member of the Essential Services Commission's
Consumer Insights Panel. Paul is a graduate of the Australian Institute of Company
Directors.
Laura Hill
Laura Hill (M.Mktng) draws on her marketing and sociology background to conduct
consumer behaviour research for Deakin University. She first studied a Bachelor of
Arts as a Participant in the Dean’s Scholars Program at Monash University and
obtained Honours in Sociology, specialising in qualitative research. Her thesis, which
was awarded First Class Honours, examined attitudes of young people towards
relationships. Laura then spent three years undertaking behavioural analysis for the
Australian Department of Defence, examining the behaviour of individuals and groups
of interest to the Australian Government to inform policy and senior minister decision-
making. Laura then obtained a Master of Marketing from Deakin University and was
awarded a place on the Dean’s Merit List of postgraduate business students. She also
works in the marketing team of a management consulting firm.
Charles Gray
Charles Gray is a PhD candidate at La Trobe University. She lectures mathematics and
statistical modelling. Her current research interests are meta-analysis, data
visualisation, and interactive simulations.