omputers in

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Computers in Human Behavior 23 (2007) 318–332

www.elsevier.com/locate/comphumbeh

Human Behavior

Email end users and spam: relations ofgender and age group to attitudes and actions

Galen A. Grimes, Michelle G. Hough, Margaret L. Signorella *

Penn State McKeesport, 4000 University Drive, McKeesport, PA 15132, USA

Available online 18 November 2004

Abstract

As the problem of spam email increases, we examined users� attitudes toward and experi-

ence with spam as a function of gender and age. College-age, working-age, and retirement-age

men and women were surveyed. Most respondents strongly disliked receiving spam yet took

few actions against it. There were fewer gender differences than predicted, but age was a sig-

nificant predictor of several responses. Retirement age men rated themselves as significantly

lower in expertise than did working age men, and the oldest and youngest age groups took

fewer actions against spam, used the computer less often, and spent fewer hours online than

did the working age respondents. Older respondents were more likely than younger ones to

report making a purchase as a result of a spam email and received the same amount of spam

as other age groups in spite of lower overall use of the computer. The results suggest both that

older computer users may be more vulnerable to spam, and that the usability of email for all

users may be threatened by the inability of users to effectively take action against spam.

� 2004 Elsevier Ltd. All rights reserved.

Keywords: Age differences; Attitudes; Electronic communication; Gender differences; Internet; Consumer

behavior

0747-5632/$ - see front matter � 2004 Elsevier Ltd. All rights reserved.

doi:10.1016/j.chb.2004.10.015

* Corresponding author. Tel.: +1 412 675 9052; fax: +1 412 675 9085.

E-mail address: msignorella@psu.edu (M.L. Signorella).

G.A. Grimes et al. / Computers in Human Behavior 23 (2007) 318–332 319

1. Introduction

Many people regard electronic mail, or email, as an integral part of their daily

lives, providing a mechanism for low-cost, near instantaneous communication over

distance (e.g., Olson & Olson, 2003). Although email provides great benefits to thosewho use it, one aspect is receiving increasing attention: the proliferation of unsolic-

ited bulk email, or spam. Email administrators report frustration in dealing with this

expanding problem (e.g., Cranor & LaMacchia, 1998; Karr, 2003). Users are also

exposed to dangers, such as cases where malicious code was embedded in MP3 files

or other attachments to spam (Whitman & Mattord, 2003). Societal issues encom-

pass the loss of productivity in dealing with spam, the effect of sending pornographic

spam to e-mail addresses accessed by young children, and the use of spam as a mech-

anism to perpetrate fraud on socially isolated groups of individuals such as seniorcitizens.

A single definition for spam is still not universally accepted. Various legislative

bodies have struggled with defining spam in attempts to control it, and at the urging

of direct mail lobbyists such as the Direct Marketing Association, differentiate

‘‘spam’’ from so-called legitimate marketing email messages. Butler (2003) defines

spam as unsolicited bulk email, or junk email, typically coming from organizations

we neither know nor want to know. Butler differentiates spam from mass emails sent

by companies to communicate to established customers, noting that an electronicmessage is spam if:

� the recipient�s personal identity and context are irrelevant because the message is

equally applicable to many other potential recipients;

� the recipient has not verifiably granted deliberate, explicit, and still revocable per-

mission for it to be sent, and;

� the transmission and reception of the message appears to the recipient to give a

disproportionate benefit to the sender (p. 388).

Spam derives its colorful name from a Monty Python sketch in which patrons of a

restaurant are served spam, which presumably they do not want, with everything that

they legitimately order (episode 25, 15 December 1970). Spam is hardly a new phe-

nomenon; in fact, the first junk or marketing email message was sent in 1978 on the

Arpanet, the Department of Defense�s forerunner to today�s Internet (Hinde, 2003).

What is truly noteworthy, however, is the exponential growth of spam in recent years.

Hinde (2003) quotes European Union Enterprise Commissioner Erkki Liikanen ascautioning that spam is now generating important costs for the IT industry, estimating

that spam increases now exceed the growth of normal email traffic. In the IT and tel-

ecommunications industries, Liikanen estimated that spam accounts for one of every

2.66 messages sent (Hinde, 2003). Further, the growth of spam is unlikely to subside

on its own in the near future. Spam industry experts predict an estimated two trillion

spam messages will be sent this year alone (Spamhaus Project, n.d.).

It is understandable why organizations, especially those of a questionable nature,

are drawn to spamming. Spam is a particularly inexpensive mass marketing tool.

320 G.A. Grimes et al. / Computers in Human Behavior 23 (2007) 318–332

From the organization�s perspective, the cost of creating an e-mail campaign for a

product or service is an order of magnitude less than that for a direct mail campaign.

Additionally, the three-week development time for an email campaign is greatly

more attractive than the three-month development cycle for a direct mail effort

(Reynolds, 2003). Further, the reach of a spam campaign is virtually limitless andis constrained only by the organization�s ability to obtain email address lists. Spam-

mers insist their campaigns are tremendously profitable. Of particular note is the

accidental revelation of the order log of one online company resulting from one

spam message advertising an herbal sexual enhancement supplement. The log

detailed some 6000 requests for the product (McWilliams, 2003).

Clearly, the practice of spamming would not continue to grow exponentially with-

out significant resulting profit. It follows then that at least some email users must

read spam and even purchase the advertised products. Who might these users be?The McAfee Americans and Spam survey ranked pornography as the type of spam

message most frequently received by consumers, followed in order by refinancing,

credit counseling, and sexual enhancement products (Hinde, 2003). This product list

appears somewhat targeted toward working age males, consistent with Schumacher

and Morahan-Martin�s (2001) findings that males exhibit greater competency and

comfort with the Internet and computers, spend more time online than females,

and are more likely to own a computer. Similarly, DeYoung and Spence�s (2004)

work supports these findings, noting that women are more anxious and less confi-dent than men about using computers and the Internet. Regarding age, the market-

ing of pornography, refinancing, and credit counseling are typically not targeted

toward the elderly, although sexual enhancement products such as Viagra increas-

ingly are.

Spammers, however, for obvious reasons, cannot differentiate by demographics

such as gender and age when sending spam emails. Part of the allure of spam as a

marketing device is that its low cost and broad reach negates the need to target par-

ticular consumer segments for increased effectiveness. Because the content of themajority of spam messages seems targeted toward working age males, females and

retirement age individuals may exhibit increased dislike of spam because the spam

content is less likely to pertain to them (Fallows, 2003). Further, because of data sug-

gesting that gender differences in computer-related behaviors have lessened in more

recent cohorts (e.g., Schumacher & Morahan-Martin, 2001), gender effects may be

more pronounced in older age groups. Finally, older adults may be more vulnerable

to spam in general. Older individuals are considered to be at higher risk for various

types of fraud because of both social and cognitive factors (e.g., Cohen, 1998), inaddition to showing less general ease in using computers (e.g., Zhang, 2004).

To date, spam-related research has focused on tracking increases in messages sent,

reviewing spam as a legislative issue, and evaluating technological mechanisms to

block or filter spam, but little attention has been given to the attitudinal issues

regarding spam. One public opinion survey did examine attitudes toward spam

and associated behaviors across age groups (Fallows, 2003), but these results did

not provide unequivocal support for the notion that users strongly dislike spam

(see p. 27), possibly because the opinion question had two dimensions. Nonetheless,

G.A. Grimes et al. / Computers in Human Behavior 23 (2007) 318–332 321

the issues raised by Fallows (2003), that use of email may be negatively impacted due

to spam, and that these impacts may differentially affect female and older users, are

important ones. Therefore, in the present research, we examined the following:

1. How do email users feel about spam?2. What actions do email users take in dealing with spam?

3. What computer-related characteristics or behaviors predict spam attitudes and

actions?

4. Do gender and/or age significantly alter these relationships?

We hypothesized that email users would exhibit negative attitudes toward spam,

but that female and older users would be especially likely to exhibit these attitudes

while at the same time be less likely to engage in effective behaviors against spam.

2. Methods

2.1. Participants

A convenience sample of participant volunteers was recruited from academic set-

tings including faculty meetings and classes, and from nonacademic venues such assenior citizen, political organizing meetings, social gatherings, and workplace set-

tings where approved by management. Completed surveys were collected from 207

individuals. Two of the respondents, men ages 60 and 82, did not have email. As

the focus of this research is on reactions to email spam, the responses of these two

participants will not be reported further.

The remaining 205 participants included 93 females (45.4%), 101 males (49.3%),

and 11 (5.4%) who did not specify gender. Respondents ranged in age from 18 to

83 (M = 38.6, SD = 17.1), with 21 respondents declining to report their ages.

2.2. Measures

Participants completed a two-page survey on their attitude toward and experience

with spam, general computer use and experiences, and demographic information.

2.2.1. Spam questions

The questionnaire began with a definition of spam as ‘‘unsolicited email messagesoffering or attempting to sell you a product or service.’’ Participants were asked if

they receive spam and if they ever purchased anything as a result of a spam adver-

tisement. They were then given a 5-point Likert scale on which to indicate how they

feel about receiving spam, anchored by 1 = strongly dislike and 5 = strongly like.

To assess actions users might take against spam, respondents were asked to indi-

cate, from a list of common responses (nothing, delete, filter, contact ISP, contact

legislators, other) all actions that they took against spam, and if other was chosen,

were asked to specify the action. Respondents were also asked to estimate the

322 G.A. Grimes et al. / Computers in Human Behavior 23 (2007) 318–332

number of spam emails received daily, and were given a list of 25 common types of

spam (e.g., Bergstein, 2004; CAUCE, n.d.; Hinde, 2003; The most annoying, 2003),

and asked to indicate each that they had received.

2.2.2. General computer questions

Respondents were asked to indicate which email accounts they use, estimate their

daily hours of computer usage, indicate whether they engaged in any of the following

online activities (shopping, publishing web pages, newsgroup postings), and rate

their level of computer expertise on a 5-point Likert scale anchored by 1 = beginner

and 5 = computer expert.

2.2.3. Demographic questions

The following information was requested: sex, age, and occupation.

2.3. Procedure

University student participants were recruited by their instructors in classes

(none of the authors recruited from their own classes). Non-student participants

were recruited at meetings or gatherings of the organizations sampled. An informed

consent form described the study and instructed respondents to return the question-

naires if they agreed to participate. As a result, the response rate cannot becalculated.

For analyses on age, groups were formed representing traditional college age

(623), working age (24–60), and retirement age (P60). The traditional college age

cut-off followed that used by the university from which the students were recruited.

The retirement age cut-off was chosen after comparing the participants� ages with

their stated occupations.

The 11 respondents who did not give their genders also did not provide ages.

The other 10 respondents who did not report their ages consisted of 4 malesand 6 females. Analyses on the effects of gender and age exclude these

participants.

Descriptive results for categorical variables are reported as frequencies, and for

ordinal or higher variables as means, standard deviations, and ranges. For the

questions about numbers of spam messages and numbers of hours of computer

use, respondents frequently gave ranges instead of a single number. For answers

given as ranges, a single number was derived from the midpoint of the range. To

provide an overall indicator of number of actions taken against spam, a compositeaction variable was created. Those whose only action was to do nothing received a

total action score of zero. Those who took one or more actions besides nothing re-

ceived a total action score that was the sum of other actions taken. Gender (male,

female) X age group (college, working, retirement ages) between subjects ANOVAs

were used to examine relations of gender and age to other responses. Pearson cor-

relations were also used to examine interrelationships among variables, including

within age groups.

G.A. Grimes et al. / Computers in Human Behavior 23 (2007) 318–332 323

3. Results

3.1. Descriptive information and relations with gender and age

Almost all of respondents, or 95% of 204 email users who answered the ques-tion, said they received spam. There were not enough nonrecipients of spam to

examine gender X age group effects, but neither age nor gender were signifi-

cantly correlated with reception of spam (r(181) = �0.03 and r(191) = 0.04,

respectively).

Similarly, almost all respondents reported either strongly disliking (81%) or dislik-

ing (14%) spam. The average rating of 1.3 (SD = 0.6) was close to the minimum of 1

(strongly dislike) on the 5-point scale, and the distaste for spam was uniform, with

no significant gender or age effects (see Table 1). Respondents estimated receivingan average of 13.5 spam e-mail messages per day (SD = 16.1), with ranges from al-

most none to 150. Again, there were no significant mean variations due to gender or

age. Even though it appears in Table 1 that the oldest age group is reporting receiv-

ing somewhat less spam, there is also high variability within all age groups. There

was no overall relation between attitudes and the estimated number of spam mes-

sages received, r(183) = 0.06, nor were there any within the three age groups (see Ta-

bles 2 and 3).

The types of spam reported received by the respondents matched that reported inother surveys. The top 10 received by this sample included the categories of financial,

pornographic and other sexual, health, entertainment, and computer hardware and

software (e.g., Bradley, 2002). These common types of spam were used to group the

responses into five categories of spam, each of which was compared to gender and

age group (see Table 1).

There were significant gender or age effects for three of the five spam categories.

For the category of sexual or pornographic spam (possible range = 0�4), there were

main effects for both gender (F(1, 172) = 10.5, p = 0.001) and age group (F(2,172) = 6.5, p = 0.002). Men reported receiving more sexual spam (M = 2.7) than

did women (M = 2.3), and both the college age group (M = 2.8) and the working

age group (M = 2.6) reported receiving more sexual spam than did the retirement

age group (M = 1.8). Much of the age group effect appears to be due to the retire-

ment age women, but the interaction of gender and age group was only of borderline

significance (p = 0.06).

For the category of financial spam (possible range = 0�4), men reported receiving

more financial spam (M = 2.8) than did women (M = 2.5), F(1, 172) = 5.6, p = 0.02.Although it appears that similar to sexual spam, older women report receiving less,

the main effect for gender was of only marginal significance, and the interaction did

not even approach marginal levels.

The only other significant difference occurred for entertainment spam (possible

range = 0�3). There was a main effect for age group (F(2, 172) = 4.4, p = 0.01).

The college age group reported receiving significantly more entertainment spam

(M = 1.7) than did the working age group (M = 1.4) or the retirement age group

(M = 1.1). For health spam (possible range = 0�6, overall M = 2.0) and computer

Table 1

Mean rating for computer expertise, hours of computer use, attitude toward spam, number of spam emails

received, and total number of actions against spam as a function of gender and age group (college,

working, and retired)

Male Female

College Working Retired College Working Retired

Spam att. 1.2 1.2 1.4 1.4 1.2 1.3

SD (0.5) (0.4) (0.6) (0.9) (0.7) (0.8)

n 27 52 14 33 45 7

Spam no. 14.2 16.7 9.9 13.8 13.0 5.4

SD (15.9) (24.6) (7.3) (15.1) (10.2) (3.3)

n 27 46 12 31 43 7

Sexual spam 3.0 2.7 2.4 2.6 2.4 0.6

SD (1.5) (1.2) (1.5) (1.3) (1.5) (1.0)

N 27 52 14 33 45 7

Fin. spam 2.8 2.8 2.6 2.7 2.5 1.3

SD (1.3) (1.3) (1.4) (1.2) (1.5) (1.0)

n 24 52 14 33 45 7

Health spam 2.2 1.9 1.6 2.0 2.0 1.9

SD (1.7) (1.6) (1.3) (1.0) (1.3) (1.9)

n 24 52 14 33 45 7

Comp. spam 3.2 2.8 2.4 3.0 2.2 1.9

SD (2.4) (2.0) (2.1) (1.8) (1.8) (1.6)

n 24 52 14 33 45 7

Entertain. spam 1.9 1.4 1.1 1.6 1.3 1.1

SD (1.0) (1.0) (1.1) (0.8) (0.9) (0.9)

n 24 52 14 33 45 7

Expertise 3.7 4.3 2.7 3.5 3.2 2.4

SD (0.6) (0.8) (1.2) (0.6) (1.1) (0.8)

n 29 52 15 33 46 7

Hours use 3.0 6.0 2.5 2.5 4.6 2.6

SD (2.4) (3.7) (1.1) (1.1) (3.4) (1.8)

n 28 48 14 32 43 7

Total email 1.8 2.3 1.5 1.9 1.8 1.1

(0.6) (1.1) (0.6) (0.7) (1.0) (0.4)

29 53 15 33 47 7

Total online behaviors 0.8 1.5 0.5 0.9 1.1 0.3

SD 0.7 0.8 0.8 0.6 0.8 0.5

n 29 53 15 33 47 7

Total actions 1.1 1.4 1.1 1.3 1.4 0.9

SD (0.5) (0.7) (0.7) (0.5) (0.7) (0.4)

n 27 52 14 33 45 7

Note. The following abbreviations were used: Comp. spam = computer spam, Entertain. spam = enter-

tainment spam. Fin. spam = financial spam. Cell ns vary due to respondents not answering all items.

324 G.A. Grimes et al. / Computers in Human Behavior 23 (2007) 318–332

hardware/software spam (possible range = 0�7, overall M = 2.0) gender and age

groups reported similar numbers.

The members of the sample generally rated themselves as computer literate or bet-

ter (on a 1–5 scale, 87% P3, M = 3.6, SD = 1.1). There was an interaction of age and

Table 2

Correlations among gender, attitude, expertise, online behaviors, and spam numbers and types by age group (college, working)

Variable 1 2 3 4 5 6 7 8 9 10 11 12 13 14

1. Gender – 0.14 �0.14 –a �0.13 0.12 0.02 �0.01 �0.13 �0.06 �0.06 �0.05 �0.16 0.16

2. Spam attitude 0.01 – �0.27* –a 0.01 �0.16 0.08 0.10 0.04 �0.12 0.01 0.13 �0.07 �0.08

3. Expertise �0.52** 0.02 – –a 0.31* 0.11 0.03 0.03 �0.00 0.02 �0.11 0.02 �0.09 0.11

4. Purchase �0.10 0.10 0.06 – –a –a –a –a –a –a –a –a –a –a

5. Hours use �0.20 0.16 0.48** �0.06 – 0.02 0.02 0.04 �0.00 �0.03 �0.08 0.14 0.06 0.08

6. Email total �0.22* �0.09 0.16 0.05 0.25* – 0.36** �0.01 0.05 0.19 �0.00 �0.04 �0.13 0.06

7. Online total �0.29** �0.02 0.55** �0.06 0.36* 0.11 – 0.06 0.11 0.08 �0.02 0.12 0.05 0.21

8. Spam number �0.10 0.07 0.16 �0.11 0.22* 0.14 0..30** – 0.29* 0.37** 0.34** 0.36** 0.28* 0.15

9. Sexual spam �0.10 �0.03 0.07 0.19 �0.02 0.17 0.16 0.28** – 0.42** 0.53** 0.50** 0.49** 0.20

10. Financial spam �0.09 �0.00 0.09 0.01 0.07 0.14 0.21* 0.32** 0.51** – 0.32* 0.45** 0.39** 0.04

11. Health spam 0.03 �0.13 �0.10 0.14 0.00 0.12 0.13 0.49** 0.52** 0.52** – 0.52** 0.44** 0.04

12. Computer spam �0.15 �0.04 0.12 0.29** 0.12 0..14 0.23* 0.39** 0.46** 0.53** 0.67** – 0.49** 0.06

13. Entertain. spam �0.07 �0.08 0.12 0.29** �0.04 0.14 �0.00 0.31** 0.40** 0.48** 0.59** 0.64** – �0.16

14. Total actions 0.04 �0.28** 0.10 �0.05 0.08 �0.12 0.22* �0.06 0.16 0.09 0.13 0.28** 0.20* –

Note. Responses for the college age group are above the diagonal; for the working age group below the diagonal. The following abbreviations were used: Entertain

spam = entertainment spam. Variables were scored as follows: gender, higher = female; spam attitude, lower = more disliked; expertise, higher = more expert; purchase, high-

er = yes; hours use to total actions, higher = higher numbers of yes responses or actions. Cell n s range from 58 to 62 for college age respondents and from 89 to 100 for working age

respondents.a All individuals in this age group answered no to the purchase question.* p < 0.05.** p < 0.01.

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Table 3

Correlations among gender, attitude, expertise, online behaviors, and spam numbers and types in the retirement age group

Variable 1 2 3 4 5 6 7 8 9 10 11 12 13 14

1. Gender –

2. Spam attitude �0.05 –

3. Expertise �0.10 �0.09 –

4. Purchase �0.16 �0.05 0.08 –

5. Hours use 0.04 0.41 0.09 0.25 –

6. Email total �0.32 0.00 0.56** 0.22 0.15 –

7. Online total �0.16 0.10 0.12 0.18 0.21 0.32 –

8. Spam number �0.35 �0.13 0.30 0.45* �0.06 0.18 0.09 –

9. Sexual Spam �0.56** �0.08 0.43* 0.31 0.03 0.72** 0.41 0.58** –

10. Financial spam �0.46* �0.07 0.17 0.29 0.05 0.48* 0.39 0.40 0.71** –

11. Health spam 0.07 �0.26 0.17 0.35 �0.13 0.20 0.03 0.40 0.27 0.33 –

12. Computer spam �0.14 �0.26 0.39 0.57** �0.22 0.56** �0.04 0.28 0.44* 0.40 0.69** –

13. Entertain. spam 0.03 �0.34 0.29 0.42* �0.23 0.33 �0.12 0.19 0.07 0.25 0.47* 0.76** –

14. Total actions �0.23 �0.04 0.56** 0.37 0.09 0.65** 0.52** 0.26 0.65** 0.34 �0.04 0.34 0.07 –

Note. The following abbreviations were used: Entertain spam = entertainment spam. Variables were scored as follows: gender, higher = female; spam attitude,

lower = more disliked; expertise, higher = more expert; purchase, higher = yes; hours use to total actions, higher = higher numbers of yes responses or actions.

Cell ns range from 18 to 21.* p < 0.05.** p < 0.01.

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G.A. Grimes et al. / Computers in Human Behavior 23 (2007) 318–332 327

gender in self-reported expertise, (F(2, 176) = 6.9, p = 0.001, see Table 1). Simple ef-

fects tests indicated that there was a significant gender difference among the working

age group, with men rating themselves higher in expertise than did women

(F(1,181) = 38.9, p < 0.001). There was also an age difference in men

(F(2,181) = 30.1, p < 0.001). Follow-up tests indicated that the working age menrated themselves as significantly higher in expertise than did retirement age men.

For reported hours using a computer per day, however, there were no gender differ-

ences. Instead, a main effect for age group (F(2, 166) = 17.2, p < 0.001) occurred be-

cause the working age group reported using computers for significantly more hours

than did either the college age or retirement age group (see Table 1).

Not surprisingly, given the students and faculty in the sample, university accounts

were the most frequent type of email account (60% of respondents possessed this type

of account), followed by AOL (30%), ISP based (21%), company accounts (20%),Hotmail (19%), and Yahoo (18%). Most respondents (45%) reported having two

email accounts. The next largest group (34%) reported having only one email account.

There were no notable differences by age in the types of email accounts used by

the respondents. The only relationships reaching significance were that the college

age group members were more likely to have university accounts (v2(2,

N = 184) = 47.3, p < 0.001), and the working age group members were more likely

to have company accounts (v2(2, N = 184) = 19.5, p < 0.001) or ISP accounts

(v2(2, N = 184) = 21.5, p < 0.001). Among accounts sometimes linked to spam prob-lems, such as AOL or Hotmail, there were no significant age differences. There was,

however, an age difference in the total number of email accounts, F(1, 178) = 5.5,

p = 0.005, with working age respondents reporting more email accounts that retire-

ment age respondents.

Online behaviors that may inadvertently result in spam increases besides type of

email account include those such as participating in online shopping, creating a web

page, and posting in newsgroups. Examination of responses to these three items showed

a relation to age, namely that the working age group tended to engage more in suchbehaviors than did the other two age groups. These responses were summed into a var-

iable representing total online behaviors (see Table 1). A gender X age group ANOVA

revealed a main effect for age (F(2, 178) = 15.5, p < 0.001), with follow-up tests showing

that the working age group engaged in significantly more such behaviors (M = 1.3)

than did either the college age group (M = 0.8) or the retirement age group (M = 0.5).

An online behavior that is a direct consequence of spam is the purchase of some-

thing advertised in a spam email. Only eight respondents out of 205 (4%) said they

made such a purchase. There were insufficient numbers of purchasers to examinegender X age group effects, but age bore a small significant correlation with purchase

(r(175) = 0.16, p = 0.04), because none of the eight purchasers was in the youngest

age group. Gender was not related to purchasing behavior.

3.2. Actions against spam

Respondents were given a choice of five possible actions they could take against

the spam email messages they receive, and also offered the opportunity to specify

328 G.A. Grimes et al. / Computers in Human Behavior 23 (2007) 318–332

their own. The most frequent action was to delete the spam (66%). The percentages

of respondents who used filters (16.7%), or contacted their ISP (11.7%) or the gov-

ernment (0.5%) were much lower. Among those who reported taking actions not on

the original list, the most common action was to attempt to unsubscribe from the list.

According to the composite action variable, the numbers of different actions takenwas very low, with a mean of only 1.3 (SD = 0.6) and a mode of only one action ta-

ken. Analysis of age group and gender effects on total actions showed only a main

effect for age group (F(2, 172) = 4.1, p = 0.02), with both the retirement and college

age groups reporting taking fewer actions against spam than did the working age

group (see Table 1).

3.3. Computer use, expertise, and online behaviors in relation to spam and spam

attitudes

Very few user characteristics or actions were related to attitudes toward or actions

against spam, and the patterns of relations varied by age group (see Tables 2 and 3).

Among the college age group, the only significant predictor of attitude toward spamwas expertise, with those with more expertise exhibiting a more negative attitude,

whereas among the working age group the only significant predictor of attitude

was numbers of actions taken against spam, with more negative attitudes associated

with more actions taken. Among the retirement age group, attitude toward spam did

not predict any other responses.

There were no significant correlations between numbers of actions against spam

and other variables among college age respondents, but there were for both working

age and retirement age respondents. The latter two groups, however, showed differ-ent patterns of responses. For the working age group, in addition to the correlation

described above between more actions and more negative attitudes, there were also

significant correlations between more actions and more online behaviors, computer

spam, and entertainment spam. In contrast, the retirement age group took more

actions when self-rated expertise, numbers of email accounts, numbers of online

behaviors, and amount of sexual spam were higher.

Another pattern difference among the age groups occurred in the interrelations of

number of spam emails and types of spam. For both the college age and working agegroups, the estimated numbers of spam emails correlated with receipt of specific

types of spam, and there were consistent intercorrelations among the five types of

spam. In contrast, among the retirement age group, total number of spam emails sig-

nificantly predicted only sexual and computer spam, and there were fewer significant

correlations among the five types of spam.

Finally, unlike the college age group, there was some evidence among the working

age and retirement age groups that specific behaviors were linked to total spam or

types of spam, but the behaviors differed somewhat between the latter two agegroups. Purchasing items from a spam email was related to receiving more spam

in both age groups. Engaging in other behaviors linked to spam such as purchasing

online, making a web page, and posting in a newsgroup, was significantly correlated

G.A. Grimes et al. / Computers in Human Behavior 23 (2007) 318–332 329

with receiving spam in the working age group only. Having more email accounts was

significantly correlated with receiving spam in the retirement age group only.

4. Discussion

The results showed that the vast majority of email users strongly dislike spam,

independent of the estimated number received. Although the hypothesized age and

gender effects on attitudes toward spam were not observed, there were several other

findings that suggest that older users, but not women, may be more vulnerable to

spam.

The only significant relations with gender were that women reported receiving less

sexual and financial spam than did men, and that working age women rated them-selves as lower in expertise than did working age men. Although the data appeared

to show that retirement age women differed on several measures, these apparent dif-

ferences did not reach statistical significance. Other data do show that gender differ-

ences in computer expertise are smaller in more recent cohorts (e.g., Schumacher &

Morahan-Martin, 2001), and it is possible that the small numbers of women in the

oldest age group prevented gender-related cohort effects from reaching significance.

Nonetheless, the general finding was that gender did not play a major role in the pat-

tern of results.Age effects were more prominent and included the finding that the oldest men

were lower in self-reported expertise than the working age men, and the oldest

and youngest age groups took fewer actions against spam, used the computer less

often, and spent fewer hours online than did the working age respondents. Although

most respondents denied making a purchase as a result of a spam email, in contra-

diction to the Direct Marketing Association claim that 36% of all users who receive

spam make a purchase (Johnson, 2003), older respondents were more likely than

younger ones to report making a purchase as a result of a spam email.Further, the relations among the measures differed across ages. For the working

age group, attitude, engaging in on-line behaviors linked to spam, and receipt of

computer and entertainment spam predicted numbers of actions against spam.

For the retirement age group, the receipt of sexual spam, numbers of email accounts

and online behaviors linked to spam, and self-reported expertise predicted numbers

of actions against spam. The strong relationship in the retirement age group between

self-reported expertise (and other indicators of computer expertise, such as multiple

email accounts) and taking more actions against spam may reflect that there are few-er outside resources, such as instructors or co-workers or the IT department, to pro-

vide assistance to those who are retired.

Even though the retirement age group reported receiving less sexual spam than

did the other age groups, the tendency was for receipt of sexual spam, rather than

the other types, to predict more anti-spam actions among this age group. Note that

the perceptions of types of spam received followed stereotypical notions and are con-

trary to practice for most spam marketers, who cannot tell the age or gender of a

recipient by the email address. One possibility is that computer users� own actions,

330 G.A. Grimes et al. / Computers in Human Behavior 23 (2007) 318–332

such as college students posting in entertainment news groups or purchasing games

online, may result in the receipt of different types of spam. Alternatively, as Fallows

(2003) concluded, certain types of spam may be more distasteful to some age groups

than others.

Although most respondents in this sample took few actions against spam, retire-ment age respondents reported taking the fewest actions, and retirement age men re-

ported lower expertise than working age men. At the same time, within the

retirement age group, there were significant associations between engaging in online

behaviors and receiving higher numbers of spam overall, as well as higher numbers

of some types of spam. Respondents in older age groups are typically also considered

more vulnerable to telemarketing and other fraud due to socially isolating circum-

stances (e.g., Cohen, 1998; Lee & Geistfield, 1999). This vulnerability may be

extended to fraudulent spam schemes, in spite of the lower overall use of the com-puter by older age groups, because the lower use appears to be accompanied by

engaging in some risky behaviors while exhibiting computer lower expertise and tak-

ing fewer actions against spam.

The results of this study support the contention that users genuinely dislike spam

yet take little action against it. This apathy in dealing with spam seems in direct con-

tradiction to consumers� fervent responses against telemarketing. The Federal Trade

Commission reported that more than 50 million consumers registered on the Na-

tional Do Not Call registry in its opening months, June through September 2003(Federal Trade Commission, 2003). To be included on the Do Not Call registry, con-

sumers must provide contact information either by calling a toll-free telephone num-

ber or logging on to the Do Not Call website, actions seemingly more labor-intensive

than pressing the delete key to eliminate a spam email message, the only action

engaged in with any frequency by the present sample.

Given this apparent inconsistency in dealing with telemarketing versus spam

solicitations, it would be worthwhile to compare consumers� relative dislike of tele-

marketing and spam solicitations. Further, particularly with respect to users whoself-report lower levels of computer expertise, a follow-up study could be con-

ducted to determine if, at some volume, the number of spam messages becomes

so overwhelming that it hinders an individual�s use of email or forces a more

aggressive stance in dealing with spam. Finally, it is worthwhile to investigate

whether improvements in spam filtering programs correlate to user aggressiveness

in dealing with the unsolicited bulk email, especially among older age groups with

less computer expertise.

Clearly from these preliminary results, spam can be perceived as a societal detri-ment, much like its lower-tech cousin, telemarketing. These detrimental effects range

from wasted time and resources in attempts to eliminate and/or filter annoying,

insulting, or pornographic spam, wasted time and damage to PCs from malicious

code (i.e., viruses and worms) inadvertently attached to spam, and wasted economic

resources associated with fraudulent schemes, outright scams, and bogus health

claims. The vast majority of email users, at least in this study, did not have to receive

a significant quantity of spam – an average of 13.7 spam email messages per day

(SD = 16.2) – to become irate over its presence in their inboxes.

G.A. Grimes et al. / Computers in Human Behavior 23 (2007) 318–332 331

Interestingly, efforts to minimize unwanted telemarketing solicitations have to

date been more successful than similar efforts regarding spam, potentially because

of the technological difficulties in successfully identifying, tracking, or blocking the

sources of spam. These technological difficulties seem to support the contention from

many experts that stronger or more punitive anti-spam legislation will be largelyineffective and will simply drive most spammers offshore.

Many experts now contend that the greatest hope for controlling spam will result

from technological efforts – filtering and/or blocking spam (e.g., see Grimes, 2004).

Currently, some of the most promising efforts in the direction of anti-spam filtering is

being developed using Bayesian sampling of email content to try to differentiate

spam messages from non-spam messages, and so-called ‘‘whitelist’’ and ‘‘blacklist’’

blocking techniques with combinations of interactive challenge-response testing.

User attitudes toward and experiences with spam intersect with usability issues.Hackos and Redish (1998) note:

To be usable, an interface must let people who use the product (users), workingin their own physical, social, and cultural environments, accomplish their goalsand tasks effectively and efficiently. To be usable, an interface must also be per-ceived as usable by those who must use it or choose to use it (p. 6).

Users� dislike of spam, coupled with the quantity and type of spam received, and

the difficult and/or ineffective filtering methods available to stop spam, can be con-sidered a usability issue. Rubin concludes that ‘‘[u]sers are more likely to perform

well on a product that meets their needs and provides satisfaction than one that does

not’’ (1994, p. 19). Olson and Olson (2003) warned that ordinary email was

approaching ‘‘information overload’’ (p. 505) and that ‘‘substantial human interven-

tion’’ (p. 505) was required to manage it. As the users studied strongly disliked spam

but took little action to deal with it, additional investigation is required to determine

if, at some volume, spam impedes usability to the extent that users refrain from com-

municating via email, or alternately step up pressure to control it via technological orlegislative processes.

Acknowledgement

This research was supported in part by the Penn State Minority Faculty Assis-

tance Program. We would like to thank Barbara Fenton, Roderick Nixon, and

Katherine Piesek for their assistance with this study. An earlier version of this paperwas presented in November 2003 at the ACM Conference on Universal Usability,

Vancouver, Canada.

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