christopher wilcox - cm 723 advanced communication research home shopping data analysis

7/31/2019 Christopher Wilcox - CM 723 Advanced Communication Research Home Shopping Data Analysis

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CM 723Advanced Communication

Research

ASSIGNMENT 4: Understanding and predicting shopping behaviors

Boston University

Christopher WilcoxMeredith Knight

Xue Teng (Portia)Yajie Li (Jessie)

May 1, 2012


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Table of Contents

I. Introduction1. Client problem2. Research questions3. Logic of analysis

II. Sample descriptionDemographics

III. Data Analysis1. Review of variables2. Selection and testing of multiple item measures

Correlations

Factor Analysis

Reliability

3. Building a Regression Model4. Cluster Analysis

IV. Cluster DescriptionV. Final Story and Key Takeaways

VI. Limitations

Appendices


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I. IntroductionElectronic Shopping Incorporated (ESI) is developing technology to enable individuals to

shop interactively from home using their television. ESI faces two key business challenges in

their initiative to transform home shopping through this new technology. The first challenge is

to identify the individuals most likely to be early adopters of this new technology. The second

challenge is to explore how American consumers may adopt this new technology. We used an

existing dataset as a resource to answer the following research questions:

1. What are the profiles of different groups of potential customers based on their currentattitudes, beliefs, and behaviors?

2. What factors cause individuals within those groups to be more likely to adopt thistechnology?

The existing dataset was reviewed to identify variables directly related to shopping at

home using the television. Three related items were combined into one composite dependent

variable. All other variables were reviewed to identify potential predictors, and each predictor

was evaluated to determine whether it was suitable for a regression model by determining how

well those variables correlated with the dependent variable. Finally, the dataset was segmented to

identify major groups within the sample who would be likely to adopt the technology.


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II. Overall sample descriptionRace, ethnicity, and gender

Ninety three percent of the respondents report that they are not Hispanic (93.2%,

SE:1.1, 95% CI). Eighty six percent report that they are white (86.3% SE:1.5, 95% CI), 6.7%

are black (SE:1.1, 95% CI), 1.3% are Asian (SE:0.4, 95% CI), 4.4% are other (SE:0.9, 95%

CI), and 1.4% refused to respond (SE:1.1, 95% CI). The overall gender ratio for the sample was

48.2% male and 51.8% female (both SE:2.3, 95% CI).

Age

The data for age was gathered in categories rather than absolute age. The highest

percentage of respondents was between the ages of 30-39, representing 23.9% of the total sample

(SE:2.0, 95% CI). The second highest percentage of respondents were between the ages of 40-

49, representing 21.8% of the total sample (SE:1.9, 95% CI), and the third highest percentage

was for the 20-29 age group, representing 18.3% (SE:1.8, 95% CI).

Income

Over sixty percent of the sample reported income levels of between $15,000 and $75,000

(63.7% SE:2.1, 95% CI). Only five percent reported an income over $100,000 (SE:0.9, 95%

CI), and 18.8% refused to answer the question (SE:1.7, 95% CI).

Marital status

Over half (56.3%) of the sample is married (SE:2.1, 95% CI), and slightly over a quarter

are single (26%, SE:2.6, 95% CI). Nine percent (9.5%SE:1.3, 95% CI)are divorced, 6.2% are


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widowed (SE:1.1, 95% CI) and 1.7 are separated (SE:0.6, 95% CI). 0.3 refused to answer the

question (SE:0.8, 95% CI).

Home ownership

Seventy two percent of the respondents own the home in which they live (72.4%,

SE:1.9, 95% CI), while 27.6% rent (SE:1.9, 95% CI). Seventy two percent of the respondents

live in single-family homes (72.8%, SE:1.9, 95% CI), 15.7% live in an apartment (SE:1.6,

95% CI), 6.4% live in a multifamily house (SE:1.1, 95% CI), 2.6% are in a townhouse

(SE:0.7, 95% CI) and 2.0% are in a condo apartment (SE:0.6, 95% CI).

Household demographics

The mean number of people in the household is 2.85, with a standard deviation of 1.5,

and a median of 2. Sixteen percent of households have only one person (16.6%, SE:1.6, 95%

CI), 33.6% of households have 2 people (SE:2.0, 95% CI), 19.5% have 3 people (SE:1.7, 95%

CI), 17.3% have 4 (SE:1.6, 95% CI), 17% have 1 (SE:1.6, 95% CI). Eighty four percent

(84.6%) of households do not have children under 5 (84.6% (SE:1.6, 95% CI). Only 10.6% of

households have one person under 5 (SE:1.3, 95% CI), and 3.9% have two people under 5

(SE:2.1, 95% CI).

Shopping patterns

Most people shop locally. Ninety two percent go to local shopping stores (92.3%SE:1.2,

95% CI) and 91.0% go to the mall (SE:1.2, 95% CI). Forty two percent (42.4% SE:2.1, 95%

CI) use the phone to shop from a catalog, and 42.5% shop through the mail (SE:2.1, 95% CI).

Fewer than fifteen percent of people shop by calling 800 numbers (14.5% SE:1.5, 95% CI).


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Very few people bought merchandise from infomercials (6.1% SE:1.0, 95% CI), home

shopping networks (8.8% SE:1.2, 95% CI), or through online computer services (4.5%,

SE:0.9, 95% CI). Seventy one percent (71.4%) of the respondents look at the Yellow Pages for

information about where to purchase items (SE:1.9, 95% CI). Thirty six percent of the sample

watch infomercials (36.5% SE:2.2, 95% CI), and 30.1% watch home shopping channels.

Personal preferences

Over half of the respondents describe themselves as outgoing (56.0%, SE:2.2, 95% CI),

and almost three quarters describe themselves as cautious (73.5% SE:2.2, 95% CI). Fifty eight

percent of the people say they enjoy staying at home in their free time (58%, SE:2.1, 95% CI),

31.6% prefer going out (SE:2.0, 95% CI), and 10.4% said neither in particular (SE:1.3, 95%

CI). Eighty percent of the sample (80.5%, SE:1.7, 95% CI) wait before buying new products

and 14.4% are among the first to buy new products (SE:1.5, 95% CI).

Education

Most people are either high school graduates (29.8% SE:2.0, 95% CI), have attended

some college (24.5%, SE:1.9, 95% CI), or a college degree (21.0%, SE:1.8, 95% CI). Only

15.3% of the respondents are currently in school (SE:2.2, 95% CI), 84.7% are not currently in

school. The level of school mentioned often is college at 48%, high school at 28% and graduate

school at 16.2% (SE:21.6, 95% CI).

Employment

Sixty seven percent are employed (67.5%, SE:2.0, 95% CI), and 32.5% (SE:2.0, 95%

CI are not). Of those who are employed, 81.9% (SE:1.6, 95% CI) are employed full time and


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18.1% are part time. Seventy seven percent of those who are working work for someone else

(77.2% SE:1.8, 95% CI), 14.1% are self-employed (SE:1.5, 95% CI), and 8.6% responded that

they are both self-employed and work for someone else (SE:1.2, 95% CI). Fifty eight percent

(58.9%) of people work someplace other than home (SE:2.7, 95% CI), while 41.1% work at

home (SE:2.7, 95% CI). Respondents were also asked whether they occasionally worked from

home. Slightly under half replied that they never work from home (47.4%, SE:2.8, 95% CI)

20.2% occasionally work from home (SE:2.2, 95% CI), and 14.5% rarely worked from home

(SE:2.0, 95% CI).

The top three occupations cited among respondents were technical, sales, or

administrative support (32.7%, SE:2.5, 95% CI), management (30.9%, SE:2.4, 95% CI) or

service (15.9%, SE:1.8, 95% CI). Seventy percent of respondents reported that there is another

member in the household who is also employed (SE:2.4, 95% CI), 29.9% do not (SE:2.4, 95%

CI).

Of the respondents who are not currently working, 47.4% are retired (SE:3.8, 95% CI),

21.1% are homemakers (SE:3.1, 95% CI), 10.9% are unemployed (SE:2.4, 95% CI), and 6.0%

are looking for work (SE:1.8, 95% CI). Over sixty five percent of people who have someone

working from home do not also work from home themselves (65.4%, SE:2.6, 95% CI), and

34.6% have two people working from home (SE:2.6, 95% CI).


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III. Data Analysis1. Identifying the Dependent Variable

The ideal dependent variable should predict the participants likelihood of using the new

TV technology to shop at home. In the surveys, four items met those requirements. The four

measures are Likelihood of looking for consumer information about something you want to

buy, Likelihood of looking up the store that sells an item you need to buy, Likelihood of

looking through catalogs and ordering items using your TV remote and Likelihood of seeing a

video shopping mall and have a sales person demonstrate the products for you. These four

measures each have a distinct meaning but also share an overlap in meaning, so the items were

combined into a new construct called Likelihood of using new TV technology to shop at home.

After first checking the frequency distributions for each of the four measures for outliers and

skew, a three-step process was used to confirm our assumption about the multiple item measures.

First, the multiple item measures were validated through an inter-item correlation test, then a

factor analysis was run, and finally, the reliability of the measure was tested using Cronbachs

alpha.

According to Table 1, inspection of the correlation matrix revealed the presence of the

coefficients of .50 to.70, which show a relatively large overlap in meaning of the four measures.

According to the output of factor analysis (see Table 2), Principal component analysis revealed

that these four measures explained 71.4% of the total variance, with all four factor loadings

around .80-.90. Based on the robust results of inter-correlation test and factor analysis, the

reliability of these four measures was tested. The construct Likelihood of using new TV

technology to shop at home has good internal consistency, with the Cronbachs alpha

coefficient reported of .87. There is no significant improvement if any item deleted. Therefore,


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the assumption that the four measures belong to the construct Likelihood of using new TV

technology to shop at home was confirmed. Two new variables, home shopping sum and home

shopping average, were created by computing the true scores of this construct in two ways for

different analysis purposes.

Table 1: Inter-item Correlations for Proposed Dependent Variable

FUT-Q.2Look forconsumer

informationabout

something

you want tobuy

FUT-Q.2Look upthe storethat sellsan item

you needto buy

FUT-Q.2Look

throughcatalogs

and orderitems

using yourremote

FUT-Q.2See a

"video"shoppingmall andhave a

salespersondemonstrate

the productsfor you

FUT-Q.2 Look for consumerinformation about something youwant to buy

PearsonCorrelation

1 .658** .524** .529**

Sig. (2-tailed)

.000 .000 .000

FUT-Q.2 Look up the store thatsells an item you need to buy

PearsonCorrelation

.658** 1 .672** .620**

Sig. (2-tailed)

.000 .000 .000

FUT-Q.2 Look through catalogs

and order items using yourremote

Pearson

Correlation

.524** .672** 1 .702**

Sig. (2-tailed)

.000 .000 .000

FUT-Q.2 See a "video" shoppingmall and have a salespersondemonstrate the products for you

PearsonCorrelation

.529** .620** .702** 1

Sig. (2-tailed)

.000 .000 .000

a. Listwise N=2005


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Table 2: Component Matrixa

Component

1FUT-Q.2 Look for consumerinformation about something youwant to buy

.795

FUT-Q.2 Look up the store that sellsan item you need to buy

.876

FUT-Q.2 Look through catalogs andorder items using your remote

.861

FUT-Q.2 See a "video" shopping malland have a salesperson demonstratethe products for you

.845

2. Identify and Verify the Multiple Item MeasuresWhen reviewing the telephone and mail questionnaires, possible multiple item measures

were identified in three different sections: general beliefs and attitudes, technology, and

shopping. When items could potentially have an overlap in meaning, those items were tested by

seeing whether respondents interpreted (and thus answered) the questions in a similar way. First,

the possible multiple item measures were grouped conceptually. To confirm and revise the initial

grouping a quantitative approach was used, running a correlation between the items, a principal

components factor analysis with varimax rotation, and checking the reliability of the measure

using Cronbachs alpha. If the proposed measures met the requirements for each of these steps,

the individual items were combined into a new variable.

The proposed constructs and corresponding measures of the general beliefs and attitudes

are organized in Table 3.


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Table 3: Proposed Constructs and Corresponding Measures of General Beliefs and Attitudes

Proposed Constructs Measures

Lack of spare time yl5: I Feel Like I Never Have Any Spare Time

yl28: On Most Days I Can't Get Everything Done I Need to Do

yl57: I Never Have Time to do the Things I Really Want to Do

yl9: Free Time is Something I Make Certain I Allow Myself (Reversed)

Attitude toward anorganized life

yl38: I Always Finish What I Start

yl43: I'm the Most Organized Person I Know

Seeking sense ofachievement fromwork

yl8: I Find My Job Extremely Fulfilling

yl11:What I Do is More Important Than What I Earn

yl7: I Think of My Job as a Means to a Paycheck, Not as Part of aCareer (Reversed)

yl19: If I Were Rich, I Wouldn't Work (Reversed)

Satisfaction with

current life

yl10: I Like My Life Just the Way it is Now

yl39: I Have More Self-Confidence Than Most People My Ageyl40: I Consider Myself a Happy Person

yl46: I Live Life to the Fullest

Positive attitudetoward watching TV

tv9b: TV is My Link to the Outside World

tv9c: I Would Love To Be Able To Watch TV 24 Hours a Day

tv9h: It's the Best Free Entertainment I Can Get

tv9i: I'm a Real "Couch Potato"

Likelihood to be anearly adopter

sh4: I like to try new products when they first come out

sh15: I am very interested in any new products and services

yl32: I Consider Myself a Trend Setter

yl58: I am Usually the First to Try New Things

ct1v: I love cutting edge, high tech thingsAttitude toward timesaving

sh16: Saving time is more important than saving a few dollars

ct1u: Anything that saves me time is important

yl54: I Hate Waiting in Line

yl56: Anything That Saves Me Time is Important

Concern of takingrisks

yl3: It Bothers Me When Something Unexpected Interrupts My DailyRoutine

yl4: I Feel Uncomfortable Trying New Things

yl12: Everything is Changing Too Much These Days

yl14: I'm a Person Who Does Not Take Risks

yl44: I Get Easily Annoyed When Things Don't Go as I Planned

Attitude towardinformationsuperhighway

q48: Interest in Learning More About the Information Superhighway

q49: Importance of Information Highway to Your Future


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The inspection of the correlation matrix revealed the presence of the coefficients of

different ranges for each of the proposed group of measures, most of which have comparable

coefficients that can be accepted. The details of the correlation matrix can be found in Appendix

1. A few measures shown relatively low coefficients compared to other measures within their

proposed groups, so we raised red flags for those measures. The problematic measures with their

Pearson coefficients are shown below.

According to Table 4, for the proposed construct Lack of Spare Time, the correlation

coefficients drop dramatically for the measure Free time is something I make certain I allow

myself. Therefore, a red flag was raised to this measure at this stage. The same situation was

also discovered for the construct Seeking sense of achievement from work with the measure

If I were rich, I wouldn't workand the construct Concern oftaking risks with the measure I

get easily annoyed when things don't go as I planned. (See Table 5 and Table 6) Therefore, red

flags were also raised for those two measures.

Table 4: Inter-item Correlation of Lack of Spare Time

YL-Q.1 Psychographics-I Feel Like I Never Have Any Spare Time 1 .359 .570

.000 .000

YL-Q.1 Psychographics-On Most Days I Can't Get Everything Done INeed to Do

.359 1 .356

.000 .000

YL-Q.1 Psychographics-I Never Have Time to do the Things I ReallyWant to Do .570 .356 1

.000 .000

RE: YL-Q.1 Psychographics-Free Time is Something I Make

Certain I Allow Myself

.370 .132 .239


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.000 .000 .000

Table 5: Inter-item Correlation of Seeking Sense of Achievement from Work

RE: YL-Q.1 Psychographics-I Think of My Job as a Means to aPaycheck, Not as Part of a Career

1 .404 .241

.000 .000

YL-Q.1 Psychographics-I Find My Job Extremely Fulfilling .404 1 .274

.000 .000

YL-Q.1 Psychographics-What I Do is More Important Than What IEarn

.241 .274 1

.000 .000

RE: YL-Q.1 Psychographics-If I Were Rich, I Wouldn't Work .239 .150 .177

Table 6: Inter-item Correlation ofConcern of Taking Risks

YL-Q.1 Psychographics-It Bothers Me When SomethingUnexpected Interrupts My Daily Routine

1 .364 .308 .230

.000 .000 .000

YL-Q.1 Psychographics-I Feel Uncomfortable Trying NewThings

.364 1 .272 .314

.000 .000 .000

YL-Q.1 Psychographics-Everything is Changing Too MuchThese Days

.308 .272 1 .230

.000 .000 .000

YL-Q.1 Psychographics-I'm a Person Who Does Not Take Risks .230 .314 .230 1

.000 .000 .000

YL-Q.1 Psychographics-I Get Easily Annoyed When Things

Don't Go as I Planned

.408 .170 .216 .089


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Besides the inspection within each proposed group, the coefficient across the groups was

reviewed to identify whether each measure was assigned to the appropriate group. Since no other

measure has a coefficient greater than .60, the assignment of the measures is acceptable.

A factor analysis was then conducted for these measures. Principal component analysis

revealed that these measures explained 54.2% of the total variance, which is acceptable (greater

than 50%). Generally, the factor loading distribution (see Table 7) shows a consistency within

each proposed group, which means that the measures should belong to the same construct. The

details of the factor loading distribution can be found in Appendix 2. However, there are also

some problematic measures found in this stage. They are discussed below.

According to Table 7, the researchers found that the three red flags raised in the inter-

item correlation test were confirmed by the low factor loadings (Bold in the Table 7) of these

measures when compared to other measures in their groups. Therefore, the researchers were

more confident about the decision to remove those items from the groups.

Table 7: Factor loadings for the measures

Component

1 2 3 4 5 6 7 8 9

YL-Q.1 Psychographics-I Feel Like INever Have Any Spare Time .017

-.082

.156 .792 .102-

.031-

.049-

.027.045

YL-Q.1 Psychographics-On MostDays I Can't Get Everything Done INeed to Do

.073 .155 .110 .589 .056 .040 .055-

.011-

.357

YL-Q.1 Psychographics-I Never Have

Time to do the Things I Really Wantto Do

.047 -.048 .235 .731 .178 .008 .083 -.105 -.032

RE: YL-Q.1 Psychographics-Free

Time is Something I Make Certain I

Allow Myself

-.070

-.377

-.162

.588-

.078-

.053-

.028-

.032.097

YL-Q.1 Psychographics-I'm the MostOrganized Person I Know .083 .092 .184

-.058

.117-

.051-

.035.070 .730


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YL-Q.1 Psychographics-I AlwaysFinish What I Start

.002 .224-

.022-

.008.003 .045 .023 .011 .720

RE: YL-Q.1 Psychographics-I Thinkof My Job as a Means to a Paycheck,Not as Part of a Career

-.056

.079-

.171-

.057.009 .021 .003 .712 .067

YL-Q.1 Psychographics-I Find MyJob Extremely Fulfilling

-.031

.246 .047 .003 .037-

.017.109 .678 .145

YL-Q.1 Psychographics-What I Do isMore Important Than What I Earn .011 .236 .128

-.093

.008 .018-

.023.591

-.128

RE: YL-Q.1 Psychographics-If I

Were Rich, I Wouldn't Work .138-

.203-

.145.009 .011 .040

-.231

.580 .028

YL-Q.1 Psychographics-I Like MyLife Just the Way it is Now

-.069

.611-

.046-

.226.027

-.166

.044 .318 .046

YL-Q.1 Psychographics-I Have MoreSelf-Confidence Than Most PeopleMy Age

.183 .535 -.045

.016 .100 .114 -.049

.116 .373

YL-Q.1 Psychographics-I ConsiderMyself a Happy Person .005 .740

-.180

-.009

.085-

.085-

.083.095 .106

YL-Q.1 Psychographics-I Live Life tothe Fullest

.281 .664 .002-

.018.005 .061

-.049

.031 .097

TV-Q.9 TV is My Link to the OutsideWorld

.096 .042 .149 .104-

.048.057 .604 .100 .116

TV-Q.9 I Would Love To Be Able ToWatch TV 24 Hours a Day .139 .032 .119 .044

-.028

.155 .646-

.041-

.025

TV-Q.9 It's the Best FreeEntertainment I Can Get

-.002

-.027

-.001

.007 .129-

.191.661

-.069

.023

TV-Q.9 I'm a Real "Couch Potato" -.029

-.171

.041-

.107.056 .010 .679

-.059

-.149

SHP-Q.1 Iike to try new productswhen they first come out .679

-.012

-.110

.011 .184-

.133.190

-.021

-.056

SHP-Q.1 I am very interested in anynew products and services .562 .010

-.046

.003 .466-

.037.136 .006

-.017

YL-Q.1 Psychographics-I Consider

Myself a Trend Setter .613 .212 .262

-

.064

-

.085 .217 .045

-

.003 .142

YL-Q.1 Psychographics-I am Usuallythe First to Try New Things .758 .157 .014 .129 .106 .101 .018 .043 .128

CT-Q.1 I love cutting edge, high techthings

.330-

.025-

.189.002 .509 .407 .059 .004 .062


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SHP-Q.1 Saving time is moreimportant than saving a few dollars .124

-.004

.002-

.028.578 .034 .001 .105 .022

CT-Q.1 Anything that saves me timeis important

.026 .078-

.022.167 .714 .154 .039 .029 .088

YL-Q.1 Psychographics-I Hate

Waiting in Line .046 .035 .324 .035 .471 .173

-

.034

-

.149

-

.051YL-Q.1 Psychographics-AnythingThat Saves Me Time is Important

-.039

.203 .221 .355 .532 .019 .075-

.034.071

YL-Q.1 Psychographics-It Bothers MeWhen Something UnexpectedInterrupts My Daily Routine

-.037

-.095

.715 .141 .110-

.032.139 .022 .053

YL-Q.1 Psychographics-I FeelUncomfortable Trying New Things

-.258

-.112

.504 .137-

.070-

.206.112

-.061

.100

YL-Q.1 Psychographics-Everything is

Changing Too Much These Days

-

.008 .100 .590 .141

-

.183

-

.234 .103

-

.018

-

.147

YL-Q.1 Psychographics-I'm a

Person Who Does Not Take Risks-

.426-

.035.284 .041 .043

-.329

.155 .025 .097

YL-Q.1 Psychographics-I Get EasilyAnnoyed When Things Don't Go as IPlanned

.115-

.114.653 .022 .200 .062 .036

-.089

.129

Q.48 Interest in Learning More Aboutthe Information Superhighway .063

-.060

-.057

-.010

.160 .832 .019 .027 .025

Q.49 Importance of Information

Highway to Your Future .033 .000-

.089 .001 .165 .825 .037 .017-

.007

Then, the reliability for each of the constructs was tested. The Cronbachs alpha of the

proposed construct Lack ofspare time is .652. The Cronbachs alpha goes up to .680 when the

reversed measure Free time is something I make certain I allow myself is deleted. According

to this result and the red flags for this measure in the previous stage, the researchers took this

measure out and computed the true scores for the construct Lack ofspare time with the other

three measures, which are I feel like I never have any spare time, On most days I can't get

everything done I need to do, and I never have time to do the things I really want to do.


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The Cronbachs alpha of the proposed construct Attitude toward an organized life

is .54, which is acceptable. Since this construct only have two measures, its not possible to

verify the consistency of the measures by conducting Cronbachs alpha if item is deleted.

Therefore, the true score of the construct was computed Attitude toward an organized life with

the two measures, which are I Always Finish What I Start and I'm the most organized person

I know.

The Cronbachs alpha of the proposed construct Seeking sense of achievement from

work is .53. The Cronbachs alpha goes up to .54 when the reversed measure If I were rich, I

wouldn't work is deleted. Although this is not a significant improvement, according to the red

flags for this measure in the previous stage, the researchers took this measure out and computed

the true scores for this construct with the other three measures, which are I find my job

extremely fulfilling, What I do is more important than what I earn and I think of my job as a

means to a paycheck, not as part of a career (Reversed).

The Cronbachs alpha of the proposed construct Satisfaction with current life is .66,

showing a good internal consistency of the measures. There is no significant improvement if any

of the items were deleted. Therefore, the researchers computed the true scores for this construct

with the four measures, which are I like my life just the way it is now, I have more self-

confidence than most people my age, I consider myself a happy person and I live life to the

fullest.

The Cronbachs alpha of the proposed construct Positive attitude toward watching TV

is .586, showing a mediocre internal consistency of the measures. There is no significant

improvement if any item was deleted. Therefore, the researchers computed the true scores for


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this construct with the four measures, which are TV is my link to the outside world, I would

love to be able to watch TV 24 hours a day, It's the best free entertainment I can get and I'm

a real couch potato.

The Cronbachs alpha of the proposed construct Likelihood to be an early adopter

is .694, showing a good internal consistency of the measures. There is no significant

improvement if any items are deleted. Therefore, the researchers computed the true scores for

this construct with the five measures, which are I like to try new products when they first come

out, I am very interested in any new products and services, I consider myself a trend setter,

I am usually the first to try new things and I love cutting edge, high tech things.

The Cronbachs alpha of the proposed construct Attitude toward time saving is .545,

showing a mediocre internal consistency of the measures. There is no significant improvement if

any item is deleted. Therefore, the researchers computed the true scores for this construct with

the four measures, which are Saving time is more important than saving a few dollars,

Anything that saves me time is important, I hate waiting in line and Anything that saves me

time is important.

The Cronbachs alpha of the proposed construct Concern of taking risks is .649,

showing a good internal consistency of the measures. There is no significant improvement if any

item is deleted. However, the measure I get easily annoyed when things don't go as I planned.

gained two red flags at the previous stages. Therefore, the researchers decided to take it out and

computed the true scores for this construct with the four measures, which are It bothers me

when something unexpected interrupts my daily routine, I feel uncomfortable trying new

things, Everything is changing too much these days, I'm a person who does not take risks.


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The Cronbachs alpha of the proposed construct Attitude toward Information

Superhighway is .804, showing an excellent internal consistency of the measures. Since this

construct only has two measures, its not possible to verify the consistency of the measures by

conducting Cronbachs alpha ifitem is deleted. Therefore, the researchers computed the true

score of this construct with the two measures, which are Interest in learning more about the

Information Superhighway and Importance of Information Highway to your future.

For all the constructs mentioned above, one construct of sum and one construct for

average were created respectively for different analysis purposes in the following stages.

Here are the final groupings and their factor loading for the shopping part. With these

groupings, 65.3% of the total variance was explained.

Table 8: Final factor loading (shopping section)

Component

1 2 3 4 5

SHP-Q.1 I worry more about spending nowbecause of the state of the economy

0.085 0.13 0.102 0.761 0.017

SHP-Q.1 I sometimes worry about not havingenough money to pay my bills

-0.076 -0.025 -0.047 0.82 0.014

SHP-Q.1 I tend to do a lot of research and shoparound before I make big purchases

0.036 0.069 0.833 0.05 -0.086

SHP-Q.1 I usually know where to get a good price 0.034 0.113 0.832 0.005 0.035

SHP-Q.1 I usually stick to the brands I know 0.021 0.806 0.032 -0.024 -0.014

SHP-Q.1 If a company has been around for a longtime, I feel it has better products or services

0.019 0.789 0.051 0.01 0.02

SHP-Q.1 If I've found something that's good, thenI don't like to change

0.03 0.635 0.123 0.138 -0.058

SHP-Q.1 I am more likely to buy something soldin a store than sold in a catalog

0.701 0.206 0.187 0.175 0.066

RE: SHP-Q.1 I like to shop by mail so that I don'thave to deal with salespeople

0.828 -0.049 -0.037 -0.096 -0.162


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RE: SHP-Q.1 I'm too busy to shop in stores, sobuying through the mail is a real convenience

0.858 -0.038 -0.026 -0.039 0.032

The last group was deleted because of its low reliability. Four new variables of the multiple-item

measures were created.

Worry about money (The Cronbachs alpha coefficient is .47.) Likely to buy from store (The Cronbachs alpha coefficient is .60.) Money concern (The Cronbachs alpha coefficient is .64.) Brand loyalty (The Cronbachs alpha coefficient is .72)

Table 9: New multiple item measures

Items Construct

SHP-Q.1 I worry more about spending now because of thestate of the economy

Worry about money

SHP-Q.1 I sometimes worry about not having enough money

to pay my billsSHP-Q.1 I tend to do a lot of research and shop around beforeI make big purchases

Money concern

SHP-Q.1 I usually know where to get a good price

SHP-Q.1 I usually stick to the brands I know Brand loyalty

SHP-Q.1 If a company has been around for a long time, I feelit has better products or services

SHP-Q.1 If I've found something that's good, then I don't liketo change

SHP-Q.1 I am more likely to buy something sold in a storethan sold in a catalog

Likely to buy from store

RE: SHP-Q.1 I like to shop by mail so that I don't have todeal with salespeople

RE: SHP-Q.1 I'm too busy to shop in stores, so buyingthrough the mail is a real convenience

The Cronbachs alpha coefficient of Worry about money is .47 with two items. The

Cronbachs alpha coefficient of Likely to buy from store is .60 with two items. The

Cronbachs alpha coefficient of Money concern is .64 with two items. The Cronbachs alpha

coefficient of Brand Loyalty: is .72 with two items. Since all these constructs only have two

measures, its not possible to verify the consistency of the measures by conducting Cronbachs

alpha if item is deleted. The true score was computer for all the constructs.


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Here are the final groupings and their factor loadings for the technology attitude part. In

this case, 69.4% of the total variance was explained.

Table 10: Final factor loading (technology section)

Component

1 2 3 4 5 6

CT-Q.1 My answering machine is an essential partof my daily living -- I couldn't do without it

-0.03 0.212 0.141 0.711 0.31 0.141

CT-Q.1 I'm comfortable retrieving messages frommy answering machine when I'm away from home

-0.063

0.07 0.171 0.744 0.109 0.251

CT-Q1 I resent it when I call and get an answering

machine

0.318 -

0.044

0.014 0.737 -

0.186

-

0.062CT-Q.1 The telephone is my main contact with theoutside world

-0.042

0.093 0.034 0.01 0.83 -0.114

CT-Q.1 The telephone is an essential part of mydaily living -- I couldn't do without

0.024 -0.053

0.002 0.117 0.792 0.206

I prefer to deal directly with a teller for mybanking needs

0.325 0.108 0.728 0.068 -0.101

-0.171

CT-Q.1 I prefer to use an ATM machine for allmy banking needs

-0.007

0.147 0.836 0.113 0.107 0.153

CT-Q.1 I am very comfortable using an ATM tomake deposits or pay bills

0.005 0.066 0.808 0.13 0.027 0.331

Ct1m Computers and technology will ruin thequality of my life

0.742 -0.065

-0.039

0.023 -0.049

0.298

Computers and technology will eventually be usedto limit personal freedom

0.795 0.024 0.104 0.045 -0.009

-0.085

Computers and technology control too much ofour lives already

0.817 0.11 0.138 0.071 0.033 0.025

PC-Q.1 My personal computer is an essential partof my daily living -- I couldn't do without it

0.154 0.753 0.08 0.122 0.042 0.292


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CT-Q.1 I often spend hours playing games on mypersonal computer

-0.021

0.76 0.125 -0.004

0.017 0.077

PC-Q.1 I spend more time with my personalcomputer than I do with people

-0.011

0.866 0.076 0.081 0.014 0.063

CT-Q.1 Computers and technology will improvethe quality of my life

0.27 0.173 0.331 0.134 0.163 0.664

CT-Q.1 I was nervous about using a personalcomputer at first, but now that I'm used to it, I loveit

-0.011

0.282 0.069 0.165 -0.013

0.736

The true scores were computed and 6 new variables were created of the following multiple

item measures.

Role of computers in life (The Cronbachs alpha coefficient is .63.) Bad attitude about computers (The Cronbachs alpha coefficient is .54.) Time spent on computer (The Cronbachs alpha coefficient is .61.) Attitude towards using ATM (The Cronbachs alpha coefficient is .72) Attitude towards using telephone (The Cronbachs alpha coefficient is .74.) Attitude towards using answering machine (The Cronbachs alpha coefficient is .59.)


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Table 11: Multiple item measures for technology

Items Construct

CT-Q.1 My answering machine is anessential part of my daily living -- I couldn'tdo without it

Attitude towards using answering machine

CT-Q.1 I'm comfortable retrieving messagesfrom my answering machine when I'm awayfrom homeCt1b I resent it when I call and get ananswering machineCT-Q.1 The telephone is my main contactwith the outside world

Attitude towards using telephone

CT-Q.1 The telephone is an essential part ofmy daily living -- I couldn't do without

I prefer to deal directly with a teller for my

banking needs

Attitude towards using ATM

CT-Q.1 I prefer to use an ATM machine forall my banking needsCT-Q.1 I am very comfortable using anATM to make deposits or pay bills

Ct1m Computers and technology will ruinthe quality of my life

Bad attitude about computer

Computers and technology will eventually beused to limit personal freedom

Computers and technology control too muchof our lives already

PC-Q.1 My personal computer is an essentialpart of my daily living -- I couldn't dowithout it

Time spent on computer

CT-Q.1 I often spend hours playing gameson my personal computer

PC-Q.1 I spend more time with my personalcomputer than I do with people

CT-Q.1 Computers and technology willimprove the quality of my life

Role of computer in life

CT-Q.1 I was nervous about using a personalcomputer at first, but now that I'm used to it,

I love it

The Cronbachs alpha coefficient of Role of computer in life is .63.The Cronbachs

alpha coefficient of Bad attitude about computer is .54.The Cronbachs alpha coefficient of

Time spent on computer is .61.TheCronbachs alpha coefficient of Attitude towards


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using ATM is .72. The Cronbachs alpha coefficient of Attitude towards using telephone

is .74.The Cronbachs alpha coefficient of Attitude towards using answering machine

is .59. There is no increase of Cronbachs alpha value if each of the items in the same

constructs was deleted. In this way, the true score for all the constructs was computed.

3. Building a Regression ModelIn order to answer the research question, it is essential to determine the variables that

influence the behavior of home shopping, and how much each of those variables influence home

shopping. To this end, the multiple regression was used to evaluate the variables for their

contribution to the overall explanation of likelihood of home shopping behavior. The phone and

mail survey items were again reviewed for variables that could have a direct effect on attitude

and use of the new home shopping technology. A list of 43 variables was compiled from the mail

and phone survey. Each of these 43 variables were evaluated for its contribution to the

explanation, represented by the beta value, and to see whether the variable made a statistically

significant and unique contribution to the explanation, represented by the significance level. The

results from the regression showed that 11 of these variables were significant at the 95 percent

confidence level. Table 12 lists all of the predictors with the highlighted variables as significant.

After removing the variables that did not have significance at the 95 percent confidence

level, a second regression was run to select the relevant variables. This process of removing non-

significant variables and re-running the model was iterated until there were nine predictors that

were significant at the 95 percent confidence level. These nine predictors explained a 22.5

percent variation in answers from respondents in the surveys.


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The next step was to examine the standardized beta coefficients to determine the impact

each of the variables had on the 22.5 percent of variation explained. After carefully removing

only the variables that effected the variation less than one percent, five variables were selected.

These five variables explained nearly 21 percent (20.9%) of the variation in answers from

respondents in the surveys. The final list of variables is in Table 13.

Table 12: List of predictors


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Table 13: Final list of predictors

4. Cluster Analysis5.

The final regression model identified five explanatory variables of interest relevant to this

investigation: having choices, being able to work from home, attitude towards the information

superhighway, preference of buying from a store, and the likelihood to be an early adopter.

Those five variables were used to assign all individuals to specific groups using cluster analysis.

Cluster analysis is a method of looking for similarities among groups of individuals within the

sample to enable the analysis of the characteristics of those different groups. Grouping the

sample into clusters provides a different lens to explore and interpret the available data for

strategic planning to reach these different groups.

A k-means cluster analysis was run on 1,818 cases in the sample. In k-means cluster

analysis, the software forms the most distinct groups possible based on the proposed number of

groupings. The researchers evaluated three, four and five possible groupings, considering both

the number of individuals who were in the proposed group and also whether the groupings made

qualitative sense. After evaluating the different potential arrangements, the researchers decided

on four clusters. The four clusters are detailed in Table 14.


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Table 14: Cluster Analysis results

Cluster

1 2 3 4

YL-Q.1 Psychographics-I LikeHaving Choices Available to Me

slightlyhigh

moderate moderate slightly high

YL-Q.1 Psychographics-If I CouldEarn a Living Without Leaving MyHome, I Would

high low high low

Prefer buy from store Don't Buyfrom mail or with phone avg (sh12sh19 sh23)

slightlylow

high high moderate

Attitude toward the super highway

avg (q48, q49)

slightly

highlow slightly low high

Likelihood to be an early adopteravg (sh4, sh15, yl58, yl32, ct1v)

high low slightly low high

New Dependent variable avg - useof shopping (fu2a5DV fu2a6DVfu2a7DV fu2a4)

high low low slightly high

Cluster namesoptimistic

trendsetterstraditionalroutiners

skepticallatecomers

tech friendlyopen

consumers

Number of Cases in each Cluster

Cluster 1 593

2 331

3 538

4 356

Valid 1818

Missing 318


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1) DemographicsAge

Each cluster had a majority of respondents within a different age category. Twenty nine

percent of the optimistic trendsetters fell within the 30-39 age category (29.3%, SE:2.1, 95%

CI). Tech friendly open consumers were almost exactly split between 20-29 (21.1%, SE:1.9,

95% CI) and 40-49 (21.6%, SE:1.9, 95% CI). Skeptical latecomers had a majority concentrated

within the 40-49 age category 23.2%, and traditional routiners had the oldest majority, falling

within the 60-69 age group (22.1%,SE:1.9, 95% CI). A chi square test of significance

confirmed that optimistic trendsetters are significantly more likely to be within the 30-39 group

than the 60-69 age group when compared with tech friendly open consumers, 2(1, N=949) =

15.44, p


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SE:2.3, 95% CI), and the traditional routiners were 51.4% male and 48.6% female (both

SE:2.3, 95% CI). There was no statistically significant difference in gender between any of the

groups.

Ethnicity

Respondents were asked to report whether they were Hispanic. A majority of all four

groups were not of Hispanic ethnicity: 95.8% of traditional routiners (SE:0.9, 95% CI), 94.1%

of skeptical latecomers (SE:1,1, 95% CI), 93.4% of optimistic trendsetters (SE:1.1, 95% CI),

and 91.0% of tech friendly open consumers (SE: 1.3, 95% CI). Tech friendly open consumers

and optimistic trendsetters were more likely to have Hispanic people within the group than the

other two groups.

Race

A majority of respondents in all four groups reported they were white when asked about

race: 92.1% of traditional routiners (SE:1.2, 95% CI), 88.3% of skeptical latecomers (SE:1.5,

95% CI), 86.5% of tech friendly open consumers (SE:1.6, 95% CI) and 83.1% of optimistic

trendsetters (SE:1.7, 95% CI). The optimistic trendsetters were the most diverse group, with

8.4% black respondents (SE:2.2, 95% CI), 1.9% Asian (SE:1.1, 95% CI), 4.4% other

(SE:1.7, 95% CI), and they had the highest percentage of individuals that refused to answer the

question.

Marital status

The majority of respondents in all four groups are married. Sixty eight percent (68.0%,

SE:2.1, 95% CI) of skeptical latecomers, 56.5% of traditional routiners (SE:2.3, 95% CI),


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56.2% of optimistic trendsetters (SE:2.3, 95% CI), and 43.0% of tech friendly open consumers

reported that they were married (SE:2.3, 95% CI). The second most popular status was single,

never married. Tech friendly open consumers were the highest percentage of single individuals

with 40.2% (SE:2.3, 95% CI), optimistic trendsetters were second at 29.2% (SE:2.1, 95% CI),

traditional routiners were 22.7% (SE:1.9 95% CI), and 16.5% of skeptical latecomers were

single (SE:1.7, 95% CI). Since married and single and never married were the two top choices,

a chi square test of independence was used to determine whether there was a significant

difference between optimistic trendsetters and tech friendly open consumers, our two top target

groups. The results of the test were significant

2

(1, N= 802) =15.6, p


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25.3% of optimistic trendsetters (SE:2.0, 95% CI).Chi square tests of independence showed no

significant difference among any of the four groups in the three income groups (below $35,000,

between $35,000-$75,000, and over $75,000).

Education

There was a wide range of education levels among the four groups, with responses in

categories ranging from grade school or less to post graduate education. After reviewing the

frequencies, the responses were recoded into three categories: did not graduate from high school,

high school graduate, and college graduate. Over sixty percent of the respondents reported

graduating from high school in three of the four clusters: sixty one percent of optimistic

trendsetters (61.2%, SE:2.2, 95% CI), sixty percent of traditional routiners (60.6%, SE:2.2,

95% CI) and sixty one percent of skeptical latecomers (61.3%, SE:2.2, 95% CI). Only 49.6% of

tech friendly open consumers reported they had graduated from high school (SE:2.3, 95% CI),

however that cluster had the highest number reporting graduating from college or additional

education (36.8%, SE:2.2, 95% CI)). The other three clusters had a lower percentage of college

graduates: traditional routiners had 30.9% (SE:2.1, 95% CI), optimistic trendsetters had 28.6%

(SE:2.1, 95% CI), and skeptical latecomers had 26.6% (SE:2.0, 95% CI).

A series of chi square tests of independence demonstrated that none of the groups had a

significance difference in high school graduates versus percentage of college graduates. A

similar series of tests showed no significant difference in the percentage of college graduates

versus percentage of post-graduates in each of the groups.

Own or rent primary residence


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When asked whether they own or rent their primary residence, 78.2% of the traditional

routiners reported owning their home (SE:1.9, 95% CI), compared to 75.8% of skeptical

latecomers (SE:2.0, 95% CI), 68.8% of tech friendly open consumers (SE:2.1, 95% CI), and

67.6% of optimistic trendsetters (SE:2.2, 95% CI). A chi square test of independence showed

no significant difference between owning and renting among optimistic trendsetters and tech

friendly open consumers.

Number of people in household

The phone survey also included questions about other individuals in the household,

including the number of people who share the residence. A one-way between-groups analysis of

variance was conducted to determine whether there was a difference between the four clusters.

The result showed a statistically significant difference at the p


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reported 62.9% (SE:2.2, 95% CI). Twenty-one (21.3%, SE:1.9, 95% CI) of the tech friendly

open consumers and 16.9% (SE:1.7, 95% CI) of optimistic trendsetters reported living in an

apartment, while 13.6% of traditional routiners (SE:1.6, 95% CI), and 11.9% of skeptical

latecomers reported living in an apartment (SE:1.2, 95% CI).

A chi square test of independence was used to determine whether dwelling type was

significantly different between optimistic trendsetters and tech friendly open consumers; the

results were significant 2(1, N = 831) =4.85, p


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Free Time

All respondents were asked whether they like to stay at home in their spare time or

whether they enjoy staying home in their spare time. Of the four groups, the skeptical latecomers

(64.7%, SE:2.2, 95% CI) had the highest percentage who would choose to stay at home in their

free time, the optimistic trendsetters had the second highest likelihood of staying at home

(60.7%, SE:2.2, 95% CI), the traditional routiners were third (55.6%, SE:2.3, 95% CI) and the

tech friendly open consumers were the least likely to stay at home (45.2%, SE:2.3, 95% CI).

There is a statistically significant difference between the optimistic trendsetters and the tech

friendly open consumers, the two groups most likely to use the new technology, 2(1, N = 848) =

25.70, p .05.

Role of computers in life


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A one-way between subjects ANOVA was conducted to test mean difference on the

Role of computer in life among the individuals in the four clusters. The results of this test

showed a significant difference across the four clusters, F (3, 1763) = 22.77, p = .000. Tukey

post-hoc comparisons of the four clusters indicate that computers play a more important role in

the lives of the optimistic trendsetters (M = 2.52, 95% CI [2.36, 2.68]) than the skeptical

latecomers (M = 1.83, 95% CI [1.69, 1.97]), p = .000 and the traditional routiners (M = 1.69,

95% CI [1.55, 1.83]), p = .000. The optimistic trendsetters and tech friendly open consumers

have a very similar and positive attitude towards computers, as the comparison between those

two groups was not statistically significant (p > .05).

A one-way between-subjects ANOVA was conducted to test mean difference on

Attitude toward computers among the four clusters. The means of this variable differed

significantly across the four clusters, F (3, 1754) = 53.18, p = .000. Tukey post-hoc comparisons

of the four clusters indicate that the optimistic trendsetters (M = 5.89, 95% CI [5.69, 6.09]) show

significantly more positive attitudes towards computers than the traditional routiners (M = 4.50,

95% CI [4.23, 4.78]), p = .000 and the skeptical latecomers (M = 4.43, 95% CI [4.22, 4.65]), p =

.000. Comparisons between the optimistic trendsetters and the tech friendly open consumers

were not statistically significant (p > .05).

Concern of money

A one-way between subjects ANOVA was conducted to test the difference in the means

regarding Concern of money among the individuals in the four clusters. The there were

significant differences in the mean for concern of money across the four clusters, F (3, 1794) =

9.31, p = .000. Tukey post-hoc comparisons of the four clusters indicate that the optimistic

trendsetters (M = 7.56, 95% CI [7.41, 7.72]) show significantly more concern of money than the


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traditional routiners (M = 6.84, 95% CI [6.61, 7.08]), p = .000. Comparisons between the

optimistic trendsetters and the skeptical latecomers, the tech friendly open consumers were not

statistically significant at p > .05.

Price consciousness

Respondents were also asked to comment on their ideas about price when shopping. A

one-way between-groups analysis of variance was conducted to determine whether there was a

difference in concern about money between the four clusters. The result showed a statistically

significant difference at the p


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saving time among traditional routiners was also significantly different from the mean score for

tech friendly open consumers (M= 3.11, SD = 1.15). Optimistic trendsetters are more interested

in saving time than the traditional routiners and skeptical latecomers. Tech friendly open

consumers are also willing to save more time then the traditional routiners.

Lack of spare time

Respondents were also asked a slightly different question, whether they felt that they

lacked spare time. The result showed a statistically significant difference for the four groups: F

(3, 1754) = 17.3, p=.00. The mean difference between each group was compared using Tukeys

HSD test. The average degree for optimistic trendsetters (M=6.28, SD = 1.86) was significantly

different from the mean score for traditional routiners (M=5.43, SD = 1.82), and from tech

friendly open consumers (M= 5.7, SD = 1.87). The average degree of lack of spare time for

traditional routiners was also significantly different from the mean score for tech friendly open

consumers and skeptical latecomers (M = 6.08, SD = 1.82). Optimistic trendsetters have the least

amount of spare time, skeptical latecomers are the group with the second least amount of spare

time, and traditional routiners and tech friendly open consumers are the least busy of the clusters.

Pay more for quality

The results showed a statistically significant difference at the p


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tech friendly open consumers (M = 8.15, SD = 1.60) and skeptical latecomers. Optimistic

trendsetters and tech friendly open consumers are more willing to pay for higher quality than

skeptical latecomers.

Pay more for convenience

The results showed a statistically significant difference at the p


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significantly different from the mean score for tech friendly open consumers (M = 2.42, SD =

2.04).The average degree of buying expensive things to impress friends for skeptical latecomers

was also significantly different from the mean score for tech friendly open consumers and

skeptical latecomers. Optimistic trendsetters and tech friendly open consumers are more likely to

buy expensive things to impress their friends than the traditional routiners and skeptical

latecomers.

Overall privacy concern

Each cluster was analyzed for how concerned they are about privacy overall. The result

showed a statistically significant difference at the p


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mean difference between each group was compared using Tukeys HSD test. The average degree

for optimistic trendsetters (M=7.16, SD = 2.32) was significantly different from the mean score

for traditional routiners (M = 6.53, SD = 2.43) and skeptical latecomers (M=6.67, SD = 2.40).

The average degree for traditional latecomers was significantly different from the mean score for

tech friendly open consumers (M = 7.28, SD = 2.08).The average degree of importance of

relationships with people for skeptical latecomers was also significantly different from the mean

score for tech friendly open consumers and skeptical latecomers. Optimistic trendsetters and tech

friendly open consumers are more likely to consider personal relationships as extremely

important than the traditional routiners and skeptical latecomers.

Privacy and computers and technology

A one-way between subjects ANOVA was conducted to test mean difference on Feelings

about invasion of privacy concerning computers and technology among the four clusters. The

means of this variable differed significantly across the four clusters, F (3, 1814) = 3.47, p = .015.

Tukey post-hoc comparisons of the four clusters indicate that the optimistic trendsetters (M =

2.60, 95% CI [2.50, 2.69]) show significantly higher concern about invasion of privacy by

computers and technology than the tech friendly open consumers (M = 2.39, 95% CI [2.27,

2.51]), p = .046. Comparisons between the optimistic trendsetters and the traditional routiners,

the skeptical latecomers and were not statistically significant at p > .05.

Effect computers and technology have on control of life

A one-way between-subjects ANOVA was conducted to test the mean difference on Effect

computers and technology have on control of life among the four clusters. The means of this

variable differed significantly across the four clusters, F (3, 1814) = 22.56, p = .000. Tukey post-


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hoc comparisons of the four clusters indicate that the optimistic trendsetters (M = 1.42, 95% CI

[1.37, 1.48]) think that computers and technology have significantly more control of their lives

than the traditional routiners (M = 1.62, 95% CI [1.53, 1.71]), p = .001 and the skeptical

latecomers (M = 1.69, 95% CI [1.62 1.75]), p = .000. Comparisons between the optimistic

trendsetters and the tech friendly open consumers were not statistically significant at p > .05.

3) BehaviorsATM and debit cards

Respondents were asked if they currently had an ATM card and if they currently had a

debit card. The responses to these questions are important in two ways. First, debit cards are a

relatively new technology at the time and are a sign that respondents are more open to new

means to pay for goods and services. Second, ATM cards can only be used to make physical

transactions, whether it be at the bank or a store. There is no way to use an ATM card over the

phone, by catalog, etc. This means that respondents would need a debit or a credit card to pay for

goods while home shopping. It is more important to look at the purchasing behavior with debit

cards, since that is more closely related to the way customers would be purchasing products

through ESIs new technology.

Fifty three percent (SE: 2.1, 95% CI) of respondents in the survey have ATM cards,

while only 17.6% (SE: 1.6, 95% CI) have debit cards. Chi-square analyses were run to see if

there were differences between groups in who had debit cards. There is no statistical significance

between optimistic trendsetters (our target group) and traditional routiners (p > .05). There is also

no statistical significance between optimistic trendsetters and skeptical latecomers (p > .05)

meaning that our target group and the other clusters are similar. There is also no significant


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Optimistic trendsetters are more likely to have bought merchandise from a home shopping

channel on television, bought merchandise or services from a brief radio or television

commercial where you call an 800 number to order and bought merchandise through an on-line

computer service. All of these differences are statistically significant and are detailed in Table

15.

Table 15: Confidence intervals and chi squared for differences in shopping between optimistic

trendsetters and traditional routiners

Go out for a romantic evening

The result showed a statistically significant difference at the p


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latecomers. Optimistic trendsetters and tech friendly open consumers are more likely to go out

for a romantic evening than the traditional routiners and skeptical latecomers.

Number of televisions in households

A one-way between subjects ANOVA was conducted to test the mean difference on the

Number of televisions in households among the four clusters. The results showed a significant

difference between the number of televisions in the households among the four groups, F (3,

1814) = 9.65, p = .000. Tukey post-hoc comparisons of the four clusters indicate that the

optimistic trendsetters (M = 1.11, 95% CI [1.09, 1.14]) have fewer televisions at home than the

traditional routiners (M = 1.18, 95% CI [1.14, 1.22]), p = .025 and the skeptical latecomers (M =

1.22, 95% CI [1.18, 1.25]), p = .022. Comparisons between the optimistic trendsetters and the

tech friendly open consumers were not statistically significant at p > .05, suggesting that those

two clusters have similar numbers of televisions in their households.

Media use among different clusters

For the sake of reaching the target consumers more effectively, the researchers explored the

types of media regularly used by the different clusters. In the survey, a general description of the

media usage frequency distributions by clusters was given first. Then each medium (TV, Radio,

Magazine, Newspaper) was discussed in detail. Since most of the relevant variables are either

nominal or ordinal, cross tabulations and chi-square tests are conducted to examine whether

significant differences exist across the four clusters in these four areas, especially between the

target cluster and the other clusters.

Overall media usage


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Table 16 shows the media usage frequency distribution by clusters. According to this

table, among the four main media, optimistic trendsetters prefer television to radio, magazines,

and newspapers. To test whether the optimistic trendsetters use a specific medium more than the

respondents in other clusters, cross tabulations and corresponding chi-square tests between the

optimistic trendsetter and other clusters with respect to the usage of

TV/radio/magazine/newspaper were conducted. Among the four media, no significant

differences across the clusters with respect to TV/Radio/Newspaper usage were found. However,

for magazines, there is a significant difference on whether optimistic trendsetters read magazines

compared to the respondents in other clusters

2

(1, N=1818) = 14.36, p


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TV

Respondents to the telephone survey were asked a series of questions about their access to

cable television. Analyzing the different behaviors in television access and programming choices

between the four different clusters revealed differences among them.

Comparing how respondents answered whether or not their household has cable TV,

there was a significant difference between the target group of optimistic trendsetters and

skeptical latecomers. Sixty nine percent (69.1%) of optimistic trendsetters have cable compared

with 60% of skeptical latecomers. The difference was significant at the level 2 (1, N=1131) =

10.25, p< .001.

Out of those respondents who do not have cable TV, but it is available in their area, there

are no significant differences between optimistic trendsetters or tech-friendly open consumers

and traditional routiners or skeptical latecomers (p > .05) in the likelihood they get basic cable or

premium cable in the next year. Among all four groups, the responses for not likely at all are

very high with 51.3% (SE: 9.1, 95% CI) of optimistic trendsetters not likely at all to get basic

yes no

Count 527 66 593

% within

Cluster 1

versus the

other

clusters

88.9 .1 1.0

Count 1004 221 1225

% within

Cluster 1

versus the

other

clusters

82.0 .2 1.0

Count 1531 287 1818

% within

Cluster 1

versus the

other

clusters

84.2 .2 1.0

Cluster 1 versus the other clusters * MAG-Q.1 Whether Read Magazines Crosstabulation

Read Magazines

Total

Cluster 1 versus the other

clusters

optimistic trendsetters

others

Total


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cable and 62.9% (SE: 8.8, 95% CI) of optimistic consumers not likely at all to get premium

cable within the next year.

The top ten most popular TV programs were Local news [News/Information](54.90%),

Home Improvement (45.60%) [Comedy], Public Television (43.50%) [Educational/Cultural],

20/20 (43%) [News/Information], Sports (38.4%), Discovery Channel (37%) [Cable Channels],

Network Evening News (36.60%) [News/Information], CNN (34.10%) [Cable Channels], Oprah

Winfrey (31%) [Talk Shows], Roseanne (31%) [Comedy].

Among the ten programs, the respondents in the target cluster were significantly more likely

to watch the following programs than those in other clusters: Home Improvement, Roseanne,

20/20, Discovery Channel, Oprah Winfrey show.

There were some significant differences in television viewing tendencies among the

optimistic trendsetters and both traditional routiners and skeptical latecomers on watching

several specific TV programs. Optimistic trendsetters are more likely to watch Oprah Winfreys

talk show,2(1, N=1462) = 26.4, p< .01. Optimistic trendsetters are also more likely to watch the

cable channel A&E than the traditional routiners,2(1, N=924) = 4.72, p< .01. Optimistic

trendsetters are more likely to watch the Discovery Channel than both the traditional routiners

and skeptical latecomers,2(1, N=924) = 12.4, p< .01. The tech friendly open consumers are

more likely to watch CNN than the traditional routiners and skeptical latecomers 2(1, N=1225)

= 7.4, p< .01.


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Radio

To explore how clusters differ in the number of hours they listened to the radio, we used

a one-way between-groups analysis of variance. There was no statistically significant difference

between the three groups: F(3, 1817) = .95, p =.42.

We also analyzed the types of radio shows that the different clusters reported listening to.

The most popular type of radio programming across all four groups is music programming. The

tech friendly open consumers were the most likely to listen to music programming on the radio

(80.1% SE:1.8, 95% CI), the optimistic trendsetters were slightly less likely (78.8% SE:1.9,

95% CI), 70% of skeptical latecomers listened to music (SE:2.1, 95% CI), and 69.8% of

traditional routiners listen to musical programing (SE:2.1, 95% CI). A chi square test of

independence evinced a significant difference between listening patterns, 2(1, N = 924) = 9.24,

p


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between the optimistic trendsetters and the tech friendly open consumers were not statistically

significant at p > .05.

As for the preference of magazines read on regular basis, generally, the top ten most popular

magazines among the respondents are Readers Digest (24.2%), Better Homes and Gardens

(16.3%), Good Housekeeping (14.2%), TV Guide (13.5%), National Geographic (13.2%),

People (10.6%), Ladies Home Journal (10.4%), Sports Illustrated (9.6%), Newsweek (9.5%),

and Consumer Reports (8.1%).

Among the ten magazines, cross tabulations and chi-square tests were conducted to

examine whether the target cluster (optimistic trendsetters) were more likely to read particular

magazines. According to the results, the respondents in the target cluster were more likely to

read the following magazines than those in other clusters: Better Homes and Gardens, Good

Housekeeping, TV Guide, and People.

A chi-square test was used to determine whether optimistic trendsetters or the other

clusters were more likely to read the magazine Better Homes and Gardens. The result was

statistically significant, 2 (1, N=1818) = 17.46, p


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this magazine. Therefore, the optimistic trendsetters are more likely to read this magazine than

the respondents in other clusters.

A chi-square test between optimistic trendsetters/other clusters and whether the

respondents read the magazine TV Guide was statistically significant 2(1, N=1818) = 4.73,

p


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Most of the tech-friendly open consumers (56.6%) (SE = 2.7%, 95% CI) spent more

than one hour reading newspapers in a week. We found a significant relationship when

comparing the optimistic trendsetters and the tech friendly open consumers on the length of time

they spent reading newspaper 2(1, N=713) = 5.18, p< .05. We also found a significant

relationship when comparing the tech friendly open consumers and both the traditional routiners

and skeptical latecomers on their length of reading newspaper 2(1, N=713) = 5.2, p< .05. Tech

friendly open consumers spend more time reading the newspaper than other groups.

Table 17: Newspaper sections most frequently read by optimistic trendsetters

Sections %

Local News 74.7%

World News 56.5%Advertisement 56%

Classified 48.4%

Comics 42.8%

Different clusters read different sections of the newspaper. Table 17 shows the most

popular sections of the newspaper from the overall sample. The optimistic trendsetters and the

traditional routiners both read the technology section, and the relationship is significant 2(1,

N=924) = 7.4, p< .01. The tech friendly open consumers and the traditional routiners also both

read the technology section, and also have a significant overlap 2(1, N=1225) = 15.6, p< .01.

Optimistic trendsetters and the tech friendly open consumers are more likely to read the

technology section of the newspaper. Optimistic trendsetters are also more likely to read

advertisement sections than the traditional routiners 2 (1, N=924) = 26.4, p< .01.

We found a significant relationship when comparing the tech friendly open consumers

and non-target groups on reading the fashion section 2 (1, N=1225) = 16.3, p< .01. Tech friendly


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open consumers are more likely to read the fashion section than the traditional routiners and the

skeptical latecomers.

IV. Cluster Description

Optimistic trendsetters

Optimistic trendsetters are the top target group for ESI because they are the group most

likely to buy items from home and also the group most likely to try new things. Optimistic

trendsetters like having choices, they have an optimistic view of the information superhighway,

and they are slightly more likely to buy from the mail than other groups. Optimistic trendsetters

tend to be between 30-39 years old, have graduated from college, be married, have children at

home, and be working full time. They are most likely to work from home or all of the four

groups. They are most likely to live in a single family home. This group is very busy, and would

like more spare time. When they have any free time, they prefer to stay at home. Optimistic

trendsetters are both price conscious and quality conscious, they are willing to pay more for

convenience, and they willing to spend more if they are assured of high quality goods, but they

are not as willing as other groups (such as the tech friendly open consumers). Optimistic

trendsetters are influenced by their peers. They place a high value on their relationships with

other people, and they are likely to buy expensive things in order to impress their friends.

Optimistic trendsetters are more diverse ethnically and racially than the three other clusters.


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Tech friendly open consumers

Tech friendly open consumers are the second most likely group to adopt this new

shopping technology. They like having choices available to them, they have a positive attitude

towards technology and like to try new things. They like to work outside of the home, and are

open to shopping in the store or by mail or phone. They have the highest percentage of overall

employment among the four groups, but the lowest percentage of full time employment. The

people who are employed are more likely to be part time or have some flexibility. There are

more people with post college educational level than the optimistic trendsetters. Of all of the

clusters, they were the second most likely to shop from home and were equally likely to adopt

new things as the optimistic trendsetters. Although they are similar optimistic trendsetters, but

there are some key differences. First, they tend to be slightly older (40-49) or slightly younger

(20-29) than the optimistic trendsetters. Second, they are less likely to be married, and also have

fewer people living in the household. They are less likely to live in a single-family house, and

more likely to live in an apartment than the optimistic trendsetters. Similar to the optimistic

trendsetters, Tech friendly open consumers are very concerned about saving time, and are willing

to pay more for convenience. They are the most willing of all the clusters to pay more for higher

quality. Tech friendly open consumers are extremely social, when they have free time, they like

to go out, and they place a very high value on personal relationships. They are most concerned

among the clusters about their own personal privacy, but least concerned about invasion of

privacy related to technology use. They are very likely to be an early adopter.


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Skeptical latecomers

Skeptical latecomers are the second least likely group to use the new technology. They

are not as likely to try new things as the top two groups, but they are more likely to try new

things than the traditional routiners. They have a low attitude about the information

superhighway. They are more likely to shop from a store, and are neutral about having choices.

They would like to earn a living from home, if possible. The highest percentage of skeptical

latecomers is between 40-49. They are more homogenous racially and ethnically than the

optimistic trendsetters. They tend to live in single-family homes that they own and tend to be

employed full time. They are extremely price conscious, are unlikely to pay more for high

quality, and less likely to pay more for convenience than the other two groups. They do not buy

expensive things to try and impress their friends. They are less social than the two previous

groups, being less likely to value personal relationships or go out for romantic dinners. Although

they report that that they are very busy and do not have a lot of time, they also report a low

interest in adopting time saving measures. They have a less positive attitude towards TV.

Computers are less important to them than to the optimistic trendsetters or the tech friendly open

consumers. They are very concerned about potential privacy invasion through technology.

Traditional routiners

Traditional routiners are the least likely group to adopt the new technology. They are

neutral about choices, they like to work outside the home. They are the most likely cluster to

shop at a store. They have the lowest attitude towards the information superhighway, and the

lowest likelihood to be an early adopter. The majority of people in this group are in the 60-69

age category, and it is not surprising that they report fewer numbers of people in the household,


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as any children they may have had have moved out. They have the highest percentage of people

reporting they live in a single-family home. They tend to be homogenous both racially and

ethnically. Many of them are employed full time. They like to stay at home in their free time.

They have a less positive attitude towards TV than the two target clusters, and they report the

least number of hours of TV of all the clusters. They are the least likely to sue all types of media

(TV, radio, newspaper, magazines). Computers do not play an important role in their lives. They

are concerned about privacy invasion related to technology. They are not concerned about the

price of goods, but are less likely to be willing to pay more for higher quality items. They report

the most amount of spare time, have a low interest in time saving measures, and are unwilling to

pay more for convenience.

V. The Story and the Takeaways for the ClientAfter analyzing the overall data and the patterns among the clusters, a clear story emerges

about potential users of the new home shopping technology, and possible strategies for reaching

those users. Practical communication and marketing strategies are provided for the two target

clusters, optimistic trendsetters and tech friendly open consumers, and opportunities and barriers

are provided for the client. Opportunities and barriers are also provided for the two non-target

groups, to enable the client to devise strategies for reaching those groups in the future.

The target clusterOptimistic Trendsetters

Appealing messages:

1. Develop marketing promotions in different languages and take cultural differences into

consideration to appeal to different racial and ethnic backgrounds.


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2. Focus the marketing and promotion on the convenience and timesaving characteristics of the

new home shopping technology.

3. Stress the benefits to customers of home shopping technology in the areas of value and

price, since the target cluster is both price consciousness and quality consciousness.

4. Incorporate social interaction into the home shopping technology to appeal to the social

nature of the target groups.

How to reach them:

1. Optimistic trendsetters read magazines. Better Homes and Gardens, Good Housekeeping, TVGuide and People are especially popular among the group. It would be efficient and effective

if put the ads of the new home shopping technology on these media platforms.

2. Optimistic trendsetters watch television. Some popular TV programs or TV channels amongthem are Home Improvement, Roseanne, 20/20, Discovery and Oprah Winfrey talk show and

A&E.

Opportunities:

1. Appeal to their desire to stay at home and find ways to save time. The new home shoppingtechnology would allow them to shop without leaving home and it would also save their time

since they dont need to go to real stores.

2. Highlight the novel, technology-powered characteristics of this technology. Optimistictrendsetters have positive attitude toward new technology and electronic devices.

Barriers:

1. They tend to have fewer TVs at home.


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2. While most people in the cluster have cable at home, about 30% still do not have cable andtherefore cannot use ESIs product.

3. They have high concern of privacy invasion concerning new technology.4. Most of them dont have debit cards, which might be a necessary paying tool for the new

home shopping technology.

5.The Second Target ClusterTech Friendly Open Consumers

Appealing messages:

The same strategies developed for the optimistic trendsetters can be used for tech friendly

open consumers

How to reach them:

1. Tech friendly open consumers watch television and listen to the radio, so those are effectivechannels to reach this group. CNN is popular TV channel among them. They tend to listen to

music programs on the radio.

2. Tech friendly open consumers spend more time reading the newspaper than the other groups,especially the fashion and technology section. The newspaper would be a good place to put

ads for the new home shopping technology.

Opportunities:

1. Tech friendly open consumers are extremely social people. A word-of-mouth marketing

strategy can be used to persuade them.


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2. Tech friendly open consumers also have positive attitudes toward new technology and

electronic devices.

3. Although they have a high concern of personal privacy, when comes to the new

technologies, they are captivated by new technologies, so would be willing to take a risk to

experience them.

Barriers:

1. They prefer going out instead of staying at home at their free time.2. Most of them dont have a debit card, which might be a necessary paying tool for the new

home shopping technology.

The Cluster of the LatecomersSkeptical Latecomer

Opportunities:

1. Skeptical latecomers are most price conscious group. If they can be convinced that the homeshopping technology can help them save money on shopping, they might be interested in

trying this new technology.

2. Skeptical latecomers tend to be busy and have less spare time. If the home shoppingtechnology can help them save time on shopping, they might be interested in trying this new

technology.

Barriers:

1. Skeptical latecomers are less likely to use media, including TV. They tend to spend less timewatching TV.


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2. Skeptical latecomers are highly concerned about possible privacy invasion concerning newtechnology.

3. Most of the skeptical latecomers

christopher wilcox - cm 723 advanced communication research home shopping data analysis

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