AreaSurvey SourceNumber of items Cognitive Absorption (CA)Agarwal and Karahanna, 2000)20 questions...

Foraging time

Time to Review (tr)Cognitive HCI

factors

Demographic HCI factors

Age(a)

Perceived site usefulness based on

previous experience (pu)Gender

(g)Education

(e)

Personal Innovativeness

(PIIT)

Cognitive Absorption

(CA)

Likelihood of Surrender /

Acquisition (sa)

CognitivePlayfulness

(CPS)

Computer Self-Efficacy (CSE)

Patch Exhaustion (se)

(access order)

Information Load

Number of items (q)

Site controlled time (ta,to,te,tf,tz)

H1: a (+)

Exploration Behavior

Number of sites visited

(ns)

Number of pages visited

(np)

H2: a (+)

H3: a (λ)

H4: a (-)

H5: a (-)

H6: a (M+ / F-)

H1: b (+)H2: b (+)H3: b (λ)H4: b (-)H5: b (-)H6: b (M+ / F-)H7: b (+)

H7: a (+)

H8:

(δ)

Given: (-)

H9: a (+)

H9: b (+)

+ Positive relationship- Negative relationshipλ Direction not hypothesizedδ Non-linear relationship

H10: a (+)

H10: b (+)

H10: c (+)

H11:a-e(surrender +/ acquisition -)

H12: (δ)

Area Survey Source Number of items

Cognitive Absorption (CA) Agarwal and Karahanna, 2000) 20 questions

Computer Self-Efficacy (CSE) Compeau and Higgings (1995) 10 questions

Personal Innovativeness (PIIT) Agarwal and Prasad (1998) 4 questions

Cognitive Playfulness () Webster and Martocchio (1992) 7 questions

Perceived Usefulness based on previous experience (PU)

Adapted from Agarwald & Prasad (1999)

5 questions

Airline ticket purchase(time cost $0.50)


Car rental purchase(time cost $0.50)


Reliability (re- test)

Generalizability Foraging costs manipulation

Group 1 Group 2

Rotate50% in each group to cancel learning effect.


$0.75

Area Survey Source Number of items

Cognitive Absorption (CA) Agarwal and Karahanna, 2000) 20 questions

Computer Self-Efficacy (CSE) Compeau and Higgings (1995) 10 questions

Personal Innovativeness (PIIT) Agarwal and Prasad (1998) 4 questions

Cognitive Playfulness () Webster and Martocchio (1992) 7 questions

Perceived Usefulness based on previous experience (PU)

Adapted from Agarwald & Prasad (1999)

5 questions

Airline ticket purchase(NY – WAS)

Car rental purchase(WAS)

Generalizability

Group 1 Group 2



Reliability

Airline ticket purchase(NY – WAS)

Airline ticket purchase(NY – SFO)

Reliability

Max Min

Age 46 17 17

Income $145,000 $0 22

Years worked part-time 20 0 109

Years worked full-time 10 0 2

12 1

3 25

9 1

16 125

25

82

8

8

Race

African American

Asian

Associates

Number of countries

Education

Freshman

Sophomore

Caucasian

Hispanic

Middle-East

Doctorate

Junior

Senior

Bachelors

Married

Marital Status

Divorced

Single

n Mean Median Mode Std. Dev Min Max Range

Number of sites visited (actual) 295 4.01 4.00 4.00 1.94 1 16 15

number of pages visited (actual) 295 26 26 27 15.28 2 81 79

No. of searches 295 10 8 7 6.67 1 42 41

No. of Items reviewed 295 70 45 13 95.54 1 756 755

Review time per item 295 4.38 2.88 1.43 4.27 0.05 26.00 25.95

Acquisition time (sec.) 295 139 125 120 79.95 62 408 346

Total Search time (sec.) 295 693 638 315 361.43 86 2682 2596

Total Time (sec.) 295 833 760 420 390.34 189 2988 2799

Price difference 295 $116.49 $50.00 $26.00 $157.90 $0.00 $1,217.30 $1,217.30

Search costs 295 $8.19 $6.86 $3.50 $4.63 $1.58 $37.35 $35.78

Foraging surplus 295 -$124.68 -$59.51 -$93.24 $158.08 -$1,224.13 $38.65 $1,262.77

Table 6.3.1. Cognitive Absorption Correlation MatrixCA CO CU FI HE TD CA CO CU FI HE TD

CO .762 Sites (stated) .358 .331 .377 .314 .373 .237P .00 P .00 .00 .00 .00 .00 .00

CU .890 .612 Pages (stated) .390 .295 .445 .315 .393 .293P .00 .00 P .00 .00 .00 .00 .00 .00

FI .852 .663 .670 Sites (actual) -.020 -.076 -.008 .014 -.023 -.019P .00 .00 .00 P .73 .19 .89 .81 .69 .75

HE .887 .709 .808 .632 Pages (actual) .040 .029 .029 .026 .032 .050P .00 .00 .00 .00 P .49 .62 .62 .66 .59 .40

TD .910 .529 .779 .680 .767 No. of searches .023 .013 -.006 .031 -.006 .040P .00 .00 .00 .00 .00 P .70 .82 .91 .59 .92 .49

CPS .825 .720 .791 .651 .844 .665 No. of Items reviewed .078 .049 .043 .081 .087 .072P .00 .00 .00 .00 .00 .00 P .18 .40 .47 .17 .14 .22

Age .110 -.029 .232 .155 .033 .063 Review time per item -.056 -.009 -.051 -.087 -.052 -.035P .06 .62 .00 .01 .57 .28 P .33 .88 .38 .13 .37 .54

Education .028 -.058 -.022 .224 -.048 -.024 Acquisition time (s) .036 .050 .035 .077 -.037 .020P .63 .32 .70 .00 .41 .68 P .54 .39 .54 .19 .53 .73

PIIT .483 .521 .490 .379 .484 .339 Total Time (s) -.050 -.039 -.042 -.076 -.035 -.027P .00 .00 .00 .00 .00 .00 P .39 .50 .48 .19 .55 .65

CSE .306 .234 .267 .337 .277 .224 Price difference -.033 -.053 -.007 -.045 .015 -.040P .00 .00 .00 .00 .00 .00 P .57 .37 .90 .44 .80 .49

Foraging surplus .033 .053 .008 .046 -.015 .040P .57 .37 .89 .44 .80 .49

H1a: IS REJECTEDH1b: IS REJECTED

H1a: The CA score is positively related to the number of sites visited while foraging. Individuals with a high CA score are likely to visit more websites than those with low CA scores.H1b: The CA score is positively related to the number of pages accessed while foraging. Individuals with a high CA score are likely to visit more web pages within a site than those with low CA scores.

H2a: IS REJECTEDH2b: IS REJECTED

H2a: The CPS score is positively related to the number of sites visited while foraging. Individuals with a high CPS score are likely to visit more web sites than those with low CPS scores.H2b: The CPS score is positively related to the number of pages accessed while foraging. Individuals with a high CPS score are likely to visit more web pages than those with low CPS scores.

Table 6.3.2. Computer Playfulness Correlation Matrix

CPS CPSAge .112 No. of searches -.021

P .05 P .72Education -.055 No. of Items reviewed .017

P .35 P .78PIIT .655 Review time per item (sec.) -.032

P .00 P .59CSE .402 Acquisition time (sec.) -.007

P .00 P .91Sites (stated) .513 Total Time (sec.) -.046

P .00 P .43Pages (stated) .459 Price difference .021

P .00 P .72Sites (actual) .059 Foraging surplus -.020

P .31 P .73Pages (actual) .037

P .52

• H3a: IS SUPPORTED• H3b: IS SUPPORTED

H3a: Individuals with lower PIIT score is likely visit a different number of websites site than those with higher PIIT scores (no direction is hypothesized)H3b: Individuals with lower PIIT score is likely visit a different number of web pages within a site than those with higher PIIT scores (no direction is hypothesized)

Table 6.3.3. Personal Innovativeness with Information Technology Correlation Matrix

PIIT PIIT

CA .483 Pages (actual) -.103

P .00 P .08

CPS .655 No. of searches -.109

P .00 P .06

Age .277 No. of Items reviewed -.113

P .00 P .05

Education .095 Review time per item (sec.) -.013

P .10 P .82

CSE .393 Acquisition time (sec.) -.003

P .00 P .96

Sites (stated) .425 Total Time (sec.) -.134

P .00 P .02

Pages (stated) .428 Price difference .092

P .00 P .12

Sites (actual) .095 Foraging surplus -.088

P .105 P .13

• H4a: IS REJECTED• H4b: IS REJECTED

H4a: The CSE score is negatively related to the number of sites visited while foraging. Individuals with a high CSE score are likely to visit fewer websites than those with low CSE scores.H4b: The CSE score is negatively related to the number of pages accessed while foraging. Individuals with a high CSE score are likely to visit fewer web pages than those with low CSE scores.

Table 6.3.4. Computer Self-Efficacy Correlation Matrix

CSE CSEPIIT .393 Pages (actual) -.080

P .00 P .17CA .306 No. of searches -.130

P .00 P .03CPS .402 No. of Items reviewed -.062

P .00 P .29Age .199 Review time per item (sec.) -.005

P .00 P .93Education .106 Acquisition time (sec.) .030


P .00 P .04Pages (stated) .257 Price difference .013

P .00 P .82Sites (actual) .049 Foraging surplus -.011

P .40 P .86

• H5a: IS REJECTED• H5b: IS REJECTED

H5a: Age is negatively related to the number of sites visited. Older individuals visit fewer websites than younger individuals. H5b: Age is negatively related to the number of pages visited. Older individuals visit fewer web pages than younger individuals.

Table 6.3.5. Age Correlation Matrix

Age AgeCSE .199 Pages (actual) -.035

P .00 P .55PIIT .277 No. of searches -.061

P .00 P .30CA .110 No. of Items reviewed -.076

P .06 P .19CPS .112 Review time per item (sec.) -.091

P .05 P .12Education .531 Acquisition time (sec.) -.050


P .00 P .49Pages (stated) .178 Price difference -.008

P .00 P .89Sites (actual) .044 Foraging surplus .008

P .45 P .88

H6a: IS REJECTED H6b: IS REJECTED, BUT SUPPORTED IN THE OPPOSITE DIRECTION (When purchasing on-line, males visit less web pages than females)

H6a: When foraging, males visit more websites than females. H6b: When foraging, males visit more web pages than females.

Mean St. Dev

Female - Sites (stated) 3.14 1.75Female - Pages (stated) 14.70 13.02Female - Sites (actual) 3.91 1.55Female - Pages (actual) 28.06 14.55Male - Sites (stated) 3.16 1.49Male - Pages (stated) 15.49 15.01Male - Sites (actual) 4.14 2.35Male - Pages (actual) 24.08 16.12

Table 6.3.6b. T-scores Gender Difference of Means (assuming unequal variance)

Female - Sites (stated)

Female - Pages (stated)

Female - Sites (actual)

Female - Pages (actual)

Male - Sites (stated) -0.130Male - Pages (stated) -0.471Male - Sites (actual) -0.960

Male - Pages (actual) 2.189* *97%

confidence

H7a: IS REJECTED

H7a: Education level is positively related with the number of sites visited. Individuals with higher levels of education are more likely to visit more websites than those with lower levels of education.

Table 6.3.7a. Education level and Observed Number of Websites visited.

Level of Education 1-2 3-4 5-6 7 or more Total Pct.Less than 2-years higher-education 12 28 9 6 55 19%Juniors in college 10 17 16 6 49 17%Seniors or graduates 35 102 43 11 191 65%

Total 57 147 68 23 295

Table 6.3.7b. Education level and Expected Number of Websites visited.

Level of Education 1-2 3-4 5-6 7 or more Total Pct.Less than 2-years higher-education 10.6 27.4 12.7 4.3 55 19%Juniors in college 9.5 24.4 11.3 3.8 49 17%Seniors or graduates 36.9 95.2 44.0 14.9 191 65%

Total 57 147 68 23 295

Based on this data we calculated a Chi-Squared value of 9.562 and 6 degrees of freedom [(k-1)*(n-1)]. Based on this, there appears to only an 85.5% confidence that differences in the number of websites visited exists between the various education levels

H7b: IS REJECTED, BUT SUPPORTED IN THE OPPOSITE DIRECTION (When purchasing on-line, higher educated individuals visit less web pages than those less educated)

H7b: Education level is positively related with the number of pages visited. Individuals with higher levels of education are more likely to visit more web pages than those with lower levels of education.

Based on this data we calculated a Chi-Squared value of 14.861 and 6 degrees of freedom [(k-1)*(n-1)]. Based on this, we found that differences do in fact exists between the various education levels and the number of web pages accessed when buying on-line (99% confidence).

Table 6.3.7c. Education level and Observed Number of Web pages visited.

Level of Education 0-20 21-30 31-40 40 or more Total Pct.Less than 2-years higher-education 23 15 4 13 55 19%Juniors in college 14 14 15 6 49 17%Seniors or graduates 76 47 32 36 191 65%

Total 113 76 51 55 295

Table 6.3.7d. Education level and Expected Number of Web pages visited.

Level of Education 0-20 21-30 31-40 40 or more Total Pct.Less than 2-years higher-education 21.1 14.2 9.5 10.3 55 19%Juniors in college 18.8 12.6 8.5 9.1 49 17%Seniors or graduates 73.2 49.2 33.0 35.6 191 65%

Total 113 76 51 55 295

H8: A non-linear relationship exists between the number of pages accessed and the likelihood of surrender.

(x - β2)2

x21 - *β1P(x) =

The best estimates of beta 1 (model scaling in P(x) direction, or ‘y’) and beta 2 (model top point location, or ‘x’ direction), were found to be located at β1= 0.989 and β2= 23.98.

Table 6.3.8a Non-Linear Prediction Model Accuracy of Purchases based on Web Pages visited

H8: IS SUPPORTED

Figure 6.3.8b Predictive Probability distribution

Based on web pages Visited

0.00

0.25

0.50

0.75

1.00

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

14.1 80.7

H9a: The perceived usefulness based on previous experience is positively related to the time a user will spend on reviewing search results at an e-commerce site.

H9b: The perceived usefulness based on previous experience is positively related to the site time the user will spend at an e-commerce site (patch). (site time is defined as the foraging time less the reviewing time).

Table 6.3.9 Perceived Usefulness and Patch Exhaustion Correlation Matrix

H9a: IS SUPPORTEDH9b: IS SUPPORTED

H10a: The order of access of a site is positively related with perceived usefulness based on previous experience. Users perform patch exhaustion based on perceived usefulness of sites based on previous experience.

Table 6.3.10a Patch Exhaustion Correlation Matrix

H10a: IS SUPPORTED

It is interesting to see that there are no significant correlations between the participant’s experience with a website and the order they access the sites. However, there is a significant correlation between the Perceived Usefulness rank of the websites and how the participants accessed the sites (r = 0.502, 99.9% confidence). This supports the Optimal Foraging Theory predictions from biology (Smith and Dawkins, 1971; Smith and Sweatman, 1974), which suggested that such as behavior should also be exhibited by humans.

H10b: The order of site access is positively related to the time a person will spend reviewing search results. Users are accessing sites that they are willing to spend more time reviewing items from first.H10c: The order of site access is positively related to the site time the user will spend at an e-commerce site. Users are accessing sites that they are willing to spend more time at first (site time is defined as the foraging time less the reviewing time).

Table 6.3.10b Access Order and Site time Correlation Matrix

Participants actually spent less time at the websites they accessed first, and more time at later sites. Since, we separated the ‘acquisition time’ in the analysis, the correlations does not originate by the fact that most purchases were done by the later websites. The reason for this finding may be due to the ‘state of mind’ by the participants when executing a search. Initially, a person may be in an exploration mode where the focus is on exploration breath and the belief that more websites should be examined. As a result, less time may be devoted to the first websites visited. At subsequent websites, this desire may have been met and the individual become more focused on exploration depth.

• H10b: IS REJECTED BUT SUPPORTED IN THE OPPOSITE DIRECTION (user spent less time reviewing items offered for sale at websites they access first, more at subsequent

sites)• H10c: IS REJECTED BUT SUPPORTED IN THE OPPOSITE DIRECTION (user spent

less time at websites they access first and more at subsequent sites)

H11a: The time to access a web page is positively related with the likelihood of site

surrender and negatively related with the likelihood of acquisition. The longer a web page takes to load, the

more likely that it will be abandoned and the less likely that the user will make a purchase from it.

H11b: The time needed to orient at a web page is positively related with the likelihood of site surrender and

negatively related with the likelihood of acquisition. The longer it takes to orient, the more likely that it will be

abandoned and the less likely that the user will make a purchase from it.

H11c: The time needed to enter a search at a web page is positively related with the likelihood of site surrender and

negatively related with the likelihood of acquisition. The longer it takes to enter all required search criteria, the


H11d: The time needed to execute a search at a web page is positively related with the likelihood of site surrenders

and negatively related with the likelihood of acquisition. The longer it takes to execute a search, the more

likely that it will be abandoned and the less likely that the user will make a purchase from it.

H11e: The time needed to review the results from a website is positively related with the likelihood of site surrender

and negatively related with the likelihood of acquisition. The longer it takes to review items from a search, the


Table 6.3.11a Binary Regression Coefficients for PredictedBuy Vs. Don’t Buy Decisions (first step)

is P(x) = B0 + β1*X1 + β2*X2+…. βn*Xn . Where P(x) is defined as the probability that a binomial value is set (P(x)>0.5 = 1 and P(x)<0.5 = 0).

H11a: IS REJECTED

Table 6.3.11b Binary Regression Coefficients for PredictedBuy Vs. Don’t Buy Decisions (second and final step)

Figure 6.3.11a Final Binary Regression Model for Predicted Buy Vs. Don’t Buy Decisions

P(x) = -15.887 + 7.401 * time to orient - 0.269 * time to enter - 1.305 * time to find + 0.019 * time to review

Table 6.3.11c Airline Ticket Purchasing Binary Logistic Regression Model Accuracy based on Time Factors

H11b: IS REJECTED BUT SUPPORTED IN THE OPPOSITE DIRECTION (the longer it takes to orient, the less likely that a website will be abandoned and the more likely that the user will make a purchase from it).

H11c: IS SUPPORTED

H11d: IS SUPPORTED

H11e: IS REJECTED BUT SUPPORTED IN THE OPPOSITE DIRECTION (the longer a person spend reviewing items at a site, the less likely that a website will be abandoned and the more likely that the user will make a purchase from it).

H12: There is a non-linear relationship between the number of items returned by a search and the likelihood of surrender from a site

(x - x)2

x21 - *βP(x) =

e = ε

η+ε and: ρ = 1- e

σx = n * px * qx

x= n * px

Figure 6.3.12a Generalized Predictive Purchasing Equations

given:

given:

given:

P(x) - the probability that a purchase will take place at a given site with x number of itemsx - the number of items available for purchasing on a website after a searchβ - the discriminate beta factor (context specific i.e. for car rentals, airline ticket purchasing)e - error rate of the modelε - the number of incorrectly predicted binomal values (buy/surrender)η - the number of correctly predicted binomal values (buy/surrender)

ρ - the predictable power of the equation σ - the standard deviation of the probability q - the probability that a purchase will not occur at this type of site [1-P(x)]n - number of web pages visited

(x - 8.86)2

8.8621 - *0.532

21

2

r

nrt

2nv

Figure 6.3.12b Predictive Purchasing Equation for Car rentals

Figure 6.3.12c Discrete Betas of Car Rentals based on Number of Sales Items on a Web page

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

50.0%

55.0%

60.0%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 4142 43 44 45 46 47 48 49 50

Items

18.14.35

(x - 13.01)2

13.0121 - *0.5413 21

2

r

nrt

2nv

Figure 6.3.12d Predictive Purchasing Equation for Airline Ticket Purchases

Figure 6.3.12c Discrete Betas of Airline Ticket salesbased on Number of Sales Items on a Web page

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0.55

0.60

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99

Items

4.99 33.9

Figure 6.3.12f Confidence Intervals of web pages accessed Prior to

Purchase of based on Number of Airline Tickets on a Web page

0

2

4

6

8

10

12

14

16

18

20

22

24

26

28

30

1 2 3 4 4.99 10 13 20 25 30 33.90 40 50 60 70 80 90 100

Nu

mb

er

of

we

b s

ite

s v

isit

ed

Mean

Min

Max

13.01

Number of sales items on a web page

H12: IS SUPPORTED

Figure 6.3.13a Cox and Snell’s R-Square Equation

Figure 6.3.13b Nagelkerke R-Square Equation

Site visit: sample of 170 from 1,134 site visits

Predictive Linear Model

Test of Model accuracy against sample (170 site visits)

Test of Model accuracy against remaining

observations (964 site visits)

Explanatory Linear Model

Predictive Non-Linear Model

Individuals: sample of 51 from 151site visits by unique visitors (one

sample per participant)

Predictive Linear Model

Test of Model accuracy against sample (51 site

visits by unique visitors)

Test of Model accuracy against remaining

observations (100 site visits by unique visitors)

Explanatory Linear Model

Sample Data Model Type Model ValidationFor Model Build

Model DefinitionSample

typeSample

size

Nagel-kerke

r2Test Type

Test size (n)

Surrender Accuracy

Buy Accuracy

Overall Accuracy

Data-fit 170 98.3% 96.4% 97.6%

Remaining samples

964 97.1% 93.4% 96.2%

Data-fit 170 98.3% 90.9% 95.9%

Remaining samples

964 95.6% 89.9% 94.1%

Data-fit 170 98.3% 69.1% 88.8%

Remaining samples

964 96.0% 66.4% 88.5%

Data-fit 51 96.0% 92.3% 94.1%Remaining

samples100 93.9% 82.4% 88.0%

Data-fit 51 96.0% 96.2% 96.1%Remaining

samples100 86.3% 81.6% 84.0%

Logistic Stepwise Regression Models

51 of 151

0.921

ExplanatoryLinear

-21.27 + 9.898(timetoOrient) - 0.78(FI)

Unique51 of 151

0.912

Predictive Linear

-29.916 + 11.95(timetoOrient)+ 0.457(CO) + 0.396(CU) - 1.256(FI)

Unique

170 of 1,134

0.940

Predictive non-Linear

-25.051 + 27.29(timetoOrient) -5.886(timetoFind)

- 0.824(timetoEnter) - 0.703(CPS) +0.423(CO) +2.079(CU) -

2.93(HE) + 0.642(TD) + (1-((NoItemsReviewed-0.967)2)

/(NoItemsReviewed2)) *1.708))

Non-unique

170 of 1,134

0.860

Explanatory Linear

-15.465 + 17.96(timetoOrient) -3.541(timetoFind) - 0.531(timetoEnter)

+0.771(CU) - 2.345(HE) + 0.498(TD)

Non-unique

Model Build Model Accuracy

Predictive Linear

-25.051 + 27.29(timetoOrient) -5.886(timetoFind) - 0.824(timetoEnter)

- 0.703(CPS) +0.423(CO) +2.079(CU) - 2.93(HE) + 0.642(TD)

Non-unique

170 of 1,134

0.944

ECONOMICS

Price difference -Flight 1 Price difference -Flight 2Mean 109.44 127.75 Variance 17,363.23 37,629.63 Observations 149.00 95.00 df 149.00 t Stat (0.81) P(T<=t) one-tail 0.21 t Critical one-tail 1.66 P(T<=t) two-tail 0.42 t Critical two-tail 1.98

Total Time -Flight 1 Total Time -Flight 2Mean 932.05 816.58 Variance 196,700.18 132,147.65 Observations 149.00 95.00 df 172.00 t Stat (2.12) P(T<=t) one-tail 0.02 t Critical one-tail 1.65 P(T<=t) two-tail 0.04 t Critical two-tail 1.97

t-Test: Two-Sample Assuming Unequal Variances

* No statistical significant differences between the means

* Significant differences between the means


Overall differences - Flight #1 = $0.50 search cost per minute Flight #2 is $0.75 search costs per minute)

Review time -Flight 1 Review time -Flight 2Mean 251.51 159.76 Variance 44,190.02 17,478.41 Observations 149.00 95.00 df 142.00 t Stat (3.80) P(T<=t) one-tail 0.00 t Critical one-tail 1.66 P(T<=t) two-tail 0.00 t Critical two-tail 1.98

Acquisition time -Flight 1 Acquisition time -Flight 2Mean 150.99 146.98 Variance 6,329.89 5,220.04 Observations 149.00 95.00 df 214.00 t Stat 0.41 P(T<=t) one-tail 0.34 t Critical one-tail 1.65 P(T<=t) two-tail 0.68 t Critical two-tail 1.97




* No significant differences between the means


Sites (actual) Sites (actual)Mean 3.88 4.06 Variance 3.70 2.76 Observations 149.00 95.00 df 221.00 t Stat (0.79) P(T<=t) one-tail 0.21 t Critical one-tail 1.65 P(T<=t) two-tail 0.43 t Critical two-tail 1.97

Pages (actual)-flight 1 Pages (actual)-flight 2Mean 23.48 30.08 Variance 186.35 280.97 Observations 149.00 95.00 df 171.00 t Stat (3.22) P(T<=t) one-tail 0.00 t Critical one-tail 1.65 P(T<=t) two-tail 0.00 t Critical two-tail 1.97




* No significant differences between the means


Number of searches-flight 1 Number of searches-flight 2Mean 9.04 11.41 Variance 40.46 61.14 Observations 149.00 95.00 df 171.00 t Stat (2.48) P(T<=t) one-tail 0.01 t Critical one-tail 1.65 P(T<=t) two-tail 0.01 t Critical two-tail 1.97

Number of Items reviewed -flight 1 Number of Items reviewed-flight 2Mean 47.28 123.43 Variance 1,964.01 20,657.61 Observations 149.00 95.00 df 105.00 t Stat (5.01) P(T<=t) one-tail 0.00 t Critical one-tail 1.66 P(T<=t) two-tail 0.00 t Critical two-tail 1.98






Source of Variation SS df MS F P-value F critGender 12927.36 1 12927.360 0.456 0.501 3.932Stress (Foraging costs per minute) 63219.6 1 63219.599 2.232 0.138 3.932Interaction 4951.714 1 4951.714 0.175 0.677 3.932Within 2945516 104 28322.267Total 3026614 107

Net Price and Gender (Anova: Two-Factor)

There are no significant overall differences between genders and the Net price paid (Price paid - best price available). There is an indication that stress affected the Net price achieved (p=0.138), but the finding is not conclusive. For the first flight when foraging costs were $0.50 per minute, females paid on average $122.94 more than the best price available and males paid $87.51 more than the best price (standard deviations of $130.25 and $82.28 respectively). For flight #2 when foraging costs were increased to $0.75, females paid 28.3% ($157.78) more than for flight #1, while males paid 70.8% ($149.44) more than flight #1. The standard deviation increased by 75% for females to $228.52, while the standard deviation for males increase by 134% to $193.22. However, there is no evidence that stress had a statistical significant different impact on Net price by the genders (interaction p=0.677).

There is also insufficient evidence that females and males achieve different prices under different foraging

Source of Variation SS df MS F P-value F critGender 302.1706 1 302.171 14.956 0.000 3.932Stress (Foraging costs per minute) 770.5799 1 770.580 38.140 0.000 3.932Interaction 46.21687 1 46.217 2.288 0.133 3.932Within 2101.199 104 20.204Total 3220.166 107

Search costs and Gender (Anova: Two-Factor)

There are significant differences in search costs between males and females for flight #1 with foraging costs of $0.50 per minute. Females had $7.21 in search costs of flight 1, while males had an average of $5.17 with standard deviations of $3.31 and $1.99 respectively.

For flight #2 with foraging costs of $0.75 per minute Females had $13.86 in search costs, while males had an average of $9.21 with standard deviations of $6.76 and $4.49 respectively. It is interesting to note that the standard deviation of foraging costs doubled when the foraging costs increased by only 50%.

There are only a slight indication (p=0.13) that the increased 'stress' affected the genders differently between flight one and two (measured by search costs).

Source of Variation SS df MS F P-value F critGender 17182.39 1 17182.387 0.601 0.440 3.932Stress (Foraging costs per minute) 77949.51 1 77949.514 2.728 0.102 3.932Interaction 4041.159 1 4041.159 0.141 0.708 3.932Within 2971872 104 28575.696Total 3071045 107

There is no statistical significant differences between foraging surplus and the genders (p=0.44). The indication that there is a difference between foraging surplus and stress in trivial since it simply reflects the higher search costs per minute. There is also no significant interaction between gender and stress with respect to foraging surplus.

Foraging Surplus and Gender (Anova: Two-Factor)

Source of Variation SS df MS F P-value F critAge 2,084,055.47 53 39,321.80 2.370 0.001 1.578Stress (Foraging costs per minute) 63,219.60 1 63,219.60 3.810 0.056 4.023Error 879,339.42 53 16,591.31 Total 3,026,614.49 107

Net Price and Age (Anova: Two-Factor)

There are a significant overall differences between Age and the Net price paid (Price paid - best price available). For example, participants under 23 years of age paid an average of $97.97 more than best price available for the first flight, while participants over 23 yeas of age paid $117.56 more.

When additional stress (search costs of $0.75 pre minute) was applied for the second flight, the net costs for participants under 23, increased by 21% to $118.58, while the net costs for participants over 23 years of age increased by 81% to $213.17. This indicate that net costs increases by age and that stress affects older people more than younger individuals.

Source of Variation SS df MS F P-value F critAge 1,193.27 53 22.51 0.950 0.574 1.578Stress (Foraging costs per minute) 770.58 1 770.58 32.508 0.000 4.023Error 1,256.31 53 23.70 Total 3,220.17 107

Search costs and Age (Anova: Two-Factor)

There is no indication that search costs increases due to age. The relationship between increased search cost per minute and search costs overall is trivial, since one is a function of the other.

Source of Variation SS df MS F P-value F critAge 2,101,237.13 53 39,645.98 2.356 0.001 1.578Stress (Foraging costs per minute) 77,949.51 1 77,949.51 4.632 0.036 4.023Error 891,858.83 53 16,827.53 Total 3,071,045.48 107Due to the differences in age and the search costs as illustarted before, the overall foraging costs are significantly different by age. For example the foraging surplus for the first flight was -104.16 for participants under 23 years of age and -123.76 for those above. For flight number two, when search costs were increased to $0.75 per minute, the foraging surplus decreased 21% to -$130.8 for those under 23. For those above 23 years of age, the foraging surplus decreased (81%) to -$223.55. (note: all changes were attributable to 'worse' purchasing decisions and not to the search costs).

Foraging costs and Age (Anova: Two-Factor)

Source of Variation SS df MS F P-value F critEducation 1,657,060.59 53 31,265.29 2.269 0.002 1.578Stress (Foraging costs per minute) 3,938.93 1 3,938.93 0.286 0.595 4.023Error 730,175.10 53 13,776.89 Total 2,391,174.62 107

Net Price and Education (Anova: Two-Factor)

There is a strong relationship between education and Net price paid (defined as price paid - best price available in the market).

For example, for the first flight participants with 3 or less years of higher education paid an average of $123.16 more for tickets then the best price available in the market. Participants with senior standing or graduates paid only $97.12 more. This relationship held true when foraging costs per minute was increased to $0.75 per minute. For the less educated, the net price increased 49% to $184.15, but for the better educated it only increased by 18% to 114.77.

The increase in standard deviation was the same for both groups. For flight 1 the standard deviation for the less educated was $133.8 and for the better educated it was lower at $101.1. For flight number 2, the standard deviation increased by 73% and 71% to $232.0 and $173.1 respectively. The ANOVA confirms this relationship and we must conclude that increased stress have does not appear to affect people of the various education level differently.

Source of Variation SS df MS F P-value F critEducation 1,054.83 53 19.90 0.827 0.754 1.578Stress (Foraging costs per minute) 1,056.49 1 1,056.49 43.921 0.000 4.023Error 1,274.88 53 24.05 Total 3,386.20 107

Search costs and Education (Anova: Two-Factor)

There is no indication that search costs changes with relation to education. The relationship between increased search cost per minute and search costs overall is trivial, since one is a function of the other.

Source of Variation SS df MS F P-value F critEducation 1,667,606.84 53 31,464.28 1.929 0.009 1.578Stress (Foraging costs per minute) 45,567.48 1 45,567.48 2.794 0.101 4.023Error 864,449.04 53 16,310.36 Total 2,577,623.36 107The differences in Foraging surplus is a function of the better purchasing decisions made by those with higher education and not by any changes in search costs incurred.

Foraging costs and Education (Anova: Two-Factor)

AreaSurvey SourceNumber of items Cognitive Absorption (CA)Agarwal and Karahanna, 2000)20 questions...

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