1. Theory Building (hypotheses testing) Model Building (LP) Simulations (Probabilistic) Proposal by April 23 Data collection by June 30 Hypotheses testing by August 31 Model built by Sept. 30Simulation completed by Nov. 15 Seminal draft due Nov. 22 Final edits and defense Dec. 11

2. Airline ticket purchase (time cost $0.50) Airline ticket purchase (time cost $0.50) Car rental purchase (time cost $0.50) Airline ticket purchase (time cost $1.00) Reliability (re-test) Generalizability Foraging costs manipulation Group 1Group 2 Rotate 50% in each group to cancel learning effect. Rotate 50% in each group to cancel learning effect. Experimental Design

3. H2: a, b H4 H3: a,b,c H7: a,b,c, d,e,f H6: a H6: b,c H12: a,b H8: a,b H9: a,b H11: a,b H1: a,b,c, d,e H10: a,b Foraging time Time to Review (tr) Site Time to Orient (to) Time to Access (ta) Time to Enter (te) Time to Search (tf) Time to Acquire (tz) Cognitive HCI factors Foraging Surplus (s) Demographical HCI factors Age Perceived Usefulness based on previous experience GenderEducation Propensity to Explore (e) Personal Innovativeness (PIIT) Cognitive absorption (CA) Temporal Dissociation (TD) Focused Immersion (FI) Heightened Enjoyment (HE) Control (CD) Curiosity (CU) Surrender / Acquisition (sa) Cognitive Playfulness (CPS) Computer Self- Efficacy (CSE) Usage Consolidation (uc) Patch Exhaustion (se) Information Load Number of patches (p) Number of items (q) Access order H5: a, b

4. Error rates in model (FIND time can be removed) Made up data HYPOTHESES 1: a,b,c,d,e

5. HYPOTHESES 2: a Number of events (one observation per site). In this example, more people abandoned the site when prices were higher than best available in market. The differences are in fact so high, that it could not be due to randomness in the sample.

6. HYPOTHESES 2: a,b Number of events (one observation per site). There could be many for each forager In this example there is sufficient evidence to suggest that the participants correctly surrenders when the foraging surplus is decreasing. I.e. the time to forage is not offset by better prices and sites are correctly abandoned.

7. HYPOTHESES 3: a,b,c Using the SPSS Levenberg-Marquardt non-linear regression modeling (LSE method) with adjusted r-squared and 95% confidence (F), our non-linear model has an r-square of 0.9245 In this SPSS example we see that if the number of items returned for a given search exceeds 75 or less than 25 there is more than 50% chance of site abandonment.

8. HYPOTHESES 5: a,b Spearman correlation coefficients and t-test of significance

9. HYPOTHESES 6: a,b,c Spearman correlation coefficients and t-test of significance Example is for 6a. Hypothesis 6b will follow same logic with order of access correlated with total time spend at a site. Hypothesis 6c follows same logic, with order of access correlated with total time spend on reviewing items provided at a site.