McCann Erickson and Ad Promises Experiential Learning Exercise McCann Erickson Worldwide Advertising is the world’s largest advertising agency network. With offices in over 130 countries and more than 75 years of global experience, McCann Erickson handles more global accounts than any other ad agency. Joe Plummer is Executive VP and Director of Research & Insight Development for McCann Erickson WorldGroup in New York. Recently, Joe has become concerned that viewers may be overcome with the “dazzle” of television ads at the expense of actually learning about the brands featured in such ads. With all of the special effects tools at the disposal of advertising creators these days, what may become lost in the creative execution of the television ads is the key selling idea for the brand. To address the need for improved measures of viewer response to ads, researchers have developed a set of multi-item scales called Ad Promises1. Eight-point Likert type scales were used to measure each of the eight subscales of Ad Promises. The end-points of these Likert scales were anchored by “strongly disagree” and “strongly agree”. The Ad Promise scale items were prefaced on the viewer response questionnaire in the following way: “For each statement, indicate your degree of agreement that the statement pertains to the advertiser’s implied or stated promises about the brand in the preceding commercial.” A list of the Ad Promise scale items and other items used by McCann Erickson follow: Ad Promise Scale Items 1. Problem removal a. The brand would help remove a problem one may encounter b. One would go from feeling annoyance to feeling relief as a result of using the brand. c. Using the brand would NOT appeal to one’s desire for resuming the pursuit of a goal. 2. Problem avoidance a. Using the brand would enable one to avoid a potential problem. b. The brand would dissipate fear and bring one a feeling of relaxation. c. One’s desire for a threat-free pursuit of a goal would NOT be met by using the brand. 3. Incomplete satisfaction a. The brand would bridge the gap between one’s expectations and the existing circumstances in a situation. b. The brand would bring an optimistic outlook to an otherwise disappointing situation. c. One’s desire for more complete satisfaction would NOT be met by using the brand. 4. Mixed approach-avoidance a. The brand would take care of one of the remaining negative aspects of a situation. b. Using the brand would avoid impending conflict and bring peace of mind. c. One’s desire for more consistency in thoughts about an object or issue would NOT be met by using the brand. 5. Sensual gratification a. A sample of the brand would make one want more. b. Using the brand would take one from a neutral state to a pleasurable state. c. The brand would NOT appeal to one’s bodily senses. 6. Intellectual stimulation a. The brand would stimulate one’s intellect. b. The brand would relieve boredom. c. The brand would NOT appeal to my sense of adventure or risk. 7. Social approval a. One would be considered more fashionable by using the brand. b. The brand would help one feel less apprehensive in social situations. c. The brand would NOT appeal to one’s desire for social approval. 8. Intrinsic satisfaction a. Using the brand is its own reward. b. The brand would be enjoyed for its own sake, not for what it will bring one. c. Pure enjoyment of the brand would NOT be the only thing in it for someone. Additional Items 9. Gender 10. Marital Status 11. Children in house 12. Age 13. Zip Code 14. Student Status 15. Formal education 16. Cultural Group The Ad Promise scales were first evaluated by showing 413 consumers a 30-second television ad for Mountain Dew in a lab setting and then having these consumers answer the 24 items of the Ad Promise scales along with some demographic questions. Some of the theory behind the Ad Promise scale items follows. Eight constructs are each measured by three items. The third item defining each construct needs is reversed-scaled. In other words, in analyzing the results, the reverse-scaled items need to be recoded. This can be done by creating a new column in a spreadsheet where the total possible scale points (Here, eight) receive an additional point (Here, summing to nine). Then, the value for each respondent in the original variable to be recoded is subtracted from one-plus-the total scale points). For example, in the current example, whenever a respondent recorded a “7”, this would become a “2” after recoding (8 + 1 – 7 = 2). Likewise, in the current example, whenever a respondent recorded a “1”, this would become an “8” after recoding (8 + 1 – 1 = 8). Go to the textbook’s website and download Ad Promises Not Reversed.xls from chapter 10. Create eight new variables corresponding to q1c, q2c, q3c, q4c, q5c, q6c, q7c, and q8c. Name these q1cnew, q2cnew, q3cnew, q4cnew, q5cnew, q6cnew, q7cnew, and q8cnew. Reverse code the original eight variables into these eight new variables. For example, the first cell corresponding to the first respondent’s answer would be recoded by subtracting the respondent’s answer from “9” (the number of scale points plus one). The results for this cell could then be copied to the remaining 412 rows in this column for q1cnew by clicking on the lower-right corner of the first cell and dragging down to the row for the last respondent. 1. Recode the last item defining each Ad Promise construct (q1c, q2c, q3c, q4c, q5c, q6c, q7c, and q8c) into new variables (q1cnew, q2cnew, q3cnew, q4cnew, q5cnew, q6cnew, q7cnew, and q8cnew). Save your Excel file as “Ad Promises Reversed Coded.xls”, and then import its contents into SPSS in the following way: Import into SPSS Open SPSS. File Open. Data Open Files Files of Type window (All Files) Select “Ad Promises Reversed Coded.xls”. Text Import Wizard Step 1 – accept default settings – Next. Text Import Wizard Step 2 – variables arranged delimited – Yes; – variable names at top of file – Yes. Note: variable names will be taken from the first row of the spreadsheet. Text Import Wizard Step 3 – accept default settings - Next Text Import Wizard Step 4 – accept default settings - Next Text Import Wizard Step 5 – accept default settings - Next Text Import Wizard Step 6 – accept default settings – Finish. Note: variable names will be at the top of the columns. To make sure you do not inadvertently refer to the original variables, select “Data View” tab of the SPSS window, and then select the top of each column of the original variables, right click on the mouse and then select “Clear”. Now, the original variables are removed from the matrix. To understand how consistent each of the eight subscales was measured by its corresponding three items, reliability analysis needs to be performed on each of the eight sets of items. 2. Compute Cronbach’s Alpha for each of these eight sets of items. Cronbach’s Alpha Menu bar Analyze Scale Reliability Analysis Select the three items defining each construct in the Reliability Analysis window Put a check mark in the empty box “List item Labels” Click OK. Composite variables can also be computed. In essence, a composite variable is a combination of several variables into one new variable. Do this by using the Compute command in SPSS in the following way: 3. Create a composite variable representing each of the eight Ad Promise constructs. Creating a Composite Variable Retaining the Original Scale Menu bar Transform Compute Enter the name of the new composite variable in the “Target Variable” box of the Compute window. Enter the formula for the composite variable in the Numeric Expression box of the Compute window [For example, (q1a + q1b + q1cnew) / 3)]. Click OK. Checking the Creation of Composite Variables Menu bar Analyze Reports Case Summaries Select the composite variables you created and move them into the “variables” box in the Summarize Cases window by first highlighting these composite variables and then clicking on the > button. Click OK. You will receive a report on the first 100 cases in the dataset. Inspect these values to make sure they are all within the original scale of 1 to 8. 1. Mark Peterson and Naresh K. Malhotra, “Measuring the Appraisal of Ad- Based Affect with Ad Promises”, Journal of Business Research, vol. 42, (1998), 227-239.
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