netrashetty
Netra Shetty
Arbitron is a consumer research company in the United States that collects listener data on radio audiences. It was founded as American Research Bureau (ARB) by Jim Seiler in 1949 and became national by merging with L.A. based Coffin, Cooper and Clay in the early 1950s. The company's initial business was the collection of television broadcast ratings.
The company changed its name to Arbitron in the mid 1960s, the namesake of the Arbitron System - a centralized statistical computer with leased lines to viewers' homes to monitor their activity. Deployed in New York, it gave instant ratings data on what people were watching. A reporting board would light up to indicate what home was listening to what broadThe primary purpose of the tradeoff phase was to obtain a direct comparison between alternate modes of transportation, including maglev, from the airport. This phase also was designed to better understand some of the opinions derived from the focus groups.
Specifically, the objectives included:
* Comparison of competing modes of transportation from the airport based on travel time, drop-off convenience and cost.
* Obtaining consumer opinion of the sensitivity of automatic baggage transfer from the inbound airline to the hotel and its impact on ridership decisions.
* Measuring the train's appeal as a tourist attraction.
* Obtaining data on consumer travel patterns.
* Obtaining basic demographic, geographic and psychographic information on the respondent.
This phase used conjoint methodology and involved personal interviews with travelers at the Orlando International Airport. The questionnaire used a series of questions based on a paired trade-off between two transportation options (see Figure 2). The layout was based on a factorial design having four factors of three to four levels each. Fare is an example of a factor that has up to four levels.
FIGURE 2 Paired Comparison -- Preference Scale
Strongly Prefer Mode A 1 2 3 4 5 6 7 8 9 Strongly Prefer Mode B
Several Likert scale questions were positioned between introductory travel related questions and respondent demographics. The scale ranged from 1, which denoted strong preference toward transport mode A, to 9, which indicated a strong preference for mode B. Each respondent was shown three transportation pairs. Four combinations of tradeoff pairs were then randomly distributed across the airport and rotated by day parts (morning, midday and evening hours).
At an appropriate time during the interview, the respondent was shown a characteristics grid (Figure 3 at bottom) displaying information about each transportation mode. As can be seen, an attempt was made to provide the respondent with an unbiased set of features for each mode.
The study's result is based on 400 completed interviews, 100 per pair combination set. Quotas were set for both inbound and outbound respondent type, and the final dataset was weighted to overcome disparities. The data were also adjusted for the seasonal variation between vacation and business traffic.
Beyond basic descriptive statistics, the study used the Student's t test to identify significant differences about the Likert scale centroid. The conjoint methodology identified a favorable fare structure and its elasticity. Factorial analysis was used to discern if clustering existed among variables such as: reason for being in Orlando, method of transportation to/from the hotel, attitude toward automatic baggage transfer, likelihood of taking an advanced technology transport, attitude toward the maglev train as an attraction, and select demographics. Regression analysis also was helpful in understanding the relationship among variables.
The airport intercept research developed the ridership preference/likelihood grid (Fig. 4). The grid resulted from a series of crosstabulations using the set of trade-off questions and an "educated" ridership opinion question asked at the end of the survey.
FIGURE 4 Preference/Likelihood Grid
$21 Fare $16 Fare $12 Fare
Combination
Strong & Very Likely 12% 17% 20%
Moderate & Very Likely 9% 8% 9%
Neutral & Very Likely 1% 3% 1%
Based on the data illustrated in the grid, 12 percent of respondents indicated that they had a strong preference for maglev compared to the alternative mode presented at a $21 fare, and they were very likely to take the maglev train, regardless of fare. Moreover, 20 percent had a strong preference at a $12 fare and were very likely to use the train. Well over half (59 percent) had a neutral to strong preference for maglev, and were at least somewhat likely to use it from the airport to International Drive.
The study's data then was used as input for two models that provided ridership estimates. The first, a trend & cycle model, was based on analysis of linear and cyclical times series at various lag times. This model used historic airport statistics to project inbound traffic (domestic and international) through the year 2000. The second model was for actual ridership. The ridership model combined elements of the preference/ likelihood grid and the trend & cycle projections to estimate market size then ridership at various fare levels for several points in time.
Strong advantage
The study's results coincided very closely to the qualitative research. Both were conclusive: The maglev concept has merit and usability. Based on this research, a maglev train has strong competitive advantage against its main competition, the rental car. When taking into account the upper fare limit (from the fare elasticity findings), its position remains especially strong if there is seamless transfer of baggage from the airport to the visitor's hotel.
Information from this research formed an integral part of ridership projections, financial structuring and strategic transportation planning.
Postscript: During 1994 (about six months after completion of this research phase) Maglev Transit changed technology from that based on Germany's Transrapid to Japan's HSST system.cast
The company changed its name to Arbitron in the mid 1960s, the namesake of the Arbitron System - a centralized statistical computer with leased lines to viewers' homes to monitor their activity. Deployed in New York, it gave instant ratings data on what people were watching. A reporting board would light up to indicate what home was listening to what broadThe primary purpose of the tradeoff phase was to obtain a direct comparison between alternate modes of transportation, including maglev, from the airport. This phase also was designed to better understand some of the opinions derived from the focus groups.
Specifically, the objectives included:
* Comparison of competing modes of transportation from the airport based on travel time, drop-off convenience and cost.
* Obtaining consumer opinion of the sensitivity of automatic baggage transfer from the inbound airline to the hotel and its impact on ridership decisions.
* Measuring the train's appeal as a tourist attraction.
* Obtaining data on consumer travel patterns.
* Obtaining basic demographic, geographic and psychographic information on the respondent.
This phase used conjoint methodology and involved personal interviews with travelers at the Orlando International Airport. The questionnaire used a series of questions based on a paired trade-off between two transportation options (see Figure 2). The layout was based on a factorial design having four factors of three to four levels each. Fare is an example of a factor that has up to four levels.
FIGURE 2 Paired Comparison -- Preference Scale
Strongly Prefer Mode A 1 2 3 4 5 6 7 8 9 Strongly Prefer Mode B
Several Likert scale questions were positioned between introductory travel related questions and respondent demographics. The scale ranged from 1, which denoted strong preference toward transport mode A, to 9, which indicated a strong preference for mode B. Each respondent was shown three transportation pairs. Four combinations of tradeoff pairs were then randomly distributed across the airport and rotated by day parts (morning, midday and evening hours).
At an appropriate time during the interview, the respondent was shown a characteristics grid (Figure 3 at bottom) displaying information about each transportation mode. As can be seen, an attempt was made to provide the respondent with an unbiased set of features for each mode.
The study's result is based on 400 completed interviews, 100 per pair combination set. Quotas were set for both inbound and outbound respondent type, and the final dataset was weighted to overcome disparities. The data were also adjusted for the seasonal variation between vacation and business traffic.
Beyond basic descriptive statistics, the study used the Student's t test to identify significant differences about the Likert scale centroid. The conjoint methodology identified a favorable fare structure and its elasticity. Factorial analysis was used to discern if clustering existed among variables such as: reason for being in Orlando, method of transportation to/from the hotel, attitude toward automatic baggage transfer, likelihood of taking an advanced technology transport, attitude toward the maglev train as an attraction, and select demographics. Regression analysis also was helpful in understanding the relationship among variables.
The airport intercept research developed the ridership preference/likelihood grid (Fig. 4). The grid resulted from a series of crosstabulations using the set of trade-off questions and an "educated" ridership opinion question asked at the end of the survey.
FIGURE 4 Preference/Likelihood Grid
$21 Fare $16 Fare $12 Fare
Combination
Strong & Very Likely 12% 17% 20%
Moderate & Very Likely 9% 8% 9%
Neutral & Very Likely 1% 3% 1%
Based on the data illustrated in the grid, 12 percent of respondents indicated that they had a strong preference for maglev compared to the alternative mode presented at a $21 fare, and they were very likely to take the maglev train, regardless of fare. Moreover, 20 percent had a strong preference at a $12 fare and were very likely to use the train. Well over half (59 percent) had a neutral to strong preference for maglev, and were at least somewhat likely to use it from the airport to International Drive.
The study's data then was used as input for two models that provided ridership estimates. The first, a trend & cycle model, was based on analysis of linear and cyclical times series at various lag times. This model used historic airport statistics to project inbound traffic (domestic and international) through the year 2000. The second model was for actual ridership. The ridership model combined elements of the preference/ likelihood grid and the trend & cycle projections to estimate market size then ridership at various fare levels for several points in time.
Strong advantage
The study's results coincided very closely to the qualitative research. Both were conclusive: The maglev concept has merit and usability. Based on this research, a maglev train has strong competitive advantage against its main competition, the rental car. When taking into account the upper fare limit (from the fare elasticity findings), its position remains especially strong if there is seamless transfer of baggage from the airport to the visitor's hotel.
Information from this research formed an integral part of ridership projections, financial structuring and strategic transportation planning.
Postscript: During 1994 (about six months after completion of this research phase) Maglev Transit changed technology from that based on Germany's Transrapid to Japan's HSST system.cast
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