Merging Explicit Declarations With Implicit Response Time to Better Predict Behavior

Abstract

Declarations and actions do not always overlap, and thus, predicting future behavior solely on the basis of self-reported measures seems to be ineffective. The authors propose a confidence index (CI): a measure based on Fazio's attitude accessibility model. CI integrates explicit and implicit perspectives and captures how long a person hesitates when stating an opinion. The more certain someone is the stronger the attitude-behavior link is likely to be. A study was conducted to uncover differences in attitudes between average- and top-performing sales agents from the automotive industry. The results for declarative data did not show any significant differences; however, the CI results yielded interesting significant differences between groups. Random decision forests analyses confirmed that merging explicit and implicit measures increases predictive power of the tool. The study provided actionable insights on how to improve sales team performance, which were then implemented and eventually validated by sales results.

COVID-19 Endemism and the Control Skeptics

“…3 Besides data on socio-demographics (e.g., gender, occupation, income) and Covid-19 exposure, the responses regarding the 50 statements were structured on a 3 point scale (yes (Y), hard to tell, no (N)). Here, we adjust responses further in function of response time (RT) collected through an iCode Smart test (Ohme et al, 2020). In practice, hard to tell is coded as 0; reaction time data are rescaled by the z-score, z, of log(RT), with mean = 0 and two standard deviations = 1, with a positive answer Yes is z(Y) = 1 -z and a positive No becomes z(No) = z -1.…”

Perceptive Risk Clusters of European Citizens and NPI Compliance in Face of the COVID-19 Pandemics

“…Last, but not least, our analysis uses an online survey which makes us at the mercy of the validity of the answers provided. We leverage a powerful data collection technique based on the neuroeconomics field to both filter the non-credible answers and assess the information strength of the answers received (Ohme et al, 2020). Neuro-economics suggest among others that people's speed of answers guides the reliability of the information responses.…”

“…Neuro-economics suggest among others that people's speed of answers guides the reliability of the information responses. Our analysis uses an algorithm (Ohme et al, 2020) that measures both, the explicit answer as well as the speed of answering making it possible to identify the confidence of attitudes.…”

“…As shown by Fazio et al (1989) correlations between attitudes and behaviors are higher among people with fast reaction time when expressing their opinions. iCode Smart test was used to collect the data (Ohme et al, 2020), with response time (RT) being measured for each answer, making it possible to derive a measure of the reliability of attitudes. In this respect, responses given too fast suggest speeding through the test without giving meaningful answers, or responses given too slow, suggest a person got distracted from the test (Greenwald et al, 2003).…”

Challenges in Decoding Consumer Behavior with Data Science

“…Besides, this shift allows us to collect more data at different stages of the customer journey, which further allows us to analyze behavior in ways that were not previously available (Malter et al, 2020;Tong et al, 2020). Not only have technological advancements changed the nature of consumption but they have also significantly influenced the methods used in consumer research by adding both new sources of data and improved analytical tools (Ding et al, 2020;Ohme et al, 2020). The adoption of contemporary methods in consumer data analytics is slow and many businesses fail to understand their consumers as well as they want.…”