1. What are the arguments for and against using this technology to surreptitiously change the consumers intention, attitude, and purchase patterns?
2. Are there any significant differences between positive versus negative empathy use? Explain.

Kenta was hired as a marketing analyst on the consumer behavior team to analyze data for clients at Emotions Analytics Coporation (EAC). EAC is an artificial intelligence (AI) firm specializing in analyzing consumer data. The company was founded by a group of PhD marketing consumer behaviorists who were experts in the fields of consumer decision making, product-consumer involvement, attitude-changing behavior, emotion, and empathy-driven decision making. They had become incredibly successful over five years of business as client companies started giving them access to millions of data points of past and real-time data, yielding thousands per consumer.

For each consumer on file, they had created a profile of purchases, online activities, and chat room interaction, as well as social media data. EACs most popular product was its empathy algorithm (EA-I).

The firms objective to clients is learning, retaining, servicing, nurturing, and adding value to them and their customers. The EA-I had the capacity to significantly interact with and change the following positive empathies in consumers: joy, gratitude, serenity, hope, pride, altruism, contentment, and optimism. With this tool, EAC could change a consumers attitude, intention, and behavior toward a product, brand, or company. Predictive behavioral changes varied by customer psychological profile, length of data point time, and data variety. Prediction rates also vary by attitude, intention, and behavior.

Because of its success, EAC wanted to develop EA-I further, so it created a new version called EA-II that was assigned to Kenta and a team of six others. The new empathy algorithm was to be focused on negative empathies such as disgust, fear, anger, sadness, rage, loneliness, melancholy, and annoyance. The service would be marketed to the same client firms. The groups assignment was to analyze past data to help increase intention, attitude, and behavioral prediction rates. Recently, EAC had purchased approximately one zettabyte of data from the number one social media network in the world, SnapBook, that it used for the project.

As the EA-II team was discussing strategy, Kenta asked Ann, the team manager, a question.

Arent we really doing something negative here? Were manipulating peoples emotions. I mean, couldnt this cause people that are predisposed to depression to be more depressed, for example? said Kenta. What happens when the competition finds out what were doing? Or the public?

Others started to murmur in agreement when Ann interjected, First, the same argument could be used for our positive algorithm as well. That hasnt happened, and EAC has been in business for five years. It has more than 1,000 highly paid employees, plus the company has no plans to go public. Thats in our credo. Also, whos going to say anything? Each of you are being paid well, and you have a five-year noncompete clause for that express purpose. Employees who have left have been unsuccessful in their careers.

Kenta knew he had just painted a target on his back. Later that week, while he was dining at a restaurant, he was approached by a couple who represented SnapBook who offered him a job at three times his current salary. If he could entice others from the EA-II team to join, he would get a very large bonus for each recruit.

But what about my five-year noncompete clause? he argued.

SnapBook is a social media company, not an AI company, so the noncompete clause does not apply. If EAC tries to take us to court, this will be our argument.

Kenta knew litigation of this sort took years. Later that evening, Kenta received a text message from a team member who had spotted Kenta talking with the SnapBook representatives. His team member wanted to know what Kenta was up to.