One of the research papers that helped Michael (Zhan) Shi earn a doctorate in economics last year at the University of Texas at Austin was earlier titled “Shall I Go?” It discussed the effects that friends’ check-ins on social networks have on whether someone goes to that same place. Now he had his own “Shall I Go?” question to ponder as he sought a job in what he describes as his “personal passion,” the analysis of data from social media.
At the International Conference on Information Systems, in Orlando, Fla., last December, he had 12 interviews for academic positions: one in China, two in Singapore, two in Hong Kong, and the rest in the United States.
His first offer, and the one he immediately accepted, was for a job as an assistant professor of information systems at Arizona State University, teaching in its new nine-month master’s program in business analytics.
Such programs are being created at universities around the world to train students to analyze “big data.” Their goal is to quickly prepare students to sift through the great volume of information companies have been collecting on consumers and identify patterns that will help inform new business strategies. Arizona State’s program is a collaboration between two departments in the W.P. Carey School of Business, information systems and supply-chain management.
Both departments have hired new faculty members who will teach in the program. In addition to Mr. Shi, they include Scott Webster, a professor of supply-chain management who came from Syracuse University; a married couple, Pei-Yu Chen and Shin-yi Wu, associate professors of information systems who came from Temple University; and Robert G. Hornyak, an assistant professor of information systems who earned his Ph.D. last year from Georgia State University.
To determine whether Arizona State would be the best place for him, Mr. Shi says, he did not turn to what he calls the “weak relationships” of social media but “strong real-world personal relationships.” In particular, he knew Bin Gu, an associate professor of information systems at Arizona State. The two had discussed research ideas when both were at Austin, and Mr. Shi looked forward to a chance to work with him again. Mr. Gu told Mr. Shi that the information-systems faculty at Arizona State was very collaborative and collegial. And Mr. Shi liked the idea of being able to attend research seminars in the economics department, which is also part of the Carey School.
One trait that made Mr. Shi a particularly attractive candidate was his ability to analyze unstructured data like the retweets on Twitter, says Michael Goul, chair of information systems.
“So immediately there, students are going to get the benefit of, This is a guy who knows real big data, he’s experienced with it, and he’s applied it in a setting that they’re quite familiar with because they know how Twitter works,” Mr. Goul says.
About 55 students are enrolled in the program this first year. Mr. Shi is teaching them a three-credit course this session, “Introduction to Enterprise Analytics.”
The field is new enough that many of the first applicants to the program didn’t seem so sure what business analytics was, says Mr. Goul.
Mr. Shi, who is 27, would have counted himself among them when he came to Austin, in 2007, from China, after earning bachelor’s degrees in economics and mathematics at Peking University. But he found that analyzing data was an ideal way to combine his interests in economics, mathematics, and information systems. He wrote a paper with a fellow graduate student at Austin, Huaxia Rui, and their adviser there, Andrew B. Whinston, on content sharing on Twitter. In it they concluded that users were more likely to share information through retweets if they were weakly connected to the person who had posted the original tweet than if they were strongly connected.
Mr. Shi’s and Mr. Whinston’s “Shall I Go?” paper, under the new title “Network Structure and Observational Learning: Evidence From a Location-Based Social Network,” is scheduled to be published in the Journal of Management Information Systems in March. It says that with location-based networks, in which people “check in” via their cellphones to let their friends know where they are, users are not as heavily influenced by the numbers of people who check in somewhere as they are by the influential power of the particular friends who recommend the place. That power depends on how well the users’ tastes align with their friends’.
Mr. Shi says his professional goal had been to “work on some real product” and have it used by a lot of people. But Mr. Whinston told him that “a professor is just like an individual entrepreneur,” he says. “Your papers will be your product. And you have to do all the marketing work to promote your product.” Mr. Shi liked the analogy.
