University of Utah researcher Xiao Fang and his colleagues were faced with a mystery: What factors have fueled the explosive growth of Facebook and other social networking sites over the past two years?
On October 4, Facebook reported it had topped 1 billion users, while runners-up Google+ and LinkedIn cleared 400 million and 175 million accounts, respectively. For the world’s most popular social network, Facebook, the past two years have seen user totals more than double.
Still, while retailers, game designers and marketing and sales executives salivate over the business potential of the burgeoning, worldwide social media space, no one has been able to decipher this phenomenon’s DNA. Even more of an enigma, Fang said of the revelations contained in a new paper he co-authored along with fellow Information Systems instructors Paul Hu and Weiyu Tsai and doctoral student Lionel Li, is how acceptance of opinions or products on one social media site spreads to another.
"This social contagion is the fundamental feature driving the new dynamic — for business marketing and sales, and for the democratic ideal of the public forum," Fang said, citing the recent ability of freedom fighters to spread political messages and defeat regime media controls during the Arab Spring.
The paper, Predicting Adoption Probabilities in Social Networks — accepted for future publication in the Information Systems Research Journal — not only explores factors behind social media users’ choices in products and opinions, but "proposes a novel and highly effective method to predict future adoption probabilities" for individuals who remain undecided, Fang added.
"The novelty of the proposed method lies in its consideration of a more complete set of factors driving adoption decision in a social network," Fang said. "Traditional methods focus on the factor of peer influence for adoption prediction. Our proposed method seamlessly integrates a more comprehensive set of factors, including peer influence, profile similarity between individuals — such as similarities in age, gender, or taste, and the structural position of an individual in a social network — for predicting adoption probabilities."
Such theoretical and analytical tools could revolutionize social network-based targeted marketing, bolster viral marketing techniques to better overlap numerous social networks, and give teeth to product demand predictions within specific social networking groups.
So informed, firms "could predict the expected number of adopters in the next time period, allowing them to gauge whether an offering is likely to go viral," Fang said. "If the offering is likely to go viral, the firm could act proactively to leverage it with appropriate marketing strategies, such as product bundling or cross-selling."