Introduction to Case Study

This paper is the first of a series of three articles relating to a case study conducted at Fairfield University to assess aspects of the rapid introduction of Information Technology at the institution. This article deals with the nature of the problem faced by Fairfield University, the characteristics of the case methodology, and lays the foundation for the selection of this research technique for the current study. The paper begins with an Introduction section to familiarize the reader with the case organization. The following section on Case Methodology explores the history, and some of the applications of the technique. The section ends with specific research protocols for researchers

Results of a Case Study on Information Technology at a University

In the first article in this series (Tellis, 1997a) the author presented the background on the history and importance of the use of the case method of research. The second article (Tellis, 1997b) presented a proposed methodology based on the literature and an application of the methodology in an information technology case. The current article will present a suggested format for reporting case research results. The article will review the goals and objectives of the research project and present various tables containing the results of the data analysis conducted for the project. The article will finally present conclusions drawn from the results, and what future researchers might wish to pursue

Bayesian approach to SETI

The search for technosignatures from hypothetical galactic civilizations is going through a new phase of intense activity. For the first time, a significant fraction of the vast search space is expected to be sampled in the foreseeable future, potentially bringing informative data about the abundance of detectable extraterrestrial civilizations, or the lack thereof. Starting from the current state of ignorance about the galactic population of non-natural electromagnetic signals, we formulate a Bayesian statistical model to infer the mean number of radio signals crossing Earth, assuming either non-detection or the detection of signals in future surveys of the Galaxy. Under fairly noninformative priors, we find that not detecting signals within about $1$ kly from Earth, while suggesting the lack of galactic emitters or at best the scarcity thereof, is nonetheless still consistent with a probability exceeding $10$ \% that typically over $\sim 100$ signals could be crossing Earth, with radiated power analogous to that of the Arecibo radar, but coming from farther in the Milky Way. The existence in the Galaxy of potentially detectable Arecibo-like emitters can be reasonably ruled out only if all-sky surveys detect no such signals up to a radius of about $40$ kly, an endeavor requiring detector sensitivities thousands times higher than those of current telescopes. Conversely, finding even one Arecibo-like signal within $\sim 1000$ light years, a possibility within reach of current detectors, implies almost certainly that typically more than $\sim 100$ signals of comparable radiated power cross the Earth, yet to be discovered.Comment: Published in PNAS ahead of print October 1, 2018. Preprint has 13 pages, 7 figures + 7 pages of Supplementary Information with 5 figure

Indirect Network Effects in New Product Growth

Indirect network effects are of prime interest to marketers because they affect the growth and takeoff of software availability for, and hardware sales of, a new product. While prior work on indirect network effects in the economics and marketing literature is valuable, these literatures show two main shortcomings. First, empirical analysis of indirect network effects is rare. Second, in contrast to the importance the prior literature credits to the chicken-and-egg paradox in these markets, the temporal pattern – which leads which? – of indirect network effects remains unstudied. Based on empirical evidence of nine markets, this study shows, among others, that: (1) indirect network effects, as commonly operationalized by prior literature, are weaker than expected from prior literature; (2) in most markets we examined, hardware sales leads software availability, while the reverse almost never happens, contradicting existing beliefs. These findings are supported by multiple methods, such as takeoff and time series analyses, and fit with the histories of the markets we studied. The findings have important implications for academia, public policy and management practice. To academia, it identifies a need for new, and more relevant, conceptualizations of indirect network effects. To public policy, it questions the need for intervention in network markets. To management practice, it downplays the importance of the availability of a large library of software for hardware technology to be successful.Chicken-and-Egg;New Product Growth;Indirect Network Effects;Takeoff

Application of a Case Study Methodology

In the preceding article (Tellis, 1997), the goals and objectives were presented and explained in detail. In this article, the methodology to accomplish those goals and objectives will be examined. The reader will become familiar with the specific techniques that are used in the current study, and supported by the literature that was reviewed in the previous article. That methodology will follow the recommendation of Yin (1994) and has four stages: Design the case study, Conduct the case study, Analyze the case study evidence, and Develop the conclusions, recommendations and implications. The article begins with an introduction, that includes some of the background information that is intended to inform the reader. Following that section, each step of the methodology will be explored in detail. Finally a summary will connect all the information in a concise manner

“The new bipolarity between the US and China poses challenges for India” – Ashley Tellis

Ashley J. Tellis featured on the foreign policy panel at India @ 70: LSE India Summit 2017. Following the session, he spoke to Sonali Campion about the new US-China bipolarity in the international environment, the impact of Trump on the US-India relationship and the question of India’s membership of the Nuclear Supplier’s group

Does Online Chatter Really Matter? Dynamics of User-Generated Content and Stock Performance

User-Generated Content in online platforms or chatter for short provides a valuable source of consumer feedback on market performance of firms. This study examines whether chatter can predict stock market performance, which metric of chatter has the strongest relationship, and what the dynamics of the relationship are. The authors aggregate chatter (in the form of product reviews) from multiple websites over a four year period across six markets and fifteen firms. They derive multiple metrics of chatter (volume, positive chatter, negative chatter, and 5-start ratings) and use multivariate time series models to assess the short and long term relationship between chatter and stock market performance. They use three measures of stock market performance: abnormal returns, risk, and trading volume. The findings reveal that two metrics of chatter can predict abnormal returns with a lead of a few days. Of four metrics of chatter, volume shows the strongest relationship with returns and trading volume, followed by negative chatter. Whereas negative chatter has a strong effect on returns and trading volume with a short “wearin” and long “wearout,” positive chatter has no effect on these metrics. Negative chatter also increases volatility (risk) in returns. A portfolio analysis of trading stocks based on their chatter provides a return of 8% over and above normal market returns. In addition to the investing opportunities, the results show managers that chatter is an important metric to follow to gauge the performance of their brands and products. Because chatter is available daily and hourly, it 2 can provide an immediate pulse of performance that is not possible with infrequent sales and earnings reports. The fact that negative chatter is more important than positive, indicates that negatives are more diagnostic than positives. The negatives suggest what aspects of the products managers should focus on

Indirect Network Effects in New Product Growth

Indirect network effects are of prime interest to marketers because they affect the growth and takeoff of software availability for, and hardware sales of, a new product. While prior work on indirect network effects in the economics and marketing literature is valuable, these literatures show two main shortcomings. First, empirical analysis of indirect network effects is rare. Second, in contrast to the importance the prior literature credits to the chicken-and-egg paradox in these markets, the temporal pattern – which leads which? – of indirect network effects remains unstudied. Based on empirical evidence of nine markets, this study shows, among others, that: (1) indirect network effects, as commonly operationalized by prior literature, are weaker than expected from prior literature; (2) in most