The effects of the social structure of digital networks on viral marketing performance

Viral marketing is a form of peer-to-peer communication in which individuals are encouraged to pass on promotional messages within their social networks. Conventional wisdom holds that the viral marketing process is both random and unmanageable. In this paper, we deconstruct the process and investigate the formation of the activated digital network as distinct from the underlying social network. We then consider the impact of the social structure of digital networks (random, scale free, and small world) and of the transmission behavior of individuals on campaign performance. Specifically, we identify alternative social network models to understand the mediating effects of the social structures of these models on viral marketing campaigns. Next, we analyse an actual viral marketing campaign and use the empirical data to develop and validate a computer simulation model for viral marketing. Finally, we conduct a number of simulation experiments to predict the spread of a viral message within different types of social network structures under different assumptions and scenarios. Our findings confirm that the social structure of digital networks play a critical role in the spread of a viral message. Managers seeking to optimize campaign performance should give consideration to these findings before designing and implementing viral marketing campaigns. We also demonstrate how a simulation model is used to quantify the impact of campaign management inputs and how these learnings can support managerial decision making.

Design of a valid simulation for researching physical, physiological and cognitive performance in volunteer firefighters during bushfire deployment.

Every year, Australian firefighters protect our nation from the devastation of bushfire. Understanding the impact of consecutive long shifts in hot, smoky conditions is essential for making decisions during campaign fires. At present, the evidence-base for such decisions is limited to laboratory studies with little relevance to bushfire suppression or field research where the impact of environmental and workload stressors cannot be measured. To counter these limitations, we have developed a three-day simulation that mimics the work and environment of campaign bushfire suppression. Construction of the simulation involved three stages; 1) data collection and analysis; 2) design and development; and 3) trial and refinement. The frequency, intensity, duration and type of physical work performed on the fireground is well documented and a modified applied cognitive task analysis, using experienced firefighters was used as a framework to describe in detail the non-physical aspects of the work. The design and development of the simulation incorporated the physical and non-physical aspects of the work into simulated tasks. Finally, experienced firefighters participated in trials of the simulation and reviewed digital recordings to ensure that the simulation accurately represented campaign bushfire suppression work. The outcome of this project is a valid, realistic, and reliable simulation of the physiological, physical and cognitive aspects of a volunteer firefighter on a three-day bushfire deployment.

Comparing churn prediction techniques and assessing their performance: a contingent perspective

Customer retention has become a focal priority. However, the process of implementing an effective retention campaign is complex and dependent on firms’ ability to accurately identify both at-risk customers and those worth retaining. Drawing on empirical and simulated data from two online retailers, we evaluate the performance of several parametric and nonparametric churn prediction techniques, in order to identify the optimal modeling approach, dependent on context. Results show that under most circumstances (i.e., varying sample sizes, purchase frequencies, and churn ratios), the boosting technique, a nonparametric method, delivers superior predictability. Furthermore, in cases/contexts where churn is more rare, logistic regression prevails. Finally, where the size of the customer base is very small, parametric probability models outperform other techniques.

Influence of fuel-borne oxygen on European stationary cycle: diesel engine performance and emissions with a special emphasis on particulate and NO emissions

Exploration of sustainable fuels and their influence on reductions in diesel emissions are nowadays a challenge for the engine and fuel researchers. This study investigates the role of fuel-borne oxygen on engine performance and exhaust emissions with a special emphasis on diesel particulate and nitric oxide (NO) emissions. A number of oxygenated-blends were prepared with waste cooking biodiesel as a base oxygenated fuel. Triacetin, a derivative from transesterified biodiesel was chosen for its high oxygen content and superior fuel properties. The experimental campaign was conducted with a 6-cylinder, common rail turbocharged diesel engine equipped with highly precise instruments for nano and other size particles and other emissions. All experiments were performed in accordance with European Stationary Cycle (ESC 13-mode). A commercial diesel was chosen as a reference fuel with 0% oxygen and five other oxygenated blends having a range of 6.02–14.2% oxygen were prepared. The experimental results revealed that the oxygenated blends having higher a percentage of fuel-borne oxygen reduced particulate matter (PM), particle number (PN), unburned hydrocarbon (UBHC) and carbon monoxide (CO) emissions to a significantly low level with a slight penalty of NO emissions. The main target of this study was to effectively utilise triacetin as an additive for waste cooking biodiesel and suppress emissions without deteriorating engine performance. The key finding of this investigation is the significant reductions in both particle mass and number emissions simultaneously without worsening engine performance with triacetin-biodiesel blends. Reductions in both particle mass and number emissions with a cost-effective additive would be a new dimension for the fuel and engine researchers to effectively use triacetin as an emission suppressor in the future.