Optimal Resource Allocation Over Time and Degree Classes for Maximizing Information Dissemination in Social Networks

We study the optimal control problem of allocating campaigning resources over the campaign duration and degree classes in a social network. Information diffusion is modeled as a Susceptible-Infected epidemic and direct recruitment of susceptible nodes to the infected (informed) class is used as a strategy to accelerate the spread of information. We formulate an optimal control problem for optimizing a net reward function, a linear combination of the reward due to information spread and cost due to application of controls. The time varying resource allocation and seeds for the epidemic are jointly optimized. A problem variation includes a fixed budget constraint. We prove the existence of a solution for the optimal control problem, provide conditions for uniqueness of the solution, and prove some structural results for the controls (e.g. controls are non-increasing functions of time). The solution technique uses Pontryagin's Maximum Principle and the forward-backward sweep algorithm (and its modifications) for numerical computations. Our formulations lead to large optimality systems with up to about 200 differential equations and allow us to study the effect of network topology (Erdos-Renyi/scale-free) on the controls. Results reveal that the allocation of campaigning resources to various degree classes depends not only on the network topology but also on system parameters such as cost/abundance of resources. The optimal strategies lead to significant gains over heuristic strategies for various model parameters. Our modeling approach assumes uncorrelated network, however, we find the approach useful for real networks as well. This work is useful in product advertising, political and crowdfunding campaigns in social networks.Comment: 14 + 4 pages, 11 figures. Author's version of the article accepted for publication in IEEE/ACM Transactions on Networking. This version includes 4 pages of supplementary material containing proofs of theorems present in the article. Published version can be accessed at http://dx.doi.org/10.1109/TNET.2015.251254

Operator-based approaches to harm minimisation in gambling: summary, review and future directions

In this report we give critical consideration to the nature and effectiveness of harm minimisation in gambling. We identify gambling-related harm as both personal (e.g., health, wellbeing, relationships) and economic (e.g., financial) harm that occurs from exceeding one’s disposable income or disposable leisure time. We have elected to use the term ‘harm minimisation’ as the most appropriate term for reducing the impact of problem gambling, given its breadth in regard to the range of goals it seeks to achieve, and the range of means by which they may be achieved. The extent to which an employee can proactively identify a problem gambler in a gambling venue is uncertain. Research suggests that indicators do exist, such as sessional information (e.g., duration or frequency of play) and negative emotional responses to gambling losses. However, the practical implications of requiring employees to identify and interact with customers suspected of experiencing harm are questionable, particularly as the employees may not possess the clinical intervention skills which may be necessary. Based on emerging evidence, behavioural indicators identifiable in industryheld data, could be used to identify customers experiencing harm. A programme of research is underway in Great Britain and in other jurisdiction

Time-Series Models in Marketing

Marketing data appear in a variety of forms. An often-seen form is time-series data, like sales per month, prices over the last few years, market shares per week. Time-series data can be summarized in time-series models. In this chapter we review a few of these, focusing in particular on domains that have received considerable attention in the marketing literature. These are (1) the use of persistence modelling and (2) the use of state space models.Marketing;Persistence;State Space;Time Series

Electronic word of mouth in online social networks: strategies for coping with opportunities and challenges

In today's world, the widespread success of the Internet, social media, and online social networks (OSN) provide the basis for electronic word of mouth (EWOM). EWOM can be seen as a digital enhancement of traditional word of mouth that makes communication more efficient and involves less effort by its users. The resulting speed of diffusion and information transparency have caused transformative changes in consumer behaviour in all types of markets, which requires the development of new business strategies for adequately dealing with the new circumstances. This doctoral dissertation is divided into three overall subject areas that concern the investigation of capable strategies for coping with the emerged opportunities and challenges of EWOM in OSN. The first subject area concerns negative electronic word of mouth in OSN and investigates capable countermeasure strategies for firms to adequately address claims of unsatisfied customers. For this, three simulation studies are conducted in which the propagation of a negative message and its countering by a positive message published by the firm are numerically analysed. The results reveal that, in general, the persuasiveness of a firm's response is more important than a quick response with a less persuasive counter-message. To some extent, this also holds if the number of OSN members who initially disseminate the counter-message on behalf of the firm is increased. In the second subject area, an optimisation model for individualised pricing is developed for an online store whose customers are interconnected in an OSN and can share price information via EWOM. The model is solved numerically by artificial intelligence solution methods. The results indicate that personalised prices can be financially worthwhile even under price transparency. The third subject area investigates market entry strategies for social media apps and services that are advertised in an OSN for acquiring new users and examines the role of EWOM in this context. A diffusion model is developed and analysed numerically by simulation. Three different targeting approaches are compared to each other regarding their ability to reach a high share of active users in the OSN: (1) a random marketing strategy, where randomly chosen members in the OSN are presented the advertisement, (2) cluster marketing, where whole clusters of members who are densely connected to each other are simultaneously shown the advertisement, and (3) influencer marketing, where the most influential users in the OSN are selected to share sponsored posts about the app in the OSN. The results suggest that EWOM can have detrimental effects if OSN members are too early informed about the app or service. If the information about the app reaches clusters in the OSN prematurely where a sufficient level of activity is not present yet, it can deplete the excitement of the users. The lack of excitement, in turn, can significantly reduce the effect of subsequent marketing campaigns. However, if applied appropriately, a higher level of EWOM about the app or service can increase the performance of the random marketing strategy to the extent that it outperforms cluster and influencer marketing

Barriers to Entry

Entry of firms into a market is an important economic mechanism that influences industry dynamics and contributes to allocative and dynamic efficiency. However, there are barriers that can prevent companies from entering a market, hampering the competitive process. Therefore, it is clear that barriers to entry are an important issue in competition policy. In this report, we studied a number of 37 different barriers with a special focus on the possible size effect of the barrier, the sustainability of the barrier, the way it can be measured and the relation with other barriers to entry.

Awareness or Persuasion? How Free Sampling Affects Crowdfunding Performance

Free sampling, which has been widely applied and studied in marketing, has recently been adopted by entrepreneurs on crowdfunding platforms. Using a dataset from a unique crowdfunding platform, we study whether opting to provide free samples to potential backers increases the likelihood of a campaign meeting its goal. We also investigate how free sampling influences the fundraising dynamics. Using a matched sample of crowdfunding campaigns, we demonstrate that providing free samples significantly improves crowdfunding performance. And among those projects which opt for free sampling, persuasion effect during the report period contributes to the positive effect. The higher the score from the evaluator, the more successful the campaign will be. We also find that the longer the investors are exposed to the evaluation report, the more money is raised