Three Essays on Retransmission of Brand Messages in Social Media

Social networks have emerged as an important channel for brands to communicate with customers both directly as well as secondarily through customers who share the communications with others. A value of this channel to brands, therefore, depends on how effective they are in increasing the retransmission of their messages by customers. This is the issue that we investigate through three essays using the social media site Twitter as our research setting. Our investigation is based on the theory that retweeting is a choice made by consumers who rely on constructive preferences (Bettman, Luce and Payne 1998) while seeking three intangible benefits: altruism, self-enhancement, and social interaction. They also have two overarching metagoals of accuracy maximization and effort minimization (Bettman et al 1998) as they seek the benefits. Within this theoretical context, we examine the design attributes of tweets that increase retweeting. Specifically, we investigate more than 14000 tweets by 62 brands, across four product categories, over periods ranging from 18 to 400 days. Our empirical results are consistent with the theory and suggest that brand and tweet characteristics that increase the recipients’ ability to maximize the benefits of retweeting, and minimize the cognitive effort required to decide whether to retweet or not, increase retweets. The key managerial implication of our findings therefore is that brands should design tweets carefully to increase altruism, self-enhancement, and social interaction benefits while reducing the amount of effort that recipients need to undertake to assess whether the tweet offers these benefits. We replicate and extend these findings in essay two in the context of celebrities as brands by investigating the volume and duration of retweets of more than 2900 tweets by 65 celebrities across seven categories of the entertainment industry. Our results from this essay suggest that traits of the sources, i.e., the celebrities, also play a role in how recipients assess whether a tweet can deliver the three benefits while realizing the two metagoals. The third essay focuses on brands’ desire to generate retweets at a rapid rate before the tweet loses its relevance. In addition to volume and duration, therefore, we also investigate the rate at which a tweet is retweeted in this essay. Our investigation examines the retweet rates, in fifteen minutes intervals over a 24-hour period, of more than 2400 tweets posted by 62 celebrities using a Modulate Poisson Process model (Soyer and Tarimcilar 2008). Our results suggest that tweets that do not need recipients to interact with them and are related to significant cultural events are retweeted at a faster rate than others

Collaborative framework in computer aided innovation 2.0 : Application to process system engineering

In economy nowadays, the act of innovation is in general social; it requires the management of knowledge, and the techniques and methodologies to drive it. Innovation is not the product of one isolated intelligence, instead, it is the result of a multi-disciplinary workgroup lead by a process or a methodology. The conceptual design, which is found in the first stages of the innovation process, represents one of the most important challenges in industry nowadays. One of the main challenges faced by chemical industries related to the conceptual design phase is to provide the means in the form of methods and computational tools, for solving problems systematically, at the same time that benefiting from the collective efforts of individual intelligences involved. Hence, the main objective of this work is to provide a solution to improve the creative capacity of a team involved in the innovation process, in particular the preliminary (critical) phase of conceptual design. Consequently, it is important to understand the techniques, methods and tools that best support the generation of novel ideas and creative solutions. In addition, it is necessary to study the contribution of information and communication technologies as the mean to support collaboration. Web technologies are considered as complementary tools to implement methods and techniques in collaborative design, and particularly in the conceptual design stage. These technologies allow setting up distributed collaborative environments to bring together the resources and the experts who can relate the existing pieces of knowledge to new contexts. It is the synergy created in this kind of environment, which allow producing valuable concepts and ideas in the form of Collective Intelligence. Nevertheless in most existing solutions for collective intelligence or crowdsourcing environments, they do not report the use of a particular methodology to improve the participants' creativity. The solution in this work describes a social network service that enables users to cooperatively solve problems oriented (but not limited) to the phase of conceptual design. In this work we propose that the use of Collective Intelligence in combination with the model TRIZ-CBR could lead the creative efforts in a team to develop innovative solutions. With this work we are looking for connecting experts from one particular field, TRIZ practitioners and stakeholders with the objective to solve problems in collaboration unlashing the collective intelligence to improve creativity. This work uses the basis of the concept named "Open CAI 2.0" to propose a solution in the form of a theoretical framework. The contributions seek to move the development of the field in Computer Aided Innovation a step forward

Task Recommendation in Crowdsourcing Platforms

Task distribution platforms, such as micro-task markets, project assignment portals, and job search engines, support the assignment of tasks to workers. Public crowdsourcing platforms support the assignment of tasks in micro-task markets to help task requesters to complete their tasks and allow workers to earn money. Enterprise crowdsourcing platforms provide a marketplace within enterprises for the internal placement of tasks from employers to employees. Most of both types of task distribution platforms rely on the workers' selection capabilities or provide simple filtering steps to reduce the number of tasks a worker can choose from. This self-selection mechanism unfortunately allows for tasks to be performed by under- or over-qualified workers. Supporting the workers by introducing a task recommender system helps to solve such deficits of existing task distributions. In this thesis, the requirements towards task recommendation in task distribution platforms are gathered with a focus on the worker's perspective, the design of appropriate assignment strategies is described, and innovative methods to recommend tasks based on their textual descriptions are provided. Different viewpoints are taken into account by analyzing the domains of micro-tasks, project assignments, and job postings. The requirements of enterprise crowdsourcing platforms are compiled based on the literature and a qualitative study, providing a conceptual design of task assignment strategies. The demands of workers and their perception of task similarity on public crowdsourcing platforms are identified, leading to the design and implementation of additional methods to determine the similarity of micro-tasks. The textual descriptions of micro-tasks, projects, and job postings are analyzed in order to provide innovative methods for task recommendation in these domains