65,629 research outputs found
Learning Strategies in Coopetitive Environments
The objective of this chapter is to explore the learning strategies that can be deployed by firms in coopetitive configurations with no other choice than deploying an “adverse learning” mechanism to reach their customers through cooperation with their competitors. After exploring the mechanisms of asymmetric learning in a first section, the chapter adopts an ecological perspective (Hawley, 1950) in drawing parallels between animal organization and groups of firms in gaining a strategic advantage through asymmetric learning.coopetition; Learning Behavior; Learning Strategy.
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Couldn't or Wouldn't? the Influence of Privacy Concerns and Self-Efficacy in Privacy Management on Privacy Protection
Sampling 515 college students, this study investigates how privacy protection, including profile visibility, self-disclosure, and friending, are influenced by privacy concerns and efficacy regarding one's own ability to manage privacy settings, a factor that researchers have yet to give a great deal of attention to in the context of social networking sites (SNSs). The results of this study indicate an inconsistency in adopting strategies to protect privacy, a disconnect from limiting profile visibility and friending to self-disclosure. More specifically, privacy concerns lead SNS users to limit their profile visibility and discourage them from expanding their network. However, they do not constrain self-disclosure. Similarly, while self-efficacy in privacy management encourages SNS users to limit their profile visibility, it facilitates self-disclosure. This suggests that if users are limiting their profile visibility and constraining their friending behaviors, it does not necessarily mean they will reduce self-disclosure on SNSs because these behaviors are predicted by different factors. In addition, the study finds an interaction effect between privacy concerns and self-efficacy in privacy management on friending. It points to the potential problem of increased risk-taking behaviors resulting from high self-efficacy in privacy management and low privacy concerns.Radio-Television-Fil
How Emotions Unfold in Online Discussions After a Terror Attack
In the wake of a terror attack, social media is used for sharing thoughts and emotions, accessing and distributing information, and memorializing victims. Emotions are a big part of this, but there is a gap in our understanding on how those emotions evolve and what kinds of social media uses they are related to. Better understanding of the emotional and topical developments of online discussions can serve not only to fill the aforementioned gap, but also assist in developing better collective coping strategies for recovering from terror attacks. We examine what types of conversations unfolded online after the Boston Marathon Bombing and what kinds of emotions were associated with them, accounting for regional differences, and present a process model covering the general trends of such conversations. Although the phases apply to reactions to terror attacks on a general level, there are proximity-based differences to the location of the terror attack
Behavior Change and HIV Prevention: (Re)Considerations for the 21st Century
Outlines the evidence base for the effectiveness of HIV-prevention programs aimed at reducing high-risk behaviors. Discusses elements of successful programs, challenges and limitations, and recommendations for expanding behavioral prevention programs
A Decentralized Mobile Computing Network for Multi-Robot Systems Operations
Collective animal behaviors are paradigmatic examples of fully decentralized
operations involving complex collective computations such as collective turns
in flocks of birds or collective harvesting by ants. These systems offer a
unique source of inspiration for the development of fault-tolerant and
self-healing multi-robot systems capable of operating in dynamic environments.
Specifically, swarm robotics emerged and is significantly growing on these
premises. However, to date, most swarm robotics systems reported in the
literature involve basic computational tasks---averages and other algebraic
operations. In this paper, we introduce a novel Collective computing framework
based on the swarming paradigm, which exhibits the key innate features of
swarms: robustness, scalability and flexibility. Unlike Edge computing, the
proposed Collective computing framework is truly decentralized and does not
require user intervention or additional servers to sustain its operations. This
Collective computing framework is applied to the complex task of collective
mapping, in which multiple robots aim at cooperatively map a large area. Our
results confirm the effectiveness of the cooperative strategy, its robustness
to the loss of multiple units, as well as its scalability. Furthermore, the
topology of the interconnecting network is found to greatly influence the
performance of the collective action.Comment: Accepted for Publication in Proc. 9th IEEE Annual Ubiquitous
Computing, Electronics & Mobile Communication Conferenc
Effective medical surplus recovery
We analyze not-for-profit Medical Surplus Recovery Organizations (MSROs) that manage the recovery of surplus (unused or donated) medical products to fulfill the needs of underserved healthcare facilities in the developing world. Our work is inspired by an award-winning North American non-governmental organization (NGO) that matches the uncertain supply of medical surplus with the receiving parties’ needs. In particular, this NGO adopts a recipient-driven resource allocation model, which grants recipients access to an inventory database, and each recipient selects products of limited availability to fill a container based on its preferences. We first develop a game theoretic model to investigate the effectiveness of this approach. This analysis suggests that the recipient-driven model may induce competition among recipients and lead to a loss in value provision through premature orders. Further, contrary to the common wisdom from traditional supply chains, full inventory visibility in our setting may accelerate premature orders and lead to loss of effectiveness. Accordingly, we identify operational mechanisms to help MSROs deal with this problem. These are: (i) appropriately selecting container capacities while limiting the inventory availability visible to recipients and increasing the acquisition volumes of supplies, (ii) eliminating recipient competition through exclusive single-recipient access to MSRO inventory, and (iii) focusing on learning recipient needs as opposed to providing them with supply information, and switching to a provider-driven resource allocation model. We use real data from the NGO by which the study was inspired and show that the proposed improvements can substantially increase the value provided to recipients
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