Does corporate reputation matter? Role of social media in consumer intention to purchase innovative food product

The exponential growth of the corporate reputation in food industry has resulted in innovations in every link of its supply chain. There have been studies that have characterized innovation in various industries from the perspective of technology, but far fewer in the area of corporate reputation, consumer perception, and intention towards innovations in food products. This research analyses the innovations in the food industry from the perspective of the consumer and provides a conceptual framework of food innovation stages. The study also investigates the relationship between corporate reputation and intention towards food innovation along with the other components of TPB model with an extension of social media engagement. The results from India and US samples confirm that social media engagement have a significant role to play in creating intention to purchase innovative food products. The study compares the US and Indian samples and identifies differences in subjective norms and perceived behavioural control

On Data Clustering Analysis: Scalability, Constraints and Validation

this paper we discuss some very recent clustering approaches and recount our experience with some of these algorithms. We also present the problem of clustering in the presence of constraints and discuss the issue of clustering validatio

Dynamics of Neuronal Models in Online Neuroevolution of Robotic Controllers

<p><strong>Dynamics of Neuronal Models in Online Neuroevolution of Robotic Controllers</strong>, by Fernando Silva, Luís Correia, and Anders Lyhne Christensen.</p> <p>To appear in: EPIA 2013. 16th Portuguese Conference on Artificial Intelligence. Springer-Verlag.</p> <p> </p> <p><strong>Abstract: </strong>In this paper, we investigate the dynamics of different neuronal models on online neuroevolution of robotic controllers in multirobot systems. We compare the performance and robustness of neural network-based controllers using summing neurons, multiplicative neurons, and a combination of the two. We perform a series of simulation-based experiments in which a group of e-puck-like robots must perform an integrated navigation and obstacle avoidance task in environments of different complexity. We show that: (i) multiplicative controllers and hybrid controllers maintain stable performance levels across tasks of different complexity, (ii) summing controllers evolve diverse behaviours that vary qualitatively during task execution, and (iii) multiplicative controllers lead to less diverse and more static behaviours that are maintained despite environmental changes. Complementary, hybrid controllers exhibit both behavioural characteristics, and display superior generalisation capabilities in simple and complex tasks. </p> <p> </p> <p><strong>Description of the dataset:</strong></p> <p>The dataset contains a number of figures and .gv files that represent artificial neural networks used as evolved robotic controllers. There are also a number of .txt files describing each network as a set of connections. Each line in a given file describes a connection as a triple (input neuron, output neuron, connection weight).</p> <p> </p> <p>For a given experimental setup (see the paper), each controller evolved is named as "S.R", where S represents the sample/run in which the controller was evolved, and R is the id of the robot.</p> <p> </p> <p>Networks are described in the following manner:</p> <p>- Ri denotes sensor i for robot detection, with i in [1:8]</p> <p>- Wi denotes sensor i for wall/obstacle detection, with i in [1:8]</p> <p>- E denotes the virtual energy level sensor</p> <p>- Hx denotes the hidden neuron H with id x</p> <p>- LW represents the output neuron that controls the robot's left wheel</p> <p>- RW represents the output neuron that controls the robot's right wheel</p