Top-down attention is the mechanism that allows us to selectively process goal-relevant aspects of a scene while ignoring irrelevant aspects. A large body of research has characterized the effects of attention on neural activity evoked by a visual stimulus. However, attention also includes a preparatory phase before stimulus onset in which the attended dimension is internally represented. Here, we review neurophysiological, functional magnetic resonance imaging, magnetoencephalography, electroencephalography, and transcranial magnetic stimulation (TMS) studies investigating the neural basis of preparatory attention, both when attention is directed to a location in space and when it is directed to nonspatial stimulus attributes (content-based attention) ranging from low-level features to object categories. Results show that both spatial and content-based attention lead to increased baseline activity in neural populations that selectively code for the attended attribute. TMS studies provide evidence that this preparatory activity is causally related to subsequent attentional selection and behavioral performance. Attention thus acts by preactivating selective neurons in the visual cortex before stimulus onset. This appears to be a general mechanism that can operate on multiple levels of representation. We discuss the functional relevance of this mechanism, its limitations, and its relation to working memory, imagery, and expectation. We conclude by outlining open questions and future directions.

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This study looks for signals of economic awareness on online social media and tests their significance in economic predictions. The study analyses, over a period of 2 years, the relationship between the West Texas Intermediate daily crude oil price and multiple predictors extracted from Twitter; Google Trends; Wikipedia; and the Global Data on Events, Location and Tone (GDELT) database. Semantic analysis is applied to study the sentiment, emotionality and complexity of the language used. Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) models are used to make predictions and to confirm the value of the study variables. Results show that the combined analysis of the four media platforms carries valuable information in making financial forecasting. Twitter language complexity, GDELT number of articles and Wikipedia page reads have the highest predictive power. This study also allows a comparison of the different fore-sighting abilities of each platform, in terms of how many days ahead a platform can predict a price movement before it happens. In comparison with previous work, more media sources and more dimensions of the interaction and of the language used are combined in a joint analysis.

Prior research has emphasised the importance of informal advice networks for knowledge sharing and peer learning. We use Social Network Analysis to detect individuals who play a strategic role in advice networks. Even if roles have been extensively described, how to identify people within them is still an open issue. Furthermore, we investigate whether an association between key players and the big five personality traits exists, by means of nonparametric statistics. To achieve this, we present a case study which involves roughly 180 university students. We found 21 of them playing a key role. Results give evidence of significant associations between key positions and Conscientiousness, Neuroticism and Agreeableness; whereas no evidence was found for a relationship with Extraversion or Openness to Experience. Consistently, personality emerges as a relevant indicator for predicting people who are more likely to play a strategic role, even when connection patterns are unknown.

In this paper we investigate the most prominent drivers of brand equity, from a consumerbased point of view. We present a new approach for measuring brand equity, which can be applied regardless of the brand sector and is based on the Analytic Hierarchy Process. This approach has the main advantage of allowing for comparisons to be made between non‐directly measurable elements and also has the advantage of enabling the ranking of intangible criteria, such as consumers’ feelings or purchase intentions. We focus on the fashion industry, since we believe in the higher value of our approach when applied to brands which offer products with less tangible characteristics. Thanks to a case study – which involved about 250 interviewees – we succeed in finding and prioritizing the elements which can have an impact on the brand value. We also provide a global ranking for three apparel brands: Gap, H&M and Zara. The results from our model are consistent with other popular ratings and can be extremely useful for brand managers

There is evidence that the deployment of attention can be biased by the content of visual working memory. Recently, it has been shown that focusing internal attention to a specific item in memory not only increases the accessibility of that specific item for retrieval, but also results in increased attentional guidance toward matching external stimuli by that item. Here, we investigated the time course of attentional guidance by cued memories. Following a retro-cue that prioritized one of two memory items, participants performed a visual search task. The interval between the cue and the search display was varied. Consistent with earlier findings, we observed memory-related capture when the search display contained a distractor that was related to the cued item in memory. No such effects were found for the uncued items or when none of the memory items were prioritized by a retro-cue. Results suggest that the prioritization by a retro-cue is a very rapid process that starts to affect perceptual selection within the first 100-200 ms following the cue.

The success of a New Product Development (NPD) process strongly depends on the deep comprehension of market needs and Analytic Hierarchy Process (AHP) has been commonly used to find weights for customers’ preferences. AHP best practices suggest that low‐consistency respondents should be considered untrustworthy; however, in some NPD cases – such as the one presented here – this stake can be extremely big. This paper deals with the usage of AHP methodology to define the weights of customer needs connected to the NPD process of a typical impulse buying good, a snack. The aim of the paper is to analyse in a critical way the opportunity to exclude or include non‐consistent respondents in market analysis, addressing the following question: should a non‐consistent potential customer be excluded from the analysis due to his inconsistency or should he be included because, after all, he is still a potential consumer? The chosen methodological approach focuses on evaluating the compatibility of weight vectors among different subsets of respondents, filtered according to their consistency level. Results surprisingly show that weights do not significantly change when non‐consistent respondents are excluded

In this paper we investigate the possible relationships among some optimization techniques used in Operations Management and the performance of SMEs that operate in the manufacturing sector. A model based on the Structural Equation Modelling (SEM) approach is used to analyse a dataset of small and medium-sized Italian enterprises. The model is expressed by a system of simultaneous equations and is solved through regression analysis. Taking advantage of the contributions presented previously, we focus our research on the Italian economy, highlighting the importance of Operations Management practices, which are relevant drivers of these firms’ performances