38,399 research outputs found
An empirical investigation of Network-Oriented Behaviors in Business-to-Business Markets
This study is concerned with the extent to which network-oriented behaviors directly and/or indirectly affect firm
performance. It argues that a firm's interaction behaviors in relation to an embedded network structure are key
mechanisms that facilitate the development of important organizational capabilities in dealing with business
partners. Such network-oriented behaviors, which are aimed at affecting the position of a company in the
network, are consequently important drivers of firm performance, rather than the network structure alone. We
develop a conceptual model that captures network-oriented behaviors as a driving force of firm performance
in relation to three other key organizational behaviors, i.e., customer-oriented, competitor-oriented and
relationship-oriented behaviors. We test the hypothesized model using a dataset of 354 responses collected
via an on-line questionnaire from UK managers, whose organizations operate in business-to-business markets
in either the manufacturing or services sectors. This study provides four key findings. First, a firm's networkoriented
behaviors positively affect the development of customer-oriented and competitor-oriented behaviors.
Secondly, they also foster relationship coordination with its important business partners within the network.
Thirdly, the effective management of the firm's portfolio of relationships is found to mediate the positive impact
of network-oriented behaviors on firm profitability. Lastly, closeness to end-users amplifies the positive effect of
network-oriented behaviors on relationship portfolio effectiveness
Are black friday deals worth it? Mining twitter users' sentiment and behavior response
The Black Friday event has become a global opportunity for marketing and companies’
strategies aimed at increasing sales. The present study aims to understand consumer behavior
through the analysis of user-generated content (UGC) on social media with respect to the Black Friday
2018 offers published by the 23 largest technology companies in Spain. To this end, we analyzed
Twitter-based UGC about companies’ offers using a three-step data text mining process. First, a Latent
Dirichlet Allocation Model (LDA) was used to divide the sample into topics related to Black Friday.
In the next step, sentiment analysis (SA) using Python was carried out to determine the feelings
towards the identified topics and offers published by the companies on Twitter. Thirdly and finally,
a data-text mining process called textual analysis (TA) was performed to identify insights that could
help companies to improve their promotion and marketing strategies as well as to better understand
the customer behavior on social media. The results show that consumers had positive perceptions of
such topics as exclusive promotions (EP) and smartphones (SM); by contrast, topics such as fraud (FA),
insults and noise (IN), and customer support (CS) were negatively perceived by customers. Based on
these results, we offer guidelines to practitioners to improve their social media communication.
Our results also have theoretical implications that can promote further research in this area
Correlated Cascades: Compete or Cooperate
In real world social networks, there are multiple cascades which are rarely
independent. They usually compete or cooperate with each other. Motivated by
the reinforcement theory in sociology we leverage the fact that adoption of a
user to any behavior is modeled by the aggregation of behaviors of its
neighbors. We use a multidimensional marked Hawkes process to model users
product adoption and consequently spread of cascades in social networks. The
resulting inference problem is proved to be convex and is solved in parallel by
using the barrier method. The advantage of the proposed model is twofold; it
models correlated cascades and also learns the latent diffusion network.
Experimental results on synthetic and two real datasets gathered from Twitter,
URL shortening and music streaming services, illustrate the superior
performance of the proposed model over the alternatives
Continuous Interaction with a Virtual Human
Attentive Speaking and Active Listening require that a Virtual Human be capable of simultaneous perception/interpretation and production of communicative behavior. A Virtual Human should be able to signal its attitude and attention while it is listening to its interaction partner, and be able to attend to its interaction partner while it is speaking – and modify its communicative behavior on-the-fly based on what it perceives from its partner. This report presents the results of a four week summer project that was part of eNTERFACE’10. The project resulted in progress on several aspects of continuous interaction such as scheduling and interrupting multimodal behavior, automatic classification of listener responses, generation of response eliciting behavior, and models for appropriate reactions to listener responses. A pilot user study was conducted with ten participants. In addition, the project yielded a number of deliverables that are released for public access
NAIS: Neural Attentive Item Similarity Model for Recommendation
Item-to-item collaborative filtering (aka. item-based CF) has been long used
for building recommender systems in industrial settings, owing to its
interpretability and efficiency in real-time personalization. It builds a
user's profile as her historically interacted items, recommending new items
that are similar to the user's profile. As such, the key to an item-based CF
method is in the estimation of item similarities. Early approaches use
statistical measures such as cosine similarity and Pearson coefficient to
estimate item similarities, which are less accurate since they lack tailored
optimization for the recommendation task. In recent years, several works
attempt to learn item similarities from data, by expressing the similarity as
an underlying model and estimating model parameters by optimizing a
recommendation-aware objective function. While extensive efforts have been made
to use shallow linear models for learning item similarities, there has been
relatively less work exploring nonlinear neural network models for item-based
CF.
In this work, we propose a neural network model named Neural Attentive Item
Similarity model (NAIS) for item-based CF. The key to our design of NAIS is an
attention network, which is capable of distinguishing which historical items in
a user profile are more important for a prediction. Compared to the
state-of-the-art item-based CF method Factored Item Similarity Model (FISM),
our NAIS has stronger representation power with only a few additional
parameters brought by the attention network. Extensive experiments on two
public benchmarks demonstrate the effectiveness of NAIS. This work is the first
attempt that designs neural network models for item-based CF, opening up new
research possibilities for future developments of neural recommender systems
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