11,501 research outputs found
The Synesthesia effects of Online Advertising Stimulus Design on Word-of-Mouth and Purchase Intention: From the Perspective of Consumer Olfactory and Gustatory
Multisensory marketing has been seen as an approach improving advertising effect in the social science, neuroscience, and marketing literature. For examining visual/audio synesthesia, the effect of smelling and tasting an online product, this study first developed design elements of digital video advertising: rational/emotional appeals and fast/slow tempo. Moreover, it strives to investigate empirically the effects of various online advertisement contexts on consumer emotion, attitude, and behavioral intention. We used event-related potentials (ERPs) in a scenario-based laboratory experiments. Data collected from 166 customers provide strong support for the research model. Through EEG and SEM analyses, in rational advertisings, consumers’ olfactory was triggered and both arousal and pleasure of the emotions affected the attitudes; in emotional advertisings, not only olfactory but gustatory were triggered and only pleasure affected the attitudes. By understanding online advertising design and synesthesia, insights from the findings can benefit designers and marketers in implementing more effective marketing strategies
The diverse magneto-optical selection rules in bilayer black phosphorus
The magneto-optical properties of bilayer phosphorene is investigated by the
generalized tight-binding model and the gradient approximation. The vertical
inter-Landau-level transitions, being sensitive to the polarization directions,
are mainly determined by the spatial symmetries of sub-envelope functions on
the distinct sublattices. The anisotropic excitations strongly depend on the
electric and magnetic fields. A perpendicular uniform electric field could
greatly diversify the selection rule, frequency, intensity, number and form of
symmetric absorption peaks. Specifically, the unusual magneto-optical
properties appear beyond the critical field as a result of two subgroups of
Landau levels with the main and side modes. The rich and unique
magneto-absorption spectra arise from the very close relations among the
geometric structures, multiple intralayer and interlayer hopping integrals, and
composite external fields
3,6-Dihydroxy-2′-[(2-hydroxy-1-naphthyl)methyleneamino]xanthene-9-spiro-1′-isoindolin-3′-one acetonitrile solvate
The title compound, C31H20N2O5·C2H3N, was synthesized by the reaction of fluorescein hydrazide and excess 2-hydroxy-1-naphthaldehyde in acetonitrile. The spirolactam ring is planar and is nearly at right angles to the two benzene rings of the xanthene system. The dihedral angle between the two benzene rings of the xanthene system is 9.92 (4)°. In the crystal structure, the molecules are linked into extended two-dimensional networks by intermolecular hydrogen bonding. Acetonitrile molecules are located in the voids between the two-dimensional networks
Noise-aware neural network for stochastic dynamics simulation
In the presence of system-environment coupling, classical complex systems
undergo stochastic dynamics, where rich phenomena can emerge at large
spatio-temporal scales. To investigate these phenomena, numerical approaches
for simulating stochastic dynamics are indispensable and can be computationally
expensive. In light of the recent fast development in machine learning
techniques, here, we establish a generic machine learning approach to simulate
the stochastic dynamics, dubbed the noise-aware neural network (NANN). One key
feature of this approach is its ability to generate the long-time stochastic
dynamics of complex large-scale systems by just training NANN with the one-step
dynamics of smaller-scale systems, thus reducing the computational cost.
Furthermore, this NANN based approach is quite generic. Case-by-case special
design of the architecture of NANN is not necessary when it is employed to
investigate different stochastic complex systems. Using the noisy Kuramoto
model and the Vicsek model as concrete examples, we demonstrate its capability
in simulating stochastic dynamics. We believe that this novel machine learning
approach can be a useful tool in investigating the large spatio-temporal
scaling behavior of complex systems subjected to the influences of the
environmental noise.Comment: 7 pages, 3 figure
Design for data acquisition system of gear measuring center
A data acquisition system for Computerized Numerical Control Gear Measurement Center was developed, in which Field-Programmable Gate Array was used as the control core instead of traditional single-chip microcomputer or Digital Signal Processor, First-In First-Out buffer to hold data, and personal computer bus as data transmission unit. The system performs multichannel data acquisition more efficiently, higher precision and higher rate in real time than the traditional ones. Experimental comparison indicates that the data gathering rate increase twice to 10 kHz, and the uniformly-spaced error is in 1 μm
Visual Presentation Modes in Online Product Reviews and Their Effects on Consumer Responses
Online product reviews posted by consumers are becoming a staple part of e-commerce websites. Researchers demonstrate that the volume and strength of online reviews, among others, have a significant impact on consumer responses. These studies have focused on the effect of text-based online reviews, but current information technologies enable the posting of online reviews with higher visual content, such as with images and videos. Using the Elaboration Likelihood Model and Dual Coding theory, we examine the effects of three visual modes for presenting online reviews with three products – backpack, digital camera and video game. Our results indicate that video-based online reviews are perceived as being more credible, helpful, persuasive, and providing a great sense of involvement, compared to text-based and image-based online reviews, but with no significant differences among the latter two. The influence of presentation modes on consumer responses is partially moderated by product type
The Impact of Online Recommendations and Consumer Feedback on Sales
Quality uncertainty and high search costs for identifying relevant information from an ocean of information may prevent customers from making purchases. Recognizing potential negative impacts of this search cost for quality information and relevant information, firms began to invest in creating a virtual community that enables consumers to share their opinions and experiences to reduce quality uncertainty, and in developing recom- mendation systems that help customers identify goods in which they might have an interest. However, not much is known regarding the effectiveness of these efforts. In this paper, we empirically investigate the impacts of recommendations and consumer feedbacks on sales based on data gathered from Amazon.com. Our results indicate that more recommendations indeed improve sales at Amazon.com; however, consumer ratings are not found to be related to sales. On the other hand, number of consumer reviews is positively associated with sales. We also find that recommendations work better for less-popular books than for more-popular books. This is consistent with the search cost argument: a consumer’s search cost for less-popular books may be higher, and thus they may rely more on recommendations to locate a product of interest
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