Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Framing Effects

Framing Effects

Information is everywhere and its presentation—such as how and when items are presented—can impact our perceptions and decisions surrounding the info. This broad concept umbrellas framing effects—influences that occur due to the way information is framed in its appearance, whether it’s purely the order or the specific wording of a message. Let’s take a look at numerous ways in which two versions of something can objectively say the same thing, yet we respond in...

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

Protein Networks

Protein Networks

Network Covalent Solids

Network Covalent Solids

Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...

Buffer Effectiveness

Buffer Effectiveness

Buffer solutions do not have an unlimited capacity to keep the pH relatively constant . Instead, the ability of a buffer solution to resist changes in pH relies on the presence of appreciable amounts of its conjugate weak acid-base pair. When enough strong acid or base is added to substantially lower the concentration of either member of the buffer pair, the buffering action within the solution is compromised.
The buffer capacity is the amount of acid or base that can be added to a given volume...

Network Function of a Circuit

Network Function of a Circuit

Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Optimal decisions on price and inventory for a newsboy-type retailer with identifiable information and discount promotion.

PloS one·2023

Same author

Competitive analysis of online revenue management with two hierarchical resources and multiple fare classes.

PloS one·2022

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 30, 2026

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

A pricing model for group buying based on network effects.

¹College of Management, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China.

|January 25, 2019

Summary

This summary is machine-generated.

Group buying (GB) offers advantages over individual buying (IB) when positive network effects are high or low-valuation consumers are numerous. A mixed strategy (MIX) combining GB and IB is superior to IB alone.

More Related Videos

Behavioral and Network Pharmacology-Based Analyses for the Traditional Mongolian Medicine Zadi-5 in a Rat Model of Depression

Behavioral and Network Pharmacology-Based Analyses for the Traditional Mongolian Medicine Zadi-5 in a Rat Model of Depression

Published on: February 24, 2023

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Related Experiment Videos

Last Updated: Jan 30, 2026

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

Behavioral and Network Pharmacology-Based Analyses for the Traditional Mongolian Medicine Zadi-5 in a Rat Model of Depression

Behavioral and Network Pharmacology-Based Analyses for the Traditional Mongolian Medicine Zadi-5 in a Rat Model of Depression

Published on: February 24, 2023

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Area of Science:

E-commerce
Consumer behavior
Marketing strategy

Background:

Group buying (GB) is a prevalent e-commerce model.
Consumer choice in GB is increasingly influenced by social network effects over price discounts.
Both positive and negative network effects impact consumer decisions in GB.

Purpose of the Study:

To segment consumers based on network effects in group buying.
To analyze optimal pricing and sales strategies for e-commerce platforms.
To compare the performance of individual buying (IB), group buying (GB), and mixed (MIX) strategies.

Main Methods:

Consumer segmentation considering positive and negative network effects.
Game theory or economic modeling to determine optimal pricing.
Comparative analysis of different sales strategies (IB, GB, MIX).

Main Results:

GB strategy outperforms IB when positive network effects are strong or low-valuation consumers are abundant.
The MIX strategy consistently surpasses the IB strategy.
The relative performance of MIX versus GB strategies is contingent on market conditions.

Conclusions:

Understanding consumer network effects is crucial for optimizing e-commerce sales strategies.
GB and MIX strategies offer significant potential for e-commerce platforms.
Market-specific analysis is necessary to determine the most effective sales approach.