The possibility of using search traffic information to explore consumer product attitudes and forecast consumer preference

Jun, Seung-pyo; Park, Do-Hyung; Yeom, Jaeho

doi:10.1016/j.techfore.2013.10.021

Cited by 60 publications

(27 citation statements)

References 20 publications

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“…Companies working on the large data seek to measure supplier risk and the best options, predict demand for products or services, optimize inventory, observe capacity constraints, analyze workforce, optimize distribution across routes and schedules, alternative transportation, vehicle maintenance, analyze customer behavior, avoid unnecessary financial risks, among many other purposes. Among all previous applications, demand forecasting is of major interest to researchers (Jun et al, 2014;Sagaert et al, 2016). When a company can predict the exact demand, it manages the perfect management of warehouse, transport, optimizing and satisfying the needs of the final consumer, get loyalty.…”

Section: Big Data In Supply Chain Managementmentioning

confidence: 99%

Big Data Analysis in Supply Chain Management in Portuguese SMEs “Leader Excellence”

Azevedo¹,

Reis²

2019

J INFORM SYSTEMS ENG

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With the market becoming increasingly competitive, companies are looking for ways to differentiate themselves from competitors, thereby increasing the interest of organizations in analysing large data and their potential benefits to supply chain management. The objective of this study is to understand the involvement of the Portuguese SMEs "Leader Excellence" with the Big Data theme and their analysis as a function of the Supply Chain Management, as well as to understand if these companies are in the same line as the companies of worldwide reference, in what concerns to the topic in question. For this study, a survey was carried out with 18 questions and applied to 80 SMEs distinguished as "SMEs Leader Excellence" certified by IAPMEI. With this random sample, from the analysis of the results, it was possible to verify that the Portuguese SMEs are not yet at the level of the big world companies with respect to the use of Big Data in the management of the supply chain. Although the Big Data theme is often explored by the scientific community, since they are relatively recent concepts, when the focus is on SMEs, the number of available studies is scarce, hence this work is a further contribution to broadening the body knowledge in this area.

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Section: Big Data In Supply Chain Managementmentioning

confidence: 99%

Big Data Analysis in Supply Chain Management in Portuguese SMEs “Leader Excellence”

Azevedo¹,

Reis²

2019

J INFORM SYSTEMS ENG

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“…Goel [10] found that, in music ranks of Billboard Hot 100 chart and first month sales of video games, it is possible to predict consumer behaviors by using search traffic. Web search traffic has a merit that it enables investigating a population because there are many users per day, so it could be considered to be a good analytical method in understanding consumer behaviors [11].…”

Section: Advanced Research Using Search Trafficmentioning

confidence: 99%

A Study on the Activities of Touch Point and Consumer Information Search in the Multimedia Environment

Baek¹,

Kim

2015

IJMUE

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“…They search words related to a new product using a search engine to obtain detailed and objective information. Thus, for forecasting sales in the near future, in the era of big data, web search traffic is recognized as a new indicator of the degree of consumer recognition and interest, and its effectiveness has been demonstrated in related studies [ 1 , 8 – 10 ]. Therefore, in this study the web search traffic was considered a measure of interest diffusion.…”

Section: Introductionmentioning

confidence: 99%

Forecasting new product diffusion using both patent citation and web search traffic

Lee

Choi

2018

PLoS ONE

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Accurate demand forecasting for new technology products is a key factor in the success of a business. We propose a way to forecasting a new product’s diffusion through technology diffusion and interest diffusion. Technology diffusion and interest diffusion are measured by the volume of patent citations and web search traffic, respectively. We apply the proposed method to forecast the sales of hybrid cars and industrial robots in the US market. The results show that that technology diffusion, as represented by patent citations, can explain long-term sales for hybrid cars and industrial robots. On the other hand, interest diffusion, as represented by web search traffic, can help to improve the predictability of market sales of hybrid cars in the short-term. However, interest diffusion is difficult to explain the sales of industrial robots due to the different market characteristics. Finding indicates our proposed model can relatively well explain the diffusion of consumer goods.

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The possibility of using search traffic information to explore consumer product attitudes and forecast consumer preference

Cited by 60 publications

References 20 publications

Big Data Analysis in Supply Chain Management in Portuguese SMEs “Leader Excellence”

Big Data Analysis in Supply Chain Management in Portuguese SMEs “Leader Excellence”

A Study on the Activities of Touch Point and Consumer Information Search in the Multimedia Environment

Forecasting new product diffusion using both patent citation and web search traffic

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