Consumer information search behavior and purchasing decisions: Empirical evidence from Korea

Jun, Seung-pyo; Park, Do-Hyung

doi:10.1016/j.techfore.2016.03.021

Cited by 53 publications

(48 citation statements)

References 35 publications

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“…Relative to searching on a personal computer, however, search using a mobile device is increasing dramatically (Jun & Park, ). Consumers are replacing traditional searches (e.g., use of magazine reviews, newspapers, guidebooks) with smartphone searching (Bachrach, Ogilvie, Rapp, & Calamusa, ).…”

Section: Hypothesesmentioning

confidence: 99%

“…Indeed, Huang, Lu, and Ba () discerned that customers have a preference for a mobile platform versus a Web platform, with the mobile platform allowing for more appealing features to attract consumers. Jun and Park () ascertained that consumer searches on a mobile device for durable, higher priced consumer products are related to purchase behavior. Moreover, Kim et al.…”

Section: Hypothesesmentioning

confidence: 99%

“…Relative to searching on a personal computer, however, search using a mobile device is increasing dramatically (Jun & Park, 2016).…”

Section: Mobile Search and Purchase Intentionmentioning

confidence: 99%

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Under the sway of a mobile device during an in‐store shopping experience

Rippé

Weisfeld‐Spolter

Yurova

et al. 2017

Psychology and Marketing

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Mobile device technology is transforming the retail shopping experience. Today's consumers are mobile dependent, preferring to consult with their phone, instead of using the salesperson, while shopping at the retail store. In the absence of literature investigating how the salesperson might sell to this omni‐channel, mobile‐connected consumer, this paper proposes a conceptual model and tests its proposed linkages. The empirically tested model presents a dyadic view of the omni‐channel consumer and the salesperson. Uniquely contributing to the omni‐channel and marketing literature, samples from an emerging economy (India) and developed (United States) economy represent the contexts. Specifically, the research examines the salesperson's role in selling to a mobile‐dependent consumer who uses mobile technology to search for information during the sales meeting. Findings show that adaptive selling can affect purchase intention and customer predisposition to comply with salesperson input. Results also reveal how perceived control, mobile dependence, and customer predisposition to comply with mobile device input affect purchase intention. Managerial and research implications specifically appropriate for the omni‐consumer retail setting are offered.

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Section: Hypothesesmentioning

confidence: 99%

Section: Hypothesesmentioning

confidence: 99%

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Under the sway of a mobile device during an in‐store shopping experience

Rippé

Weisfeld‐Spolter

Yurova

et al. 2017

Psychology and Marketing

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“…Online search traffic data have been proven effective in analyzing online interest [43], and have been highly used in research over the last few years for analyzing online behavior. Data from Google Trends are suggested to be valid [44] and significantly contributing to forecastings [45] and in measuring public interest, subject to the careful selection of the terms to be examined [46].…”

Section: Introductionmentioning

confidence: 99%

Quantifying the UK Online Interest in Substances of the EU Watchlist for Water Monitoring: Diclofenac, Estradiol, and the Macrolide Antibiotics

Mavragani

Sypsa

Sampri

et al. 2016

Water

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Due to the increased interest in micropollutants, this paper aims at quantifying and analyzing the UK online interest in Diclofenac, Estradiol, Azithromycin, Clarithromycin, and Erythromycin, substances included in the EU watchlist for monitoring, in order to examine if the public's online behavior and the use of these substances, in terms of issued prescriptions, are correlated. Using time series data from Google Trends from January 2004 to December 2014, an analysis of these substances in the UK, and in each UK region, i.e., England, Wales, Scotland, and Northern Ireland, is at first performed, followed by an analysis of interest by substance. The results show high interest in Diclofenac with a slight decline, while the Macrolides are significantly less popular though increasing. For Estradiol, the interest is low and declining throughout the examined period, in contrast to the scientific community, where Estradiol is the most studied substance. Prescription items and Google hits are highly correlated in the UK for Diclofenac, Azithromycin, and Clarithromycin, while no correlation is observed for Estradiol. Results from this study indicated that online search traffic data can be valuable in examining the public's online behavior towards the monitored micropollutants, and could assist with the evaluation and forecasting of their concentrations in the waste, surface, and ground water in the UK.

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“…A popular way to access and use this vast amount of information is the analysis of online search queries [4,5], with the most notable research on the subject taking into account data from Google Trends [6]; a tool that measures variations in the online interest. The validity of Google Trends' data has been undoubtedly shown [7], while many studies have suggested that they are valuable, accurate, and of high benefit for forecastings [8-13], predictions [14] and nowcastings [15], in analyzing online interest [8], and in decision making [3].Google Trends as a tool is highly integrated in research, with notable contributions in the fields of health and medicine [16][17][18][19][20][21][22][23], economics and finance [4,[24][25][26][27], and the environment [28]. The use of Google Trends in political science in general and in predicting election and referendum outcomes in specific is showing promising results so…”

mentioning

confidence: 99%

Predicting referendum results in the Big Data Era

Mavragani

Tsagarakis

2019

J Big Data

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IntroductionBig Data are large data volumes characterized by the 8 Vs: 'Volume' [1], Variety, Velocity [2], Variability, Value, Volatility, Validity, and Veracity [3]. A popular way to access and use this vast amount of information is the analysis of online search queries [4,5], with the most notable research on the subject taking into account data from Google Trends [6]; a tool that measures variations in the online interest. The validity of Google Trends' data has been undoubtedly shown [7], while many studies have suggested that they are valuable, accurate, and of high benefit for forecastings [8-13], predictions [14] and nowcastings [15], in analyzing online interest [8], and in decision making [3].Google Trends as a tool is highly integrated in research, with notable contributions in the fields of health and medicine [16][17][18][19][20][21][22][23], economics and finance [4,[24][25][26][27], and the environment [28]. The use of Google Trends in political science in general and in predicting election and referendum outcomes in specific is showing promising results so AbstractIn addressing the challenge of Big Data Analytics, what has been of notable significance is the analysis of online search traffic data in order to analyze and predict human behavior. Over the last decade, since the establishment of the most popular such tool, Google Trends, the use of online data has been proven valuable in various research fields, including -but not limited to-medicine, economics, politics, the environment, and behavior. In the field of politics, given the inability of poll agencies to always well approximate voting intentions and results over the past years, what is imperative is to find new methods of predicting elections and referendum outcomes. This paper aims at presenting a methodology of predicting referendum results using Google Trends; a method applied and verified in six separate occasionsSaid referendums were of importance for the respective country and the EU as well, and received wide international attention. Google Trends has been empirically verified to be a tool that can accurately measure behavioral changes as it takes into account the users' revealed and not the stated preferences. Thus we argue that, in the time of intelligence excess, Google Trends can well address the analysis of social changes that the internet brings. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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Consumer information search behavior and purchasing decisions: Empirical evidence from Korea

Cited by 53 publications

References 35 publications

Under the sway of a mobile device during an in‐store shopping experience

Under the sway of a mobile device during an in‐store shopping experience

Quantifying the UK Online Interest in Substances of the EU Watchlist for Water Monitoring: Diclofenac, Estradiol, and the Macrolide Antibiotics

Predicting referendum results in the Big Data Era

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