Comparing shopping experiences in department stores and street markets: a big data analysis of TripAdvisor reviews

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chayanon Phucharoen ◽  
Tatiyaporn Jarumaneerat ◽  
Nichapat Sangkaew
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Learning Algorithm ◽  
Big Data Analysis ◽  
Department Stores ◽  
Shopping Malls ◽  
Content Type ◽  
Shopping Experience ◽  
The Mean

Purpose Based on big data analytical and statistical techniques, this study aims to examine tourists’ shopping experiences at department stores and street markets in Phuket. Design/methodology/approach A Naïve Bayes machine learning algorithm was used to identify the most frequently used terms in TripAdvisor reviews of both department stores and street markets contributed by the same pool of 729 tourists. Findings A total of 18 out of 62 terms used were common in reviews of both shopping settings. However, the study found significant differences in the mean use of the 18 common terms and the likelihood of those terms being used in overall positive reviews. Practical implications The study’s findings indicate differences in tourist shopping experiences at department stores and street markets. Several concrete recommendations are made, including a greater focus on the linkage to the national characteristic of street markets, and particularly the quality of local fruit, to enhance the tourist shopping experience. Originality/value Understanding the differences between shopping malls and street markets from the tourist’s perspective would further enhance the coexistence of shopping malls and street markets in tourism-led growth cities. As such, using reviews of both shopping malls and street markets from an identical pool of tourists, the present study will analyse and compare tourists’ actual shopping experiences, thereby addressing this gap in the research canon via integrated statistical and big data analysis techniques.

Understanding the role of big data analytics for coordination of electronic retail service supply chain

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fatao Wang ◽  
Di Wu ◽  
Hongxin Yu ◽  
Huaxia Shen ◽  
Yuanjun Zhao
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Data Analysis ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Coordination Mechanism ◽  
Market Demand ◽  
Content Type ◽  
Service Supply Chain ◽  
Retail Service

PurposeBased on the typical service supply chain (SSC) structure, the authors construct the model of e-tailing SSC to explore the coordination relationship in the supply chain, and big data analysis provides realistic possibilities for the creation of coordination mechanisms.Design/methodology/approachAt the present stage, the e-commerce companies have not yet established a mature SSC system and have not achieved good synergy with other members of the supply chain, the shortage of goods and the greater pressure of express logistics companies coexist. In the case of uncertain online shopping market demand, the authors employ newsboy model, applied in the operations research, to analyze the synergistic mechanism of SSC model.FindingsBy analyzing the e-tailing SSC coordination mechanism and adjusting relevant parameters, the authors find that the synergy mechanism can be implemented and optimized. Through numerical example analysis, the authors confirmed the feasibility of the above analysis.Originality/valueBig data analysis provides a kind of reality for the establishment of online SSC coordination mechanism. The establishment of an online supply chain coordination mechanism can effectively promote the efficient allocation of supplies and better meet consumers' needs.

scholarly journals The study of the effect of online review on purchase behavior

2020 ◽  
Vol 4 (1) ◽  
pp. 73-86 ◽  
Author(s):  
Jinghuan Zhang ◽  
Wenfeng Zheng ◽  
Shan Wang
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Online Reviews ◽  
Big Data Analysis ◽  
Influential Factors ◽  
Purchase Behavior ◽  
Behavioral Experiment ◽  
Content Type ◽  
Online Purchase ◽  
Network Big Data

Purpose The purpose of this paper is to explain the difference and connection between the network big data analysis technology and the psychological empirical research method. Design/methodology/approach This study analyzed the data from laboratory setting first, then the online sales data from Taobao.com to explore how the influential factors, such as online reviews (positive vs negative mainly), risk perception (higher vs lower) and product types (experiencing vs searching), interacted on the online purchase intention or online purchase behavior. Findings Compared with traditional research methods, such as questionnaire and behavioral experiment, network big data analysis has significant advantages in terms of sample size, data objectivity, timeliness and ecological validity. Originality/value Future study may consider the strategy of using complementary methods and combining both data-driven and theory-driven approaches in research design to provide suggestions for the development of e-commence in the era of big data.

scholarly journals A Novel on Transmission Line Tower Big Data Analysis Model Using Altered K-means and ADQL

2019 ◽  
Vol 11 (13) ◽  
pp. 3499 ◽  
Author(s):  
Se-Hoon Jung ◽  
Jun-Ho Huh
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Transmission Line ◽  
Clustering Algorithm ◽  
Learning Algorithm ◽  
Principal Component ◽  
Big Data Analysis ◽  
Standard Normal Distribution ◽  
Analysis Model ◽  
Q Learning

This study sought to propose a big data analysis and prediction model for transmission line tower outliers to assess when something is wrong with transmission line tower big data based on deep reinforcement learning. The model enables choosing automatic cluster K values based on non-labeled sensor big data. It also allows measuring the distance of action between data inside a cluster with the Q-value representing network output in the altered transmission line tower big data clustering algorithm containing transmission line tower outliers and old Deep Q Network. Specifically, this study performed principal component analysis to categorize transmission line tower data and proposed an automatic initial central point approach through standard normal distribution. It also proposed the A-Deep Q-Learning algorithm altered from the deep Q-Learning algorithm to explore policies based on the experiences of clustered data learning. It can be used to perform transmission line tower outlier data learning based on the distance of data within a cluster. The performance evaluation results show that the proposed model recorded an approximately 2.29%~4.19% higher prediction rate and around 0.8% ~ 4.3% higher accuracy rate compared to the old transmission line tower big data analysis model.

scholarly journals An Area-Context-Based Credibility Detection for Big Data in IoT

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Bo Zhao ◽  
Xiang Li ◽  
Jiayue Li ◽  
Jianwen Zou ◽  
Yifan Liu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Detection Method ◽  
Big Data Analysis ◽  
Detection Methods ◽  
High Detection ◽  
Data Credibility ◽  
High Level

In order to improve the credibility of big data analysis platform’s results in IoT, it is necessary to improve the quality of IoT data. Many detection methods have been proposed to filter out incredible data, but there are certain deficiencies that performance is not high, detection is not comprehensive, and process is not credible. So this paper proposes an area-context-based credibility detection method for IoT data, which can effectively detect point anomalies, behavioral anomalies, and contextual anomalies. The performance of the context determination and the data credibility detection of the device satisfying the area characteristics is superior to the similar algorithms. As the experiments show, the proposed method can reach a high level of performance with more than 97% in metrics, which can effectively improve the quality of IoT data.

scholarly journals Big Data Analysis of Water Quality of Secondary Water Supply

2019 ◽  
Vol 154 ◽  
pp. 744-749
Author(s):  
Liu Ying ◽  
Shu Shihu ◽  
Wang Hongyu ◽  
Zhao Xin ◽  
Yan Qi
Keyword(s):  
Water Quality ◽  
Big Data ◽  
Data Analysis ◽  
Water Supply ◽  
Big Data Analysis

scholarly journals A big-data analysis of political rhetoric relating the developments of the United States, China, and global powers

2020 ◽  
Vol 23 (3) ◽  
pp. 227-243
Author(s):  
Patrick Carter ◽  
Jeffrie Wang ◽  
Davis Chau
Keyword(s):  
United States ◽  
Big Data ◽  
Data Analysis ◽  
Political Rhetoric ◽  
The United States ◽  
Big Data Analysis ◽  
Content Type ◽  
World Power ◽  
The U.S ◽  
Over Time

PurposeThe similarities between the developments of the United States (U.S.) and China into global powers (countries with global economic, military, and political influence) can be analyzed through big data analysis from both countries. The purpose of this paper is to examine whether or not China is on the same path to becoming a world power like what the U.S. did one hundred years ago.Design/methodology/approachThe data of this study is drawn from political rhetoric and linguistic analysis by using “big data” technology to identify the most common words and political trends over time from speeches made by the U.S. and Chinese leaders from three periods, including 1905-1945 in U.S., 1977-2017 in U.S. and 1977-2017 in China.FindingsRhetoric relating to national identity was most common amongst Chinese and the U.S. leaders over time. The differences between the early-modern U.S. and the current U.S. showed the behavioral changes of countries as they become powerful. It is concluded that China is not a world power at this stage. Yet, it is currently on the path towards becoming one, and is already reflecting characteristics of present-day U.S., a current world power.Originality/valueThis paper presents a novel approach to analyze historical documents through big data text mining, a methodology scarcely used in historical studies. It highlights how China as of now is most likely in a transitionary stage of becoming a world power.

Big data analysis on the business process and management for the store layout and bundling sales

2019 ◽  
Vol 25 (7) ◽  
pp. 1783-1801 ◽  
Author(s):  
Shu-hsien Liao ◽  
Yi-Shan Tasi
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Business Process ◽  
Process Management ◽  
Business Processes ◽  
Analysis Data ◽  
Database System ◽  
Big Data Analysis ◽  
Content Type

Purpose In the retailing industry, database is the time and place where a retail transaction is completed. E-business processes are increasingly adopting databases that can obtain in-depth customers and sales knowledge with the big data analysis. The specific big data analysis on a database system allows a retailer designing and implementing business process management (BPM) to maximize profits, minimize costs and satisfy customers on a business model. Thus, the research of big data analysis on the BPM in the retailing is a critical issue. The paper aims to discuss this issue. Design/methodology/approach This paper develops a database, ER model, and uses cluster analysis, C&R tree and the a priori algorithm as approaches to illustrate big data analysis/data mining results for generating business intelligence and process management, which then obtain customer knowledge from the case firm’s database system. Findings Big data analysis/data mining results such as customer profiles, product/brand display classifications and product/brand sales associations can be used to propose alternatives to the case firm for store layout and bundling sales business process and management development. Originality/value This research paper is an example to develop the BPM of database model and big data/data mining based on insights from big data analysis applications for store layout and bundling sales in the retailing industry.

Big Data promises value: is hardware technology taken onboard?

2015 ◽  
Vol 115 (9) ◽  
pp. 1577-1595 ◽  
Author(s):  
Wasim Ahmad Bhat ◽  
S.M.K. Quadri
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
High Performance ◽  
Relevant Literature ◽  
Big Data Analysis ◽  
Theory And Practice ◽  
Full Potential ◽  
Content Type ◽  
Current Trends ◽  
Technology Trends

Purpose – The purpose of this paper is to explore the challenges posed by Big Data to current trends in computation, networking and storage technology at various stages of Big Data analysis. The work aims to bridge the gap between theory and practice, and highlight the areas of potential research. Design/methodology/approach – The study employs a systematic and critical review of the relevant literature to explore the challenges posed by Big Data to hardware technology, and assess the worthiness of hardware technology at various stages of Big Data analysis. Online computer-databases were searched to identify the literature relevant to: Big Data requirements and challenges; and evolution and current trends of hardware technology. Findings – The findings reveal that even though current hardware technology has not evolved with the motivation to support Big Data analysis, it significantly supports Big Data analysis at all stages. However, they also point toward some important shortcomings and challenges of current technology trends. These include: lack of intelligent Big Data sources; need for scalable real-time analysis capability; lack of support (in networks) for latency-bound applications; need for necessary augmentation (in network support) for peer-to-peer networks; and rethinking on cost-effective high-performance storage subsystem. Research limitations/implications – The study suggests that a lot of research is yet to be done in hardware technology, if full potential of Big Data is to be unlocked. Practical implications – The study suggests that practitioners need to meticulously choose the hardware infrastructure for Big Data considering the limitations of technology. Originality/value – This research arms industry, enterprises and organizations with the concise and comprehensive technical-knowledge about the capability of current hardware technology trends in solving Big Data problems. It also highlights the areas of potential research and immediate attention which researchers can exploit to explore new ideas and existing practices.

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