A comparative study of online consumer behavior: a tale of two research methods

2019 ◽  
Vol 15 (4) ◽  
pp. 716-727 ◽  
Author(s):  
Kun-Huang Huarng ◽  
Tiffany Hui-Kuang Yu
Keyword(s):  
Consumer Behavior ◽  
Linear Regression ◽  
Research Methods ◽  
Data Sets ◽  
Multiple Relationships ◽  
Data Set ◽  
Content Type ◽  
Online Consumer Behavior ◽  
Online Consumer ◽  
Different Characteristics

Purpose The use of linear regression analysis is common in the social sciences. The purpose of this paper is to show the advantage of a qualitative research method, namely, structured qualitative analysis (SQA), over the linear regression method by using different characteristics of data. Design/methodology/approach Data were gathered from a study of online consumer behavior in Taiwan. The authors changed the content of the data to have different sets of data. These data sets were used to demonstrate how SQA and linear regression works individually, and to contrast the empirical analyses and empirical results from linear regression and SQA. Findings The linear regression method uses one equation to model different characteristics of data. When facing a data set containing a big and a small size of different characteristics, linear regression tends to provide an equation by modeling the characteristics of the big size data and subsuming those of the small size. When facing a data set containing similar sizes of data with different characteristics, linear regression tends to provide an equation by averaging these data. The major concern is that the one equation may not be able to reflect the data of various characteristics (different values of independent variables) that result in the same outcome (the same value of dependent variable). In contrast, SQA can identify various variable combinations (multiple relationships) leading to the same outcome. SQA provided multiple relationships to represent different sizes of data with different characteristics so it created consistent empirical results. Research limitations/implications Two research methods work differently. The popular linear regression tends to use one equation to model different sizes and characteristics of data. The single equation may not be able to cover different behaviors but may lead to the same outcome. Instead, SQA provides multiple relationships for different sizes of data with different characteristics. The analyses are more consistent and the results are more appropriate. The academics may re-think the existing literature using linear regression. It would be interesting to see if there are new findings for similar problems by using SQA. The practitioners have a new method to model real world problems and to understand different possible combinations of variables leading to the same outcome. Even the relationship obtained from a small data set may be very valuable to practitioners. Originality/value This paper compared online consumer behavior by using two research methods to analyze different data sets. The paper offered the manipulation of real data sets to create different data sizes of different characteristics. The variations in empirical results from both methods due to the various data sets facilitate the comparison of both methods. Hence, this paper can serve as a complement to the existing literature, focusing on the justification of research methods and on limitations of linear regression.

Frontiers: The Impact of Ad-Blockers on Online Consumer Behavior

2021 ◽  
Author(s):  
Vilma Todri
Keyword(s):  
Consumer Behavior ◽  
Panel Data ◽  
Online Search ◽  
Data Set ◽  
Online Consumer Behavior ◽  
Online Consumer ◽  
The Impact

This paper investigates the impact of ad-blockers on online search and purchasing behaviors by empirically analyzing a consumer-level panel data set.

E-consumer conformity and its impact on consumer attitude

2018 ◽  
Vol 12 (4) ◽  
pp. 455-468 ◽  
Author(s):  
Utkal Khandelwal ◽  
Seemant Kumar Yadav ◽  
Vikas Tripathi ◽  
Vivek Agrawal
Keyword(s):  
Consumer Behavior ◽  
Equation Modeling ◽  
Consumer Attitude ◽  
Technological Advancement ◽  
Content Type ◽  
Online Consumer Behavior ◽  
Online Mode ◽  
Conformity Behavior ◽  
Different Dimensions ◽  
Online Consumer

PurposeWith the tremendous increase in the number of netizens, online consumer behavior has become an important issue nowadays. One of the important issues of online consumer behavior is e-consumer conformity. This paper aims to explore prominent factors of e-consumer conformity and its impact on consumer attitude, which helps marketers to understand this new business arena and involve this relationship to enhance their business.Design/methodology/approachFor the purpose, convenience sampling was used with sample size of 510. Offline as well as online mode of survey was applied. The resultant hypotheses (based on the developed model depicting normative and informational consumer conformity effect on attitude) were examined by structured equation modeling.FindingsThe present study presents the different dimensions of e-consumer conformity and its difference in metro and non-metro cities on which marketers have to frame their strategies. The study revealed that the customer attitude is largely affected by others expectations (conformance with others expectations, NCC) rather others knowledge and expertise (ICC). Additionally, the comparison of virtual conformity behavior of metro and non-metro customers was made, and it was found that conformity behavior does not significantly differ in these two contexts.Practical implicationsBusiness saturation in metro cities, infrastructural growth and technological advancement in non-metro cities, companies are moving toward non-metro cities. Due to contextual differences existing between metro and non-metro market, it is difficult to trace the changes in the marketing policies and device the appropriate strategy accordingly for the marketers. In lieu of this, the present study presents the different dimensions of e-consumer conformity and its degree of difference in metro and non-metro cities on which marketers have to frame their strategies.Originality/valueGood number of research has been conducted on consumer conformity in India; however, there is a scarcity of literature in virtual consumer conformity in India. This research is not only establishing the relationship between virtual consumer conformity and consumer attitude but also establishing the difference of virtual consumer conformity in metro and non-metro cities in India.

Consumer behavior in the online context

2014 ◽  
Vol 8 (3) ◽  
pp. 169-202 ◽  
Author(s):  
Shannon Cummins ◽  
James W. Peltier ◽  
John A. Schibrowsky ◽  
Alexander Nill
Keyword(s):  
Social Network ◽  
Consumer Behavior ◽  
Network Theory ◽  
Design Methodology ◽  
Social Network Theory ◽  
Cross Cultural ◽  
User Generated Content ◽  
Content Type ◽  
Online Consumer Behavior ◽  
Online Consumer

Purpose – The purpose of this article is to review the consumer behavior and social network theory literature related to the online and e-commerce context. Design/methodology/approach – To conduct the review, the authors draw on a sample of 942 articles published from 1993 to 2012 addressing consumer behavior or social network issues in the online or social media context. The sample is analyzed by both era (incubation, expansion and explosion) and primary topic. Findings – Eight categories of online consumer behavior research are described. In the order from largest to smallest, these are: cognitive issues, user-generated content, Internet demographics and segmentation, online usage, cross cultural, online communities and networks, strategic use and outcomes and consumer Internet search. Originality/value – The literature has been summarized in each category and research opportunities have been offered for consumer behavior and social network scholars interested in exploring the online context.

scholarly journals USE OF ONLINE SHOP IN INSTAGRAM IN WOMEN'S CONSUMPTIVE BEHAVIOR IN JAKARTA

2018 ◽  
Vol 15 (2) ◽  
pp. 115-126
Author(s):  
Fauziah Fauziah
Keyword(s):  
Qualitative Research ◽  
Consumer Behavior ◽  
Research Methods ◽  
Online Shopping ◽  
Qualitative Research Methods ◽  
Online Consumer Behavior ◽  
Online Consumer ◽  
Online Stores ◽  
Consumptive Behavior ◽  
And Behavior

The rise of shopping through online stores on Instagram makes women in Jakarta a problem in this case. Generally women do online shopping not only on the mere needs, for the sake of and the lifestyle of a country called wasteful or better known as consumptive behavior or consumerism behavior. This study used qualitative research methods. The results of research conducted on six people obtained information from online consumer behavior in Jakarta has entered the consumer category or online shopping on large Instagram for women, online shopping or online stores are more interested in shopping problems with reasons and behavior because they are not want to miss the trend and offer prices from online stores even though the product is not needed.

Lady first? The gender difference in the influence of service quality on online consumer behavior

2019 ◽  
Vol 10 (3) ◽  
pp. 408-428 ◽  
Author(s):  
Weihua Wang ◽  
Saebum Kim
Keyword(s):  
Gender Differences ◽  
Consumer Behavior ◽  
Customer Satisfaction ◽  
Service Quality ◽  
Customer Loyalty ◽  
Self Regulation ◽  
Content Type ◽  
Online Consumer Behavior ◽  
Online Consumer ◽  
The Impact

Purpose This paper aims to articulate the gender differences in the influence of service quality on online consumer behavior. Design/methodology/approach Through data collected via a Web-based questionnaire survey from 330 consumers in China, this study builds and analyzes a structural equation model, using five dimensions of E-service quality, customer satisfaction and customer loyalty, and focuses on the moderation test of gender. Findings This study finds that first, efficiency dimension of e-service quality is of same importance for male and female customers; second, there are significant gender differences in the responsiveness and reliability dimensions of E-service quality, which affect customer satisfaction; third, the impact of female customer satisfaction on customer loyalty is stronger than for male customers. Practical implications Online retailers with limited service resources should preferentially respond to service requests from male customers and provide more reliable services for female consumers under the same condition. Originality/value The research validated the applicability of self-regulation theory in online consumer behavior, explored the occurrence stage and characteristics of gender differences in online consumer behavior under influence of SRT and first found some apparent gender differences in the influence of different dimensions of e-service quality on online consumer behavior.

scholarly journals Research Jungle on Online Consumer Behaviour in the Context of Web 2.0: Traceability, Frontiers and Perspectives in the Post-Pandemic Era

2021 ◽  
Vol 16 (5) ◽  
pp. 1740-1767
Author(s):  
Xi Zhang ◽  
Hongda Liu ◽  
Pinbo Yao
Keyword(s):  
Consumer Behavior ◽  
Web 2.0 ◽  
Research Network ◽  
Analysis Model ◽  
Economic Changes ◽  
Consumption Behavior ◽  
Online Consumer Behavior ◽  
Traditional Research ◽  
Online Consumer ◽  
Development Paths

In recent years, the study of online consumption behavior has gradually formed its research system and analysis model based on the inheritance of traditional research paradigms, focusing on the inner mechanism of consumption models explained by consumption activities. Online consumption is based on the research scenario of social e-commerce and forms a broad research network through the extension of consumer objects, consumer psychology, and consumer concepts. Although the theoretical constructs of online consumer behavior continue to improve, the relevant studies still do not fully grasp the research frontiers due to the lagging research nature. In the context of Web 2.0, it is impossible to run through the latest developments in online consumption research. Moreover, the study of online consumer behavior has shown a trend of diversification and multiple schools of thought, and a research jungle has emerged, which in essence is the perfection and new height of the study of consumerism. This paper analyses the origins, frontiers, and prospects of online consumer behavior research to clarify the formation principles, development paths, and future directions of the online consumer behavior research jungle. Ultimately based on the economic changes in the post-pandemic context, this paper integrates and proposes an evolving mechanism for studying online consumption behavior, intending to achieve a peek into and reveal the jungle of online consumption research.

A systematic review of machine learning-based missing value imputation techniques

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tressy Thomas ◽  
Enayat Rajabi
Keyword(s):  
Machine Learning ◽  
Selection Process ◽  
Evaluation Metrics ◽  
Correct Prediction ◽  
Data Sets ◽  
Data Set ◽  
Missing Value ◽  
Content Type ◽  
Missing Value Imputation ◽  
Literature Reviews

PurposeThe primary aim of this study is to review the studies from different dimensions including type of methods, experimentation setup and evaluation metrics used in the novel approaches proposed for data imputation, particularly in the machine learning (ML) area. This ultimately provides an understanding about how well the proposed framework is evaluated and what type and ratio of missingness are addressed in the proposals. The review questions in this study are (1) what are the ML-based imputation methods studied and proposed during 2010–2020? (2) How the experimentation setup, characteristics of data sets and missingness are employed in these studies? (3) What metrics were used for the evaluation of imputation method?Design/methodology/approachThe review process went through the standard identification, screening and selection process. The initial search on electronic databases for missing value imputation (MVI) based on ML algorithms returned a large number of papers totaling at 2,883. Most of the papers at this stage were not exactly an MVI technique relevant to this study. The literature reviews are first scanned in the title for relevancy, and 306 literature reviews were identified as appropriate. Upon reviewing the abstract text, 151 literature reviews that are not eligible for this study are dropped. This resulted in 155 research papers suitable for full-text review. From this, 117 papers are used in assessment of the review questions.FindingsThis study shows that clustering- and instance-based algorithms are the most proposed MVI methods. Percentage of correct prediction (PCP) and root mean square error (RMSE) are most used evaluation metrics in these studies. For experimentation, majority of the studies sourced the data sets from publicly available data set repositories. A common approach is that the complete data set is set as baseline to evaluate the effectiveness of imputation on the test data sets with artificially induced missingness. The data set size and missingness ratio varied across the experimentations, while missing datatype and mechanism are pertaining to the capability of imputation. Computational expense is a concern, and experimentation using large data sets appears to be a challenge.Originality/valueIt is understood from the review that there is no single universal solution to missing data problem. Variants of ML approaches work well with the missingness based on the characteristics of the data set. Most of the methods reviewed lack generalization with regard to applicability. Another concern related to applicability is the complexity of the formulation and implementation of the algorithm. Imputations based on k-nearest neighbors (kNN) and clustering algorithms which are simple and easy to implement make it popular across various domains.

Fast and accurate detection of surface defect based on improved YOLOv4

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jiawei Lian ◽  
Junhong He ◽  
Yun Niu ◽  
Tianze Wang
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Surface Defect ◽  
Steel Ingot ◽  
Industrial Applications ◽  
Data Sets ◽  
Data Set ◽  
Processing Technologies ◽  
Content Type ◽  
Public Data

Purpose The current popular image processing technologies based on convolutional neural network have the characteristics of large computation, high storage cost and low accuracy for tiny defect detection, which is contrary to the high real-time and accuracy, limited computing resources and storage required by industrial applications. Therefore, an improved YOLOv4 named as YOLOv4-Defect is proposed aim to solve the above problems. Design/methodology/approach On the one hand, this study performs multi-dimensional compression processing on the feature extraction network of YOLOv4 to simplify the model and improve the feature extraction ability of the model through knowledge distillation. On the other hand, a prediction scale with more detailed receptive field is added to optimize the model structure, which can improve the detection performance for tiny defects. Findings The effectiveness of the method is verified by public data sets NEU-CLS and DAGM 2007, and the steel ingot data set collected in the actual industrial field. The experimental results demonstrated that the proposed YOLOv4-Defect method can greatly improve the recognition efficiency and accuracy and reduce the size and computation consumption of the model. Originality/value This paper proposed an improved YOLOv4 named as YOLOv4-Defect for the detection of surface defect, which is conducive to application in various industrial scenarios with limited storage and computing resources, and meets the requirements of high real-time and precision.

scholarly journals From pole to pole: 33 years of physical oceanography onboard R/V <i>Polarstern</i>

2017 ◽  
Vol 9 (1) ◽  
pp. 211-220 ◽  
Author(s):  
Amelie Driemel ◽  
Eberhard Fahrbach ◽  
Gerd Rohardt ◽  
Agnieszka Beszczynska-Möller ◽  
Antje Boetius ◽  
...  
Keyword(s):  
Data Collection ◽  
Water Cycle ◽  
Heat Budget ◽  
Ocean Dynamics ◽  
The Arctic ◽  
Sensor Calibration ◽  
Data Sets ◽  
Calibration Data ◽  
Data Set ◽  
Different Characteristics

Abstract. Measuring temperature and salinity profiles in the world's oceans is crucial to understanding ocean dynamics and its influence on the heat budget, the water cycle, the marine environment and on our climate. Since 1983 the German research vessel and icebreaker Polarstern has been the platform of numerous CTD (conductivity, temperature, depth instrument) deployments in the Arctic and the Antarctic. We report on a unique data collection spanning 33 years of polar CTD data. In total 131 data sets (1 data set per cruise leg) containing data from 10 063 CTD casts are now freely available at doi:10.1594/PANGAEA.860066. During this long period five CTD types with different characteristics and accuracies have been used. Therefore the instruments and processing procedures (sensor calibration, data validation, etc.) are described in detail. This compilation is special not only with regard to the quantity but also the quality of the data – the latter indicated for each data set using defined quality codes. The complete data collection includes a number of repeated sections for which the quality code can be used to investigate and evaluate long-term changes. Beginning with 2010, the salinity measurements presented here are of the highest quality possible in this field owing to the introduction of the OPTIMARE Precision Salinometer.

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