scholarly journals Qualitative Marketing Research: The State of Journal Publications

2017 ◽  
Author(s):  
Maria Petrescu ◽  
Brianna Lauer
Keyword(s):  
Big Data ◽  
Consumer Behavior ◽  
Literature Review ◽  
Qualitative Methods ◽  
Quantitative Methods ◽  
Marketing Research ◽  
Research Articles ◽  
Digital Information ◽  
Online Data ◽  
International Data

Qualitative methods in marketing have become essential not only for their classical advantage in consumer behavior, but also for their benefits in dealing with big data and data Qualitative methods in marketing have become essential not only for their classical advantage in consumer behavior, but also for their benefits in dealing with big data and data mining. Research from International Data Corporation (IDC) shows that when it comes to online data, unstructured content accounts for 90% of all digital information. Under these circumstances, this study provides a literature review and analysis on the role and relation of qualitative methods with quantitative methods in marketing research. The paper analyzes research articles that include qualitative studies in the top marketing journals during the last decade and focuses on their topic, domain, methods used and whether they used any triangulation with quantitative methods. Starting from this analysis, the study provides recommendations that can help better integrate qualitative methods in marketing research, academics and practice.mining. Research from International Data Corporation (IDC) shows that when it comes to online data, unstructured content accounts for 90% of all digital information. Under these circumstances, this study provides a literature review and analysis on the role and relation of qualitative methods with quantitative methods in marketing research. The paper analyzes research articles that include qualitative studies in the top marketing journals during the last decade and focuses on their topic, domain, methods used and whether they used any triangulation with quantitative methods. Starting from this analysis, the study provides recommendations that can help better integrate qualitative methods in marketing research, academics and practice.

Big Data Overview

2018 ◽  
pp. 1-9
Keyword(s):  
Big Data ◽  
Data Structures ◽  
Large Data ◽  
Annual Growth Rate ◽  
Digital Information ◽  
Compound Annual Growth Rate ◽  
Data Architecture ◽  
International Data ◽  
Data Scientist ◽  
Multiple Skills

Big data is now a reality. Data is created constantly. Data from mobile phones, social media, GIS, imaging technologies for medical diagnosis, etc., all these must be stored for some purpose. Also, this data needs to be stored and processed in real time. The challenging task here is to store this vast amount of data and to manage it with meaningful patterns and traditional data structures for processing. Data sources are expanding to grow into 50X in the next 10 years. An International Data Corporation (IDC) forecast sees that big data technology and services market at a compound annual growth rate (CAGR) of 23.1% over 2014-19 period with annual spending may reach $48.6 billion in 2019. The digital universe is expected to double the data size in next two years and by 2020 we may reach 44 zettabytes (1021) or 44 trillion gigabytes. The zettabyte is a multiple of the unit byte for digital information. There is a need to design new data architecture with new analytical sandboxes and methods with an integration of multiple skills for a data scientist to operate on such large data.

Methods of Researching Online Communities

2020 ◽  
pp. 23-112
Author(s):  
Dariusz Jemielniak
Keyword(s):  
Big Data ◽  
Narrative Analysis ◽  
Cultural Production ◽  
Quantitative Methods ◽  
Methodological Approach ◽  
Digital Culture ◽  
Data Sets ◽  
Online Data ◽  
Digital Communities ◽  
Ethnographic Analysis

The chapter presents the idea of Thick Big Data, a methodological approach combining big data sets with thick, ethnographic analysis. It presents different quantitative methods, including Google Correlate, social network analysis (SNA), online polls, culturomics, and data scraping, as well as easy tools to start working with online data. It describes the key differences in performing qualitative studies online, by focusing on the example of digital ethnography. It helps using case studies for digital communities as well. It gives specific guidance on conducting interviews online, and describes how to perform narrative analysis of digital culture. It concludes with describing methods of studying online cultural production, and discusses the notions of remix culture, memes, and trolling.

scholarly journals ‘Mind the Gap’: Assessing Differences between Brazilian and Mainstream IR Journals in Methodological Approaches

2021 ◽  
Vol 43 (3) ◽  
pp. 461-488
Author(s):  
Thales Carvalho ◽  
João Paulo Nicolini Gabriel ◽  
Dawisson Belém Lopes
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Quantitative Methods ◽  
World Politics ◽  
International Organization ◽  
Research Articles ◽  
Methodological Approaches ◽  
Mainstream Literature

Abstract In this article, we assess the methodological approaches employed in articles published in Brazilian and global mainstream IR journals in order to observe the differences between the two. To this end, we compare the methodological tools applied in research articles published in the top two Brazilian journals (Revista Brasileira de Política Internacional and Contexto Internacional) vis-à-vis two other top international influential mainstream publications (International Organization and World Politics), from the year 2009 to 2019. By undertaking a Systematic Literature Review, we surveyed a total of 955 articles. Our research concluded that Brazilian IR scholarship differs from the mainstream literature because (1) most articles do not mention the mobilized methods during their analyses, (2) the field of IR presents more non- and post-positivist approaches, and (3) contrary to the mainstream outlets, quantitative methods are rarely employed in Brazil.

scholarly journals TINJAUAN WARNA PADA VISUAL BRANDING WARUNG KOPI LOKAL

2019 ◽  
Vol 4 (2) ◽  
pp. 205
Author(s):  
Novena Ulita
Keyword(s):  
Big Data ◽  
Marketing Strategies ◽  
Brand Names ◽  
Direct Reaction ◽  
Digital Information ◽  
Purchasing Decisions ◽  
Online Data ◽  
Coffee Shop ◽  
Coffee Shops ◽  
Local Term

<strong>Abstract</strong><br />Color Overview on Visual Branding Local Coffee Shop. Visual power is an important part of marketing strategies. Visually gives a tremendous influence on consumer purchasing decisions. Humans have a direct reaction to color and shape to improve memory of vision, recognize, and identify brands. Color can convey messages and give special reactions to the human brain. The phenomenon of drinking coffee in coffee shops<br />is now shifting in the community. This research uses netnographic methods related to the existence and phenomenon of local coffee shops. By taking advantage in big data that can be accessed online, visual data analyzed with color branding theory, visual branding theory and sensory branding theory. The results of this study showed the increase of the local coffee shops presence in digital information media, and the tendency of certain<br />form, color and brand names using local term to put forward Indonesia local identity.<br /><div> </div><div> </div><div> </div><strong>Abstrak</strong><br />Tinjauan Warna pada Visual Branding Warung Kopi Lokal. Kekuatan visual menjadi bagian penting dalam strategi pemasaran. Visual memberikan pengaruh luar biasa terhadap keputusan pembelian konsumen. Manusia memiliki reaksi langsung terhadap warna dan bentuk, meningkatkan memori penglihatan, mengenali, dan mengidentifikasi brand. Warna dapat menyampaikan pesan dan memberikan reaksi khusus pada otak<br />manusia. Fenomena minum kopi di warung kopi saat ini sudah bergeser di masyarakat. Penelitian ini menggunakan metode netnografi terkait keberadaan dan fenomena warung kopi lokal. Dengan memanfaatkan big data yang dapat diakses secara online, data visual dianalisi menggunakan teori color branding, teori visual branding dan teori branding sensory. Hasil penelitian menunjukkan adanya peningkatan keberadaan warung kopi<br />lokal pada media informasi digital, serta kecenderungan penggunaan bentuk, warna serta penggunaan istilah lokal untuk mengedepankan identitas lokal Indonesia.<br /><br />

PENGARUH PEMASARAN LANGSUNG TERHADAP PERILAKU KONSUMEN BISNIS MENGGUNAKAN MEETING PACKAGE DI GRAND TJOKRO HOTEL BANDUNG

2019 ◽  
Vol 3 (2) ◽  
pp. 96-110
Author(s):  
Dian Candra Fatihah ◽  
Dewi Rani Desmawati
Keyword(s):  
Consumer Behavior ◽  
Quantitative Methods ◽  
Descriptive Analysis ◽  
Direct Marketing ◽  
Simple Random Sampling ◽  
Analysis Data ◽  
Coefficient Of Determination ◽  
Validity And Reliability ◽  
Sales And Marketing ◽  
Strong Relation

This research is aimed to determine The Influence of Direct Marketing to Business Consumer Behavior Using Meeting Package at Grand Tjokro Hotel Bandung. Respondents from this research are 44 consumers selected by simple random sampling. This research used quantitative methods with approach descriptive analysis. Data was collected through survey, questionnaires and interviews. The test results validity and reliability variables X and Y are valid and reliable. The data analysis used statistical test of correlation pearson product moment and the coefficient of determination. Calculated used SPSS version 21. The data of this research is obtained from consumer data Grand Tjokro Hotel Bandung. From the result obtained correlation coefficient of 0,633. This tells that there is strong relation between Direct Marketing of Business Consumer Behavior. The influence of Direct Marketing to Business Consumer Behavior to 40,0% and remaining 60,0% is influenced by other factors not examined. The problems are competition between hotels is very tight, lack of coordination between sales. The suggestions given to fix the problem are 1) Do a better promotion to attract the attention of consumers; 2) Evaluation between Manager and Sales especially in Sales and Marketing Division.

Trends and Opportunities in Health Analytics as a Service and Implications for Use in Low Resource Settings: A Literature Review Abstract (Preprint)

2019 ◽  
Author(s):  
Meghana Bastwadkar ◽  
Carolyn McGregor ◽  
S Balaji
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Intensive Care ◽  
Literature Review ◽  
Health Monitoring ◽  
Data Analytics ◽  
Neonatal Intensive Care ◽  
Big Data Analytics ◽  
Healthcare Facilities

BACKGROUND This paper presents a systematic literature review of existing remote health monitoring systems with special reference to neonatal intensive care (NICU). Articles on NICU clinical decision support systems (CDSSs) which used cloud computing and big data analytics were surveyed. OBJECTIVE The aim of this study is to review technologies used to provide NICU CDSS. The literature review highlights the gaps within frameworks providing HAaaS paradigm for big data analytics METHODS Literature searches were performed in Google Scholar, IEEE Digital Library, JMIR Medical Informatics, JMIR Human Factors and JMIR mHealth and only English articles published on and after 2015 were included. The overall search strategy was to retrieve articles that included terms that were related to “health analytics” and “as a service” or “internet of things” / ”IoT” and “neonatal intensive care unit” / ”NICU”. Title and abstracts were reviewed to assess relevance. RESULTS In total, 17 full papers met all criteria and were selected for full review. Results showed that in most cases bedside medical devices like pulse oximeters have been used as the sensor device. Results revealed a great diversity in data acquisition techniques used however in most cases the same physiological data (heart rate, respiratory rate, blood pressure, blood oxygen saturation) was acquired. Results obtained have shown that in most cases data analytics involved data mining classification techniques, fuzzy logic-NICU decision support systems (DSS) etc where as big data analytics involving Artemis cloud data analysis have used CRISP-TDM and STDM temporal data mining technique to support clinical research studies. In most scenarios both real-time and retrospective analytics have been performed. Results reveal that most of the research study has been performed within small and medium sized urban hospitals so there is wide scope for research within rural and remote hospitals with NICU set ups. Results have shown creating a HAaaS approach where data acquisition and data analytics are not tightly coupled remains an open research area. Reviewed articles have described architecture and base technologies for neonatal health monitoring with an IoT approach. CONCLUSIONS The current work supports implementation of the expanded Artemis cloud as a commercial offering to healthcare facilities in Canada and worldwide to provide cloud computing services to critical care. However, no work till date has been completed for low resource setting environment within healthcare facilities in India which results in scope for research. It is observed that all the big data analytics frameworks which have been reviewed in this study have tight coupling of components within the framework, so there is a need for a framework with functional decoupling of components.

Data Mining-based Financial Statement Fraud Detection: Systematic Literature Review and Meta-analysis to Estimate Data Sample Mapping of Fraudulent Companies Against Non-fraudulent Companies

2021 ◽  
pp. 097215092098485
Author(s):  
Sonika Gupta ◽  
Sushil Kumar Mehta
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Classification Accuracy ◽  
Meta Analysis ◽  
Financial Statement ◽  
Research Articles ◽  
Financial Statement Fraud ◽  
Data Mining Techniques

Data mining techniques have proven quite effective not only in detecting financial statement frauds but also in discovering other financial crimes, such as credit card frauds, loan and security frauds, corporate frauds, bank and insurance frauds, etc. Classification of data mining techniques, in recent years, has been accepted as one of the most credible methodologies for the detection of symptoms of financial statement frauds through scanning the published financial statements of companies. The retrieved literature that has used data mining classification techniques can be broadly categorized on the basis of the type of technique applied, as statistical techniques and machine learning techniques. The biggest challenge in executing the classification process using data mining techniques lies in collecting the data sample of fraudulent companies and mapping the sample of fraudulent companies against non-fraudulent companies. In this article, a systematic literature review (SLR) of studies from the area of financial statement fraud detection has been conducted. The review has considered research articles published between 1995 and 2020. Further, a meta-analysis has been performed to establish the effect of data sample mapping of fraudulent companies against non-fraudulent companies on the classification methods through comparing the overall classification accuracy reported in the literature. The retrieved literature indicates that a fraudulent sample can either be equally paired with non-fraudulent sample (1:1 data mapping) or be unequally mapped using 1:many ratio to increase the sample size proportionally. Based on the meta-analysis of the research articles, it can be concluded that machine learning approaches, in comparison to statistical approaches, can achieve better classification accuracy, particularly when the availability of sample data is low. High classification accuracy can be obtained with even a 1:1 mapping data set using machine learning classification approaches.

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