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تلفن: ۰۹۲۱۶۴۲۶۳۸۴

دانلود رایگان مقاله رویکردهای تحقیق درباره کلان داده در بازاریابی

عنوان فارسی: رویکردهای تحقیق درباره کلان داده در بازاریابی: متن کاوی و مدلسازی موضوع بر اساس تحلیل ادبیات
عنوان انگلیسی: Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis
تعداد صفحات مقاله انگلیسی : 7 تعداد صفحات ترجمه فارسی : ترجمه نشده
سال انتشار : 2017 نشریه : الزویر - Elsevier
فرمت مقاله انگلیسی : PDF کد محصول : E5505
محتوای فایل : PDF حجم فایل : 1 Mb
رشته های مرتبط با این مقاله: مدیریت
گرایش های مرتبط با این مقاله: بازاریابی
مجله: تحقیقات اروپایی درباره مدیریت و اقتصاد کسب و کار - europen rsearch on management and business economics
دانشگاه: Instituto Universitário de Lisboa - Portugal
کلمات کلیدی: کلان داده، بازاریابی، تحلیل ادبیات، رویکردهای تحقیق، متن کاوی
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چکیده

ABSTRACT

 Given the research interest on Big Data in Marketing, we present a research literature analysis based on a text mining semi-automated approach with the goal of identifying the main trends in this domain. In particular, the analysis focuses on relevant terms and topics related with five dimensions: Big Data, Marketing, Geographic location of authors’ affiliation (countries and continents), Products, and Sectors. A total of 1560 articles published from 2010 to 2015 were scrutinized. The findings revealed that research is bipartite between technological and research domains, with Big Data publications not clearly aligning cutting edge techniques toward Marketing benefits. Also, few inter-continental co-authored publications were found. Moreover, findings show that research in Big Data applications to Marketing is still in an embryonic stage, thus making it essential to develop more direct efforts toward business for Big Data to thrive in the Marketing arena

نتیجه گیری

5. Conclusions

This research literature analysis focused on the application of Big Data in Marketing, in an attempt to identify the trends in these applied domains through different dimensions. A total of 1560 articles published between 2010 and 2015 indexed in ScienceDirect’s database were gathered and scrutinized. The large number of articles makes the usage of text mining an adequate choice for a better assessment of the literature. The results revealed that Big Data in Marketing has seen an increasing interest over the years, with each year doubling the previous one in publication output numbers. The application of text mining and topic modeling in the collected articles provides a summarized overview of the literature, by grouping articles in logical topics characterized by key relevantterms. Authors’ affiliation assessment enabled to conclude that most of the research originates from Europe, North America and Asia. Asian authors seem to be keener on searching intercontinental research. There are few publications in South America or Africa, two largely populated continents. However, such result could rise from an overall lower interest or research output in Big Data or data analytics in general and not generally related to the marketing field. Also, energy and healthcare are receiving around half of the attention of consumer goods in North America. The findings from this study rise in the form of prescriptions for future research. First, while plenty of research is being conducted on Big Data and on Marketing, less is found in addressing specifically the benefits that marketers could potentially achieve through Big Data solutions. While Big Data adoption within the industry is taking place nowadays, there is a gap for research to clearly identify the pros and cons for organizations to invest in Big Data. As Akter and Wamba (2016) noted, after defining the boundaries for a Big Data solution, it is imperative that the implementation is perfectly aligned with the challenges posed by the specificities of the business, as each solution needs to be contextaware. The confirmation of such discovery unveiled a research gap incross-disciplinary research, withtechnological researchersneeding to better align the benefits of Big Data toward Marketing. It is interesting to note that although several specific Marketing related terms often seen as associated to data analysis were included (e.g., customer retention, customer segmentation), few appear highlighted in the topics uncovered, and the ones that did appear, show a weak relationship to the corresponding topic, paling in comparison to the relevance of the respective Big Data term. Some limitations should be pointed out which could also be addressed in future research. First, Big Data research is still in its infancy as volumes of data keep piling up. Therefore, it is a very dynamic subject, implying the results presented may need updating in a narrow time window. Also, as Big Data conceals different challenges which may be translated into the known 5 Vs (Volume, Variety, Velocity, Variability, and Value), another research direction could be to understand the main requirements in designing Marketing solutions to answer each specific challenge.