Uncovering the message from the mess of big data

Bendle, Neil; Wang, Xin

doi:10.1016/j.bushor.2015.10.001

Cited by 46 publications

(22 citation statements)

References 7 publications

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“…The research findings are consistent with the findings by (4)(5)(6)(7)(8). These results prove that the factors forming political marketing have general definite elements, and in many situations similar ones (according to its specific theory), by proper applying which and related solutions, one can guide the thought and public opinion in the intended direction.…”

Section: Fig 1 Structural Model Of Constituent Elements Of Politicasupporting

confidence: 88%

“…The third difference is in the need to get public opinion (Bendle, Wang, 2016). As specified, in business marketing, even one percent or much less is acceptable and sufficient, whereas in the political sphere, the effort is to achieve more than 50% of the votes and tendency of society.…”

Section: Differences In Commercial Marketing and Political Marketingmentioning

confidence: 99%

“…In other words, people from different nationalities have definite basic cultural principles they adhere to and value their traditions and values greatly. Various studies have shown that always in the electoral process for popular vote, candidates with slogans and programs based on preservation of cultural issues have more chances to win votes and support (Bendle, Wang, 2016).…”

Section: © Filodiritto Editore -Proceedingsmentioning

confidence: 99%

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Constituent elements of political marketing in creating a positive attitude for voting for a particular candidate

Gholipour¹,

Monem²

2017

Int Rev

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This study was conducted to draw the structural model of constituent elements of political marketing in one of the large governmental organizations in Gilan of Iran. For this purpose, we randomly selected 217 subjects who completed the questionnaire. The results showed that seven major factors, economic, political, social, cultural, marketing mix, personality, and supporting individuals and factions, are involved in political marketing. Moreover, the strongest factor influencing political marketing has been economic indicators (p<0.01). Political, social, and cultural factors, as well as marketing mix with standard coefficients of 0.39, 0.30, 0.32, and 0.33, respectively, are the factors that directly predict political marketing (p<0.01), and variables such as personality and supporting individuals and factions indirectly affect political marketing.

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Section: Fig 1 Structural Model Of Constituent Elements Of Politicasupporting

confidence: 88%

Section: Differences In Commercial Marketing and Political Marketingmentioning

confidence: 99%

Section: © Filodiritto Editore -Proceedingsmentioning

confidence: 99%

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Constituent elements of political marketing in creating a positive attitude for voting for a particular candidate

Gholipour¹,

Monem²

2017

Int Rev

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“…However, in practice, accurate and effective applications of big data generated by customers remain a major problem [13]. Current big data research divides data into structured and unstructured types [10,14,15].…”

Section: Introductionmentioning

confidence: 99%

Value co-creation between firms and customers: The role of big data-based cooperative assets

Xie

Xiao

et al. 2016

Information & Management

205

159

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A B S T R A C TTo better understand how big data interconnects firms and customers in promoting value co-creation, we propose a theoretical framework of big data-based cooperative assets based on evidence of multiple case studies. We identify four types of big data resources and four types of associated digital platforms, and we explore how firms develop the cooperative assets by transforming big data resources via the theoretical lens of service-dominant logic. This study offers a new theoretical perspective on value co-creation and an alternative competitive strategy in the era of big data for firms.2016 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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“…Such understanding assists businesses in reaching various groups, using the differences to guide different strategies for increasing brand engagement for each group (Fan & Gordon 2014). Since the success of these strategies depends on the value that customers place on features (D'Aveni 2007), the authors designed ReviewMap to develop a list of the features offered by the different brands in the market, and then gather data on how customers perceive those features utilizing the probabilistic topic modeling technique, Latent Dirichlet Allocation (LDA) (Blei, 2012;Bendle, N.T., and Wang, X. 2016).…”

Section: Literature Reviewmentioning

confidence: 99%

Competitive analysis of online reviews using exploratory text mining

Amadio¹,

Procaccino²

2016

Tour. hosp. manag.

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Purpose -This paper explores the usefulness of analyzing text-based online reviews using text mining tools and visual analytics for SWOT Analysis, as applied to the hotel industry. These results can be used to develop competitive actions. Design -The text mining/visualization tool, ReviewMap, was used to transform an archive of reviews spanning multiple suppliers into a hierarchy of data of increasing dimensionality. Visual summaries at each level were integrated to propagate selections at one level throughout the rest of the hierarchy. These visual summaries identify features required for competition at a given level and features that currently discriminate amongst competitors. Methodology -The approach was exploratory, the objective of which was to determine if useable competitive intelligence could be found in a typical collection of online reviews for a set of competing hotels. A publically available collection of reviews was subjected to a set of text mining procedures and visual analyses in order to summarize the features and opinions expressed. Originality -Prior analyses of online reviews relied solely upon numeric "star" ratings. This study utilized text mining to uncover information within the written comments and applied the information in a SWOT Analysis of three competing hotels. Findings -In the set of reviews used in this paper, a common measure of analytical power almost doubled when text mining summaries of the written comments were used in combination with numeric ratings. Visual analytics revealed the dominant features for each hotel, the features required of all hotels competing at a given level, and the features that define specific positions within the competitive landscape. This analysis of strengths, weaknesses, opportunities and threats revealed several promising competitive actions for the hotels in the study.

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Uncovering the message from the mess of big data

Cited by 46 publications

References 7 publications

Constituent elements of political marketing in creating a positive attitude for voting for a particular candidate

Constituent elements of political marketing in creating a positive attitude for voting for a particular candidate

Value co-creation between firms and customers: The role of big data-based cooperative assets

Competitive analysis of online reviews using exploratory text mining

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