The internet of everything: implications of marketing analytics from a consumer policy perspective

2020 ◽  
Vol 37 (6) ◽  
pp. 675-686
Author(s):  
Maria Petrescu ◽  
Anjala Krishen ◽  
My Bui
Keyword(s):  
Data Security ◽  
Personal Information ◽  
Legal Framework ◽  
Information Privacy ◽  
Social Media Data ◽  
Content Type ◽  
Marketing Analytics ◽  
Internet Of Everything ◽  
The Impact ◽  
Media Data

Purpose The purpose of this paper is to evaluate the impact of internet of everything (IoE) on marketing analytics, the benefits and challenges it presents and the implications of its policy and legal framework. Design/methodology/approach Qualitative research methods are used across privacy statements and consumer social media data to determine factors of concern for business and consumers. Findings The qualitative analysis of privacy statements and consumer social media data unveils factors of concern that are common for businesses and consumers, such as user consent and data security, as well as problems specific to the IoE, including the use of mobile devices and various service providers. The study also shows a differentiation in the levels of information privacy concerns for marketing practice, the use of personal information, sharing information with third parties and consumer consent and agreement to critical terms. Practical implications Recommendations for policymakers, practitioners and researchers, especially concerning the need for more studies related to the issues of data security, information privacy and personal information are addressed. Originality/value There is a need to assess the potential implications that the use of marketing analytics in the IoE can have for marketing policy, governmental regulations and industry self-regulation. The purpose of this research is to perform an exploratory evaluation of the impact of IoE on marketing analytics, the benefits and challenges it presents and the implications of its policy and legal framework.

Determining social media impact on the politics of developing countries using social network analytics

2016 ◽  
Vol 50 (4) ◽  
pp. 481-507 ◽  
Author(s):  
Collins Udanor ◽  
Stephen Aneke ◽  
Blessing Ogechi Ogbuokiri
Keyword(s):  
Social Media ◽  
Developing Countries ◽  
Developing Economies ◽  
General Election ◽  
Typical Application ◽  
Social Media Data ◽  
Content Type ◽  
The Social ◽  
The Impact ◽  
Media Data

Purpose The purpose of this paper is to use the Twitter Search Network of the Apache NodeXL data discovery tool to extract over 5,000 data from Twitter accounts that twitted, re-twitted or commented on the hashtag, #NigeriaDecides, to gain insight into the impact of the social media on the politics and administration of developing countries. Design/methodology/approach Several algorithms like the Fruchterman-Reingold algorithm, Harel-Koren Fast Multiscale algorithm and the Clauset-Newman-Moore algorithms are used to analyse the social media metrics like betweenness, closeness centralities, etc., and visualize the sociograms. Findings Results from a typical application of this tool, on the Nigeria general election of 2015, show the social media as the major influencer and the contribution of the social media data analytics in predicting trends that may influence developing economies. Practical implications With this type of work, stakeholders can make informed decisions based on predictions that can yield high degree of accuracy as this case. It is also important to stress that this work can be reproduced for any other part of the world, as it is not limited to developing countries or Nigeria in particular or it is limited to the field of politics. Social implications Increasingly, during the 2015 general election, citizens have taken over the blogosphere by writing, commenting and reporting about different issues from politics, society, human rights, disasters, contestants, attacks and other community-related issues. One of such instances is the #NigeriaDecides network on Twitter. The effect of these showed in the opinion polls organized by the various interest groups and media houses which were all in favour of GMB. Originality/value The case study the authors took on the Nigeria’s general election of 2015 further strengthens the fact that the developing countries have joined the social media race. The major contributions of this work are that policy makers, politicians, business managers, etc. can use the methods shown in this work to harness and gain insights from Big Data, like the social media data.

scholarly journals The impact of public interest in the information privacy: Analyze of the ECtHR Decisions

2017 ◽  
Vol 6 (1) ◽  
pp. 1-8
Author(s):  
Jorida Xhafaj ◽  
Almarin Frakulli
Keyword(s):  
Human Rights ◽  
Public Interest ◽  
Family Life ◽  
Personal Information ◽  
Personal Data ◽  
Information Privacy ◽  
Public Institutions ◽  
Public Security ◽  
European Court ◽  
The Impact

The main object of this paper is the tender balance that exists and arises even more between the use of personal information that people provide in the course of most public security actions and privacy. This study analyze the most famous and strong related decision of the European Court of Human Rights, with the aim to give our opinion how has to be understand the barrier between the power of individuals over information and the power of public institutions to guaranties security. The protection of personal data is of fundamental importance to a person’s enjoyment of his or her right to respect for private and family life, and how law allocates power over information in different countries, will give us the possibility to define the most important criteria’s which define the existence of abuse or not over personal data and information.

Analysing the features of negative sentiment tweets

2018 ◽  
Vol 36 (5) ◽  
pp. 782-799 ◽  
Author(s):  
Ling Zhang ◽  
Wei Dong ◽  
Xiangming Mu
Keyword(s):  
Research Area ◽  
Small Sample ◽  
Negative Word ◽  
Social Media Data ◽  
Content Type ◽  
Word Use ◽  
Part Of Speech ◽  
Negative Sentiment ◽  
Media Data

Purpose This paper aims to address the challenge of analysing the features of negative sentiment tweets. The method adopted in this paper elucidates the classification of social network documents and paves the way for sentiment analysis of tweets in further research. Design/methodology/approach This study classifies negative tweets and analyses their features. Findings Through negative tweet content analysis, tweets are divided into ten topics. Many related words and negative words were found. Some indicators of negative word use could reflect the degree to which users release negative emotions: part of speech, the density and frequency of negative words and negative word distribution. Furthermore, the distribution of negative words obeys Zipf’s law. Research limitations/implications This study manually analysed only a small sample of negative tweets. Practical implications The research explored how many categories of negative sentiment tweets there are on Twitter. Related words are helpful to construct an ontology of tweets, which helps people with information retrieval in a fixed research area. The analysis of extracted negative words determined the features of negative tweets, which is useful to detect the polarity of tweets by machine learning method. Originality/value The research provides an initial exploration of a negative document classification method and classifies the negative tweets into ten topics. By analysing the features of negative tweets, related words, negative words, the density of negative words, etc. are presented. This work is the first step to extend Plutchik’s emotion wheel theory into social media data analysis by constructing filed specific thesauri, referred to as local sentimental thesauri.

Destination image through social media analytics and survey method

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Michael S. Lin ◽  
Yun Liang ◽  
Joanne X. Xue ◽  
Bing Pan ◽  
Ashley Schroeder
Keyword(s):  
Social Media ◽  
Survey Data ◽  
Destination Image ◽  
Social Media Analytics ◽  
Tourism Destination ◽  
Social Media Data ◽  
Content Type ◽  
Holistic Understanding ◽  
Intercept Survey ◽  
Media Data

Purpose Recent tourism research has adopted social media analytics (SMA) to examine tourism destination image (TDI) and gain timely insights for marketing purposes. Comparing the methodologies of SMA and intercept surveys would provide a more in-depth understanding of both methodologies and a more holistic understanding of TDI than each method on their own. This study aims to investigate the unique merits and biases of SMA and a traditional visitor intercept survey. Design/methodology/approach This study collected and compared data for the same tourism destination from two sources: responses from a visitor intercept survey (n = 1,336) and Flickr social media photos and metadata (n = 11,775). Content analysis, machine learning and text analysis techniques were used to analyze and compare the destination image represented from both methods. Findings The results indicated that the survey data and social media data shared major similarities in the identified key image phrases. Social media data revealed more diverse and more specific aspects of the destination, whereas survey data provided more insights in specific local landmarks. Survey data also included additional subjective judgment and attachment towards the destination. Together, the data suggested that social media data should serve as an additional and complementary source of information to traditional survey data. Originality/value This study fills a research gap by comparing two methodologies in obtaining TDI: SMA and a traditional visitor intercept survey. Furthermore, within SMA, photo and metadata are compared to offer additional awareness of social media data’s underlying complexity. The results showed the limitations of text-based image questions in surveys. The findings provide meaningful insights for tourism marketers by having a more holistic understanding of TDI through multiple data sources.

Determining factors of participants' attitudes toward the ethics of social media data research

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yi Chen ◽  
Chuanfu Chen ◽  
Si Li
Keyword(s):  
Social Media ◽  
Structural Equation ◽  
Ethical Issues ◽  
Equation Modeling ◽  
Research Subjects ◽  
Social Media Data ◽  
Content Type ◽  
Mediating Role ◽  
Determining Factors ◽  
Media Data

PurposeThe purpose of this study was to investigate the participants' attitudes toward the ethical issues caused by collecting social media data (SMD) for research, as well as the effects of familiarity, trust and altruism on the participants' attitudes toward the ethics of SMD research. It is hoped that through this study, scholars will be reminded to respect participants and engage in ethical reflection when using SMD in research.Design/methodology/approachThis study adopted social media users as its research subjects and used Sina Microblog, the world's largest Chinese social media platform, as the example. Based on the 320 valid responses collected from a survey, structural equation modeling was employed to examine the research model.FindingsThe results indicated that altruism, familiarity and trust have significant influences on participants' attitudes toward the ethics of SMD research, and familiarity also influences attitudes through the mediating role of trust and altruism.Originality/valueThis study explored the mechanism underlying the relationship between the determining factors and participants' attitudes toward the ethics of SMD research, and the results demonstrated that the informed consent mechanism is an effective way to communicate with participants and that the guiding responsibility of the platform should be improved to standardize SMD research.

Forecasting daily attraction demand using big data from search engines and social media

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fengjun Tian ◽  
Yang Yang ◽  
Zhenxing Mao ◽  
Wenyue Tang
Keyword(s):  
Social Media ◽  
Big Data ◽  
Search Engine ◽  
Elastic Net ◽  
Percentage Error ◽  
Mean Square ◽  
Social Media Data ◽  
Content Type ◽  
Forecasting Error ◽  
Media Data

Purpose This paper aims to compare the forecasting performance of different models with and without big data predictors from search engines and social media. Design/methodology/approach Using daily tourist arrival data to Mount Longhu, China in 2018 and 2019, the authors estimated ARMA, ARMAX, Markov-switching auto-regression (MSAR), lasso model, elastic net model and post-lasso and post-elastic net models to conduct one- to seven-days-ahead forecasting. Search engine data and social media data from WeChat, Douyin and Weibo were incorporated to improve forecasting accuracy. Findings Results show that search engine data can substantially reduce forecasting error, whereas social media data has very limited value. Compared to the ARMAX/MSAR model without big data predictors, the corresponding post-lasso model reduced forecasting error by 39.29% based on mean square percentage error, 33.95% based on root mean square percentage error, 46.96% based on root mean squared error and 45.67% based on mean absolute scaled error. Practical implications Results highlight the importance of incorporating big data predictors into daily demand forecasting for tourism attractions. Originality/value This study represents a pioneering attempt to apply the regularized regression (e.g. lasso model and elastic net) in tourism forecasting and to explore various daily big data indicators across platforms as predictors.

A Sentiment Analysis and Role of Twitter for Health Communications

2022 ◽  
pp. 188-205
Author(s):  
Erkan Çiçek ◽  
Uğur Gündüz
Keyword(s):  
Social Media ◽  
Crisis Management ◽  
Semantic Analysis ◽  
Social Media Data ◽  
Global Pandemic ◽  
The Difference ◽  
Textual Content ◽  
The Impact ◽  
Media Data

Social media has been in our lives so much lately that it is an undeniable fact that global pandemics, which constitute an important part of our lives, are also affected by these networks and that they exist in these networks and share the users. The purpose of making this hashtag analysis is to reveal the difference in discourse and language while analyzing Twitter data and to evaluate the effects of a global pandemic crisis on language, message, and crisis management with social media data. This form of analysis is typically completed through amassing textual content data then investigating the “sentiment” conveyed. Within the scope of the study, 11,300 Twitter messages posted with the #stayhome hashtag between 30 May 2020 and 6 June 2020 were examined. The impact and reliability of social media in disaster management could be questioned by carrying out a content analysis based totally on the semantic analysis of the messages given on the Twitter posts with the phrases and frequencies used.

Combatting economic crimes in Nigeria through whistleblowing: a shift from policy to legal framework

2020 ◽  
Vol 23 (4) ◽  
pp. 819-832
Author(s):  
Sirajo Yakubu ◽  
Mohammed Kyari Dikwa
Keyword(s):  
Personal Experience ◽  
Quantitative Methods ◽  
Legal Framework ◽  
Critical Examination ◽  
Policy Document ◽  
Content Type ◽  
National Assembly ◽  
Qualitative And Quantitative Methods ◽  
Nigerian Government ◽  
The Impact

Purpose The purpose of this paper is a holistic assessment of the impact of whistleblowing policy adopted by the Nigerian Government in fighting corruption and an evaluation of the whistleblowing and witness protection bill. Design/methodology/approach This paper is a critical analysis of the whistleblowing policy and the draft whistleblowing and witness protection bill. The paper combines both qualitative and quantitative methods. It is conducted through the study of the policy and the draft bill and the critical examination of the data released by the federal Ministry of Finance. Moreover, the personal experience of the authors in the civil service and in formulating and implementing the whistleblower policy account significantly. Findings The whistleblowing policy adopted by the Federal Republic of Nigeria is promising in controlling corruption and other economically motivated crimes. However, while efforts to give whistleblowing a legal backing will strengthen the fight against corruption in Nigeria, the National Assembly must subject the bill to rigorous debate to avoid having many lacunas in would be act. Research limitations/implications The use of whistleblowing in combatting corruption in Nigeria is still at its infancy. A policy document backs implementation of the policy – there is no legislation or case law to consider. Thus, analysis is based on the policy document, the bill, statistics from the FMF and personal experience of the authors. Originality/value There is no comprehensive study on the adoption of and efforts to give legal backing to, the whistleblowing policy adopted in Nigeria. This paper is of value to the Nigerian Government and the National Assembly considering the latest efforts to institutionalise whistleblowing in Nigeria.

scholarly journals Tackling social media data analysis

2019 ◽  
Vol 22 (2) ◽  
pp. 94-113 ◽  
Author(s):  
Violetta Wilk ◽  
Geoffrey N. Soutar ◽  
Paul Harrigan
Keyword(s):  
Social Media ◽  
Data Analysis ◽  
Exploratory Research ◽  
Online Data ◽  
Social Media Data ◽  
Content Type ◽  
Advantages And Disadvantages ◽  
Brand Advocacy ◽  
Qualitative Data Analysis Software ◽  
Media Data

PurposeThis paper aims to offer insights into the ways two computer-aided qualitative data analysis software (CAQDAS) applications (QSR NVivo and Leximancer) can be used to analyze big, text-based, online data taken from consumer-to-consumer (C2C) social media communication.Design/methodology/approachThis study used QSR NVivo and Leximancer, to explore 200 discussion threads containing 1,796 posts from forums on an online open community and an online brand community that involved online brand advocacy (OBA). The functionality, in particular, the strengths and weaknesses of both programs are discussed. Examples of the types of analyses each program can undertake and the visual output available are also presented.FindingsThis research found that, while both programs had strengths and weaknesses when working with big, text-based, online data, they complemented each other. Each contributed a different visual and evidence-based perspective; providing a more comprehensive and insightful view of the characteristics unique to OBA.Research limitations/implicationsQualitative market researchers are offered insights into the advantages and disadvantages of using two different software packages for research projects involving big social media data. The “visual-first” analysis, obtained from both programs can help researchers make sense of such data, particularly in exploratory research.Practical implicationsThe paper provides practical recommendations for analysts considering which programs to use when exploring big, text-based, online data.Originality/valueThis paper answered a call to action for further research and demonstration of analytical programs of big, online data from social media C2C communication and makes strong suggestions about the need to examine such data in a number of ways.

An examination of the factors contributing to participation in online social platforms

2016 ◽  
Vol 68 (6) ◽  
pp. 793-818 ◽  
Author(s):  
Jenny Bronstein ◽  
Tali Gazit ◽  
Oren Perez ◽  
Judit Bar-Ilan ◽  
Noa Aharony ◽  
...  
Keyword(s):  
Personality Traits ◽  
Information Exchange ◽  
Political Influence ◽  
Online Participation ◽  
Social Media Data ◽  
Content Type ◽  
Internet Users ◽  
The Social ◽  
The Relationship ◽  
Media Data

Purpose The purpose of this paper is to examine participation in online social platforms consisting of information exchange, social network interactions, and political deliberation. Despite the proven benefits of online participation, the majority of internet users read social media data but do not directly contribute, a phenomenon called lurking. Design/methodology/approach A survey was administered electronically to 507 participants and consisted of ten sections in a questionnaire to gather data on the relationship between online participation and the following variables: anonymity, social value orientation, motivations, and participation in offline activities, as well as the internet’s political influence and personality traits. Findings Findings show that users with high levels of participation also identify themselves, report higher levels of extroversion, openness, and activity outside the internet, the motivations being an intermediary variable in the relationship between the variables value. Originality/value The study shows that participation in online social platforms is not only related to personality traits, but they are impacted by the nature of the motivations that drive them to participate in the particular social platform, as well as by the interest toward the specific topic, or the type or nature of the social group with whom they are communicating.

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