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The age of surveillance capitalism : the fight for a human future at the new frontier of power
\"Shoshana Zuboff, named \"the true prophet of the information age\" by the Financial Times, has always been ahead of her time. Her seminal book In the Age of the Smart Machine foresaw the consequences of a then-unfolding era of computer technology. Now, three decades later she asks why the once-celebrated miracle of digital is turning into a nightmare. Zuboff tackles the social, political, business, personal, and technological meaning of \"surveillance capitalism\" as an unprecedented new market form. It is not simply about tracking us and selling ads, it is the business model for an ominous new marketplace that aims at nothing less than predicting and modifying our everyday behavior--where we go, what we do, what we say, how we feel, who we're with. The consequences of surveillance capitalism for us as individuals and as a society vividly come to life in The Age of Surveillance Capitalism's pathbreaking analysis of power. The threat has shifted from a totalitarian \"big brother\" state to a universal global architecture of automatic sensors and smart capabilities: A \"big other\" that imposes a fundamentally new form of power and unprecedented concentrations of knowledge in private companies--free from democratic oversight and control\"-- Provided by publisher.
Book
Predictive analytics and data mining : concepts and practice with RapidMiner
This book shows how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Topics include: exploratory data analysis; visualization; decision trees; rule induction; k-nearest neighbors; naive Bayesian; artificial neural networks; support vector machines; ensemble models; bagging; boosting; random forests; linear regression; logistic regression; association analysis using Apriori and FP growth; k-means clustering; density based clustering; self organizing maps; text mining; time series forecasting; anomaly detection and feature selection. --
eBook
A deep learning-based model using hybrid feature extraction approach for consumer sentiment analysis
There is an exponential growth in textual content generation every day in today's world. In-app messaging such as Telegram and WhatsApp, social media websites such as Instagram and Facebook, e-commerce websites like Amazon, Google searches, news publishing websites, and a variety of additional sources are the possible suppliers. Every instant, all these sources produce massive amounts of text data. The interpretation of such data can help business owners analyze the social outlook of their product, brand, or service and take necessary steps. The development of a consumer review summarization model using Natural Language Processing (NLP) techniques and Long short-term memory (LSTM) to present summarized data and help businesses obtain substantial insights into their consumers' behavior and choices is the topic of this research. A hybrid approach for analyzing sentiments is presented in this paper. The process comprises pre-processing, feature extraction, and sentiment classification. Using NLP techniques, the pre-processing stage eliminates the undesirable data from input text reviews. For extracting the features effectively, a hybrid method comprising review-related features and aspect-related features has been introduced for constructing the distinctive hybrid feature vector corresponding to each review. The sentiment classification is performed using the deep learning classifier LSTM. We experimentally evaluated the proposed model using three different research datasets. The model achieves the average precision, average recall, and average F1-score of 94.46%, 91.63%, and 92.81%, respectively.
Journal Article
Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook
We describe the effect of social media advertising content on customer engagement using data from Facebook. We content-code 106,316 Facebook messages across 782 companies, using a combination of Amazon Mechanical Turk and natural language processing algorithms. We use this data set to study the association of various kinds of social media marketing content with user engagement—defined as
Likes
, comments, shares, and click-throughs—with the messages. We find that inclusion of widely used content related to brand personality—like humor and emotion—is associated with higher levels of consumer engagement (
Likes
, comments, shares) with a message. We find that directly informative content—like mentions of price and deals—is associated with lower levels of engagement when included in messages in isolation, but higher engagement levels when provided in combination with brand personality–related attributes. Also, certain directly informative content, such as deals and promotions, drive consumers’ path to conversion (click-throughs). These results persist after incorporating corrections for the nonrandom targeting of Facebook’s EdgeRank (News Feed) algorithm and so reflect more closely user reaction to content than Facebook’s behavioral targeting. Our results suggest that there are benefits to content engineering that combines informative characteristics that help in obtaining immediate leads (via improved click-throughs) with brand personality–related content that helps in maintaining future reach and branding on the social media site (via improved engagement). These results inform content design strategies. Separately, the methodology we apply to content-code text is useful for future studies utilizing unstructured data such as advertising content or product reviews.
The online appendix is available at
https://doi.org/10.1287/mnsc.2017.2902
.
This paper was accepted by Chris Forman, information systems.
Journal Article
An Empirical Analysis of Search Engine Advertising: Sponsored Search in Electronic Markets
The phenomenon of sponsored search advertising—where advertisers pay a fee to Internet search engines to be displayed alongside organic (nonsponsored) Web search results—is gaining ground as the largest source of revenues for search engines. Using a unique six-month panel data set of several hundred keywords collected from a large nationwide retailer that advertises on Google, we empirically model the relationship between different sponsored search metrics such as click-through rates, conversion rates, cost per click, and ranking of advertisements. Our paper proposes a novel framework to better understand the factors that drive differences in these metrics. We use a hierarchical Bayesian modeling framework and estimate the model using Markov Chain Monte Carlo methods. Using a simultaneous equations model, we quantify the relationship between various keyword characteristics, position of the advertisement, and the landing page quality score on consumer search and purchase behavior as well as on advertiser's cost per click and the search engine's ranking decision. Specifically, we find that the monetary value of a click is not uniform across all positions because conversion rates are highest at the top and decrease with rank as one goes down the search engine results page. Though search engines take into account the current period's bid as well as prior click-through rates before deciding the final rank of an advertisement in the current period, the current bid has a larger effect than prior click-through rates. We also find that an increase in landing page quality scores is associated with an increase in conversion rates and a decrease in advertiser's cost per click. Furthermore, our analysis shows that keywords that have more prominent positions on the search engine results page, and thus experience higher click-through or conversion rates, are not necessarily the most profitable ones—profits are often higher at the middle positions than at the top or the bottom ones. Besides providing managerial insights into search engine advertising, these results shed light on some key assumptions made in the theoretical modeling literature in sponsored search.
Journal Article
A Framework for Collaborative Artificial Intelligence in Marketing
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•AI advances from mechanical to thinking to feeling, changing how AI should be used.•AI and human intelligence (HI) complement best as collaborative teams.•Lower-level AI augments higher-level HI.•AI first augments and then replaces HI at a given intelligence level.•Move HI to a higher intelligence level when AI automates the lower level.
We develop a conceptual framework for collaborative artificial intelligence (AI) in marketing, providing systematic guidance for how human marketers and consumers can team up with AI, which has profound implications for retailing, which is the interface between marketers and consumers. Drawing from the multiple intelligences view that AI advances from mechanical, to thinking, to feeling intelligence (based on how difficult for AI to mimic human intelligences), the framework posits that collaboration between AI and HI (human marketers and consumers) can be achieved by 1) recognizing the respective strengths of AI and HI, 2) having lower-level AI augmenting higher-level HI, and 3) moving HI to a higher intelligence level when AI automates the lower level. Implications for marketers, consumers, and researchers are derived. Marketers should optimize the mix and timing of AI-HI marketing team, consumers should understand the complementarity between AI and HI strengths for informed consumption decisions, and researchers can investigate innovative approaches to and boundary conditions of collaborative intelligence.
Journal Article
The dilemma of sweet temptation: How sugar perception confusion in sweetened beverages shapes consumer avoidance behavior
Despite widespread consumption of sugar-sweetened beverages, consumers face contradictory information from health authorities, marketing, and social media, yet limited research examines how this information conflict affects purchasing decisions. This study investigates how sugar perception confusion influences purchasing avoidance through ambivalent attitudes. Based on cognitive dissonance and information processing theories, we developed a cognitive-affective-behavioral model examining relationships among sugar perception confusion, ambivalent attitudes, and purchasing avoidance behaviors. Using PLS-SEM analysis of 531 Chinese consumers, results show sugar perception confusion significantly affects ambivalent attitudes (β = 0.576, p < 0.001), which strongly predict purchasing avoidance (β = 0.593, p < 0.001). Sugar perception confusion also directly influences purchasing avoidance (β = 0.155, p < 0.001), with ambivalent attitudes serving as a significant mediator (indirect effect β = 0.342, p < 0.001). These findings advance consumer information processing theory and provide evidence-based insights for optimizing information environments to support informed decision-making.
Journal Article
Machine learning-based mathematical modelling for prediction of social media consumer behavior using big data analytics
Social media is popular in our society right now. People are using social media platforms to purchase various products. We collected the data from various social media platforms. We analyzed the data for prediction of the consumer behavior on the social media platform. We considered the consumer data from Facebook, Twitter, Linked In and YouTube, Instagram, and Pinterest, etc. There are diverse and high-speed, high volume data which are coming from social media platform, so we used predictive big data analytics. In this paper, we have used the concept of big data technology to process data and analyze it to predict consumer behavior on social media. We have analyzed consumer behavior on social media platforms based on some parameters and criteria. We analyzed the consumer perception, attitude towards the social media platform. To get good quality of result, we pre-process data using various data pre-processing to detect outlier, noises, error, and duplicate record. We developed mathematical modeling using machine learning to predict consumer behavior on the social media platform. This model is a predictive model for predicting consumer behavior on the social media platform. 80% of data are used for training purposes and 20% for testing.
Journal Article
Reducing food’s environmental impacts through producers and consumers
Food is produced and processed by millions of farmers and intermediaries globally, with substantial associated environmental costs. Given the heterogeneity of producers, what is the best way to reduce food's environmental impacts? Poore and Nemecek consolidated data on the multiple environmental impacts of ∼38,000 farms producing 40 different agricultural goods around the world in a meta-analysis comparing various types of food production systems. The environmental cost of producing the same goods can be highly variable. However, this heterogeneity creates opportunities to target the small numbers of producers that have the most impact. Science , this issue p. 987 Food producer heterogeneity on a global level creates mitigation opportunities with respect to environmental damage caused by food production. Food’s environmental impacts are created by millions of diverse producers. To identify solutions that are effective under this heterogeneity, we consolidated data covering five environmental indicators; 38,700 farms; and 1600 processors, packaging types, and retailers. Impact can vary 50-fold among producers of the same product, creating substantial mitigation opportunities. However, mitigation is complicated by trade-offs, multiple ways for producers to achieve low impacts, and interactions throughout the supply chain. Producers have limits on how far they can reduce impacts. Most strikingly, impacts of the lowest-impact animal products typically exceed those of vegetable substitutes, providing new evidence for the importance of dietary change. Cumulatively, our findings support an approach where producers monitor their own impacts, flexibly meet environmental targets by choosing from multiple practices, and communicate their impacts to consumers.
Journal Article