book.bib

@book{cbSI,
  title={Statistical inference},
  author={Casella, George and Berger, Roger L},
  edition={2},
  year={2002},
  publisher={Duxbury Pacific Grove, CA}
}

@article{aguiarPlatformsPowerPromotion2021a,
  title      = {Platforms, {{Power}}, and {{Promotion}}: {{Evidence}} from {{Spotify Playlists}}*},
  shorttitle = {Platforms, {{Power}}, and {{Promotion}}},
  author     = {Aguiar, Luis and Waldfogel, Joel},
  year       = {2021},
  journal    = {The Journal of Industrial Economics},
  volume     = {69},
  number     = {3},
  pages      = {653--691},
  issn       = {1467-6451},
  doi        = {10.1111/joie.12263},
  abstract   = {Many online markets are dominated by a handful of platforms, raising concerns about the exercise of market power in the digital age. Spotify has emerged as the leading interactive music streaming platform, and we assess its power by measuring the impact of its promotion decisions \textendash{} via platform-operated playlists \textendash{} on the success of songs and artists. We employ discontinuity and instrumental variables identification approaches and find large and significant effects of playlist inclusion on success. Our results provide direct evidence of a prominent platform's power and suggest a need for continued scrutiny of how platforms exercise their power.},
  langid     = {english},
  annotation = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/joie.12263},
  file       = {C\:\\Users\\danie\\Zotero\\storage\\39H9B5PL\\Aguiar and Waldfogel - 2021 - Platforms, Power, and Promotion Evidence from Spo.pdf;C\:\\Users\\danie\\Zotero\\storage\\M9ZU9228\\joie.html}
}
@misc{aguiarQualityPredictabilityWelfare2016,
  type       = {Working Paper},
  title      = {Quality {{Predictability}} and the {{Welfare Benefits}} from {{New Products}}: {{Evidence}} from the {{Digitization}} of {{Recorded Music}}},
  shorttitle = {Quality {{Predictability}} and the {{Welfare Benefits}} from {{New Products}}},
  author     = {Aguiar, Luis and Waldfogel, Joel},
  year       = {2016},
  month      = sep,
  series     = {Working {{Paper Series}}},
  number     = {22675},
  publisher  = {{National Bureau of Economic Research}},
  doi        = {10.3386/w22675},
  abstract   = {We explore the consequence of quality unpredictability for the welfare benefit of new products, using recent developments in recorded music as our context. Digitization has expanded consumption opportunities by giving consumers access to the ``long tail'' of existing products, rather than simply the popular products that a retailer might stock with limited shelf space. While this is clearly beneficial to consumers, the benefits are somewhat limited: given the substitutability among differentiated products, the incremental benefit of obscure products - even lots of them - can be small. But digitization has also reduced the cost of bringing new products to market, giving rise to a different sort of long tail, in production. If the appeal of new products is unpredictable at the time of investment, as is the case for cultural products as well as many others, then creating new products can have substantial welfare benefits. Technological change in the recorded music industry tripled the number of new products between 2000 and 2008. We quantify the effects of new music on welfare using a simple illustrative, but explicitly structural, model of demand and entry with potentially unpredictable product quality. Based on a range of plausible forecasting models of expected appeal, a tripling of the choice set according to expected quality adds substantially more to consumer surplus and overall welfare than the usual long-tail benefits from a tripling of the choice set according to realized quality, perhaps by more than an order of magnitude.},
  file       = {C\:\\Users\\danie\\Zotero\\storage\\MWEWAYLR\\Aguiar and Waldfogel - 2016 - Quality Predictability and the Welfare Benefits fr.pdf}
}

@book{atkinson2011beyond,
  title     = {Beyond Bullet Points, 3rd Edition},
  author    = {Atkinson, C. and Safari, an O'Reilly Media Company},
  year      = {2011},
  publisher = {Microsoft Press}
}


@misc{bergerWhatHoldsAttention2022,
  type       = {SSRN Scholarly Paper},
  title      = {What {{Holds Attention}}? {{Linguistic Drivers}} of {{Engagement}}},
  shorttitle = {What {{Holds Attention}}?},
  author     = {Berger, Jonah A. and Moe, Wendy W. and Schweidel, David A.},
  year       = {2022},
  month      = dec,
  number     = {4311202},
  address    = {{Rochester, NY}},
  doi        = {10.2139/ssrn.4311202},
  abstract   = {From advertisers and marketers to salespeople and leaders, everyone wants to hold their audience's attention. They want to make ads, pitches, presentations, and content that captivates audiences and keeps them engaged. But not all content has that effect. What makes some content more engaging? A multi-method investigation combines controlled experiments with natural language processing of 600,000 reading sessions from over 35,000 pieces of content to examine what types of language hold attention and why. Results demonstrate that linguistic features associated with processing ease (e.g., concrete or familiar words) and emotion both play an important role. Rather than simply being driven by valence, though, the effects of emotional language are driven by the degree to which different discrete emotions evoke arousal and uncertainty. Consistent with this, anxious, exciting, and hopeful language holds attention while sad language discourages it. Experimental evidence underscores emotional language's causal impact and demonstrates the mediating role of uncertainty and arousal. The findings shed light on what holds attention, illustrates how content creators can generate more impactful content, and, as shown in a stylized simulation, have important societal implications for content recommendation algorithms.},
  langid     = {english},
  keywords   = {automated textual analysis,content consumption,digital engagement,digital marketing,emotion,natural language processing,online content},
  file       = {C\:\\Users\\danie\\Zotero\\storage\\ASKK8NCE\\Berger et al. - 2022 - What Holds Attention Linguistic Drivers of Engage.pdf}
}


@article{bergerWhatMakesOnline2012,
  title     = {What {{Makes Online Content Viral}}?},
  author    = {Berger, Jonah and Milkman, Katherine L.},
  year      = {2012},
  month     = apr,
  journal   = {Journal of Marketing Research},
  volume    = {49},
  number    = {2},
  pages     = {192--205},
  publisher = {{SAGE Publications Inc}},
  issn      = {0022-2437},
  doi       = {10.1509/jmr.10.0353},
  abstract  = {Why are certain pieces of online content (e.g., advertisements, videos, news articles) more viral than others? This article takes a psychological approach to understanding diffusion. Using a unique data set of all the New York Times articles published over a three-month period, the authors examine how emotion shapes virality. The results indicate that positive content is more viral than negative content, but the relationship between emotion and social transmission is more complex than valence alone. Virality is partially driven by physiological arousal. Content that evokes high-arousal positive (awe) or negative (anger or anxiety) emotions is more viral. Content that evokes low-arousal, or deactivating, emotions (e.g., sadness) is less viral. These results hold even when the authors control for how surprising, interesting, or practically useful content is (all of which are positively linked to virality), as well as external drivers of attention (e.g., how prominently content was featured). Experimental results further demonstrate the causal impact of specific emotion on transmission and illustrate that it is driven by the level of activation induced. Taken together, these findings shed light on why people share content and how to design more effective viral marketing campaigns.},
  langid    = {english},
  file      = {C\:\\Users\\danie\\Zotero\\storage\\HTGZ5M2I\\Berger and Milkman - 2012 - What Makes Online Content Viral.pdf}
}

@article{bermanValueDescriptiveAnalytics2022,
  title      = {The {{Value}} of {{Descriptive Analytics}}: {{Evidence}} from {{Online Retailers}}},
  shorttitle = {The {{Value}} of {{Descriptive Analytics}}},
  author     = {Berman, Ron and Israeli, Ayelet},
  year       = {2022},
  month      = nov,
  journal    = {Marketing Science},
  volume     = {41},
  number     = {6},
  pages      = {1074--1096},
  publisher  = {{INFORMS}},
  issn       = {0732-2399},
  doi        = {10.1287/mksc.2022.1352},
  abstract   = {Does the adoption of descriptive analytics impact online retailer performance, and if so, how? We use the synthetic difference-in-differences method to analyze the staggered adoption of a retail analytics dashboard by more than 1,500 e-commerce websites, and we find an increase of 4\%\textendash 10\% in average weekly revenues postadoption. We demonstrate that only retailers that adopt and use the dashboard reap these benefits. The increase in revenue is not explained by price changes or advertising optimization. Instead, it is consistent with the addition of customer relationship management, personalization, and prospecting technologies to retailer websites. The adoption and usage of descriptive analytics also increases the diversity of products sold, the number of transactions, the numbers of website visitors and unique customers, and the revenue from repeat customers. In contrast, there is no change in basket size. These findings are consistent with a complementary effect of descriptive analytics that serve as a monitoring device that helps retailers control additional martech tools and amplify their value. Without using the descriptive dashboard, retailers are unable to reap the benefits associated with these technologies. History: Avi Goldfarb served as the senior editor and associate editor for this article. Funding: This work was supported by The Mack Institute for Innovation Management. Supplemental Material: The web appendix and data are available at https://doi.org/10.1287/mksc.2022.1352.},
  keywords   = {big data,descriptive analytics,difference-in-differences,e-commerce,martech,online retail,synthetic control},
  file       = {C\:\\Users\\danie\\Zotero\\storage\\IVKGTGAK\\Berman and Israeli - 2022 - The Value of Descriptive Analytics Evidence from .pdf}
}

@article{boughanmiDynamicsMusicalSuccess2021,
  title      = {Dynamics of {{Musical Success}}: {{A Machine Learning Approach}} for {{Multimedia Data Fusion}}},
  shorttitle = {Dynamics of {{Musical Success}}},
  author     = {Boughanmi, Khaled and Ansari, Asim},
  year       = {2021},
  month      = apr,
  journal    = {Journal of Marketing Research},
  pages      = {00222437211016495},
  publisher  = {{SAGE Publications Inc}},
  issn       = {0022-2437},
  doi        = {10.1177/00222437211016495},
  abstract   = {The success of creative products depends on the felt experience of consumers. Capturing such consumer reactions requires the fusing of different types of experiential covariates and perceptual data in an integrated modeling framework. In this article, the authors develop a novel multimodal machine learning framework that combines multimedia data (e.g., metadata, acoustic features, user-generated textual data) in creative product settings and apply it to predict the success of musical albums and playlists. The authors estimate the proposed model on a unique data set collected using different online sources. The model integrates different types of nonparametrics to flexibly accommodate diverse types of effects. It uses penalized splines to capture the nonlinear impact of acoustic features and a supervised hierarchical Dirichlet process to represent crowd-sourced textual tags, and it captures dynamics via a state-space specification. The authors show the predictive superiority of the model with respect to several benchmarks. The results illuminate the dynamics of musical success over the past five decades. The authors then use the components of the model for marketing decisions such as forecasting the success of new albums, conducting album tuning and diagnostics, constructing playlists for different generations of music listeners, and providing contextual recommendations.},
  file       = {C\:\\Users\\danie\\Zotero\\storage\\UJ2V4LVY\\Boughanmi and Ansari - 2021 - Dynamics of Musical Success A Machine Learning Ap.pdf}
}

@article{bradlowRoleBigData2017a,
  title    = {The {{Role}} of {{Big Data}} and {{Predictive Analytics}} in {{Retailing}}},
  author   = {Bradlow, Eric T. and Gangwar, Manish and Kopalle, Praveen and Voleti, Sudhir},
  year     = {2017},
  month    = mar,
  journal  = {Journal of Retailing},
  series   = {The {{Future}} of {{Retailing}}},
  volume   = {93},
  number   = {1},
  pages    = {79--95},
  issn     = {0022-4359},
  doi      = {10.1016/j.jretai.2016.12.004},
  abstract = {The paper examines the opportunities in and possibilities arising from big data in retailing, particularly along five major data dimensions\textemdash data pertaining to customers, products, time, (geo-spatial) location and channel. Much of the increase in data quality and application possibilities comes from a mix of new data sources, a smart application of statistical tools and domain knowledge combined with theoretical insights. The importance of theory in guiding any systematic search for answers to retailing questions, as well as for streamlining analysis remains undiminished, even as the role of big data and predictive analytics in retailing is set to rise in importance, aided by newer sources of data and large-scale correlational techniques. The Statistical issues discussed include a particular focus on the relevance and uses of Bayesian analysis techniques (data borrowing, updating, augmentation and hierarchical modeling), predictive analytics using big data and a field experiment, all in a retailing context. Finally, the ethical and privacy issues that may arise from the use of big data in retailing are also highlighted.},
  langid   = {english},
  keywords = {Big data,Predictive analytics,Pricing,Retailing},
  file     = {C\:\\Users\\danie\\Zotero\\storage\\NDW58ARG\\Bradlow et al. - 2017 - The Role of Big Data and Predictive Analytics in R.pdf;C\:\\Users\\danie\\Zotero\\storage\\8F8NVAFP\\S0022435916300835.html}
}

@article{callawayDifferenceinDifferencesMultipleTime2021b,
  title        = {Difference-in-Differences with multiple time periods},
  volume       = {225},
  issn         = {0304-4076},
  url          = {https://www.sciencedirect.com/science/article/pii/S0304407620303948},
  doi          = {10.1016/j.jeconom.2020.12.001},
  series       = {Themed Issue: Treatment Effect 1},
  abstract     = {In this article, we consider identification, estimation, and inference procedures for treatment effect parameters using Difference-in-Differences ({DiD}) with (i) multiple time periods, (ii) variation in treatment timing, and (iii) when the “parallel trends assumption” holds potentially only after conditioning on observed covariates. We show that a family of causal effect parameters are identified in staggered {DiD} setups, even if differences in observed characteristics create non-parallel outcome dynamics between groups. Our identification results allow one to use outcome regression, inverse probability weighting, or doubly-robust estimands. We also propose different aggregation schemes that can be used to highlight treatment effect heterogeneity across different dimensions as well as to summarize the overall effect of participating in the treatment. We establish the asymptotic properties of the proposed estimators and prove the validity of a computationally convenient bootstrap procedure to conduct asymptotically valid simultaneous (instead of pointwise) inference. Finally, we illustrate the relevance of our proposed tools by analyzing the effect of the minimum wage on teen employment from 2001–2007. Open-source software is available for implementing the proposed methods.},
  pages        = {200--230},
  number       = {2},
  journaltitle = {Journal of Econometrics},
  shortjournal = {Journal of Econometrics},
  author       = {Callaway, Brantly and Sant’Anna, Pedro H. C.},
  urldate      = {2023-12-19},
  date         = {2021-12-01},
  keywords     = {Difference-in-Differences, Doubly robust, Dynamic treatment effects, Event study, Semi-parametric, Treatment effect heterogeneity, Variation in treatment timing},
  file         = {ScienceDirect Full Text PDF:/Users/daniel/Zotero/storage/2PXF3B6N/Callaway and Sant’Anna - 2021 - Difference-in-Differences with multiple time periods.pdf:application/pdf;ScienceDirect Snapshot:/Users/daniel/Zotero/storage/ESBNKGSL/S0304407620303948.html:text/html}
}

@article{cinelli2020crash,
  title   = {A crash course in good and bad controls},
  author  = {Cinelli, Carlos and Forney, Andrew and Pearl, Judea},
  journal = {SSRN 3689437},
  year    = {2020}
}


@article{cleveland1984graphics,
  issn      = {01621459},
  url       = {http://www.jstor.org/stable/2288400},
  abstract  = {The subject of graphical methods for data analysis and for data presentation needs a scientific foundation. In this article we take a few steps in the direction of establishing such a foundation. Our approach is based on graphical perception-the visual decoding of information encoded on graphs-and it includes both theory and experimentation to test the theory. The theory deals with a small but important piece of the whole process of graphical perception. The first part is an identification of a set of elementary perceptual tasks that are carried out when people extract quantitative information from graphs. The second part is an ordering of the tasks on the basis of how accurately people perform them. Elements of the theory are tested by experimentation in which subjects record their judgments of the quantitative information on graphs. The experiments validate these elements but also suggest that the set of elementary tasks should be expanded. The theory provides a guideline for graph construction: Graphs should employ elementary tasks as high in the ordering as possible. This principle is applied to a variety of graphs, including bar charts, divided bar charts, pie charts, and statistical maps with shading. The conclusion is that radical surgery on these popular graphs is needed, and as replacements we offer alternative graphical forms-dot charts, dot charts with grouping, and framed-rectangle charts.},
  author    = {William S. Cleveland and Robert McGill},
  journal   = {Journal of the American Statistical Association},
  number    = {387},
  pages     = {531--554},
  publisher = {[American Statistical Association, Taylor & Francis, Ltd.]},
  title     = {Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods},
  volume    = {79},
  year      = {1984}
}

  
@article{covid19_data,
  title   = {COVID-19 Data Hub},
  year    = {2020},
  doi     = {10.21105/joss.02376},
  author  = {Emanuele Guidotti and David Ardia},
  journal = {Journal of Open Source Software},
  volume  = {5},
  number  = {51},
  pages   = {2376}
}

@book{cunningham2021causal,
  title     = {Causal inference - The Mixtape},
  author    = {Cunningham, Scott},
  year      = {2021},
  publisher = {Yale University Press},
  url       = {https://mixtape.scunning.com/index.html}
}


@manual{datasaurus,
  title  = {datasauRus: Datasets from the Datasaurus Dozen},
  author = {Steph Locke and Lucy {D'Agostino McGowan}},
  year   = {2018},
  note   = {R package version 0.1.4},
  url    = {https://CRAN.R-project.org/package=datasauRus}
}


@article{felbermayrRoleEmotionsPerceived2016,
  title    = {The {{Role}} of {{Emotions}} for the {{Perceived Usefulness}} in {{Online Customer Reviews}}},
  author   = {Felbermayr, Armin and Nanopoulos, Alexandros},
  year     = {2016},
  month    = nov,
  journal  = {Journal of Interactive Marketing},
  volume   = {36},
  pages    = {60--76},
  issn     = {1094-9968},
  doi      = {10.1016/j.intmar.2016.05.004},
  abstract = {Online customer reviews often express emotions. This can enable marketers to analyze the textual content of online reviews with the aim to understand the role of emotions and how they can affect other customers. In this paper, we present an approach to extracting emotion content from online reviews in order to measure the importance of various emotion dimensions within different product categories. The approach uses an emotion lexicon to extract emotion terms, while it also builds a classification model to measure the importance of emotion dimensions based on the quality of reviews. Review quality is measured based on the usefulness of online customer reviews, which are perceived and evaluated by other customers through their helpfulness ratings. This approach allows the identification of emotion dimensions that characterize qualitative reviews. The empirical evaluation in our study suggests that trust, joy, and anticipation are the most decisive emotion dimensions, although substantial variance across product categories can also be detected. Additionally, we compared two contrasting emotion dictionaries. One lexicon was crowd-funded and contained a large vocabulary, whereas the other was more focused and smaller, since it was created word-wise by an expert. Our empirical findings indicate that the crowd-funded solution outperforms its smaller counterpart in terms of classification precision. The main implication of this study is that it adds an emotional perspective to the broad set of existing tools that marketers employ to analyzing online reviews. Our contributions are: i) we are the first to analyze emotions' role in online customer reviews; ii) we demonstrate how to develop a big data model such as this, without external assistance; iii) we show how to interpret the results of the created model; and iv) we show which dictionary to prefer when creating the model.},
  langid   = {english},
  keywords = {Electronic word-of-mouth,Emotions,Helpfulness,Online customer reviews,Text mining},
  file     = {C\:\\Users\\danie\\Zotero\\storage\\K9AZDUV2\\Felbermayr and Nanopoulos - 2016 - The Role of Emotions for the Perceived Usefulness .pdf}
}

@article{filippasReputationInflation2022,
  title     = {Reputation {{Inflation}}},
  author    = {Filippas, Apostolos and Horton, John J. and Golden, Joseph M.},
  year      = {2022},
  month     = jul,
  journal   = {Marketing Science},
  volume    = {41},
  number    = {4},
  pages     = {733--745},
  publisher = {{INFORMS}},
  issn      = {0732-2399},
  doi       = {10.1287/mksc.2022.1350},
  abstract  = {We show that average buyer ratings of sellers have grown substantially more positive over time in five online marketplaces. Although this increase could by explained by (i) marketplace improvements that increased rater satisfaction, it could also be caused by (ii) ``reputation inflation,'' with raters giving higher ratings without being more satisfied. We present a method to decompose the growth in average ratings into components attributable to these two reasons. Using this method in one marketplace where we have extensive transaction-level data, we find that much of the observed increase in ratings is attributable to reputation inflation. We discuss the negative informational implications of reputation inflation and consider the likely causes.},
  keywords  = {market design,online marketplaces,reputation systems},
  file      = {C\:\\Users\\danie\\Zotero\\storage\\A8MQWCDK\\Filippas et al. - 2022 - Reputation Inflation.pdf}
}


@article{golderLearningDataEmpiricsFirst2022,
  title      = {Learning from {{Data}}: {{An Empirics-First Approach}} to {{Relevant Knowledge Generation}}},
  shorttitle = {Learning from {{Data}}},
  author     = {Golder, Peter N. and Dekimpe, Marnik G. and An, Jake T. and {van Heerde}, Harald J. and Kim, Darren S.U. and Alba, Joseph W.},
  year       = {2022},
  month      = sep,
  journal    = {Journal of Marketing},
  pages      = {00222429221129200},
  publisher  = {{SAGE Publications Inc}},
  issn       = {0022-2429},
  doi        = {10.1177/00222429221129200},
  abstract   = {A theory-first paradigm tends to be the dominant approach in much academic marketing research. In this approach, a theory is borrowed, refined, or developed and then tested empirically. In this challenging-the-boundaries article, the authors make a case for an empirics-first approach. ?Empirics-first? refers to research that (1) is grounded in (originates from) a real-world marketing phenomenon, problem, or observation, (2) involves obtaining and analyzing data, and (3) produces valid marketing-relevant insights without necessarily developing or testing theory. The empirics-first approach is not antagonistic to theory but rather can serve as a stepping-stone to theory. The approach lends itself well to today?s data-rich environment, which can reveal novel research questions untethered to theory. The present article describes the underlying principles of an empirics-first approach, which consists of exploring a domain purposefully without preconceptions. Using a rich set of published examples, the authors offer guidance on how to implement empirics-first research and how it can lead to valuable knowledge development. Advice is also offered to scholars on how to report empirics-first research and to reviewers and to editorial teams on how to evaluate it. The ultimate objective is to pave a way for the empirics-first approach to enter the mainstream of academic marketing research.},
  langid     = {english},
  file       = {C\:\\Users\\danie\\Zotero\\storage\\287767GN\\Golder et al. - 2022 - Learning from Data An Empirics-First Approach to .pdf}
}

@article{goldfarbConductingResearchMarketing2022,
  title     = {Conducting {{Research}} in {{Marketing}} with {{Quasi-Experiments}}},
  author    = {Goldfarb, Avi and Tucker, Catherine and Wang, Yanwen},
  year      = {2022},
  month     = may,
  journal   = {Journal of Marketing},
  volume    = {86},
  number    = {3},
  pages     = {1--20},
  publisher = {{SAGE Publications Inc}},
  issn      = {0022-2429},
  doi       = {10.1177/00222429221082977},
  abstract  = {This article aims to broaden the understanding of quasi-experimental methods among marketing scholars and those who read their work by describing the underlying logic and set of actions that make their work convincing. The purpose of quasi-experimental methods is, in the absence of experimental variation, to determine the presence of a causal relationship. First, the authors explore how to identify settings and data where it is interesting to understand whether an action causally affects a marketing outcome. Second, they outline how to structure an empirical strategy to identify a causal empirical relationship. The article details the application of various methods to identify how an action affects an outcome in marketing, including difference-in-differences, regression discontinuity, instrumental variables, propensity score matching, synthetic control, and selection bias correction. The authors emphasize the importance of clearly communicating the identifying assumptions underlying the assertion of causality. Last, they explain how exploring the behavioral mechanism?whether individual, organizational, or market level?can actually reinforce arguments of causality.},
  langid    = {english},
  file      = {C\:\\Users\\danie\\Zotero\\storage\\LPZHEZ8Z\\Goldfarb et al. - 2022 - Conducting Research in Marketing with Quasi-Experi.pdf}
}

@article{heMarketFakeReviews2022a,
  title        = {The Market for Fake Reviews},
  volume       = {41},
  issn         = {0732-2399},
  url          = {https://pubsonline.informs.org/doi/abs/10.1287/mksc.2022.1353},
  doi          = {10.1287/mksc.2022.1353},
  abstract     = {We study the market for fake product reviews on Amazon.com. Reviews are purchased in large private groups on Facebook and other sites. We hand-collect data on these markets and then collect a panel of data on these products’ ratings and reviews on Amazon, as well as their sales rank, advertising, and pricing policies. We find that a wide array of products purchase fake reviews, including products with many reviews and high average ratings. Buying fake reviews on Facebook is associated with a significant but short-term increase in average rating and number of reviews. We exploit a sharp but temporary policy shift by Amazon to show that rating manipulation has a large causal effect on sales. Finally, we examine whether rating manipulation harms consumers or whether it is mainly used by high-quality products in a manner like advertising or by new products trying to solve the cold-start problem. We find that after firms stop buying fake reviews, their average ratings fall and the share of one-star reviews increases significantly, particularly for young products, indicating rating manipulation is mostly used by low-quality products.},
  pages        = {896--921},
  number       = {5},
  journaltitle = {Marketing Science},
  author       = {He, Sherry and Hollenbeck, Brett and Proserpio, Davide},
  urldate      = {2024-01-05},
  date         = {2022-09},
  note         = {Publisher: {INFORMS}},
  keywords     = {electronic commerce, retailing, word of mouth},
  file         = {Full Text PDF:/Users/daniel/Zotero/storage/IA9PDEQJ/He et al. - 2022 - The Market for Fake Reviews.pdf:application/pdf}
}

@misc{henke_levine_mcinerney_2018,
  title   = {You Don’t Have to Be a Data Scientist to Fill This Must-Have Analytics Role},
  url     = {https://hbr.org/2018/02/you-dont-have-to-be-a-data-scientist-to-fill-this-must-have-analytics-role?autocomplete=true},
  journal = {Harvard Business Review},
  author  = {Henke, Nicolaus and Levine, Jordan and McInerney, Paul},
  year    = {2018}
}

@article{imbens_causal,
  author  = {Imbens, Guido W.},
  title   = {Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics},
  journal = {Journal of Economic Literature},
  volume  = {58},
  number  = {4},
  year    = {2020},
  month   = {December},
  pages   = {1129-79},
  doi     = {10.1257/jel.20191597},
  url     = {https://www.aeaweb.org/articles?id=10.1257/jel.20191597}
}
  
@article{kimSerendipityChanceEncounters2021,
  title        = {Serendipity: Chance Encounters in the Marketplace Enhance Consumer Satisfaction},
  volume       = {85},
  issn         = {0022-2429},
  url          = {https://doi.org/10.1177/00222429211000344},
  doi          = {10.1177/00222429211000344},
  shorttitle   = {Serendipity},
  abstract     = {Despite evidence that consumers appreciate freedom of choice, they also enjoy recommendation systems, subscription services, and marketplace encounters that seemingly occur by chance. This article proposes that enjoyment can, in some contexts, be higher than that in contexts involving choice. This occurs as a result of feelings of serendipity that arise when a marketplace encounter is positive, unexpected, and attributed to some degree of chance. A series of studies shows that feelings of serendipity positively influence an array of consumer outcomes, including satisfaction and enjoyment, perceptions of meaningfulness of an experience, likelihood of recommending a company, and likelihood of purchasing additional products from the company. The findings show that strategies based on serendipity are even more effective when consumers perceive that randomness played a role in how an encounter occurred, and not effective when the encounter is negative, the encounter occurs deterministically (i.e., planned by marketers to target consumers), and consumers perceive that they have enough knowledge to make their own choices. Altogether, this research suggests that marketers can influence customer satisfaction by structuring marketplace encounters to appear more serendipitous, as opposed to expected or entirely chosen by the consumer.},
  pages        = {141--157},
  number       = {4},
  journaltitle = {Journal of Marketing},
  author       = {Kim, Aekyoung and Affonso, Felipe M. and Laran, Juliano and Durante, Kristina M.},
  urldate      = {2024-01-05},
  date         = {2021-07-01},
  langid       = {english},
  note         = {Publisher: {SAGE} Publications Inc},
  file         = {SAGE PDF Full Text:/Users/daniel/Zotero/storage/H846TXPK/Kim et al. - 2021 - Serendipity Chance Encounters in the Marketplace Enhance Consumer Satisfaction.pdf:application/pdf}
}

@book{knaflic2015storytelling,
  title     = {Storytelling with Data: A Data Visualization Guide for Business Professionals},
  author    = {Knaflic, C.N.},
  isbn      = {9781119002253},
  year      = {2015},
  publisher = {Wiley}
}
  
@book{mcelreath2020statistical,
  title     = {Statistical rethinking: A Bayesian course with examples in R and Stan},
  author    = {McElreath, Richard},
  year      = {2020},
  publisher = {Chapman and Hall/CRC}
}
  
@article{mcgowanDesignPrinciplesData2022,
  title      = {Design {{Principles}} for {{Data Analysis}}},
  author     = {McGowan, Lucy D'Agostino and Peng, Roger D. and Hicks, Stephanie C.},
  year       = {2022},
  month      = jul,
  journal    = {Journal of Computational and Graphical Statistics},
  volume     = {0},
  number     = {0},
  pages      = {1--8},
  publisher  = {{Taylor \& Francis}},
  issn       = {1061-8600},
  doi        = {10.1080/10618600.2022.2104290},
  abstract   = {The data revolution has led to an increased interest in the practice of data analysis. While much has been written about statistical thinking, a complementary form of thinking that appears in the practice of data analysis is design thinking\textemdash the problem-solving process to understand the people for whom a solution is being designed. For a given problem, there can be significant or subtle differences in how a data analyst (or producer of a data analysis) constructs, creates, or designs a data analysis, including differences in the choice of methods, tooling, and workflow. These choices can affect the data analysis products themselves and the experience of the consumer of the data analysis. Therefore, the role of a producer can be thought of as designing the data analysis with a set of design principles. Here, we introduce design principles for data analysis and describe how they can be mapped to data analyses in a quantitative and informative manner. We also provide data showing variation of principles within and between producers of data analyses. Our work suggests a formal mechanism to describe data analyses based on design principles. These results provide guidance for future work in characterizing the data analytic process. Supplementary materials for this article are available online.},
  keywords   = {Data science,Design,Education,Statistics},
  annotation = {\_eprint: https://doi.org/10.1080/10618600.2022.2104290},
  file       = {C\:\\Users\\danie\\Zotero\\storage\\NUUYP8KZ\\McGowan et al. - 2022 - Design Principles for Data Analysis.pdf}
}

@article{meyer1995natural,
  title     = {Natural and quasi-experiments in economics},
  author    = {Meyer, Breed D},
  journal   = {Journal of business \& economic statistics},
  volume    = {13},
  number    = {2},
  pages     = {151--161},
  year      = {1995},
  publisher = {Taylor \& Francis}
}

@book{morgan2015counterfactuals,
  title     = {Counterfactuals and causal inference - Methods and Principles for Social Research},
  edition   = {2nd Edition},
  author    = {Morgan, Stephen L and Winship, Christopher},
  year      = {2015},
  publisher = {Cambridge University Press}
}

@article{morrison1974theoretical,
  title   = {A theoretical framework for cartographic generalization with the emphasis on the process of symbolization},
  author  = {Morrison, Joel L},
  journal = {International Yearbook of Cartography},
  volume  = {14},
  number  = {1974},
  pages   = {115--27},
  year    = {1974}
}

@article{ordaniniFewerBlockbustersMore2016a,
  title      = {From Fewer Blockbusters by More Superstars to More Blockbusters by Fewer Superstars: {{How}} Technological Innovation Has Impacted Convergence on the Music Chart},
  shorttitle = {From Fewer Blockbusters by More Superstars to More Blockbusters by Fewer Superstars},
  author     = {Ordanini, Andrea and Nunes, Joseph C.},
  year       = {2016},
  month      = jun,
  journal    = {International Journal of Research in Marketing},
  series     = {The {{Entertainment Industry}}},
  volume     = {33},
  number     = {2},
  pages      = {297--313},
  issn       = {0167-8116},
  doi        = {10.1016/j.ijresmar.2015.07.006},
  abstract   = {The pace of change in recorded music technology has accelerated faster than ever during the past two decades with the shift from analog to digital. Digital recordings provide consumers the unimpeded ability to access, sample, learn about, acquire, store, and share vast amounts of music as never before. Supporters of winner-take-all theory believe lower search and transaction costs brought about by digitization have led to greater convergence with fewer extraordinarily popular songs (blockbusters) and a smaller number of artists who perform them (superstars). Supporters of long-tail theory believe the same factors have led to less convergence and a greater number of songs and artists becoming blockbusters and superstars. This work pits these opposing predictions against each other empirically. More specifically, we examine how the number of songs and artists appearing annually on Billboard's Hot 100 singles chart has changed between 1974 and 2013 in relation to three major turning points in technology associated with digitization. These turning points mark consumers' shift: (1) from analog records and cassettes to digital audio with the advent of CDs, (2) from CDs to compressed digital audio MP3s, and (3) from P2P networks and illegal file sharing to legitimate online distributors of digital downloads. In general, we observe a growing winner-take-all effect for songs until the advent of MP3s in 1998, when this trend abated. This result appears largely due to greater convergence in the Top 10. The trend reverses itself as the number of songs making the chart increases steadily after the launch of legitimate online music sellers such as iTunes. The exact opposite pattern is observed for artists. Initially, an increasing number of artists made the chart, and this trend continued unabated until 2003. After the emergence of legitimate online resellers, the trend reversed as fewer and fewer artists made it onto the chart. The overall pattern is summarized as a transition from fewer blockbusters by more superstars to more blockbusters by fewer superstars.},
  langid     = {english},
  keywords   = {Blockbuster,Long-tail,Music,Recording technology,Superstar,Winner-take-all},
  file       = {C\:\\Users\\danie\\Zotero\\storage\\MUZND6ZS\\Ordanini and Nunes - 2016 - From fewer blockbusters by more superstars to more.pdf;C\:\\Users\\danie\\Zotero\\storage\\69N98CLJ\\S0167811615001081.html}
}

@article{pachaliWhatDrivesDemand2022d,
  title   = {What Drives Demand for Playlists on {{Spotify}}?},
  author  = {Pachali, Max J. and Datta, Hannes},
  year    = {2022},
  journal = {Available at SSRN},
  file    = {C\:\\Users\\danie\\Zotero\\storage\\3AVZ6S4R\\Pachali and Datta - 2022 - What drives demand for playlists on Spotify.pdf;C\:\\Users\\danie\\Zotero\\storage\\R3J472YM\\papers.html}
}

@article{pearl2000models,
  title   = {Models, reasoning and inference},
  author  = {Pearl, Judea and others},
  journal = {Cambridge, UK: CambridgeUniversityPress},
  volume  = {19},
  pages   = {2},
  year    = {2000}
}

@article{pearl2009causal,
  title     = {Causal inference in statistics: An overview},
  author    = {Pearl, Judea},
  journal   = {Statistics surveys},
  volume    = {3},
  pages     = {96--146},
  year      = {2009},
  publisher = {Amer. Statist. Assoc.}
}

@article{rocklageMassscaleEmotionalityReveals2021,
  title        = {Mass-scale emotionality reveals human behaviour and marketplace success},
  volume       = {5},
  rights       = {2021 The Author(s), under exclusive licence to Springer Nature Limited},
  issn         = {2397-3374},
  url          = {https://www.nature.com/articles/s41562-021-01098-5},
  doi          = {10.1038/s41562-021-01098-5},
  abstract     = {Online reviews promise to provide people with immediate access to the wisdom of the crowds. Yet, half of all reviews on Amazon and Yelp provide the most positive rating possible, despite human behaviour being substantially more varied in nature. We term the challenge of discerning success within this sea of positive ratings the ‘positivity problem’. Positivity, however, is only one facet of individuals’ opinions. We propose that one solution to the positivity problem lies with the emotionality of people’s opinions. Using computational linguistics, we predict the box office revenue of nearly 2,400 movies, sales of 1.6 million books, new brand followers across two years of Super Bowl commercials, and real-world reservations at over 1,000 restaurants. Whereas star ratings are an unreliable predictor of success, emotionality from the very same reviews offers a consistent diagnostic signal. More emotional language was associated with more subsequent success.},
  pages        = {1323--1329},
  number       = {10},
  journaltitle = {Nature Human Behaviour},
  shortjournal = {Nat Hum Behav},
  author       = {Rocklage, Matthew D. and Rucker, Derek D. and Nordgren, Loran F.},
  urldate      = {2024-01-05},
  date         = {2021-10},
  langid       = {english},
  note         = {Number: 10
                  Publisher: Nature Publishing Group},
  keywords     = {Business and management, Human behaviour}
}

@book{schwabish2021better,
  title     = {Better data visualizations: A guide for scholars, researchers, and Wonks},
  author    = {Schwabish, Jonathan},
  year      = {2021},
  publisher = {Columbia University Press}
}


@article{varianCausalInferenceEconomics2016a,
  title     = {Causal Inference in Economics and Marketing},
  author    = {Varian, Hal R.},
  year      = {2016},
  month     = jul,
  journal   = {Proceedings of the National Academy of Sciences},
  volume    = {113},
  number    = {27},
  pages     = {7310--7315},
  publisher = {{Proceedings of the National Academy of Sciences}},
  doi       = {10.1073/pnas.1510479113},
  abstract  = {This is an elementary introduction to causal inference in economics written for readers familiar with machine learning methods. The critical step in any causal analysis is estimating the counterfactual\textemdash a prediction of what would have happened in the absence of the treatment. The powerful techniques used in machine learning may be useful for developing better estimates of the counterfactual, potentially improving causal inference.},
  file      = {C\:\\Users\\danie\\Zotero\\storage\\VVATUBR7\\Varian - 2016 - Causal inference in economics and marketing.pdf}
}


@article{westreich2013table,
  title     = {The table 2 fallacy: presenting and interpreting confounder and modifier coefficients},
  author    = {Westreich, Daniel and Greenland, Sander},
  journal   = {American journal of epidemiology},
  volume    = {177},
  number    = {4},
  pages     = {292--298},
  year      = {2013},
  publisher = {Oxford University Press}
}


@article{wlomertInternationalHeterogeneityAssociations2019,
  title    = {International Heterogeneity in the Associations of New Business Models and Broadband {{Internet}} with Music Revenue and Piracy},
  author   = {Wl{\"o}mert, Nils and Papies, Dominik},
  year     = {2019},
  month    = sep,
  journal  = {International Journal of Research in Marketing},
  series   = {Marketing {{Perspectives}} on {{Digital Business Models}}},
  volume   = {36},
  number   = {3},
  pages    = {400--419},
  issn     = {0167-8116},
  doi      = {10.1016/j.ijresmar.2019.01.007},
  abstract = {Broadband Internet has fundamentally changed business models in many industries. In the music industry, for instance, old business models were challenged by illegal competitors, and broadband Internet has enabled value creation through new business models. The changes that established business models experienced in the wake of broadband Internet, however, differed vastly across national markets, and these differences are not well understood. We build a conceptual framework and study the extent to which differences in economic and cultural factors are associated with different market outcomes in the wake of the proliferation of broadband Internet. Thus, we compile two unique data sets from the music industry, comprising (1) revenue data for 36 countries and 22\,years and (2) piracy data for 47 countries and {$>$}2\,years. We use a Bayesian multilevel model to explore between-country heterogeneity in the associations between these variables and broadband Internet adoption and business model innovations. Our results show that the negative association between broadband Internet penetration and music revenue is weaker in high-income countries, where income restrictions are less likely to drive demand towards illegitimate piracy services. In terms of cultural factors, we find that a market's response to the introduction of broadband Internet is less negative in countries scoring high on Hofstede's individualism and uncertainty avoidance dimensions. Furthermore, we find that overall revenues only recover after the latest generation of streaming services (e.g., Spotify) has been introduced, and the adoption of these services is associated with lower levels of online music piracy.},
  langid   = {english},
  keywords = {Bayes,Business models,Multilevel modeling,National culture,Online piracy,Panel data},
  file     = {C\:\\Users\\danie\\Zotero\\storage\\C7RK6VKJ\\Wlömert and Papies - 2019 - International heterogeneity in the associations of.pdf}
}


@book{wright1928tariff,
  title     = {Tariff on animal and vegetable oils},
  author    = {Wright, Philip G},
  year      = {1928},
  publisher = {Macmillan Company, New York}
}