Is Tom Cruise Threatened? An Empirical Study of the Impact of Product Variety on Demand Concentration

Tan, Tom Fangyun; Netessine, Serguei; Hitt, Lorin M.

doi:10.1287/isre.2017.0712

Cited by 62 publications

(44 citation statements)

References 72 publications

(31 reference statements)

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“…The study theoretically and empirically advances research on consumer demand in online markets and peer-to-peer marketplaces. First, I contribute by uncovering an important contingency that can reconcile opposing findings in previous long tail studies (Brynjolfsson et al 2011;Elberse and Oberholzer-Gee 2006;Hinz et al 2010;Tan et al 2017). Under high uncertainty, consumers will be disproportionally choose the small number of highly reputable producers and products.…”

Section: Introductionmentioning

confidence: 84%

“…Compared to the Pareto distribution, the long tail effect will lead to a more equally distributed distribution where the 20% most popular products (blockbusters) will account for less than 80% of sales. Various studies empirically confirmed that a reduction in search costs shifts demand towards the long tail (Brynjolfsson et al 2011;Kumar et al 2014;Oestreicher-Singer and Sundararajan 2012a;Tan et al 2017;Zentner et al 2013). For instance, a study by Brynjolfsson et al (2011) showed that the 80% least-selling products of a fashion retailer's online channel accounted for almost half of the total sales (47%)significantly more than via the same retailer's catalog channel and the predicted the 80/20 distribution.…”

Section: Demand Concentration In Online Marketsmentioning

confidence: 93%

“…Thus, related research often applies an 80/20 distribution as a benchmark for comparing sales distributions in online markets (Brynjolfsson et al 2003;Brynjolfsson et al 2006;Brynjolfsson et al 2011). Chris Anderson's observation that sales of many online retailers were much less concentrated than the 80/20 distribution would suggest (Anderson 2006) caused subsequent research to explore the drivers behind this phenomenon (Brynjolfsson et al 2006;Brynjolfsson et al 2011;Hinz et al 2010;Lee et al 2011;Tan et al 2017). Hinz et al (2010) and Brynjolfsson et al (2010) provide excellent reviews of these drivers.…”

Section: Demand Concentration In Online Marketsmentioning

confidence: 99%

“…Some scholars, however, provide conflicting evidence and challenge the validity of the long tail effect (Elberse 2013;Tan et al 2017). In fact, internet markets may engender rich-getricher effects that disproportionately benefit superstar producers and thus shift sales towards the head of the curve (Brynjolfsson et al 2010).…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty kills the long tail: demand concentration in peer-to-peer marketplaces

Taeuscher

2019

Electron Markets

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Theory on the BLong tail effect^predicts that consumer demand in online markets spreads over a long tail of niche products. Recent research, however, provides opposing evidence and questions the theory's validity. In this paper, I aim to reconcile these opposing findings by proposing that consumer uncertainty represents a hidden yet important boundary condition for the long tail effect. Under high uncertainty, demand will be much more concentrated as consumers disproportionally choose the most reputable producers and products. I develop these arguments to predict the demand concentration in peer-to-peer marketplaces, a context in which consumers face high uncertainties about their transaction partners. Testing my predictions with a self-collected dataset of 862,755 transactions on a peer-to-peer marketplace for skillsharing supports my hypotheses. I find that a small share of producers disproportionately benefits from marketplace participation. During the observation period, twenty percent of producers generated 94% of sales. These findings suggest that an opposing rich-get-richer effect overrides the long tail effect in peer-to-peer marketplaces and other uncertain environments. I discuss how these findings inform research on peer-to-peer marketplaces and the sharing economy more broadly.

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Section: Introductionmentioning

confidence: 84%

Section: Demand Concentration In Online Marketsmentioning

confidence: 93%

Section: Demand Concentration In Online Marketsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Uncertainty kills the long tail: demand concentration in peer-to-peer marketplaces

Taeuscher

2019

Electron Markets

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“…Due to the advent of information technology, retail business models have been changed in this decade [1]. Most retailers tend to search for the Omni-channel strategy to provide their customers with a seamless shopping experience [2]. This is an integrated approach that allows customers to use channels or touch points interchangeably on any device or in the physical place [3] [4].…”

Section: Introductionmentioning

confidence: 99%

How Albert Heijn Applied the Omni-Channel Strategy: A Case Study

Feddema¹,

Yen²

2019

TEL

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This study aims to develop the Omni-channel strategy for the supermarket stores. We used Albert Heijn, a leading supermarket chain in the Netherlands, as the research case in the study. Currently, AH has set three goals for the Omni-channel services: more customers, more choices and more places. However, there are still some improvements to be made and ideas that can be given. Therefore, this study provides three ideas suggested to AH, including E-bike delivery, live cooking and cash payment at the self-scan cashiers. E-bike delivery can lower the minimal order value and delivery costs. Live cooking can allow customers to eat in stores. Cash payment at the self-scan cashiers can provide the option of cash payments, which satisfies more customers.

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Product variety in local grocery stores: Differential effects on stock‐keeping unit level sales

Lee

Son

2021

J of Ops Management

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We empirically examine the effect of product variety on sales at the stock‐keeping unit (SKU) level. Prior empirical operations management studies generally treat product variety as a single‐dimensional measure (e.g., the number of SKUs carried by the store, distribution center, or firm) and study its impact on aggregate sales (e.g., sales at the store, distribution center, or firm). As such, SKUs from different brands are assumed to contribute equally to total sales, which is not always the case. To address this, we need to conceptualize product variety within brands and investigate its impact on sales at a more granular level. In this study, we propose and theoretically articulate two product variety dimensions (within‐brand variety [WV] and average WV of other brands [AWVO]) to investigate the mechanisms that connect product variety to SKU‐level sales. Using a detailed transaction dataset from a local grocery retailer, we first show that product variety conceptualized within brands (i.e., the mean of WVs [MWV]) does have an impact on aggregate subcategory sales, as the literature suggests. At the SKU level, we find WV increases same‐brand SKU sales, while AWVO reduces it. Our work provides an alternative explanation to the inverted U‐shaped relationship between product variety and aggregate sales observed in the literature: Increasing variety only increases sales of SKUs belonging to the brand whose variety has increased, while it decreases sales of the other SKUs. We also find that variety effects vary significantly across different profitability levels and product categories. Product variety appears to have the strongest positive effect on high‐margin products. The impact of product variety on specific product category sales varies from as high as 11% to negligible. We further discuss the practical value of our findings and show the robustness of our results through multiple tests while addressing endogeneity using the control function approach.

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Is Tom Cruise Threatened? An Empirical Study of the Impact of Product Variety on Demand Concentration

Abstract: Multiple studies suggested that expanding product variety due to adoption of the

Cited by 62 publications

References 72 publications

Uncertainty kills the long tail: demand concentration in peer-to-peer marketplaces

Uncertainty kills the long tail: demand concentration in peer-to-peer marketplaces

How Albert Heijn Applied the Omni-Channel Strategy: A Case Study

Product variety in local grocery stores: Differential effects on stock‐keeping unit level sales

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