Zusammenfassung
Das Internet und damit verbundene Technologien ermöglichen es, verglichen mit den Sortimentsgrößen traditioneller Einzelhändler der letzten Jahrzehnte, eine deutlich höhere Anzahl an Produkten zu produzieren, zu bewerben und profitabel zu verkaufen. Darüber hinaus erlaubt das Internet neue Such- und Empfehlungssysteme, die die Suchkosten von Konsumenten reduzieren. Konsumenten können so ihre Suche nach geeigneten Produkten von einer geringen Anzahl relativ einfach zu findender Blockbuster auf sehr spezielle Nischenprodukte ausdehnen. Als Resultat dieser Entwicklung sind häufig Long-Tail-Absatzverteilungen zu beobachten, die eine hohe Nachfrage nach Nischenprodukten widerspiegeln. In diesem Beitrag wird mithilfe einer agentenbasierten Simulation, die mit Verkaufsdaten eines Video-on-Demand-Anbieters kalibriert wird, gezeigt, inwiefern verschiedene Klassen von Such- und Empfehlungssystemen Einfluss auf die Absatzverteilung, den Gesamtabsatz, den Gewinn und die Konsumentenrente haben. Unsere Ergebnisse zeigen, dass Suchtechnologie je nach Funktionalität den Absatz von Nischen zu Blockbustern (Suchfilter und Empfehlungssysteme) oder aber von Blockbustern hin zu Nischen (Charts und Toplisten) verschieben kann. Wir zerlegen Absatzveränderungen in Substitution und realen Mehrkonsum und zeigen, dass Such- und Empfehlungssystemen zu substanziellen Gewinnsteigerungen führen können. Unsere Ergebnisse machen auch deutlich, dass sinkende Suchkosten durch Such- und Empfehlungssysteme stets zu einer steigenden Konsumentenrente führen. Anbieter können demnach solche Systeme zum Aufbau von Wettbewerbsvorteilen nutzen.
Abstract
The Internet and related technologies have vastly expanded the variety of products that can be profitably promoted and sold by online retailers. Furthermore, search and recommendation tools reduce consumers’ search costs in the Internet and enable them to extend their search from a few easily found best-selling products (blockbusters) to a large number of less frequently selling items (niches). As a result, Long Tail sales distribution patterns emerge that illustrate an increasing demand in niches. We show in this article how different classes of search and recommendation tools affect the distribution of sales across products, total sales, and consumer surplus. We hereby use an agent-based simulation which is calibrated based on real purchase data of a video-on-demand retailer. We find that a decrease in search costs through improved search technology can either shift demand from blockbusters to niches (search filters and recommendation systems) or from niches to blockbusters (charts and top lists). We break down demand changes into substitution and additional consumption and show that search and recommendation technologies can lead to substantial profit increases for retailers. We also illustrate that decreasing search costs through search and recommendation technologies always lead to an increase in consumer surplus, suggesting that retailers can use these technologies as competitive advantage.
This is a preview of subscription content, log in via an institution to check access.
Notes
Der Anbieter möchte nicht genannt werden.
Literatur
Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-srt and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17(6):734–749
Ancarani F (2002) Pricing and the Internet: frictionless commerce or pricer’s paradise? European Management Journal 20(6):680–687
Anderson C (2006) The long tail. Hyperion Press, New York
Ansari A, Essegaier S, Kohli R (2000) Internet recommendation systems. Journal of Marketing Research 37(3):363–375
Arrow KJ (1962) Economic welfare and the allocation of resources for invention. In: Nelson RR (Hrsg) The rate and direction of inventive activity. NBER, New York
Bajari P, Hortacsu A (2003) The winner’s curse, reserve prices, and endogenous entry: empirical insights from eBay auctions. RAND Journal of Economics 347(2):329–355
Bakos Y (1997) Reducing buyer search cost: implications for electronic marketplaces. Management Science 43(12):1676–1692
Brynjolfsson E, Hu Y, Smith MD (2000) Frictionless commerce? – An exploratory analysis of Internet pricing behaviour. Management Science 46(4):563–585
Brynjolfsson E, Hu Y, Smith MD (2003) Consumer surplus in the digital economy: estimating the value of increased product variety at online booksellers. Management Science 49(11):1580–1596
Brynjolfsson E, Dick A, Smith M (2004) Search and product differentiation at an Internet shopbot. Arbeitspapier der Carnegie Mellon University
Brynjolfsson E, Hu Y, Smith MD (2006) From niches to riches: the anatomy of the long tail. Sloan Management Review 47(4):67–71
Brynjolfsson E, Hu Y, Simester D (2007) Goodbye pareto principle, hello long tail: the effect of search costs on the concentration of product sales. Arbeitspapier der MIT Center for Digital Business
Clay K, Krishnan R, Wolff E, Fernandes D (2002) Retail strategies on the web: price and non-price competition in the online book industry. Journal of Industrial Economics 50:351–367
Eliashberg J, Jonker J-J, Sawhney M-S, Wierenga B (2000) MOVIEMOD: an implementable decision-support-system for prerelease market evaluation of motion pictures. Marketing Science 19(3):226–243
Fleder D, Hosanagar K (2009) Blockbuster culture’s next rise of fall: the impact of recommender systems on sales diversity. Management Science 55(5):697–712
Ghose A, Gu B (2007) Search costs, demand structure and long tail in electronic markets: theory and evidence. Arbeitspapier der Stern School of Business, New York University
Hann IH, Terwiesch C (2003) Measuring the frictional costs of online transactions: the case of a name-your-own-price channel. Management Science 49(11):1563–1579
Häubl G, Trifts V (2000) Consumer decision making in online shopping environments: the effects of interactive decision aids. Marketing Science 19(1):4–21
Hinz O, Hann IH, Spann M (2010) Price discrimination in e-commerce? An examination of dynamic pricing in name-your-own-price markets. MIS Quarterly (forthcoming)
Hotelling H (1929) Stability in competition. Economic Journal 39(153):41–57
Kamakura W (2009) How Americans and Europeans spend their time. Marketing Seminar Series of the University of Technology in Sydney
Leunga PL, Lau KN (2004) Estimating the city-block two-dimensional scaling model with simulated annealing. European Journal of Operational Research 158(2):518–524
Nelson BL, Matejcik FJ (1995) Using common random numbers for indifference-zone selection and multiple comparisons in simulation. Management Science 41(12):1935-1945
Netflix (2009) What is the selection like? http://www.netflix.com/HowItWorks. Abruf am 2009-09-28
Oestreicher-Singer G, Sundararajan A (2006) Network structure and the long tail of electronic commerce. Arbeitspapier
Pizam A (2005) International encyclopedia of hospitality management. Butterworth-Heinemann, Oxford
Reinartz W, Kumar V (2000) On the profitability of long-life customers in a noncontractual setting: an empirical investigation and implications for marketing. Journal of Marketing 64(4):17–35
Resnick P, Varian H (1997) Recommender systems. Communications of the ACM 40(3):56–58
Spann M, Skiera B, Schäfers B (2004) Measuring individual frictional costs and willingness-to-pay via name-your-own-price mechanisms. Journal of Interactive Marketing 18(4):22–36
Stigler GJ (1961) The economics of information. Journal of Political Economy 69(3):213–225
Tellis GJ (1986) Beyond the many faces of price: an integration of pricing strategies. Journal of Marketing 50(4):146–160
Tesfatsion L (2002) Agent-based computational economics: growing economies from the bottom up. Artificial Life 8(1):55–82
Thurstone L (1927) A law of comparative judgement. Psychological Review 34:273–286
Varian H (1980) A model of sales. American Economic Review 70(4):651–659
Wu D, Ray G, Geng X, Whinston A (2004) Implications of reduced search costs and free riding in e-commerce. Marketing Science 23(2):255–262
Author information
Authors and Affiliations
Corresponding author
Additional information
Angenommen nach einer Überarbeitung durch Prof. Dr. Buhl.
This article is also available in English via http://www.springerlink.com and http://www.bise-journal.org: Hinz O, Eckert J (2010) The Impact of Search and Recommendation Systems on Sales in Electronic Commerce. Bus Inf Syst Eng. doi: 10.1007/s12599-010-0092-x.
Zusätzliche Information
Ergänzendes Onlinematerial kann unter folgendem Link abgerufen werden.
Rights and permissions
About this article
Cite this article
Hinz, O., Eckert, J. Der Einfluss von Such- und Empfehlungssystemen auf den Absatz im Electronic Commerce. WIRTSCHAFTSINFORMATIK 52, 65–77 (2010). https://doi.org/10.1007/s11576-010-0213-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11576-010-0213-7