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Tuning into the digital channel: evaluating business model characteristics for Internet firm survival

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Abstract

More than 5,000 Internet firms have failed since the beginning of 2000. One common perception is that the downturn in the economy drove many firms out of business. But then, why have some firms survived? In this research, we provide an empirical analysis by examining how the business model characteristics of an Internet firm affect its survival. We analyze a panel data set of 130 public Internet firms using two different techniques: non-parametric survival analysis, and the semiparametric Cox proportional hazards model. We characterize the survival rates throughout the lifetimes of the public Internet firms in our sample. Our results reveal that smaller firms that facilitate customer-provider interactions, are transaction brokers, and that rely on advertising as their primary source of revenue sources have had a lower likelihood of bankruptcy or failure. In addition, the detrimental effects on failing to serve as interaction platforms for individuals and businesses, and a larger firm size diminish over time as Internet firms mature, and the weaker ones are forced out of the marketplace. Our research also points out important dimensions of an Internet firm’s business model that affect its survival.

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Notes

  1. For example, the NASDAQ Composite Index increased by 238% from 1,520 in the beginning of 1996 to its highest of 5,132 on March 10, 2000. Then within a short two and a half year period, it lost 78% of its value and decreased to a low of 1,136 on October 9, 2002. Seventy-nine out of our sample of 130 Internet firms went public during the five-quarter period from the beginning of 1999 to the first quarter of 2000, after which Internet firm IPOs dramatically fell in number. The 6-month U.S. Treasury bill interest rate went from a high of 6.25% in the third quarter of 2000 to a low of 0.94% in the second quarter of 2004, further indicating the changing fundamentals of the American economy.

  2. Barua et al. [10] define a dotcom as a firm that generates 100% of its revenues via the Internet. In another related study, they recognized that Internet firms also generate revenues through traditional channels and used 95% as the cutoff. In this research, we use 90% as the cutoff point so that we are able to include in our sample companies that are generally considered Internet firms by most observers of the digital economy. The 95 and 100% levels for Internet-based revenue generation actually leave out a number of well-known names among these firms. Some examples are Garden.com and GlobalNetFinancial.com.

  3. For example, de Koning [18] uses the concept of survival of the fittest to examine the success or failure of organizational collaborations and acquisitions. Lawless and Finch [34] also examine how the suitability of an organization’s strategy to its environment affects its survival.

  4. An interaction platform is not business model-specific. It can be a B2B, B2C, or C2C firm, as long as it provides a platform that permits individuals and businesses to interact with one another.

  5. The reader should note that each of our hypotheses might include two additional qualifiers, to ensure the accuracy in interpretation of the tests that we carried out in this research. Our theoretical assertions pertain to the time frame of our data, and should not be mistaken as “once and for all” assertions; as the times and the technologies change, so will the predictions of theory change. In spite of many changes that have occurred since the dotcom era, this actually is true with different theories in many different industry settings. So, although all of our hypotheses are stated in the present tense, it is appropriate for the reader to think of the tests of theory that we will conduct as having an historical or past tense flavor to them. Nevertheless, we believe that the bulk of what we have asserted will continue to be true. Still, there are changes and nuances that have crept into the current online marketplace and the broader digital economy that reflect the major efforts with reintermediation on the part of well-established leading companies with very large resource bases. As a result, we have seen pure-play Internet firms increasingly combined with other more traditional firms, such as Pets.com with PETsMART, and other emerging technology and emerging services partners, such as eBay and Skype.

  6. We want to point out that new Internet-only business models are not equivalent to pure-play Internet firms. Examples of companies that are pure-players, but did not use brand new business models are Amazon.com and Buy.com. They only exist online, but they are retailers. So their business models are not new; they existed before the Internet.

  7. Non-parametric survival analysis does not involve any assumption about the functional form of the hazard function. In contrast, semiparametric survival analysis places restrictions and assumptions on the functional forms of portions of the hazard function. In addition, parametric survival analysis permits an analyst to fully specify the functional forms of the hazard and survival functions, which may depend on either a set of covariates or time, or both. Typical models that are used include the Weibull and exponential models. Other models that can be used to specify statistical distributions for the hazard include the log-logistic, log-normal, and gamma distributions. For additional details on the rationale for using these different models, the interested reader should see Le [35], Hosmer and Lemeshow [25], and Lawless [33].

  8. In age-based analysis, we compare firms at the same age, that is, the same number of quarters after IPO. Performing age-based comparison allows us to eliminate the impact on the hazard rate and survival of the learning effect associated with firm age. In calendar time-based analysis, we compare Internet firm survival on a calendar quarter-by-calendar quarter basis. This way, we can compare the survival and failure outcome of all firms that were at the risk of failure during a specific calendar quarter. Performing a calendar time-based analysis allows us to eliminate the confounding effects of environmental factors on firm survival. This is especially important for our research since our sample period includes periods of “boom and bust” for the Internet firms.

  9. There is no need for an intercept because the baseline hazard absorbs all the variations in the hazard rate that are the same for all firms at the same age. The PL function we present assumes no tied durations. That is, no two firms exited at the same duration after IPO. Adjustments to the PL function can be made to account for the conjoint probability of observing two or more events at the same duration. Our empirical results reflect this adjustment.

  10. We admitted Barua et al.’s [8, 9] Layers 3 and 4 firms in our sample. Layer 3 firms are e-intermediaries that provide e-markets to facilitate buyers and sellers to meet and conduct transactions. Layer 4 firms are e-commerce firms that engage in online selling of products or services. See Appendix 1 for a list of firms in our data set.

  11. We assessed a couple of different coding approaches for the underlying construct here. One approach that we evaluated was to use two variables, PerishableProduct and Non-PerishableProduct. The variables represent perishable physical products (1,0) and non-perishable physical products (0,1) as the alternative cases, with digital products as the base case when the variables are (0,0). We settled on coding DigitalGoods in continuous categorical form, as follows: 3 if the company provides digital products or services that can be directly presented or downloaded via the Internet; 2 if the company sells non-perishable physical products that have to be physically delivered to the customers; and 1 if the company sells perishable products such as groceries, flowers, and plants that have limited delivery radius or high delivery costs. The results of the models which we present later in the article were essentially the same for both codings, so we chose to present the leanest, one-variable version [28].

  12. The two authors independently coded each Internet firm in our sample for values of the independent variables. We then discussed our differences until we reached consensus. The inter-coder consistencies were 90, 99, 100, and 98% for InteractionPlatform, NewNetBusMod, DigitalGoods, and TransBrokerBusMod, respectively.

  13. Many online retailers are categorized as “catalog and mail-order houses” with the exception of Barnes and Noble.com (www.bn.com), which is categorized as a “miscellaneous shopping goods store.”

  14. It is standard in survival analysis to add time-dependent variables to correct for violations of the proportionality assumption [3, 4, 13, 29]. This is usually done by adding an interaction term between a variable with time or the logarithm or natural logarithm of time. We chose to do this with the latter specification of the variables.

  15. We observed large coefficient estimates for a few of our variables, including InteractionPlatform, ln(Employee), and InteractionPlatform·ln(Time). This was mainly due to the inclusion of InteractionPlatform, ln(Employee), as well as their interaction terms with ln(Time) together in the same model. When we only included InteractionPlatform and ln(Employee) without the interaction terms, the coefficients were much smaller. However, the model violated the proportional hazards assumption. When we added the interaction terms, the coefficients for each variable and its corresponding interaction term with ln(Time) had opposite signs and became much larger. This is due to these coefficients picking up changes in the impacts of these variables over time as firms grew.

  16. AgeSinceIPO was significant with positive parameter estimates and greater than one HRs in both models. AgeSinceIPO 2 was significant with a negative parameter estimate and smaller than one HR in both models. These results suggest that our sample Internet firms’ likelihood due to failure first increased with age, then it decreased as the weaker ones exited the market and the more successful ones were left.

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Acknowledgments

We are indebted to Paul Tallon and the anonymous reviewers of this article at various stages along the way for their helpful suggestions. We thank Indranil Bardhan, Alok Gupta, Dennis Ahlburg, Sudipto Banerjee, Rajiv Banker, Alina Chircu, Eric Clemons, Qizhi Dai, Gordon Davis, Rajiv Dewan, Jungpil Hahn, Yuji Honjo, Bao-Jun Jiang, M.S. Krishnan, Zining Li, Hank Lucas, Brian McCall, Chris Nachtsheim, Fred Riggins, Pervin Shroff, Eric Walden and Chuck Wood for various critical comments. We also benefited from the input of the participants of the “Competitive Strategy, Economics, and Information Technology” mini-track at HICSS, and the Workshop on Information Systems and Economics. This work was also presented at the University of Minnesota, the University of Michigan, Notre Dame University, Michigan State University and the University of Maryland, where seminar participants offered us ideas on the theory, measurement and modeling issues. We thank Tim Miller, previously CEO of Webmergers.com, for access to Internet firm bankruptcy and shutdown data. Rob Kauffman thanks the MIS Research Center at Minnesota and the Center for Advancing Business through IT at Arizona State for partial support. Bin Wang thanks the University of Minnesota Graduate School, the Carlson School of Management, and the University of Texas-Pan American for partial support. The usual disclaimers apply.

Author information

Authors and Affiliations

  1. W.P. Carey School of Business, Arizona State University, Tempe, AZ, 95287, USA

    Robert J. Kauffman

  2. College of Business Administration, University of Texas−Pan American, Edinburg, TX, 78539, USA

    Bin Wang

Authors
  1. Robert J. Kauffman
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  2. Bin Wang
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Corresponding author

Correspondence to Bin Wang.

Appendix

Appendix

Firms in Our Data Set

Company name

Business description

Company name

Business description

About.com

Internet portal

El Sitio

Internet portal

Amazon.com

Online retailer

E-Loan

Online loan broker

AmeriTrade Holding Corp.

Online stock brokerage

Emerge Interactive

Online B2B marketplace

Answers Corp.

Content site

Emusic.com

Digital music/audio content

Aptimus

Online direct marketer

eToys

Online retailer

Arbinet Thexchange Inc.

Online B2B marketplace

Excite

Internet portal

ARTISTdirect

Content site

Expedia

Online travel agency

Ashford.com

Online retailer

FactSet Research Systems

Online informediary

Audible

Digital music/audio content

Fashionmall.com

Internet portal

Audiohighway.com

Digital music/audio content

Fogdog

Online retailer

Autobytel

Online auto buying services

FreeMarkets

Online B2B marketplace

Autoweb.com

Online auto buying services

Garden.com

Online retailer

Babyuniverse, Inc.

Online retailer

GeoCities

Web hosting services

Baidu.com, Inc.

Internet search engine

GetThere.com

Online travel agency

BN.com

Online retailer

GlobalNet Financial.com

Content site

Beyond.com Corp.

Online retailer

Go2Net

Internet portal

BigStar Entertainment

Online retailer

Google, Inc.

Internet search engine

Bofi Holding, Inc.

Online bank

Headhunter.net

Online recruiting site

Blue Nile, Inc.

Online retailer

HomeGrocer.com

Online grocer

Broadcast.com

Internet broadcasting/radio

HomeStore.com

Online informediary

Buy.com

Online retailer

HotJobs.com

Online recruiting site

CareerBuilder

Online recruiting site

Housevalues, Inc.

Online informediary

CDnow (New)

Online retailer

IasiaWorks

ISP and web hosting service provider

Claimsnet.com

Online healthcare ASP

IGN Entertainment

Internet portal

Comtex News Network

Online informediary

ImproveNet

Online home improvement e-market

CoolSavings

Online direct marketer

Infoseek Corp.

Internet portal

Crosswalk.com

Internet portal

Inphonic, Inc.

Online retailer

Cybergold

Online direct marketer

InsWeb Corp.

Online insurance agency

Cyberian Outpost

Online retailer

INT Media Group

Content site

Dice (Earthweb)

Online recruiting site

iPrint Technologies

Online retailer

DrKoop.com

Content site

Launch Media

Content site

Drugstore.com

Online retailer

LendingTree

Online loan broker

E*Trade Group

Online stock brokerage

LifeMinders

Online direct marketer

eBay

Online auction house

Liquid Audio

Digital music delivery tech. provider

eChapman

Internet portal

LiveWorld

Online community service provider

eCost.com, Inc.

Online retailer

Lycos

Internet portal

EDGAR Online

Online corporate filings

MarketWatch.com

Content site

Egreetings Network

Online greeting cards

McAfee.com Corp.

Consumer software ASP

Mediconsult.com

Content site

Quotesmith.com

Online insurance agency

Medscape

Content site

Redenvelope, Inc.

Online retailer

Mortgage.com

Online loan broker

Register.com

Internet domain name registration

MotherNature.com

Online retailer

Salon Media Group

Content site

MP3.com

Digital music/audio content

Shanda Interactive Entertainment, Ltd.

Online entertainment, games, wagering

Multex.com

Content site

Shopping.com, Ltd

Online informediary

MyPoints.com

Online direct marketer

SmarterKids.com

Online retailer

N2K

Online retailer

Sohu.com

Internet portal

NetCreations

Online direct marketer

SportsLine.com

Content site

Netease.com

Internet portal

Stamps.com

Online retailer

NetRadio Corp.

Internet broadcasting/radio

Streamline.com

Online grocer

Nimbus

Online retailer

Switchboard

Online yellow pages

Odimo, Inc.

Online retailer

Telocity

Internet service provider

Onvia.com

Online B2B marketplace

TradeStation Group

Online stock brokerage

Orbitz, Inc.

Online travel agency

uBid

Online auction house

Overture Services

Online direct marketer

Uproar

Online entertainment, games, wagering

PartsBase

Online B2B marketplace

Value America

Online retailer

Paypal

Online P2P payment

Varsitybooks.com

Online retailer

Peapod

Online grocer

Vistaprint, Ltd

Online retailer

Planetout, Inc.

Virtual community

VitaminShoppe.com

Online retailer

PlanetRx.com

Online retailer

Web Street

Online stock brokerage

Preview Travel

Online travel agency

WebMD Corp./Healtheon

Online healthcare transaction ASP

Priceline.com

Online travel agency

Webvan Group

Online grocer

Provide Commerce, Inc.

Online retailer

Women.com Networks

Content site

PurchasePro.com

Online B2B marketplace

WorldQuest Networks

Internet telephony

Quepasa.com

Internet portal

Yahoo!

Internet portal

Quokka Sports

Content site

Youbet.com

Online entertainment, games, wagering

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Kauffman, R.J., Wang, B. Tuning into the digital channel: evaluating business model characteristics for Internet firm survival. Inf Technol Manage 9, 215–232 (2008). https://doi.org/10.1007/s10799-008-0040-3

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