2.4 Empirical backing
2.4.2 Contexts
There are roughly three kinds of empirical settings in industry life-cycle studies. The first one includes one industry, or a few of them, and the entire history of the industry is traced. The second setting also includes one or more industries, but the focus is on a particular phase of the industry history. The third empirical setting includes the usage of data from a large panel of industries.
Understandably, the studies concentrating on a small number of industries are more qualitative in nature and offer more extensive historical detail. Changes in industry variables are tied to specific states of affairs and event paths are explained. In panel data studies the focus is usually on building formal models or on classifying the industries according to the statistical dynamics that they show.
Table 1. Industry life-cycle studies on transportation industries.
Industry Study
Airline Tushman and Anderson 1986
Automobile Mazzucato 2002
Automobile Abernathy and Clark 1985
Automobile Filson 2001
Automobile Suárez and Utterback 1995
Automobile Utterback and Suárez 1993
Automobile Abernathy 1978
Automobile Klepper 1997
Automobile Klepper 2002a
Automobile Klepper and Simons 2005
Bicycle Dowell and Swaminathan 2000
Bicycle Dowell and Swaminathan 2006
Car tyre Jovanovic and MacDonald 1994
Civil aircraft Frenken and Leydesdorff 2000 Combined gas turbine Bergek et al. 2008
Motorcycle Wezel and van Witteloostuijn 2006
Passenger aeroplane Tushman and Murmann 1998
Tyre Klepper 2002a
Tyre Klepper and Simons 2005
Tyre Klepper and Simons 2000b
Turboprop engine Bonaccorsi and Giuri 2000
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Tables 1-5 list different industries used in the kind of industry life-cycle studies that concentrate on a small number of industries. Early studies concentrated mainly on transportation industries (Table 1), but soon ICT industries gained similar popularity (Table 2). Consumer electronics (Table 3) and medical (Table 4) sectors have also been somewhat popular. In addition to these, individual studies on miscellaneous industries from beer brewing to synthetic dye manufacturing (Table 5) have been conducted.
Table 2. Industry life-cycle studies on ICT industries.
Industry Study
Cellular phone Funk 2003
Computer monitor Filson 2001
Computer printer Filson 2001
Computer Bresnahan and Greenstein 1999
Disk drive Christensen and Rosenbloom 1995
DRAM Kim and Lee 2003
Info-communications Krafft 2004
Integrated circuit Utterback and Suárez 1993 Integrated circuit Levitas et al. 2006
Laser printer de Figueiredo and Kyle 2006 Mainframe computer Greenstein and Wade 1998
Microcomputer Lawless and Anderson 1996
Minicomputer Anderson and Tushman 1990
Minicomputer Tushman and Anderson 1986
Optical disk drive Khessina and Carroll 2008 Personal computer Mazzucato 2002
Personal computer Filson 2001
Personal computer Filson 2002
Personal computer Tegarden et al. 1999 Personal computer Henderson 1999
Personal computer Bayus and Agarwal 2007 Photolithographic alignment Henderson 1993
Photolithographic alignment Henderson and Clark 1990
Picture tube Suárez and Utterback 1995
Picture tube Utterback and Suárez 1993
Rigid disk drive Filson 2001
Rigid disk drive Christensen et al. 1998
Semiconductor Stoelhorst 2002
Supercomputer Utterback and Suárez 1993 Telecommunications equipment Dowling and Ruefli 1992 Telecommunications switching Jones 2003
Transistor Suárez and Utterback 1995
Transistor Utterback and Suárez 1993
Typewriter Suárez and Utterback 1995
Typewriter Utterback and Suárez 1993
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Table 3. Industry life-cycle studies on consumer electronics industries.
Industry Study
Amateur camera Windrum 2005
Calculator Utterback and Suárez 1993
Colour television set Willard and Cooper 1985 Electronic calculator Suárez and Utterback 1995
Television Suárez and Utterback 1995
Television receiver Klepper and Simons 2000a Television set Utterback and Suárez 1993
Television Klepper 2002a
Television Klepper and Simons 2005
Table 4. Industry life-cycle studies on medical industries.
Industry Study
Biopharmaceuticals Rothaermel 2000 Pacemaker technology Haupt et al. 2007
Pacemaker Banbury and Mitchell 1995
Penicillin Klepper and Simons 2005
Penicillin Klepper 2002a
Table 5. Industry life-cycle studies on other industries.
Industry Study
Beer brewing Horvath et al. 2001
Beer brewing Tremblay et al. 2005
Cement Anderson and Tushman 1990
Cement Tushman and Anderson 1986
Daily newspaper Van Kranenburg et al. 2002 Facsimile transmission service Baum et al. 1995
Glass Anderson and Tushman 1990
Laser Klepper and Thompson 2006
Newspaper Levinthal 1991
Pharmaceutical wholesaling Fein 1998
Synthetic dye Murmann and Homburg 2001
Typesetter Tripsas 1997
These give quite a comprehensive view of the kind of industries that have been studied. Most of the industries are traditional manufacturing industries. Even though many ICT industries have been studied, they include only hardware industries. No life-cycle studies have been published on software. Of the studies listed here only four are non-manufacturing industries. The study on facsimile transmission services by Baum et al. (1995) and on pharmaceutical wholesaling by Fein (1998) are clearly service industries. Daily newspaper (Van Kranenburg et al. 2002;
Levinthal 1991) is also not exactly a traditional manufacturing industry. Biopharmaceuticals
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(Rothaermel 2000) and beer brewing (Horvath et al. 2001; Tremblay et al. 2005) are on the borderline. The selection of industries is probably directed by the availability of data. Anderson and Tushman (1990, p. 619), for example, openly admit that they chose cement, glass and microcomputers because data were available.
The studies that concentrate on a specific phase in an industry‘s history are listed in Table 6. Which studies to include here is more or less a judgement call as they are not clearly branded as ILC studies. The studies included here have classic industry life-cycle papers in their reference lists.
Table 6. Studies on specific life-cycle stages.
Industry Issue Study
Automobile Pre-shakeout and shakeout stage Argyres and Bigelow 2007
Automobile Comparison on the survival of de novo and
de alio entrants Klepper 2002b
Bicycle Product lines of entrants Dowell 2006
Brewing Entry of specialists, microbrewery and
brewpub segments Swaminathan 1998
Cellular telephone service Evolution of intra-industry variety Noda and Collis 2001
Chemical Technological leaders and followers Fai 2007
Electrical/electronic Technological leaders and followers Fai 2007
Encryption software Birth of the industry Giarratana 2004
Fibre optics Pre-adaptation Cattani 2005
Hard disk drive Entry into new submarkets Chesbrough 2003
Household electrical equipment Late entrants Shamsie et al. 2004 Initiation systems in mines Move from traditional to electronic systems Smit and Pistorius 1998 Internet start-ups Survival in the dot com era Goldfarb et al. 2007 Local area network switch Product location of incumbents and entrants Fontana and Nesta 2006
Machine tool Mature phase Roy and McEvily 2004
Mobile telecommunications Shakeout Kim and Park 2006
Mobile telecommunications Challengers won the incumbents He et al. 2006
Pacemaker Incremental innovations and survival Banbury and Mitchell 1995
Pharmaceuticals New product introductions Nerkar and Roberts 2004
Retail banking Innovative activity and competitive
advantage Roberts and Amit 2003
Tennis racket Radical inventions Dahlin and Behrens 2005
Transport Technological leaders and followers Fai 2007
Wineries Entry into maturing industry, specialists Swaminathan 1995
These studies include many of the same industries as the tables above. However, there are proportionately more studies that focus on non-manufacturing industries, such as encryption software (Giarratana 2004), Internet start-ups (Goldfarb et al. 2007) and mobile telecommunications (Kim and Park 2006; He et al. 2006).
The third kind of empirical setting is based on panel data on several industries and these studies usually use data from the Thomas Register of American Manufacturers (e.g. Agarwal and Gort 2002; Agarwal and Bayus 2004). Such studies usually include around 20 to 50 industries. In
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Table 7 industries that have been included in panel studies are listed (Gort and Klepper 1982;
Klepper and Graddy 1990; Audretsch 1991; Agarwal 1996; Agarwal and Gort 1996; Agarwal 1998;
Agarwal and Audretsch 2001; Agarwal and Bayus 2002; Agarwal et al. 2002; Agarwal and Bayus 2004; Sarkar et al. 2006). These studies have shown that the aging patterns predicted by the industry life-cycle theory hold for a wide variety of industries. Even through the product range here is vast the products are all from traditional manufacturing sectors.
Table 7. Industries included in panel studies.
Industries
Antibiotics Fluorescent lamp Photocopy machine
Adding and calculating machinery Freezer Piezoelectric crystal
Artificial Christmas tree Freon compressor Polariscope
Automobile Garbage disposer Radar antenna assembly
Ballpoint pen Gas turbine Radiant heating baseboard
Baseboard radiant heating Guided missile Radiation meter
Betaray gauge Gyroscope Radio transmitter
Car tyre Heat pump Recording tape
Cathode ray tube Home microwave oven Rocket engine
Cellular telephone Home VCR Saccharin
Clothes dryer Jet engine Sewing machine
Clothes washer Laser Shampoo
Combination lock Magnetic recording tape Streptomycin
Compact disc player Microcomputer Styrene
Computer printer Microfilm reader Telemeter
Computer Monitor Television
Contact lens Nuclear reactor Transistor
Cryogenic tank Nylon Turbojet engine
Dase Optical disc drive Vacuum cleaner
DDT Outboard motor Video cassette recorder
Dishwasher Oxygen tent Windscreen wiper
Electric blanket Paint Zipper
Electric shaver Penicillin
Electrocardiograph Phonograph record
Of the studies on non-manufacturing industries the study by Fein (1998) on pharmaceutical wholesaling is the only one that argues that the life-cycle dynamics of non-manufacturing industries differ from those of manufacturing industries. Newspapers are treated like any manufacturing industry by Van Kranenburg et al. (2002) and Levinthal (1991). The same applies to Internet start-ups (Goldfarb et al. 2007) and mobile telecommunications (Kim and Park 2006;
He et al. 2006). Beer brewing (Horvath et al. 2001; Tremblay et al. 2005) is also seen as just another manufacturing industry even though consumer taste differences and branding are acknowledged through the emergence of specialist microbrewers. The study on facsimile transmission services by Baum et al. (1995) concentrates on positive network externalities and does not highlight the difference between manufacturing and non-manufacturing industries.
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Biopharmaceuticals (Rothaermel 2000), on the other hand, are labelled high-tech, which serves as the explanation for innovations tending to take place in partnerships. The absence of economies of scale is mentioned as a special feature of the encryption software industry (Giarratana 2004).
As specificity for a non-manufacturing industry Fein (1998) concludes that no dominant design emerged in pharmaceutical wholesaling. Instead, he conceptualised a ―dominant business model‖
that standardised channel functions within such a service industry. However, the business model was not selected from among alternative business models but evolved gradually through the one-by- one adoption of new procedures, such as automation in warehouses and generation of data about anticipated customer ordering patterns. Furthermore, Fein (1998) found that increasing returns to size operate similarly to manufacturing industries, but firms tend to exit through mergers and acquisitions instead of closing down the firm or the department completely. This is due to the geographic nature of the wholesaling industry. Growing national wholesalers were willing to buy regional players to extend their coverage to new areas.
In summary, industry life-cycle theory is based on research on mainly manufacturing industries.
The empirical backing is vast, as a very large number of industries have been studied. Studies on non-manufacturing industries are scarce and some treat non-manufacturing industries as similar to manufacturing industries whereas others argue that there are fundamental differences in their life- cycle dynamics. Many high-tech industries have been studied, but they are generally treated just as any other manufacturing industries. On the other hand, the acceleration of the aging patterns predicted by the industry life-cycle theory is proposed by some due to the transition to high tech (e.g. Day et al. 2003).