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Beyond Product-Process Innovation:

The Case of Service Innovation

Phillip C. Anderson

University of Illinois, Urbana-Champaign philca@illinois.edu

Draft: January 18, 2013

PLEASE DO NOT CITE OR QUOTE

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Beyond Product-Process Innovation: The Case of Service Innovation

Abstract In the extant innovation literature, the question of how a firm’s technological innovation

position relates to service innovation has been relatively understudied. While the extant literature finds strong support for technological innovation with respect to product and process, the question of an additional innovation search has received little attention. I seek to advance this area of research by examining factors that influence the technologically innovative product manufacturing firm to innovate in new services. Using data on entry into professional services, I find empirical evidence that platform innovators are more likely to innovate in services than device innovators. Also, as manufacturing firms diversify into non-manufacturing technologies, they will be more likely to innovate in services. The study suggests that a multiproduct-service innovation cycle is a useful complement to the classic product-process innovation model in prior research because it provides insights into a broader range of innovation choices available to the firm.

Key words: technological innovation, service innovation, search, related diversification

1 Introduction

The management of innovation literature has developed a number of important insights

concerning the patterns of innovation and how new innovations affect the evolution of firms and

industries (Abernathy & Utterback 1978; Abernathy 1978; Anderson & Tushman 1990;

Murmann & Frenken 2006; Tushman & Anderson 1986; Utterback & Abernathy 1975;

Utterback 1994). This perspective focuses on understanding the dynamics surrounding

technological change as examined across two types of innovation: product and process. The

literature traces its roots back to economist Joseph Schumpeter whose early work made the case

that new entrepreneurial firms revolutionize industries through technological innovation

(Schumpeter 1942). Schumpeter described this process as the perennial gale of creative

destruction.

Abernathy and Utterback developed a framework that moved the discussion beyond a

narrow focus on product innovation. In particular, their framework suggests that a dominant

design marks a key transition between a product innovation cycle and a process innovation cycle.

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Prior to a dominant design, multiple product designs emerge and compete for the attention of

potential customers. The pre-dominant design period is characterized by a high degree of

uncertainty and variation in product features. Once a dominant design emerges, a shakeout of

firms occurs and the remaining firms begin to shift their innovation emphasis towards efficiency

improvements in the internal manufacturing process.

The empirical studies that test the Abernathy and Utterback framework primarily focus

on the emergence of a dominant design. This framework has found empirical support across

many different product manufacturing categories such as hard disk drives (Utterback & Suárez

1993), video cassette recorders (Cusumano, Mylonadis, & Rosenbloom 1992), local area

networks (Burg & Kenney 2000), Cochlear implants (Van de Ven & Garud 1993), facsimile

machines (Baum, Korn, & Kotha 1995), and flight simulators (Miller, Hobday, Leroux-Demers,

& Olleros 1995). Despite the significance of the Abernathy-Utterback model, this perspective

has not gone unchallenged. Perhaps the biggest critique is that the framework is limited in its

ability to make causal predictions of a dominant design. While the empirical studies are able to

identify a dominant design ex post across diverse industry settings, the Abernathy-Utterback

framework lacks clarity for making ex ante predictions about which competing design is most

likely to emerge as the dominant design (Murmann & Frenken 2006). While the framework is

flexible for applying to system-level and subsystem-level technologies, some scholars view this

lack of precision in the level of analysis as a weakness (Murmann & Frenken 2006; Tushman &

Murmann 1998). With a further insight that product-process innovation has a cyclical nature that

repeats with new discontinuities (Anderson & Tushman 1990), the Abernathy-Utterback model

remains an important perspective on innovation.

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Recently, scholars have begun to suggest that service innovation is an important third

component of an innovation framework (Cusumano, Kahl, & Suarez Working paper; Suarez &

Cusumano 2009) and an important strategic management issue for value creation (Cusumano

2008; Fang, Palmatier, & Steenkamp 2008; Quinn, Doorley, & Paquette 1990; Suarez,

Cusumano, & Kahl 2012; Wise & Baumgartner 1999). For example, three of the biggest

transitions for IBM have been the introductions of the IBM System/360 mainframe computer in

the 1960’s, the IBM Personal Computer in the 1980’s, and IBM Global Services in the 1990’s.

Having been used as examples of dominant designs, the mainframe and the PC fit within the

classic product-process framework (Hagedoorn, Carayannis, & Alexander 2001; Iansiti &

Khanna 1995). However, the innovation literature is less clear how the emergence of services –

a business responsible for more than half of 2011 IBM annual revenues – fits within an

innovation framework. Thus, we lack well-developed theory about the mechanisms that affect

the decisions of product innovators to select service innovation.

The purpose of this paper is to show when a manufacturing firm (i.e., a product-process

innovator) may choose service innovation and to contribute by providing a more complete

perspective to the classic product-process innovation framework. The goal is to use the search

literature on innovation and the strategic management literature on related diversification to

develop testable hypotheses on the relationship of technological scope and service innovation.

Using a longitudinal sample of US-based firms in the IT hardware industry from 1987-2005, the

paper finds that the product-process innovator is more likely to innovate in services when (1) the

firm’s technological scope broadens into complementary non-manufacturing product categories,

(2) the firm expands into open systems and closed systems platforms, and (3) service innovation

adoption among the firm’s strategic group is high. These results offer an important extension to

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the Abernathy-Utterback innovation framework. The findings suggest that service innovation

adds a third dimension to the product-process model – with a boundary condition – only for the

multiproduct firm (Hill & Hoskisson 1987; Teece 1982). The results suggest that services that

tie together related products increase the level of stickiness (von Hippel 1994) in downstream

customer-related activities and enable an architectural innovation (Davies 2004; Henderson &

Clark 1990) across the diversified firm’s product lines.

2 Theory Development and Hypotheses

2.1 Innovation and search

Innovation search is a problem-solving activity (March & Simon 1958; Nelson & Winter

1982). Within technology-intensive industries, prior research has explored the quest for new

innovations as a problem of technological search (Fleming 2001). Prior work has illustrated how

firms create new technological breakthroughs by recombining local knowledge with distant

knowledge (Rosenkopf & Nerkar, 2001; Rosenkopf & Almeida, 2003), recombining prior

knowledge with new knowledge (Katila & Ahuja 2002), and recombining prior knowledge from

various industry contexts (Katila 2002). While the firm must continually seek new ways to

generate new combinations of knowledge, new technological knowledge alone is necessary but

not sufficient for survival. For example, Polaroid conducted a diligent innovation search in

digital imaging technologies yet was not able to appropriate the value in the marketplace (Tripsas

& Gavetti 2000).

The innovation search literature emphasizes the importance of new product innovation as

the desired outcome (Katila & Ahuja 2002; Katila 2002; Zhang & Li 2009). However, the firm’s

innovation activities often go beyond new product search. The Abernathy-Utterback model has

been an influential framework for understanding innovation patterns that consist of product

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innovation and process innovation stages (Abernathy & Utterback 1978; Utterback & Abernathy

1975). In the first stage, new firms perform technological search for new product innovation that

revolutionizes an industry in the pre-dominant design period (Schumpeter 1942) while existing

firms perform technological search for incremental product innovation in the post-dominant

design period (Tushman & Anderson 1986). In the second stage, firms perform technological

search for process innovation that creates greater efficiencies within manufacturing processes

(Utterback 1994). While the product innovation stage often gets the most attention, firms such

as Toyota and Dell have shown how process innovation search can also foster competitive

advantage (Womack, Jones, & Roos 1991). The innovation search literature suggests that

disruptive product innovations make use of local and distant search. Incremental product

innovations and process innovations are predominantly local search processes.

2.2 Innovation and services

2.2.1 A baseline: the role of services in innovation

This paper suggests that service innovation is a third option for product-process

innovators. While service innovation is prevalent within technology-intensive service sectors

(Barras 1986; Guile, Quinn, & National Academy of Engineering 1988; Hipp & Grupp 2005),

the innovation literature provides little theoretical or empirical work about service innovation

within the context of manufacturing sectors where firms conduct product innovation and process

innovation. On the other hand, the innovation literature does provide a baseline suggesting that

services are an important activity for the product-process innovator. Similar to how the

manufacturing process is an important complementary activity within a product innovation value

chain, technology services are also important complementary activities that add value for product

innovation (Teece 1986). Across multiple generations of technological discontinuities in the

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typesetter industry, the incumbent’s service network was an important complementary capability

that helped buffer the incumbent from being uprooted by start-up firms with disruptive

technologies (Tripsas 1997).

From the limited studies, the first takeaway is that services are an important

complementary activity for the product-process innovator – ranging from the start-up firm to the

established incumbent. The second takeaway is that the firm has the choice to either develop

internal service capabilities or access external service capabilities (Langlois & Foss 1999;

Williamson 1985).

While services are an important complementary activity for the product-process

innovator, the topic has received little attention from a theoretical perspective. Exceptions have

been preliminary theory development (Cusumano, Kahl, & Suarez Working paper) and a recent

empirical study in the software industry (Suarez, Cusumano, & Kahl 2012). This early work

suggests that service innovation is a third stage within the Abernathy-Utterback model.

Evidence from the software industry suggests that product firms turn to service innovation when

product innovation activities are in severe decline.

In the next section, the paper takes an evolutionary perspective to consider how the

search process for service innovation develops. However, a brief background about technology

services is required. Technology services are not one monolithic category. For simplicity, this

category can be divided into two sub-categories: tightly-coupled and loosely-coupled. While

first-order technology services such as product maintenance and product support are tightly-

coupled complementary activities attached to all products, second-order technology services

such as professional services (e.g., custom implementation, consulting, and systems integration)

are more loosely-coupled mechanisms for demonstrating how multiple loosely-coupled products

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work together. In industries with highly interdependent products such as with technology

platforms, these second-order technology services can ease the integration of complex products

(Katz & Shapiro 1986; Suarez & Cusumano 2009). This paper focuses on service innovation

within second-order technology services.

2.2.2 An innovation search process for new services

An evolutionary theory suggests that firms evolve in a path dependent manner building

off of existing resources, routines, and capabilities (Nelson & Winter 1982). Innovative search

for new second-order services is also a problem-solving activity (March & Simon 1958). On the

one hand, an evolutionary perspective suggests that service innovation involves a local search

especially if the services relate to the firm’s existing product portfolio (Cyert & March 1963).

The current product knowledge resource base can be used during service activities. On the other

hand, a new service innovation may include distant search elements as well since the new

services may require new resources and routines that currently do not reside within the firm

(Rosenkopf & Nerkar 2001). For example, EMC primarily relied on external hiring of

experienced managers and consultants to staff its new professional services business in the late

1990’s (Anderson, 2012).

2.3 Architectural innovation with services

Earlier work has explored the idea of architectural innovation as a type of innovation

distinctly different from the extreme cases of radical and incremental innovation (Abernathy &

Clark 1985). A new product contains an architectural innovation when it takes existing

components and links them together in new ways (Henderson & Clark 1990). Not limited to a

technological perspective only, architectural innovation has been used to describe the process

that corporate level managers use to determine recombinations of related product organizations

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within a multibusiness firm (Galunic & Eisenhardt 2001). The key mechanism behind

architectural innovation concerns the linking of components whether they be technological

components or organizational components.

If architectural innovation is a process of creating value through forming novel linkages

with existing components, then recent work that highlights how existing products get combined

into integrated solutions is yet another form of architectural innovation (Davies 2004). An

integrated solution is based on the unique needs of at the level of an individual customers This

suggests that unlike the product (Henderson & Clark 1990) and organizational (Galunic &

Eisenhardt 2001) forms of architectural innovation that take place within the firm, the

multiproduct integrated solution form of architectural innovation occurs at the boundary of the

firm and its customers. In industries with interdependent products, Suarez and Cusumano

suggest that second-order services provide a critical linking role between complex products

(Suarez & Cusumano 2009). While second-order services may enable another form of

architectural innovation, the product innovator has a choice on whether to develop internal

capabilities or access external service provider capabilities (Langlois & Foss 1999; Williamson

1985).

In a recent study on the innovation search activities of new ventures in China, Zhang and

Li (2009) found that an entrepreneur’s ties to service intermediaries such as technology service

firms had a positive effect on a new venture’s product innovation. Given that the technology

firm offers first-order services (i.e., product support and maintenance) with each product

innovation (Teece 1986), the Zhang and Li study suggests that new ventures treat second-order

technology services as an optional choice (Zhang & Li 2009). A resource-based view of the firm

suggests that a severely-constrained start-up product firm within a manufacturing industry is

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more likely going to target its limited resources toward its highest priority activities such as

product innovation (Baker, Miner, & Eesley 2003; Penrose 1959).

On the other hand, an established innovator who has overcome the liability of newness

(Stinchcombe 1965) and whose core competence has enabled a competitive advantage is likely

to expand into related product businesses (Montgomery & Hariharan 1991; Palich, Cardinal, &

Miller 2000; Penrose 1959; Stern & Henderson 2004; Tanriverdi & Venkatraman 2005). As the

young innovator evolves into an established diversified incumbent, the innovation literature cites

both positive and negative effects of diversification on the overall innovativeness of the firm.

Hoskisson and Hitt suggest that diversified firms have a decreasing commitment to long-term

innovation (Hoskisson & Hitt 1988). In the robotics industry, Katila found that corporate

diversification had a negative effect on robotics product innovation (Katila 2002). On the other

hand, a RBV perspective suggests that large incumbents are more likely to have resource slack –

an important condition for growth and innovation (Penrose 1959). Even Schumpeter’s later

work suggests that large firms have a resource advantage necessary for new innovation

(Schumpeter 1942).

While an established incumbent’s product innovation portfolio has different

characteristics than it did as a young start-up firm, the innovation literature suggests that the firm

is now balancing different types of innovation. In itself, process innovation in the manufacturing

production stages can enable competitive advantages for the firm (Womack, Jones, & Roos

1991). Although the product-process innovation framework suggests that established

incumbents are less likely to launch a disruptive innovation, established firms are believed to

continue advancing through incremental product innovation as they shift towards an emphasis on

process innovation (Abernathy & Utterback 1978; Anderson & Tushman 1990; Utterback &

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Abernathy 1975; Utterback 1994). Since the empirical studies of the Abernathy-Utterback

framework focus on the emergence of a dominant design, the findings call attention to a single

technology or product within an industry (Murmann & Frenken 2006). This singular focus

illuminates a dominant design yet obscures innovation at a different level of abstraction. If the

diversified firm initiates architectural innovations that link products together in novel ways, the

product-process innovation literature and innovation search literature have not considered

innovation at a corporate level of analysis (Galunic & Eisenhardt 2001).

While service innovation has received little attention in the context of technology product

industries – exceptions are (Cusumano, Kahl, & Suarez Working paper; Suarez, Cusumano, &

Kahl 2012) – evolutionary theorists would predict that service innovation will involve a local

search along closely related existing paths, resources, and routines of the firm (Cyert & March

1963; March & Simon 1958; Nelson & Winter 1982; Penrose 1959). Often lost in the related-

unrelated corporate diversification debate is the idea that related diversification was

conceptualized in two ways: related supplementary and related complementary (Salter &

Weinhold 1979; Wernerfelt 1984). Related supplementary diversification comes forth when the

firm commits (Salter & Weinhold 1979) [p. 63] “existing functional skills and resources to new,

more attractive markets.” Wernerfelt viewed supplementary as building on what already exists

in the firm (Wernerfelt 1984) [p. 175]: “get more of those resources you already have.” This is

consistent with Penrose’s view that firms expand into new opportunities where they have

resource slack. Therefore, the firm may consider architectural innovation as a flexible way to

link related supplementary product lines. Services that provide a way to integrate loosely-

coupled products may add value for the multiproduct firm (Davies 2004).

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Hypothesis 1a: As firms diversify into related manufacturing technological products,

they will be more likely to innovate in services.

On the other hand, related complementary diversification (Salter & Weinhold 1979) [p.

62] “can help that company add functional skills critical to improving its overall business

position.” While complementary was viewed as adding new things to the firm (Wernerfelt 1984)

[p. 175]: “get resources which combine effectively with those you already have.” The empirical

work for related diversification is predominantly of the related supplementary variety that

highlights resource similarity (Farjoun 1994; Miller 2006; Montgomery & Hariharan 1991;

Silverman 1999; Stern & Henderson 2004). In the software industry where platform

technologies combine with complementary products (e.g., “apps”), Suarez and Cusumano

suggest that services provide a linking mechanism for architectural innovation (Suarez &

Cusumano 2009). While a platform product firm is not likely to own all complementary

products within its ecosystem, the firm will likely consider owning some complementary

products. Therefore, the firm may consider new services as an architectural innovation that

provides a flexible way to link related complementary product lines.

Hypothesis 1b: As firms diversify into related non-manufacturing technological products

that are complementary, they will be more likely to innovate in services.

2.4 Complexity reduction in interdependent product industries

Innovation search is likely to have differences across industries and across firms.

Cusumano and Suarez (2009) suggest that services may provide unique value within industries

where platform technologies and complementary products require integration. Within a complex

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ecosystem, firms who provide the core technology are perhaps most concerned that

interdependent products get integrated effectively (Davies 2004; Iansiti 1995). Nevertheless,

platform technology firms may decide to focus on technological innovation and thereby let

specialized intermediaries provide important but lower priority services (Zhang & Li 2009). If

second-order services provide a mechanism to reduce the complexity of deploying sophisticated

systems, firms that develop platform technologies may eventually be willing to conduct an

innovation search for new services (Gawer & Cusumano 2002).

Hypothesis 2: Firms who develop platform technology-based products will be more likely

to innovate in services.

2.5 Imitating rivals

While second-order technology services provide an important set of complementary

problem-solving activities during the innovation process, service innovation is not a top priority

for the product-process innovator. In the 1990’s, EMC, at the time a young fast-growing storage

device innovator, found its entry into second-order services an extremely challenging process

(Anderson 2012). Even IBM, the exemplar of service innovation among technology innovators,

found its transition towards a greater emphasis on second-order services a very difficult process

(Gerstner 2002). Further evidence from qualitative interviews suggests that second-order service

innovation is not a painless evolution for those product-process innovators who choose this path.

Given that firms keep a watchful eye on their competitors, the diffusion of innovations

literature suggests that firms adopt similar innovations due to social processes (Rogers 1962).

Firms will mimic others considered to be in the same strategic group (Burt 1987). Firms may

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also imitate the actions of influential industry leaders considered to be using “best practice”

(Fligstein 1985; Strang & Soule 1998).

Hypothesis 3: Firms whose nearest competitors innovate in services will be more likely

to innovate in services.

3 Methods

I test these hypotheses within the setting of the Information Technology industry by

examining the context of the hardware manufacturing sectors between 1987 and 2005. This

specific context provides a rich source of innovative activity. This fast-paced industry is known

for its relentless focus on product innovation (Brown & Eisenhardt 1997). While not all product

innovations occur within manufacturing settings (Utterback 1994), this manufacturing setting is

particularly appropriate because there is sufficient variation of product innovation activity in

manufactured hardware products and non-manufactured software products. However, firms

within these manufacturing sectors also look for opportunities to create competitive advantage

via greater efficiencies in production that are facilitated through process innovations (Abernathy

& Utterback 1978; Utterback & Abernathy 1975). Most appropriate for this particular study, this

setting also provides variation in service innovation activities.

I empirically test the relationship between the firm’s technological position and its

likelihood of new service innovation. Service innovation is operationalized as a new

professional services business. While IT hardware and technology professional services are

complementary activities when considered along the value chain, the conventional wisdom in the

mid-1980’s among computer manufacturing vendors was to treat professional services such as

installation, training, systems integration, and consulting in one of two ways. First, some of the

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firms provided installation and training as part of hardware sales. In other words, it was done for

free. There was no intent to create a business and charge for these types of services (Lazar

1994). Second, some firms provided these activities on an ad hoc basis. Perhaps an engineer is

temporarily assigned to a high-profile customer who is having extreme difficulties. Generally,

this type of “ad hoc problem solving” is not considered a business, a routine or a capability

(Winter 2003).

Although an important complement to a product business, professional services had a

second-class stigma amongst the IT product suppliers during the time of this study. Although

known for its shift in strategic direction to lead with service innovation rather than product

innovation, the decision to innovate in services was met with great resistance within

IBM(Gerstner 2002). With a less attractive profit potential and the need for different skills than

what was required for a product business, most professional services were provided by

specialized third-party vendors. The following excerpt from the business press captures the

conventional wisdom about services within the IT industry during the mid 1980’s (Field &

Schares 1986):

“Professional-services companies have always been the unglamorous part of the computer business. The $10.5 billion industry that specializes in helping companies decide what computer equipment to buy – and how best to use it – didn't sell sexy technology or command the same market multiples as other computer companies. And although growth was steady – up 16% a year since 1980 – services lacked the spectacular leaps in revenue and fat profit margins that characterized computer hardware and software.”

3.1 Sample and Data

This paper will answer the research question of when do firms innovate in services by

analyzing a sample of Information Technology hardware vendors. These firms enter the sample

under a common industry affiliation based on the three-digit 357 Standard Industrial

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Classification (SIC) codes. At the four-digit SIC level, the sample is organized as follows: 3570

Computer and Office Equipment, 3571 Electronic Computers, 3572 Computer Storage Devices,

3575 Computer Terminals, 3576 Computer Communications Equipment, 3577 Computer

Peripheral Equipment, 3578 Calculating and Accounting Machines NEC (No Electronic

Computers), and 3579 Office Machines NEC. The firms in this study are U.S.-based and

publicly traded which means that they submit quarterly and annual documentation of their

business activities to the U.S. Securities and Exchange Commission (SEC). The firms represent

a wide cross-section in firm size considering that their stocks are traded through over-the-counter

(OTC) transactions or via large stock exchanges such as the New York Stock Exchange (NYSE).

The analysis omits three types of firms that make significant contributions to the IT

industry: foreign firms, private firms, and computer software firms. First, the omission of

publicly-traded foreign IT hardware vendors is not likely to bias the results. With the IT industry

being heavily led and dominated by US firms, the study may perhaps underestimate the effects

from platform vendors (Hypothesis 1) and rivalry influence (Hypothesis 3) on service

innovation. Yet this should not weaken the overall results. Second, the ommission of private IT

hardware vendors from the sample should not bias the results given that most private vendors are

either young startup firms who have yet to establish themselves as product innovators or small

established firms not interested in growing large. The sample currently includes small vendors

who trade stocks in OTC transactions. Therefore, variation across a wide range of firm sizes is

accounted for. Third, the study intentionally omits software firms (SIC 7372) for theoretical

reasons based on the fact that the majority of empirical studies have sought to theorize about

product-process innovation in manufacturing-based industries.

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3.2 Variables

3.2.1 Service innovation outcome variable

This paper seeks to theorize and test hypotheses about the factors that affect the transition

into service innovation. It is important to understand where the variation is within this setting.

By limiting the sample to SIC 357 firms, all firms are product innovators within a manufacturing

industry. On the other hand, the firms vary in the degree to which they in-source and out-source

for R&D within the value chain of activities. All product innovators within this industry provide

a baseline level of complementary customer support services that are bundled with the product.

This is part of the cost of doing business. On the other hand, these firms vary as to when and if

they choose to provide additional value-added services such as a professional services business.

This study operationalizes service innovation using a dichotomous outcome variable that

captures when a firm has initiated or established a professional services business. The

professional services measure is based on a content analysis of the firm’s self-disclosed

description of its strategy and operations as explained in annual 10-K reports that are filed with

the SEC. Using the sample of SIC 357 firms identified by Compustat, the annual 10-K reports

were downloaded from two electronic databases: SEC Electronic Data Gathering, Analysis, and

Reporting (EDGAR) and LexisNexis Academic. EDGAR contains SEC filings back to 1994

while LexisNexis Academic contains filings back to 1987. Where firm-year reports were not

available from the electronic databases, 10-K reports were obtained from microfiche archives

available at MIT Sloan and Harvard Business School libraries. Financial measures were obtained

for the sample of firms from Compustat as far back as 1985 to provide enough flexibility to

calculate lagged measures where necessary for the analysis.

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Measuring innovation into professional services

While scholars acknowledge the need for more research on services within strategic

management (Huff 2009) and innovation literatures (Tushman & Smith 2002), it’s not clear how

or whether the frameworks and methods used in product manufacturing-based empirical studies

apply. For example, one may classify search into new professional services as either unrelated

due to a lack of resource similarity or as related based on a complementarity argument. See

Table 1 for an assessment based on the operationalization scheme used in prior empirical studies.

Many studies simply assume that diversification entry doesn’t systematically exist between

manufacturing and non-manufacturing settings. The sentiment is that “the service sector requires

a different set of skills or knowledge than manufacturing industries” (Chang 1996).

Given that services require very little upfront investment, how to operationalize

diversification and search into new services is not clear. If we consider how a product

diversification event is detected, the event is signaled in one of two ways. At some point during

product development, the firm signals to the market a potential launch date and then formally

launches the product. Secondly, a firm signals its intent when it acquires another product firm.

These product diversification signals are usually picked up within industry reports and by

possibly as SIC code updates. When a services business starts is often less clear due to the low

level of upfront investment required to get started. The firm may signal the event within a press

release, but there’s much variation on how and when firms issue press releases. My empirical

scheme is to operationalize service innovation as the firm sends a signal of a formal

organizational structure or acknowledgement of revenue-generating activities. This paper

measures entry based on the firm’s description of its business within the annual 10-K report.

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The assumption is that all services beyond basic customer support are articulated as some type of

professional service activity.

Although the scope of professional services varies across firms, the following list

highlights a general consensus of activities that fall under the professional services category

(Lah, O’Connor, and Peterson, 2002).

Deployment services include any optional for fee work activities that help customers

deploy the product within their data center. Examples are installation, implementation,

configuration, systems integration, and migration services.

Custom development or design services involve customization work above and beyond

the mass market product offering.

Education and training services include product training and certification programs.

Consulting services range from technology to business advice. Consulting is often

bundled together with deployment services such as implementation and systems integration.

Outsourcing services provide customers with an opportunity to transfer installation and

day-to-day operations to the product firm or a third-party. This is also often referred to as

managed services.

3.3 Measures

3.3.1 Dependent Variable

Professional Services. The rate of entry into service innovation is operationalized using a

professional services measure as the dependent variable. For each firm-year, a professional

services dichotomous variable is set to 1 when a firm acknowledges professional services entry

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within the annual 10-K report. Otherwise, the variable is set to 0 which represents one of the

following conditions: the firm has no resources devoted to providing professional services, the

firm provides limited professional services on an ad-hoc (Winter 2003) or free basis, or the

firm’s professional services activities are not considered a strategic priority relative to other

pressing concerns. Each firm discusses its primary business initiatives within the business

overview section (i.e., Item 1) of the 10-k report that is filed with the SEC. The management

discussion section (i.e., Item 7) often contains another rehash of the key business initiatives

within the context of the financial statements. See Appendix 1 for specific examples of how the

content analysis works.

3.3.2 Explanatory Variables

Technological product diversification measures. Two variables capture the technological

diversification level within each firm-year. Similar to (2010), I make use of the fine-grained

measures provided within the CorpTech data to operationalize the level of diversification. The

Corptech data is structured within three levels of granularity: industry, technology, and product.

For example, Christensen’s study (1997) of the disk drive industry would include vendors under

the Corptech product code of COM-CM-HW. COM represents the computer industry, CM

represents computer memory systems, and HW represents Winchester disk drives. For this

study, the diversification measures are counts of the most fine-grained three-level product

activities recorded within Corptech.

Manufacturing technological product diversification is a measure of the degree to which

the firm allocates resources and develops capabilities in computer product technologies that

require manufacturing activities. Examples of manufacturing technologies range from various

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types of computers to devices such as printers, storage devices, and modems.1 See Table 2 for a

list of the industry-technology Corptech categories within the computer and telecommunications

industries used to calculate this measure. The manufacturing product diversification variable is a

count of the fine-grained industry-technology-product measures. Although this measure ranges

from 0 to 47, the mean is 3.120, reflecting a high skew. Therefore, I take the natural log of the

raw count, which results in a value that ranges from 0 to 4.025.

Non-manufacturing technological product diversification is a measure of the degree to

which the firm allocates resources and develops capabilities in computer software product

technologies. Examples of software captured by CorpTech are industry-focused applications

such as healthcare software, general-purpose software such as project management applications,

and software utilities such as development tools. See Table 2 for a list of the industry-

technology Corptech categories for the software industry. The non-manufacturing product

diversification variable is a count of the fine-grained industry-technology-product measures.

Although this measure ranges from 0 to 120, the mean is 1.8, reflecting a high skew. Therefore,

I take the natural log of the raw count, which results in a value that ranges from 0 to 4.883.

Platform vendor measures. Historically, the computer system vendors have been the

primary leaders within this industry. For many years, IBM was the pace setter amongst the

computer systems providers. While the introduction of mainframe computers, minicomputers,

and personal computers have marked major transitions within this industry, other categories of

computer platforms have also made headways such as supercomputers and workstations

(Bresnahan & Greenstein 1999). Corptech includes all of the aforementioned categories in

1 Computer communications equipment such as modems and routers are often found in two SIC categories.

Compustat includes these devices under the four-digit SIC 3576 while CorpTech classifies these same devices in SIC 3661 or 3669. For example, Cisco Systems is under 3576 in Compustat but 3669 in CorpTech. Consequently, the manufacturing diversification measures used in this study are found across the computer hardware and telecommunications industry product codes in the CorpTech data.

22

addition to special-purpose computers. The early generations of computers such as mainframes

and minicomputers are considered closed proprietary platforms while the microcomputer

initiated the open systems era of platforms (Campbell-Kelly & Aspray 2004). Using this

distinction, I create three firm dummy variables: open platforms only, closed platforms only, and

hybrid platforms for firms who supply open and closed platforms. Closed platforms only is set

to one for firms who have resources and capabilities only in proprietary platforms such as

mainframes, minicomputers, supercomputers, and special-purpose computers. Open platforms

only is set to one for firms who have resources and capabilities only in open systems such as

personal computers. Hybrid platforms is set to one for firms who have resources and capabilities

in proprietary and open systems. All platform vendors will exist in one of these three categories.

In the analysis, I omit the open platforms only category.

Competitor prior adoption is a variable that seeks to operationalize the percentage of

prior adoption among firms within the same strategic group. This measure splits the firms into

two bands within each of the historical segment categories. Firms are divided into above and

below the 75th percentile bands based on annual revenues. Within each band, the percentage of

firms that have pursued service innovation is calculated. This variable contains the percentage of

adoption given the band that it resides in.

3.3.3 Control Variables

Several other variables that might influence the firm’s decision to choose service

innovation were included as controls. Firm size is measured as the number of employees

reported by the firm in a given year. I take the natural log of employees due to a wide range of

firm sizes in the sample. Firm age is calculated as the natural log of the difference between the

23

current year and the year of incorporation. I take the natural log of age due to high skewness in

age across the sample of firms.

While empirical research within settings where both product and service innovation occur

have been under studied, prior work theorizes that firm performance influences innovation in two

ways. On the one hand, a product innovator may resort to service innovation as a way to bolster

firm performance if the product business is failing (Campa & Kedia 2002; Cusumano 2008;

Hoskisson & Hitt 1990). In this scenario, performance would have a negative relationship with

service innovation. On the other hand, firm performance may have a positive relationship with

service innovation when the product innovator has slack resources due to a strong product

business. The firm may use its slack resources to invest more deeply in related complementary

business opportunities such as services (Levinthal & March 1981; Penrose 1959). Following

other innovation studies (Hitt, Hoskisson, Johnson, & Moesel 1996; Katila & Ahuja 2002), I

include return on assets (ROA) as a firm performance measure. ROA is calculated as net income

divided by total assets.

R&D intensity is a measure of R&D expenses over sales. This measure may affect

service innovation in two ways. On the one hand, R&D intensity may have a positive influence

on service innovation but indirectly. While R&D intensity is expected to be positively related to

product innovation, an increased portfolio of products may also require new services. On the

other hand, a decrease in R&D spending may be a sign that the firm is shifting its relative

innovation spending level from new few R&D projects instead to new service opportunities

(Gerstner 2002).

I created a dummy variable, upstream supplier, to control for firms who have a

substantial investment in upstream technologies such as subassemblies, components, and

24

materials. Since services are considered a downstream set of activities within the value chain

(Wise & Baumgartner 1999), firms who are heavily invested in upstream component

technologies may be less likely to innovate in services. This control variable is set to one when

at least one-third of a firm’s technological position is in upstream technologies as measured

using the fine-grained Corptech product measures.

While the sample includes firms who have been categorized within SIC 357 segments,

any firm may evolve its strategic direction such that its primary SIC categorization is no longer

within SIC 357. Therefore, I created segment dummies to control for the SIC historical position

of firms who are not the platform vendors. Due to a small number of firms within some 4-digit

SIC 357 categories, segment dummies were created for storage devices (3572), computer

peripherals (3575 and 3577), computer communications equipment (3576), and other computing

equipment (3578 and 3579). The latter segment was the ommitted category.

Time effects were controlled through year dummies for 1987-2004, with the year 2005 as

the omitted category.

3.4 Model Estimation

A discrete-time event history methodology is used to model the firm’s services

innovation event (Castilla, 2007). The dependent variable is the instantaneous rate at which a

firm acknowledges the initiation or establishment of a professional services business. This

methodology is preferred when information on the exact timing of an event is unavailable—that

is, when interval “censoring” exists. For this study, exact adoption dates are not known since the

measures are based on annual data. A second advantage of this method is that firms who do not

embrace professional services contribute to the model exactly what is known about them. Time-

varying explanatory variables are easily included because each period during which a firm is at

25

risk is treated as a separate observation. Left censoring was an issue for 12 of the 325 (4%) firms

who experienced the event either prior to entering the risk set or during their first observation

within the risk set. In other words, firms who are already providing professional services as they

enter the model are immediately dropped. The model seeks to estimate the systematic factors

associated with entry into new service innovation.

4 Results

4.1 Descriptive Statistics

Table 1 reports descriptive statistics for the 325 firms in the overall sample. The eight

four-digit SIC codes are consolidated into five primary product segments – computer systems

platforms (3570 and 3571), storage devices (3572), computer peripherals (3575 and 3577), data

communications equipment (3576), and other machines such as calculating, accounting, and

office machines (3578 and 3579).2 The raw counts of the manufacturing and non-manufacturing

diversification measures show a broad range of technological activity. The manufacturing

measure has a mean of 3.12 while the maximum is 47 product categories. The non-

manufacturing measure has a mean of 1.80 with a maximum of 120 product categories. While

the non-manufacturing categories are nearly three times as many as the manufacturing

categories, the respective means are consistent with what to expect within a manufacturing

industry.

A firm enters the sample either as a publicly traded firm in 1987 or in later years when

the firm transitions from a private to a public firm. The mean number of employees at the time

of entry into the sample is 3420 with a range from 10 to 389,350. Six percent of firms enter the

2 All computer systems vendors are reported under 3571 in CorpTech. Only a few computer monitor firms

(3575) exist today and so the category is combined with the general peripherals category (3577). SIC 3578 and 3579 are small groups with a diverse set of firms.

26

sample with substantial resources in upstream technologies such as components, photonics, and

materials.

The mean number of observations per firm is 10.33. With the study beginning in 1987,

we experience a mild left censoring issue with 12 firms (4%). In other words, 12 firms

experience the event either prior to 1987, in 1987, or during their first year in the dataset. For

example, Unisys and IBM experience the event during or prior to 1987. Identix experiences the

event in 1993 as it enters the dataset. Firms such as Unisys, IBM, and Identix are left censored

and subsequently dropped in the analysis.

Thirty-eight percent of the firms experience the service innovation event. The mean year

of incorporation is 1979 while the median year is 1983. This means that the population of firms

is nearly evenly divided into firms founded before and after the start of the open systems era.3

----------------------------- Insert Table 1 here ----------------------------- ----------------------------- Insert Table 2 here -----------------------------

Table 3 reports the firm-year descriptive statistics and pairwise correlation matrix for the

2,476 firm-year observations. Many of the firms experience multiple service innovation events

in subsequent years, but the current analysis focuses on the first event only.

----------------------------- Insert Table 3 here -----------------------------

3 Many view the August 1981 launch of the IBM PC as the beginning of the open systems era.

27

4.2 Determinants of a service innovation event

Table 4 contains the Cox hazard rate models using robust standard errors and coefficients

rather than hazard ratios. Model 4-1 shows the baseline model with only control variables,

segment fixed effects, and year fixed effects. The controls model shows that firm size and R&D

intensity are positively related to service innovation in the sample. The upstream supplier

dummy is negatively related to service innovation. Firm performance has no significant effect

on service innovation.

Hypothesis 1a, the manufacturing technological product diversification hypothesis, is

tested across Models 4-2, 4-4, and 4-7. These models provide no support for service innovation

as an architectural innovation mechanism for product manufacturers as suggested by Hypothesis

1a. While not significant, the sign on the coefficient mildly suggests a negative relationship

between manufacturing diversification position and service innovation.

Hypothesis 1b, the non-manufacturing technological product diversification hypothesis,

is tested across Models 4-3, 4-4, and 4-7. Controlling for size, age, R&D intensity, firm

performance and upstream suppliers, firms who expanded into more non-manufacturing

technological product technologies were more likely to innovate in services. These models

provide support for service innovation as an architectural innovation mechanism for

manufacturers who diversify into software technologies. The coefficient is consistently positive

and significant across the models.

Hypothesis 2, the platform vendor hypothesis, shows that when controlling for the other

manufacturing segments, firms who manage platform technologies were more likely to innovate

in services. This suggests that not only IBM was innovating in services. However, the models

suggest that the effect is stronger among firms who take a hybrid platform approach (i.e., open

28

and closed systems platforms) rather than simply being a move for vendors who remain only in

closed proprietary platforms.

Models 4-6 and 4-7 suggest no support for the competitor prior adoption of Hypothesis 3.

The result does not appear to be robust across the models and also the sign on the coefficient is

in the opposite direction of what I hypothesized.

----------------------------- Insert Table 4 here -----------------------------

4.3 Robustness

Robustness to different product diversification measures. While the manufacturing and

non-manufacturing variables count the three-level Corptech product categories, I ran the models

using a count of the two-level major technology categories. These technology counts range from

0-16 for the manufacturing technology categories and 0-24 for the non-manufacturing

technology categories. Used in a full model such as Model 4-7, the results are very consistent

across all explanatory variables. The manufacturing technology variable is close to being

significant at a p < 0.1 level, the magnitude is slightly smaller, and the coefficient remains

negative. The non-manufacturing technology variable remains positive and significant with a

slightly higher magnitude on the coefficient. The platform measures are consistent with the

results in Model 4-7.

Robustness in competitor prior adoption measure. The current operationalization of the

competitor prior adoption variable is not robust across the models. In running models with and

without the segment and time fixed effects, the results for this measure are inconsistent.

Theoretically, an increase in service innovation among firms within the same strategic group

seems to be a plausible mechanism that captures some level of social influence (Strang & Soule

29

1998) or social learning (H. Peyton Young 2009). Future work should continue to look for a

robust operationalization of this theoretical construct.

Robustness to different firm performance measures. The models were also run using

return on sales (ROS), calculated as net income divided by annual sales, as the firm performance

measure. The results remained consistent of terms of significance, magnitudes, and signs.

Instead of operationalizing firm size into revenue bands, the natural log of the number of

employees was used in these models and the results were the same. The story of large firms is a

robust finding and seems plausible. Although the resource similarity link between product and

services firms is weak, large firms have more general slack than smaller firms and hence more

able to invest in complementary yet resource dissimilar opportunities.

The proportional hazard rate assumption was tested using the Grambsch and Therneau

global test which makes use of the Schoenfeld residuals (Grambsch and Therneau, 1994). The

test considers whether the model as a whole shows evidence of non-proportional hazards. As a

whole, Model 4-6 has a p-value of 0.85 and therefore supports the proportional hazard rate

assumption. The p-value on two of the control variables was approximately of 0.15 which is not

a complete violation but is potentially a concern.

Limitations. This paper uses one approach to operationalize service innovation within a

manufacturing industry. Given the stigma about services within this manufacturing industry

during the time of the sample, its possible that firms were inclined to downplay their service

activities within the context of their overall business strategy as reported in annual reports. By

underselling their service activities, firms can keep their investment community focused on the

30

higher margin parts of the firm that are more likely to yield higher profit margins. With this in

mind, the results may underestimate the extent of service innovation within this industry.

While content analysis software was developed and iterated upon over the course of

months, the dependent variable is somewhat narrowly defined. See Appendix 1 for a brief

discussion. Adjustments have been made to the algorithms to catch synonomous names such as

professional consulting services and systems integration services. Some firms do not clearly

distinguish between the different types of services that exist. Consequently, the term services is

often very ambiguous. In one context it refers to reactive product maintenance and support

activities. Yet, in other contexts it may refer to the value-added services business category that I

measure in this paper. A few of the service innovation events were randomly chosen and

checked against external news articles to verify the timing and consistency with the contents of

the annual reports. Future work should explore more systematic ways to comprehensively

triangulate the signal from the 10-K reports with external news articles.

5 Conclusion

This paper suggests that product-process innovation models are incomplete. While the

case for service innovation is most salient within technology-intensive service industries such as

financial services, service innovation amongst manufacturing industries has been an

understudied area of research. Unlike product innovation that can be counted as units shipped,

units sold, or patent citations, service innovation is a more elusive phenomenon. This paper

suggests how a specific type of service innovation can be tracked using standardized

documentation made available by all public firms. This study builds on prior work that has

relied on content analysis to study organizational phenomena (Duriau, Reger, & Pfarrer 2007;

Kaplan 2008).

31

This paper contributes to the innovation literature by suggesting that firms who continue

to expand may also innovate in ways beyond the classic product-process models. I find support

for service innovation as an architectural innovation enabler. The literature on architectural

innovation has examined how components are recombined in new ways (Henderson & Clark

1990) as well as how architectural innovation may occur at an organizational level within the

firm (Galunic & Eisenhardt 2001). I find that services that recombine a diversified firm’s

product portfolio in new ways for customers is another form of architectural innovation (Davies

2004). Yet, this is not an easy transition to make for a product manufacturing firm (Gerstner

2002).

While this study provides empirical support across a broad sample of manufacturing

firms, a deeper dive into this black box phenomenon may illuminate new mechanisms that

encourage and hinder a wide range of innovative activity within firms. This study examines the

factors that affect entry into service innovation. However, future research should investigate the

similarities and differences between service and product innovation. While the product-process

innovation framework is based on lifecycles, it is not clear if service innovation also

demonstrates lifecycle characteristics.

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Table 1. Select CorpTech Industry and Major Product Code Categories

Computer HardwareCOM-AI Artificial intelligence hardwareCOM-AX Comp accessories/componentsCOM-BU Business equipmentCOM-CB Computer boardsCOM-CM Computer memory systemsCOM-CN ConvertersCOM-CP CPUsCOM-IN Computer input devicesCOM-MC MicrocomputersCOM-MF MainframesCOM-MN MinicomputersCOM-MO MonitorsCOM-MS SupercomputersCOM-OU Computer output devicesCOM-PC Peripheral controllersCOM-SN Computer hardware for the handicappedCOM-SP Specialized computersCOM-TR Terminals

Telecommunications*TEL-AV Audio/video equipmentTEL-BR Broadcasting/receiving equipmentTEL-CI Communications interfacesTEL-CS Communications security devicesTEL-DC Data concentration equipmentTEL-EM Electronic mail equipmentTEL-MX Multiplexers/modemsTEL-NW Comm networks and related equipmentTEL-SI Signal-related equipmentTEL-SM Satellite & microwave comm equipmentTEL-TD Telecom distribution equipmentTEL-TE Telephone/voice equipmentTEL-TR Transmission systems/equipmentTEL-ZD Other data communications equipment nec

Subassemblies and ComponentsSUB-CE Electronic connectors/packagingSUB-CL Electrical connectors/packagingSUB-CM Mechanical connectors/packagingSUB-EM Electromechanical devicesSUB-ES Electronic subsystemsSUB-ET Electron tubesSUB-ME Nonelectronic, mechanical devicesSUB-PC Passive componentsSUB-SE Semiconductors/semiconductor devicesSUB-TR Transducers

Table 1. Select CorpTech Industry and Major Product Code Categories

Computer SoftwareSOF-AC Accounting softwareSOF-AI Artificial intelligence softwareSOF-BA Banking softwareSOF-CN Construction softwareSOF-CS Communications systems softwareSOF-DM Database/file management softwareSOF-ED Educational and training softwareSOF-FM Facilities management softwareSOF-FN Financial analysis/management softwareSOF-GO Government softwareSOF-HL Health services softwareSOF-IN Insurance softwareSOF-LE Legal softwareSOF-LI Library softwareSOF-MA Manufacturing software systemsSOF-ME Media and communications softwareSOF-NP Non-profit organization softwareSOF-NR Natural resource management softwareSOF-OA Office automation softwareSOF-PD Software development systemsSOF-PM Project management softwareSOF-PU Public utilities softwareSOF-RE Real estate softwareSOF-SM Sales/marketing softwareSOF-SR Service industry softwareSOF-TR Transportation softwareSOF-TS Technical/scientific softwareSOF-UT Utility systems softwareSOF-WD Warehousing and distribution softwareSOF-ZA Other applications software nec

PhotonicsPHO-AO Acousto-optic equipmentPHO-CA Cameras and related equipmentPHO-DI DisplaysPHO-FO Fiber optics and relatedPHO-LA Lasers/laser related equipmentPHO-OE Optoelectronic devicesPHO-OP Optics and related equipment

38

Table&2.&Firm&level&descriptive&statistics

Variables N Mean s.d. Min Max

Primary&historical&segmentComputer)systems)(platform)vendors) 325 0.17 0.38 0 1Storage)devices 325 0.13 0.33 0 1Data)communications)equipment 325 0.30 0.46 0 1Computer)peripherals 325 0.32 0.47 0 1Other)machines)(calculating)&)accounting) 325 0.09 0.28 0 1

Diversification&product&countsManufacturing)technological)product)diversification 325 3.12 5.00 0 47NonJmanufacturing)technological)product)diversification 325 1.80 7.92 0 120

Firm&status&when&entering&datasetEmployees)(thousands) 325 3.42 24.12 0.01 389.35R&D)intensity 325 13.44 14.26 0 100Year)of)incorporation 325 1979.17 14.87 1876 2000Sales)(millions) 325 437.81 3183.78 0.75 54217.00Return)on)sales 325 J0.11 0.66 J7.05 2.28Return)on)assets 325 J0.05 0.55 J8.77 0.82Upstream)supplier 325 0.06 0.24 0 1

Overall&observations&and&event&statisticsObservations)per)firm 325 9.46 4.97 3 19Left)censored)firms 325 0.04 0.19 0 1Experienced)service)innovation)event 325 0.38 0.49 0 1Total)service)innovation)events)experienced 325 2.70 4.32 0 19

39

Table 3. Overall descriptive statistics and pairwise correlation matrix of variables

Table&3.&Overall&descriptive&statistics&and&pairwise&correlation&matrix&of&variablesVariables

Means.d.

MinMax

12

34

56

78

910

1Service(innovation0.05

0.210

12Manufacturing(technological(product(diversification

1.270.72

04.03

0.063Non?manufacturing(technological(product(diversification

0.570.79

04.88

0.180.26

4Closed(platforms(only0.04

0.190

10.02

?0.040.05

5Open(and(closed(platforms0.02

0.150

10.10

0.230.21

?0.036Competitor(prior(adoption

0.170.16

01

0.200.14

0.210.03

0.117Age

2.750.65

04.79

0.030.18

0.190.01

0.020.20

8Employees5.95

1.831.10

12.870.10

0.370.33

0.130.14

0.140.19

9R&D(intensity14.06

16.180

1000.02

?0.080.05

0.04?0.05

0.06?0.10

?0.2310Firm(performance((ROA)

?0.3410.53

?523.206.68

0.00?0.01

?0.030.00

0.00?0.03

?0.020.07

?0.0111Upstream(supplier

0.070.26

01

?0.05?0.26

?0.17?0.06

?0.04?0.08

0.03?0.09

0.000.01

The(number(of(observations(=(2476

40

Table&4.&Discrete/time&event&history&analyses&of&service&innovation

VariablesManufacturing+technological+product+

diversificationNon5m

anufacturing+technological+product+diversificationClosed+platform

s+onlyOpen+and+closed+platform

sCom

petitor+prior+adoption

AgeEm

ployeesR&D+intensityFirm

+performance

Upstream+supplier

Table&4.&Discrete/time&event&history&analyses&of&service&innovation

&&Model&4/1

++

+

++

++

++

++

++

++

50.138+++

(0.161)0.298

***(0.054)

0.015**+

(0.006)0.045

+++(0.159)

51.023(0.664)

Table&4.&Discrete/time&event&history&analyses&of&service&innovation

&&Model&4/2

50.017+++

(0.135)

++

++

++

++

++

++

50.138+++

(0.161)0.300

***(0.054)

0.015**+

(0.006)0.044

+++(0.158)

51.036(0.668)

&&Model&4/3

++

+

0.416***

(0.115)+

++

++

++

++

50.245(0.173)

0.213***

(0.058)0.013

*++(0.006)

0.071+++

(0.145)50.742

+++(0.666)

&&Model&4/4

50.089+++

(0.121)

0.428***

(0.111)+

++

++

++

++

50.245(0.174)

0.226***

(0.060)0.013

*++(0.006)

0.065+++

(0.140)50.801

+++(0.667)

&&Model&4/5

++

+

++

+0.712

(0.465)1.546

***(0.327)

++

+

50.152+++

(0.159)0.296

***(0.053)

0.015**+

(0.006)0.077

+++(0.173)

50.980(0.656)

&&Model&4/6

++

+

++

++

++

++

+51.114

(0.839)

50.172+++

(0.167)0.331

***(0.062)

0.015**+

(0.006)0.036

+++(0.160)

51.025(0.667)

&&Model&4/7

50.177(0.128)

0.390***

(0.114)0.832

+++(0.471)

1.588***

(0.369)52.018

*++(1.008)

50.282(0.176)

0.293***

(0.068)0.012

*++(0.006)

0.074+++

(0.154)50.860

(0.653)

Segment+fixed+effects

Year+fixed+effects

Number+of+firm

5year+observationsNum

ber+of+firms

Number+of+service+innovation+events

Degrees+of+freedomWald+chi5squared

Log+likelihoodProb+>+chi2

+******

YESYES

215131310625

94.445494.03

0.00

The+results+display+coefficients+and+not+hazard+ratios.Robust+standard+errors+in+parentheses.p"<+.10p"<+.05p"<+.01p"<+.001

YESYES

215131310626

97.175494.02

0.00

The+results+display+coefficients+and+not+hazard+ratios.Robust+standard+errors+in+parentheses.

YESYES

215131310626

101.365488.02

0.00

The+results+display+coefficients+and+not+hazard+ratios.

YESYES

215131310627

117.445487.80

0.00

YESYES

215131310627

125.975487.70

0.00

YESYES

215131310626

88.875493.41

0.00

YESYES

215131310630

176.505480.89

0.00

41

7 Appendix 1 – Notes on operationalizing professional services innovation The professional services innovation variable is set to one in the following cases: “For example, the newly-created Professional Services Division helps customers implement open systems and provides systems integration.” (Hewlett-Packard Company 1991)

“In 1996, the Company expanded its offerings of professional services through its

Teris Consulting Group. The Company provides consulting and technical services and technology as part of providing single point-of-contact solutions. The Company’s consultants help clients plan, implement, and manage computing and storage environments.” (Storage Technology Corporation 1996)

“EMC formed its Enterprise Storage Professional Services business in 1997 to

design and deliver world class professional services to its global customer base... The Company has hired 100 Professional Services employees to date.” (EMC 1997)

Sometimes the transition into professional services is very subtle. Cylink, a young

firm with 325 employees focused on security hardware products in 1998, established a professional services group. As a small firm with employees who often have multiple duties, the vice president of human resources in 1997 also became the vice president of professional services in 1998. The signal was picked up in the description of executive officers in the 1998 10-K report. Later in 1999, the firm articulates more of its intention around professional services as they acquired a consulting firm. So indeed, the 1998 signal reflected a change in services approach for Cylink. Also, the former vice president of the professional services organization confirmed these events.4

The search criterion is flexible enough to detect various combinations of

professional service phrasing such as professional consulting services. See the case of Dell below:

“Through Dell Technology Consulting, the Company offers professional

consulting services to help customers select and implement server and storage solutions.” (Dell Computer Corporation 2000) Since this is a computer-generated measure, caution is taken to root out false positives.

For example, the following discussion equates to a 0 value: “Audit fees were for professional services rendered in connection with the company’s annual financial statement audits and quarterly reviews of financial statements for filing with the Securities and Exchange Commission.” (eRoomSystem Technologies 2003) In the above example, the firm is paying for outside assistance so that it may diligently

comply with SEC filing regulations rather than referring to a set of income-generating skills offered to customers.

4 Phone conversation on November 3, 2010.