Determinants of User Innovation and the Sharing of ... - CiteSeerX

“Determinants of User Innovation and the Sharing of Product Modifications.” Pamela D. Morrison, The University of Sydney John H. Roberts, University of New South Wales Eric von Hippel Massachusetts Institute of Technology

ISBM Report 14-1999

Institute for the Study of Business Markets The Pennsylvania State University The Smeal College of Business Administration 402 Business Administration Building University Park, PA 16802-3004 (814) 863-2782 or (814) 863-0413 Fax

Determinants of User Innovation and the Sharing of Product Modifications

Pamela D. Morrison, John H. Roberts, and Eric von Hippel

Abstract Empirical studies have shown that, in many fields, product end users rather than product manufacturers are the first to prototype and apply innovations that later become the basis of important commercial products. This fundamental finding has the potential to significantly affect the way that product developers and market researchers go about the idea generation stages of new product and service development. In this study, we explore aspects of user innovation that are central to the systematic tapping of user innovation as an input to new product and service idea-generation. By studying data drawn from a sample of 122 Australian libraries using a type of library IT system called an “OPAC,” we confirm that users do frequently innovate. What is new is that we find that innovating users have two characteristics – leading edge status and in-house technical capability - that can reliably distinguish them from non-innovating users. We also find that user-developed innovations are frequently of potential commercial interest to product manufacturing firms, and that users are often willing to share or trade information about their innovations with fellow users and inquiring manufacturers. We develop and test an economic model which explains this behavior.

We conclude by discussing how our findings might affect approaches to idea

generation in product and service development processes.

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1.0: Introduction and Overview In recent years, a number of empirical studies have been carried out that trace the origins of the most important product innovations commercialized over time in a number of fields. Such studies have shown that, in many instances, product end users rather than product manufacturers are the first to prototype and apply these important innovations. This fundamental finding has the potential to significantly affect the way that product developers and market researchers go about the idea generation stages of the product and service development process within manufacturing firms. To realize this potential, we need to know more about product and service innovation by users. More specifically, it would be useful to learn: (1) whether user innovation is a relatively frequent event; (2) whether innovating users have characteristics that distinguish them from non-innovating users apart from the fact that they innovate; (3) whether userdeveloped innovations are frequently of potential commercial interest to manufacturing firms; and (4) whether users are generally willing to share their innovations with inquiring manufacturers.

If the answer to all of these questions is positive, then we reason that a basis

exists to build practical idea-generation systems for new products and services that draw systematically upon user innovation activities as inputs. To answer these four questions, we explore the patterns that emerge when one looks comprehensively at user innovations and at innovating and non-innovating users in a given field. To this point, only one study of user innovation has taken this approach (Urban and von Hippel 1988). The study we report upon here is the second one of this general design. It corroborates findings of the first study and also goes further in a number of important respects. Firstly, we define lead users in a way independent of their innovation activity, thus providing a test of which users innovate. To this we add the moderating role of firm capabilities and demonstrate their

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importance. Finally, we examine the willingness of innovating firms to share their innovations and the degree to which they are already doing this. In brief overview, our study explores user innovation and characteristics of innovating and non-innovating users in a sample of 122 Australian libraries. The types of innovation focused on in this sample were those affecting computerized information search systems called “OPACs,” or Online Public Access systems. OPACs will be familiar to most people by function if not by name. They are the technological successors to the “card catalogs” of earlier days: the software systems that drive the computer terminals used by library patrons to search for items in a library’s collection of materials. Our explorations of the frequency of library-developed innovation and product modification activities affecting OPACs show that about 20% of all libraries in our sample of OPAC-using libraries had carried out some form of system modification or redesign. These modifications covered a range of topics related to both library management and functionality delivered to the library patron. Many of the innovating users in our sample felt that they were in a better position to develop their innovations than OPAC suppliers, due to users’ deeper understanding of their own needs, coupled with an adequate level of in-house technical skills and resources. Developers employed by manufacturers of commercial OPAC systems reviewed our sample of user-developed innovations, and judged that many provided information that could be helpful to their own product development efforts. We were able to distinguish reliably between innovating and non-innovating users in our sample, finding that innovating users were characterized by both higher “leading edge status,” and by higher in-house “technical capability” than non-innovating users. When we studied the innovation sharing behaviors displayed by innovating users, we found that 64% had shared or

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informally traded detailed information regarding the innovations they had developed with fellow users and also with inquiring product developers. We were able to explain this sharing behavior in terms of the valuation of the innovation by manufacturers and the level of connection between the innovating user and potential information recipients. We think that, taken together, these findings set a basis for development of systematic market research methods for the identification and assessment of user-developed innovations as a routine part of the product and service concept generation process. Market research has already established its credentials for discovering user needs in the new product development process. Our results suggest that marketing research can learn to play an important and systematic role in the identification of solutions to those needs as well. In this paper, we begin by briefly reviewing relevant literature on user innovation, lead users, and innovation sharing by user-innovators (section 2). Next, we develop the model of user innovation that we propose to test in this paper (section 3). We then describe the industry in which we have studied user innovation, including our data collection and analysis methods (section 4). We present our study methods and the empirical findings of our study (sections 5 and 6) before finally discussing the implications of our findings and model for the involvement of market research in the early stages of the product and service innovation process (section 7).

2.0: Literature Review The classical new product development paradigm suggests that the customer is the essential source of information regarding needs for new products (e.g., Kotler 1984). Empirical studies into the sources of innovation have now made it clear that, under some conditions, product and process users may be a source of valuable new product prototypes and field-tested

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solutions as well (Enos 1962, Knight 1963, Freeman 1968, Lionetta 1977, von Hippel 1976, 1977, 1988, VanderWerf 1990, Shaw 1985). Important reasons why users engage in product development and prototyping activities include appropriability of innovation benefit, the “stickiness” of innovation-related information, and agency costs. With respect to the impact of appropriability of innovation-related benefit, empirical studies of industrial product and process innovation have long shown that innovation is an economically-motivated activity (Schmookler 1966, 1972).

It has also been shown that the

greater the benefit an innovator expects to obtain from a needed novel product or process, the greater will be his investment in obtaining a solution (Mansfield, 1968). Also, it has been shown that innovation by users follows this general pattern, with users being more likely to innovate if and as they expect that course of action to “pay” – e.g., expect to be able to appropriate attractive economic rents (von Hippel 1988, Riggs and von Hippel 1994). With respect to the impact of information stickiness on the locus of innovation, consider that information regarding user needs for innovations is generated at user sites. Often, it is very costly to transfer that information to manufacturers completely and with good fidelity – the information is “sticky.” When this is so, it has been shown that it can pay to do problem-solving at the user site rather than attempting to transfer sticky user information to a manufacturer for manufacturer-based innovation activities (von Hippel 1994, Ogawa 1998). (The logic here is the same as that used by mining firms when deciding where to locate their ore refining facilities: In cases where ore is very bulky and costly to transport, it often makes sense to locate that processing facility right at the mine.) Finally, with respect to agency costs, consider that users are principals with respect to innovations that they want, while manufacturers have the role of user agents. Thus, when users create their own innovations for themselves, they avoid the potential of agency-related costs. For example, if a

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user develops an innovation for itself, it can be expected to have an incentive to make it exactly right for the purpose. In contrast, if that innovation is developed for them by a manufacturer, there is a chance that the manufacturer might have an incentive to develop it in a form that is not quite right for any user, but good enough to sell to many (Pratt and Zeckhauser, 1985). For fields affected by rapid change, von Hippel (1986) hypothesized that user innovations of potential commercial interest to product and service manufacturers were most likely to be generated by “lead users”. Lead users are defined as those who combine two characteristics: (1) they expect attractive economic rents from a solution to their needs and so, as just noted, are likely to innovate; (2) they experience needs prior to the majority of a target market. The first characteristic selects for users with a higher likelihood of innovating, because of their relatively high expectations of economic returns from doing so. The second characteristic insures that what lead users need today is likely to represent a general need tomorrow for users in the rapidly-moving target market. In addition, for reasons mentioned earlier, lead users are likely to be able to innovate more effectively than could suppliers with respect to their advanced needs, because of the “stickiness” of the advanced need information that they alone possess. Morrison (1995) demonstrated the reliability and validity of the Lead User construct and has developed its continuous analog, Leading Edge Status (LES). The LES construct contains four types of measures. The first two, High Benefits Expected and Benefits Recognized Early, represent the two elements of the original lead user definition. The third represents direct elicitation of the overall construct (both self-reports and from third parties). The fourth set represents measures of innovative activities that have been hypothesized by von Hippel (1988) to be associated with High Benefits Expected and Benefits Recognized Early. Morrison tested the LES construct on a sample of 464 users of library information technology systems, and found

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that these four characteristics were in fact highly correlated, and that it was therefore meaningful to view them as part of the same LES construct. She also showed that the shape of the distribution of Leading Edge Status in the population studied was unimodal. Morrison used data on the adoption of four innovations within each organization to demonstrate that the LES construct provided more discrimination relative to the more traditional marketing constructs, time of adoption and dispositional innovativeness, in terms of background respondent characteristics. Urban and von Hippel (1988) used a case study to demonstrate that lead users could be identified in a market of interest, and that innovations developed by lead users could be of significant interest to product and service manufacturers. They studied a sample of 136 users of a type of software product, called PC-CAD, used to design the circuit boards of electronic equipment.

Analysis of the responses of these users showed two well-defined clusters of

respondents: 38 fell into a “lead user” cluster having relatively high values on both lead user characteristics. The second cluster had lower values on both of these characteristics. Users in the lead user cluster were found to have much richer solution information than the non-lead users. Fully 87% of the lead users had developed their own PC-CAD software to address their own advanced in-house needs, while 99% of the non-lead users purchased and used commercially-available PC-CAD systems. Some care needs to be taken in interpreting the degree to which user innovation is concentrated in the lead user group since user innovation was one of the variables used to define the lead user cluster. The potential attractiveness of lead user innovations to non-lead users of PC-CAD was assessed by creating a PC-CAD product concept containing key novel features prototyped by lead users. The attractiveness of this lead user concept was then evaluated by a sample of 173 users of PC-CAD relative to three other concept

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choices – one of which was a description of the best system then commercially available. Over 80% of the target market users were found to prefer the lead user concept (92% of the lead users were found to prefer it). Their reported purchase probability was 51%, over twice as high as the purchase probability indicated for any other system. Lead users that have an existing, in-house technical capability to execute an innovation plus some “slack” with respect to those resources will experience lower out-of-pocket costs with respect to innovating than will lead users that are not in this position. (Cyert and March (1963) defined slack to be the difference between the available resources and those necessary for production. They point out that “slack operates to stabilize the system (and).by providing a pool of emergency resources it permits aspirations to be maintained and achieved”.) We would expect lead users having some slack in relevant in-house technical capabilities to have higher net benefit expectations for any given innovation, other things being equal, and therefore to be more likely to innovate. Lead user innovations will be of value to manufacturers only if user-innovators are willing to reveal them. Research into “informal information trading” has shown that users do sometimes informally trade and share innovation-related information with others. It has been shown that such trading and sharing can make economic sense under clearly-specifiable and commonly-encountered conditions. In brief overview, “informal know-how trading” can be modeled as an iterated prisoner’s dilemma. Reciprocal trading can be shown to pay among direct rivals when the loss of profit associated with revealing an exclusively-held innovation to a rival is more than offset by the gain in profit associated with the information received in trade. Many innovations developed by users have important benefits, but offer only relatively low competitive advantages if kept secret from rivals. Given these conditions, trading of innovations

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among users should be a paying proposition (von Hippel 1987). A study of actual trading among rival steel minimill firms has documented informal trading of innovation-related information in the pattern predicted by the model just described (Schrader 1991). In fields where users are not commercial rivals, such as the library users of OPAC systems that we have studied, all user innovations have low competitive impact by definition. Under these conditions we would expect that users will have an incentive to reveal all of their innovations to other users and/or manufacturers as long as they get something in return. Costs associated with information trading will offset expected benefits, and so we can also predict that users who established links with other users and manufacturers (for example, membership in manufacturer-sponsored user groups) will be more likely to engage in informal innovation revealing and trading, other things being equal. Rogers (1983) has identified the importance of pre-existing communication networks in the diffusion of innovation while Midgley et al (1992) demonstrated the effect of different network structures in determining the shape of the diffusion curve of an innovation.

3.0: Model The innovation and sharing decisions may be represented by a two stage process, illustrated in Figure 1. First, the user organization decides whether it will innovate and for those users that do, then whether to share that innovation with manufacturers and other users. Clearly, sharing is contingent on the innovation choice. An innovation cannot be shared if it is not undertaken. However, there may also be an interdependence in the other direction. That is, if a user looks forward to the possibility of sharing or not sharing when making an innovation decision, then the expected benefits of that latter decision may well influence its decision to

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undertake an innovation. Whether this looking forward to the possibility of sharing does take place at the level of the innovation decision is an empirical question, but we need a framework which will enable us to test it. Figure 1: The User Innovation and Diffusion Decision Hierarchy

Do Not Innovate

Innovate

Share Innovation

Do Not Share Innovation

To develop an analytical framework to study user innovation and its sharing we start with an economic, utility-maximizing model of the user organization. We model behavior at both levels of the hierarchy in Figure 1 separately, but interdependently. The framework we use is analogous to that developed for discrete choice theory in consumer behavior (e.g., see Ben Akiva and Lerman, 1985); a nested logit model.

3.1 The Sharing Decision Let us first consider the innovation sharing decision (contingent on an innovation having been developed). The utility-maximizing innovator, i, will share the innovation if the expected utility of sharing the innovation, UiS, exceeds the costs of sharing it, ciS. That is, UiS

≥

ciS or

UiS – ciS

≥

(1)

0

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UiS, the utility the user can get from sharing, will depend upon the value that the recipient (either a manufacturer or a user) places on the innovation, UiR. The innovating user may capture the economic rent from this transaction either in direct financial terms, or, more likely, to trade for other valuable information (for a discussion of information trading see von Hippel 1987). Thus, we write UiS = α1 + β1 UiR + εi1 where α1 and β1 are parameters and εi1 is a random error term.

(2) Similarly, the cost of

communicating knowledge about the innovation, ciS, will depend on the strength of the network structure between user i and other potentially interested users and manufacturers, CiR. Thus, we can write: ciS = α2 + β2 CiR + εi2

(3)

where α2 and β2 are parameters and εi2 is a random error term. Substituting equations (2) and (3) in inequality (1), we have that user i will find it worthwhile to share its innovation when

β1 UiR - β2 CiR + α1 - α2

≥

εi2 - εi1

Depending on the assumptions we make about the distributions of ε

(4) i1

and ε

i2

we can

derive a discrete choice model of user innovation. If ε follows a normal distribution then we have a probit model. If ε i1 and ε i2 follow a double exponential (extreme value) distribution and the two sources are independent then we have a binary logit model. Because of the similarity of these two error distributions (see Domencich and McFadden 1975) we choose the extreme value distribution which has a closed form analytical solution. Given the innovation utility criterion in inequality 4, Ben Akiva and Lerman (1985) show the probability of user i sharing, conditional on having innovated, PiSI, is given by

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PiSI =

eα 1 + β1U iR eα 1 + β1U iR + eα 2 + β 2 ciR

(5)

3.2 The Innovation Decision The innovation decision which precedes the sharing decision may also be examined using a cost-benefit framework. User i will innovate if the expected benefits from doing so, UiI exceed the expected costs, ciI. That is, innovation should occur if UiI – ciI

≥

0

(6)

Remember that lead users are those who expect to gain large benefits from an innovation and they expect to gain them early. Therefore, the expected utility of the innovation should be directly related to the leading edge status of the user, LESi. If the user looks forward to the possibility of sharing the innovation when making its innovation decision, we will also have to incorporate the value of the second level of the decision process of Figure 1 (the share/don’t share utility) in the first level (the innovate/don’t innovate decision). While the user may not know whether or not it will share the innovation a priori, it can work out the expected utility of this level of the decision tree, UiS*. We can obtain an estimate of this from a result in discrete choice theory (Ben Akiva and Lerman 1985): Uis* = ln (1 + exp (α1 - α2 + β1 Uis - β2 ciS))

(7)

UiS* is called the inclusive value of the sharing decision. It is that part of the utility of the sharing decision which we expect to be able to reap if we decide to innovate. It may be seen from the decision tree we do not reap it if we do not innovate because there can be no sharing in that case. Thus, we can write the utility of innovating as UiI = α3 + β3 LESi + λ3 UiS* + εi3

(8)

where α3, β3, and λ3 are parameters and εi3 is a random error term.

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As with sharing, we can also model the costs of innovation. As was mentioned in our literature review, incremental innovation investment in a given innovation will be lower for those users who already have the capabilities to undertake it in-house and have some slack with respect to those capabilities. Therefore, we express the costs of innovating to Firm i in terms of its application-specific capabilities, Capi; ciI = α4 - β4 Capi + εi4

(9)

where α4 and β4 are parameters and εi4 =is a random error term. Substituting Equations (8) and (9) in inequality (6) yields the condition for a utility-maximizing user to innovate to be:

β3 LES + λ3 UiS* + β4 Capi + α3 - α4

≥

εi4 - εi3

(10)

As with sharing, if we assume that εi3 and εi4 follow independently distributed extreme value distributions, we can write the probability of innovating, PiI, as: e α 3 + β 3 LES + λ3U iS * PiI = α + β LES + λ U 3 iS * e 3 3 + e α 4 − β 4Capi

(11)

Equations (11) and (5) for the probability of innovating and the probability of sharing given such innovative behavior together form a binary, nested logit model. The unconditional probability of user i sharing an innovation, PiS, can be written: PiS = PiI PiSI

(12)

This choice framework gives us a method of testing the effect of leading edge status and corporate capability on innovative behavior, as well as the value of an innovation and a firm’s network connectedness on sharing, posited from a review of the previous literature in this area. The inclusive value , UiS*, gives us a method of evaluating whether information sharing is influential in the original innovation decision, or just a subsequent byproduct. 13

4.0: Context of industry selected for this application Our empirical study of user innovation and user innovators is focused on a type of computerized information search system called an Online Public Access system or “OPAC.” Libraries are essentially large assemblages of information that are collected and made available to library patrons. A library patron will find a library useful on any given occasion if it has the information that he or she needs, and if that information can be efficiently identified and delivered to the patron. From this point of view, OPACs are central to the effective functioning of libraries. OPACs are the modern replacement for and improvement upon the “card catalog” (Card catalogs were a system in which information on the contents of a library’s collection was provided to patrons in the form of an index made up of small paper cards).

As initially

developed, OPACs simply substituted a system of software-based records and computer terminals for the card catalog as a search medium. Later advances built important additional functionality into OPACs. The efficiency of search was greatly increased when additional search modes, such as keyword search, were introduced into OPAC software. Breadth of search was greatly increased as OPACs were made capable of searching for information beyond the collection of a single library. Today, advanced OPACs allow library patrons to extend their searches into the Internet and to all the information repositories connected to it. OPACs were initially developed by advanced and technically sophisticated users. Development began in the US in the 1970s with work by major universities and library institutions such as the US Library of Congress (Tedd 1994). Support for the work was provided by government grants. Until roughly 1978, the only primitive OPACs in use had been developed

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by libraries for their own use although considerable work had gone in to developing compatible standards. In the late 1970s, the first commercial providers of OPAC systems appeared and by 1985 there were at least forty eight OPAC system vendors in the United States (Mathews 1985). However, in Australia most OPAC adoption occurred after the availability of these commercial systems spurred by a demonstration in 1981 of a commercial machine installed at the University of Adelaide (Tedd 1994). Australia has many sophisticated and technically-capable libraries that are OPAC users. At the same time, Australia clearly does not possess the most advanced OPAC user community in the world. Most of the major OPAC suppliers are based and have their primary R&D activities in the United States. Also, many innovations with a bearing on OPAC functionality, such as advances in Internet search procedures, tend to be developed and tried first in the U.S. As a consequence, it was seen as a useful challenge to the idea of the general prevalence of innovative activities among lead users, and the potential value of these to manufacturers, to base our empirical work upon a sample of Australian libraries. Other characteristics of Australian libraries also presented favorable conditions for this research: there is heterogeneity in organizational size and other relevant user characteristics; libraries tend to keep good records with respect to their innovations; libraries tend to have a low turnover of personnel, which contributes to good recall of innovation-related activities. At the same time, libraries do not present a stiff challenge to our hypothesis that lead users will in general tend to be willing to informally trade information regarding their innovations with other users and with manufacturers. Libraries are not direct rivals in the marketplace – they tend to specialize with respect to both geographic coverage and subject matter. As a consequence, as

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rational economic actors, they should be willing to share any innovation they may develop, given a modest level of returns from doing so.

5.0: Research sample and research methods Our sample population consists of Australian Libraries that have adopted an Online Public Access (OPAC) system. Our sampling frame was 464 Australian libraries selected on the basis of stratified random sampling. These organizations accounted for 56.5% of staff employed, and 50% of the total spending in the overall population of interest. With the exception of a few custom systems built completely by users, all OPAC systems in our sample are the products of commercial OPAC vendors. Our selection of participants in the present study proceeded as follows. First, all libraries within the sampling frame were pre-screened via phone to establish whether they had an OPAC system and to identify an appropriate key informant who, because of their particular knowledge, would be in a good position to respond accurately to our questions. On the basis of these phone contacts we identified 166 individuals ( 36% of the 463) in libraries owning an OPAC system who met our criteria for key informant. These individuals were then asked if they would be willing to participate in the study. All agreed to do so. This “selected sample” includes libraries spanning the public, private and education sectors, and all sizes. 25% of the sample had ten or fewer employees, 50% less than 25, and 75% less than 60. Our next step in data collection was to send each respondent a questionnaire together with a stamped, pre-addressed return envelope. A follow-up letter was sent three weeks later to those respondents who had yet to reply. After the second mailing we obtained a total of 122 completed surveys (a 73% return rate - a very high response rate compared to similar studies).

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The average time the current OPAC system had been installed in our final sample was 5 years and the mean knowledge of respondents was 6.3 on a 7 point scale. The survey instrument used in this study was developed via the following procedure. First, a number of personal interviews were conducted with actual OPAC system manufacturers and with users responsible for the maintenance/ usage/ upkeep of the OPAC within their library (generally these were systems librarians).

Based on these exploratory investigations and

previous research in the area we developed a draft questionnaire. This was then administered to a pilot sample of five librarians who were asked to complete the survey instrument and provide feedback on its content. Changes were consequently made to a number of questions to increase their clarity. The final survey consisted of seven pages of questions and a one page cover letter. Perceptual measures in the survey included level of satisfaction and level of customization of existing equipment, possible barriers to innovation, uniqueness of the needs of the organization, organization measures relating to leading edge status, opinion leadership, innovativeness, and culture.

Objective measures included details on recently undertaken

innovations including their development, sharing and receipt, as well as background information on the responding organization, including employees, membership of user groups, etc.

In

addition we surveyed two manufacturers of OPAC systems and asked them to evaluate user innovations based on the value of the solution content to them.

Detailed descriptions of

operationalization of these variables are provided in the Appendix.

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6.0: Findings and analysis Our findings will be described under the following headings:

innovation and

modification activity in the OPAC market, testing the model of user innovation; and examining innovation sharing.

6.1: Innovation and Modification Activity OPACs are key to the library’s functioning, with 82% of respondents agreeing with the statement that “OPACs are central to my operation”. Thus, they are a very important innovation. While on the face of it OPACs seem to represent a classical new diffusion of the type frequently described in marketing, beneath the surface we discovered a considerable level of user innovation.1 Firstly, 32 of the 108 usable responses (29.6%) agreed with the statement that their

1

On the face of it, the diffusion of online public access cataloguing systems offers a good fit to a manufacturer-driven diffusion model. Penetration is extremely high; 36% of libraries contacted owned an OPAC system. Moreover, OPAC’s follow a very typical diffusion of innovation curve suggested by the classical theory of new product adoption in marketing. The Sshaped cumulative adoption curve is typical of manufacturer-driven, user-passive innovation acceptance. The most common method of modeling this consumer uptake in marketing is the Bass (1969) model: St = (p + q Yt m).(m – Yt)

(13)

where St is the sales rate during period t (in our case a year), Yt is the cumulative penetration, and p, q, and m are parameters corresponding to the degree of external influence on the adopting system (such as advertising), the degree of networking and internal influence within the adopting population, and the potential population size respectively. The results of estimating such a model are illustrated in Table 1. External influence, p Internal influence, q Population size R2

Bass fit to cumulative adoption 0.007 (0.019) 0.368 (0.086) 1.146 (0.047) 0.467

Bass fit to new adoptors 0.012 (0.001) 0.375 (0.020) 1.124 (0.013) 0.997

Table 1: Bass model (Equation 13) fit to OPAC adoption (Figures in brackets are standard errors) 18

OPAC had been “highly customized in-house during installation to meet the needs of our library”. Perhaps more interesting than this in-house customization at installation was that a large number of users modified their system after it had been installed. When asked directly if they had undertaken any modifications post-installation, 18 out of 102 usable responses said “yes” with 7 users making at least three modifications. We asked users to describe these modifications and they are summarized in Table 2.

Table 2: The Nature of Innovations by Users (S: innovation was shared with others) Improved Management Control:

Enhanced User Benefits:

• Borrower summary statistics (S)

• Integration of images (2) (1S)

• • • • • • • • •

• • • • • • • • • • •

-

Addition of library identifiers (S) Location records for physical audit (S) Stack retrieval module CD ROM System backup (S) Copyright and access control Sorting and short title listing Search tracing and recall Graduated user passwords Interface with Other Systems: Word processing and correspondence (2) (IS) Integration with other libraries (2) (2S) Umbrella for local information collection (2) (1S) Local systems adaption (S)

Combined menu/command searches (S) Remote access by different systems (S) Users can check their status (S) Users can place reservations (2) (1S) Fast letter keys (S) Multilingual, multiscript formats Key word to search word links (2) (1S) Topic linking and subject access (S) Navigation aids Different hierarchical searches Customized web interface (9) (5S): - Hot links for topics - Extended searches - Hot links for source material

Innovations by users fell into two broad categories: those which provided improved management control and those which delivered enhanced user benefits. Improved management control included innovations to aid integration with other systems, increasing the cataloguing functionality of the manufacturer-provided OPAC, and improved management control in terms

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of better statistical information, library user management, and administration of the physical book stack. Enhanced user benefits centered around improving ease of use to the customer, increasing user functionality of the OPAC and information integration from a number of different sources. The innovation activity reported by users reflects the fact that, while most libraries are extremely happy with the benefits provided by their OPAC, most reported that they would value some modifications to it. Seventy-two percent of respondents agreed with the statement that they are satisfied with the performance of their OPAC, but 54% agreed with the statement “We would like to make additional improvements to our OPAC functionality that can’t be made by simply adjusting the standard, customer-accessible parameters provided by the supplier”. Given that over 50% of users felt a need for modification yet only 17% had actually modified their systems, we were led to consider what might be causing modification and also the lack of modification. The model derived in the previous section suggests that improved utility would be driving the need for innovation and that this would be experienced most by lead users, while the cost of implementing that improved utility would act as a dampener and those costs would be inversely related to the firm’s capability to make changes. Before examining the effect of capabilities quantitatively, we examined qualitative data as to why firms did innovate (what sort of skills they felt they had to undertake the process) and why firms did not innovate (what they considered the barriers to innovation to be). The answers to the question of “What special knowledge/capabilities do you have that you feel are useful for you to be able to improve/suggest improvement to product design?” led to 89 identified skills. These skills were coded as “We are closer to the end-user” (34 responses), “We have better industry know-how” (20), “We understand the operating environment in which the equipment

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will be installed” (17), “We have better resources and technical skills than our suppliers” (14), and “We know all of the competitors in the market not just one” (4 respondents). (Note that these reasons for user innovation are entirely consistent with the sticky information arguments for user innovation that were referenced in our review of the literature.) Most user-innovators reported that the technical resources they applied to their innovations came entirely or largely from existing organizational slack, noting that their existing in-house technical support staff simply integrated these projects into their overall workload on a time-available basis. (Specifically, in 12 of the 20 innovations for which we have this information, innovators reported that no incremental expenses were incurred to accomplish their innovations. Five reported incremental expenditures ranging from “a few dollars” to $10,000; 3 reported task-specific incremental expenses ranging from $10,000 to $60,000.) When we asked users why they did not innovate more, 74 said that the closed nature of the OPAC system made it difficult, 64 said they had no technical skills, 42 said they had no monetary resources, 27 said it was impossible to find consultants who could provide support, and 23 said that it was not encouraged by the culture of their organization.

6.2: Model of User Innovation The above examination of leading edge status and barriers to innovation led to the specification and calibration of our model of user innovation. The modeling section suggested that the need for innovation would be directly linked to leading edge status. Following Morrison (1995), we used a seven item scale to measure the construct of Leading Edge Status. The measures used are contained in the appendix and the results of the factor analytic measurement

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model are provided in Table 3 below. A standard statistical test suggests one factor and the Cronbach alpha is 0.77 suggesting good construct validity.

Table 3: Factor Analysis of Leading Edge Status Attribute

Factor Loading

We are ahead of other libraries in recognizing new solutions

0.750

We have pioneered some applications

0.749

We suggest new application to developers

0.738

We are leading edge (definition provided)

0.671

We have been used as a test site for prototypes

0.648

We have benefited from the early adoption of OPACs

0.499

Mentions by others as leading edge

0.454

In addition to asking about users’ own innovation, for innovations with which manufacturers had been associated, users were asked the percentage input that they had in the building and design stages. Figure 3 provides the distribution of leading edge status, classified by the percentage of contribution that users had in the process. A very clear relationship is evidenced from this graph; there are no observations in the top left-hand corner suggesting that leading edge status is a necessary, but not sufficient condition for innovation. The model developed in the previous section would tend to suggest that user capability might provide some of the explanation as to why some users with high leading edge status who have the need for innovation, feel unable to undertake it themselves. Therefore, we developed measures of corporate capability as described in the appendix.

A factor analysis of these

corporate capabilities (or the converse of them, barriers to innovation) yielded three factors explaining 62% of the attribute variance, as illustrated in Table 4 below. We labeled these three

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factors “lack of technical skills” (related to no technical skills, no technical capability, and the inability to penetrate a closed manufacturer system), “lack of external resources” (no money to pay vendors, unwillingness of suppliers, unmotivated suppliers, and lack of consultants), and “no need” (no need for changes and the culture not encouraging innovation). We took these three factors to represent different facets of barriers to innovation and its converse, corporate capability.

Figure 3: Scatterplot of proportion of post-installation building and design done by library as a function of Leading Edge Status (LES)

100

Proportion of Building and Design by Library

80

60

40 20

0

Leading Edge Status

23

Table 4: Factor Analysis of Barriers to Innovation (Measure of lack of capability) Attribute

Factor 1:

Factor 2:

Factor 3:

Internal

External

Perceived Need

No technical capability for major reprogramming

0.916

0.062

-0.066

No technical skills for small add-on programs

0.907

0.103

-0.035

OPAC is a closed system which prevents changes

0.599

-0.057

0.166

No monetary resources to pay vendor

0.231

0.731

0.340

Supplier is receptive to user ideas

0.261

-0.661

0.438

Suppliers not motivated to make free changes

-0.089

0.647

-0.025

Lack of suitably qualified external consultants

0.296

0.492

-0.057

Our library policy does not encourage changes

-0.051

0.312

0.810

No real need to modify OPAC

0.058

-0.332

0.702

In addition to ‘leading edge status’ and ‘capability’ as hypothesized determinants of whether users modified or not, we also included the inclusive value of whether sharing of the innovation took place. That is, we calibrated Equation 7, the inclusive value of sharing, and tested whether it was a significant factor in determining innovative behavior. Obviously, for firms that had not innovated it was impossible for us to ask the manufacturers to put a valuation on innovations that they might have made, UiR. We overcame this problem by forecasting the value of those innovations both for innovating and for non-innovating firms. For the innovating firms there was a correlation between the actual inclusive value and the inferred inclusive value of 0.895, suggesting that this approach was a good one. The results of fitting the resultant logit model are described in Table 5. It may be seen that leading edge status is highly statistically significant and important, followed by whether the firm has the internal resources. The third barrier to adoption (that of need) is also statistically significant, while the second barrier (accessibility of external resources) is marginally not significant. The inclusive value does not approach significance, suggesting that

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in this market at least innovators are not looking forward to the value they might get from sharing when they decide to develop their innovation.

Table 5: Logit Model of Innovative Behavior Coefficient

(Standard Error)

Leading Edge Status

1.866

(0.604)

Internal Resources

-1.077

(0.422)

External Resources

0.697

(0.455)

Perceived Need

-0.858

(0.459)

Shared Value (Uis|I*)

0.065

(0.706)

Constant

-2.642

(0.770)

χ25 = 33.86

ρ2 = 0.40

Classification rate = 87.78%

6.3: The Sharing of Innovations We next explore whether users who have innovated share their innovations. Of the 20 users who innovated, 5 did not share any of their innovations, while 4 shared them with the manufacturer only, 6 shared them with users only, and 5 shared them with both users and manufacturers.

If we look at modifications rather than users (see Table 2), of the 39

modifications studied, 22 of them were shared with manufacturers or users and 17 of them were not. Thus, there is a fairly high level of sharing going on. Those modifications which were shared are denoted in Table 2 by an S in brackets. There is no obvious pattern to which modifications are shared. Therefore, we reverted to the model for sharing developed in section 3.1. We would expect sharing to occur if the user thought that it was valued, if the user could do so at low cost because he had strong network connections, and if the innovation was indeed perceived to be of value to other users and manufacturers. We used membership of a user group as a surrogate for network connectedness of the user. If a user belongs to a user group, it is in a 25

much stronger position to be able to easily share any innovation with other users and the manufacturer. We have direct measures of the manufacturer’s valuation of the innovation based on a description from the user. Finally, we measured whether the innovating user thought that its innovations were of general value by its level of agreement with the statement “The needs of this library are unique”. The results of fitting the logit model of sharing behavior are described in Table 6. All of the variables are statistically significant and the model has very strong discriminating ability. It forecasts that 10 users would not share their innovations who did not, 12 users who did share their innovations would, and misforecast only 4 cases of sharing where it did not occur and 2 cases of not sharing where it did occur. That is, the overall classification accuracy was 79% which is extremely high.

Table 6: Logit Model of Innovation Sharing Coefficient

(Standard Error)

Belong to user group

2.443

1.148

Manufacturer’s evaluation

0.032

0.018

Perception needs are unique

-0.572

0.304

Constant

-0.780

1.283

χ 3 = 11.03 2

ρ = 0.28 2

Classification rate = 78.57%

As a matter of interest, we also asked respondents whether they were the recipients of the innovations of others. 16 respondents said that they had received innovations from other users, whereas 86 said that they had not. This is reasonably consistent with the number of innovations that were claimed by innovators to be shared. We undertook a χ2 statistical test to determine whether there was a positive association between the sharing of innovations and the receiving of

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them. Such a positive association would lend support to the argument that one of the major reasons to share user innovation is for information trading. The test did not have a lot of statistical power because of the relatively small number of innovations received and shared (16 and 20 respectively). The results were in the hypothesized direction (sharers were more likely to receive) but not statistically significant.

7.0: Discussion and Future Research In this paper, we have further developed a basis for systematic application of lead user information to new product and service idea generation and product design. Prior to the research reported here, considerable evidence existed that users are responsible for many commerciallysignificant innovations in a wide variety of industries. However, there has been little systematic work to determine whether those users that innovate are indeed lead users. Urban and von Hippel (1988) provide strong evidence that the innovations of lead users are highly valued by other lead users and the population as a whole, but their identification of lead users includes whether the user has innovated in the first place. In this study we have attempted to overcome any such circularity by defining lead users in attitudinal terms, as well as self-perceptions and the perceptions of others before looking at their innovative activity. Our results strongly support those of Urban and von Hippel; that users with high leading edge status are much more likely to innovate. We find that this is moderated by the capability of these users to harness their resources and those of the external environment.

We have found that manufacturers find

information contained in many of these lead user innovations to be of potential value to their inhouse product development efforts. We also have empirically explored the amount of sharing of user innovation that is going on in our sample. This appears to be quite large from the users’

27

perspective, and that is reasonably strongly validated by the fact that users also report receiving innovations from others. We are able to show that lead users are more likely to share their innovations if they have higher network connectedness, think their innovation has general utility, and if others find the innovation to be of significant value. Our findings plus the earlier findings of Urban and von Hippel make it reasonable to suggest including ideas from lead users among the numerous sources of new product and service ideas that providers might tap. However, we wish to make a stronger recommendation – that need data and solution ideas from lead users receive a central place in the development of new product and service idea generation processes. As support for this recommendation, we will first explain why we think that solutions generated by lead users are likely to often be better than ideas generated in-house by provider firms. Second, we will explain why we think that, given appropriate methods, lead user ideas can be collected by manufacturers in a cost-effective manner. Our view that lead user ideas are likely to be “better” than ideas generated in-house is based on a small amount of direct information plus three tests of reason. With respect to direct information, recall that, in the study by Urban and von Hippel (1988) reviewed earlier, they found that a new product concept created by lead users was preferred over any other system. We believe that future research which systematically compares new product ideas created by lead users with those created by non-lead users is warranted and will provide useful information for the practice of new product development. Despite the present paucity of direct evidence, we think that tests of reason support the suggestion that ideas generated by lead users will eventually be proven to be typically “better” than ideas generated by manufacturers in-house based upon need data garnered from average

28

users in a target market. Consider that we, along with Urban and von Hippel, have documented that innovations are concentrated among lead users. Also, we know by construction that the need information average users possess will not be leading-edge in nature. To the extent that the need conditions facing lead users do foreshadow general demand in the target market, and to the extent that lead users have developed prototype solutions and have found empirically that their solutions do provide them with value in use, the solution ideas lead users have developed may be “better than” ideas generated in-house for at least three reasons. First, it has been shown that the “fit” between need and solution tends to be improved when iterative design methods, such as “rapid prototyping” are used that involve cycles of prototype development followed by trial-and-error learning at user sites (Connell and Shafer 1989, 15; Gomaa 1983). Innovations that have been developed and applied by lead users at their own sites will have gone through some amount of trial-and-error development and informal assessment of value in use. In contrast, ideas generated in-house by manufacturers during the idea generation phase of development projects have typically not yet been subject to prototyping and field testing. Second, lead users tend to have less problem-solving constraints than do manufacturerbased developers. Manufacturers tend to specialize in a given type of solution, and will have a competitive advantage when they can deliver new products or services that build upon their existing design and production capabilities. In contrast, users are unlikely to have a similar level of investment to protect in a particular type of solution. For example, if a user is facing a problem of attaching two things together, it may experiment freely with adhesives, mechanical fasteners, Velcro, zippers or what have you to find the best solution: the user has no production capability investment in any approach. On the other hand, a manufacturer of adhesives, for

29

example, will have an incentive to narrowly focus on adhesive-based solutions only, and may therefore miss better solutions in which they could play a role – say, adhesives plus mechanical fasteners. Other things being equal, it is reasonable the larger solution space that is attractive to users can sometimes offer opportunities for development of better concepts. Third, consider that the development of really good ideas may be to some extent a matter of chance even given user and manufacturer-based idea generators that are equally favorably positioned to develop a given new idea. If this is so, then there is a probability that one or more lead user ideas will be better than any manufacturer-generated one simply because there are often many more lead users thinking about a problem and testing a lot of different ideas. For example, a web site recently set up by Sony to support computer “hacker-users” interested in developing and playing games on the Sony Playstation quickly attracted 10,000 active participants.

In contrast, Sony devotes the efforts of perhaps 100 in-house and contract

developers to developing games for the Playstation.

Sony’s VP of third-party R&D, Phil

Harrison, thinks that the user-developers he has gained access to will come up with “some radically new forms of creativity [that will] break the conventions that are holding the business today” (Herz 1998). Next, we consider the likelihood that cost-effective methods can be developed to systematically identify lead users and obtain access to their – arguably “better” - ideas. In our OPAC study, we have found that lead users do innovate relatively frequently, and that one can reliably identify lead users via measures of their “leading edge status” (LES). This opens the way to identification of potentially-innovating lead users via screening procedures utilizing LES. Alternatively, successful use of a networking-based approach for identification of lead users has been described (von Hippel, Churchill, Sonnack 1999, Thomke 1998). Our study data also

30

supports the potential utility of networking approaches in that we found (Table 3) that OPAC users with high measured LES also tended to have a widespread reputation among OPAC users as being at the leading edge. We reason that a screener-based approach to identification of lead users will be the most cost-effective one when lead users are not known by reputation to others and a networking approach will be preferred when lead users are known to at least some others in the user community or a manufacturer wants to identify the few “best” lead users within a large population. Once lead users that have developed potentially interesting innovations have been identified, they must be willing to reveal these innovations to inquiring manufacturers. Our study has shown a widespread willingness on the part of innovating lead users of OPACs to share their innovations both in principle and in practice. Moreover, Table 6 shows that lack of network connectedness and a belief that needs are unique to the user are at least as great a barrier to sharing as a lack of value of the innovation.

That is, simple user education and

communication is likely to be highly effective in increasing user innovation sharing. Such sharing behavior is, as was discussed earlier, more likely to occur in an industry where users are not direct rivals in the marketplace and this is, of course, the case with respect to the libraries that are the users of OPACs. However, we think that there will be adequate willingness among lead users to share innovations with manufacturers even in fields where users are direct rivals. Our basis for this judgement is twofold: first, as was discussed in our literature review, “informal know-how trading” of user-developed innovations has been shown to occur and to be economically justifiable even among direct rivals. Second, practical field experience in lead user studies to date has found lead users to be generally willing to freely reveal information about their innovations to manufacturers (von Hippel, Churchill, Sonnack 1999). Our belief that lead

31

users are willing to share their ideas even in a competitive environment should be empirically tested in future studies. We conclude by suggesting that the findings from our study, when taken in combination with the findings of earlier empirical research and tests of reason, support the likely value of methods to systematically collect both need and solution information from lead users as a central input to the idea generation processes of manufacturers.

Such methods would contain

procedures to identify lead users and acquire information and ideas from them, and procedures to assist manufacturer-based market researchers and developers to evaluate and modify and combine those ideas with other sources of information during idea generation. If such methods can indeed provide better ideas, the impact on the overall effectiveness of manufacturer innovation processes should be significant. After all, without high quality ideas as inputs, the quality of innovation process outputs must be low.

Appendix: Variable Operationalizations Measures for the variables were either adapted from the previous literature or developed specifically for this study, based on qualitative research.

Dependent Variables: Modification, and Share To address the primary research question, how many users innovated, modifying their system, and what were the drivers of this innovation we asked the question “Has your library recently developed modifications to the function of the OPAC?” with a binary Yes/No response.

32

For the second research question regarding the extent of innovation sharing and its causes we asked, “Did you share your modification with (a) other users (b) a supplier?”, again with a binary Yes/No response. Additionally, open-ended descriptions of innovations developed and shared were elicited, together with the specific skills that users thought made it useful for them to undertake the innovations themselves.

Independent Variable Measures The majority of independent variables were measured by multiple items that were reduced to single constructs using principal components factor analysis with Varimax rotation. In estimating the probability of ‘innovation activity’ there were two hypothesized independent variables, namely ‘leading edge status (LES)’ and ‘capability’. The ‘LES’ construct measures how leading edge the organization is, and consists of seven items. Five of these were measured on 5-point Likert scales anchored at strongly agree and strongly disagree (applications pioneering, suggesting new applications, use as a test site, early in recognizing solutions, and benefited from early adoption). The sixth of these was a self stated measure on a seven point scale, while the last was a count of how often other libraries referred to this one as leading edge. The second independent construct, ‘capability’, measures the user’s perceptions of the organization’s financial, technical, and policy capabilities and barriers relating to undertaking innovation. Nine items were elicited on a 7 point Likert scale: policy, finances, supplier reluctance, technical skills, technical capacity, closed systems, supplier communications, lack of third parties, and low level of need. The second model related to ‘sharing’, and it had three independent constructs: network connectedness, uniqueness of needs, and ‘value’ (were the shared modifications valued by other

33

users/manufacturers). The first construct here, network connectedness, was measured by a surrogate, whether the library belonged to a user group. ‘User group’ was a single binary (Yes/No) measure. Uniqueness of needs was elicited on a 7-point Likert scale. ‘Value’ was measured as follows. After the survey data was received from the library respondents, a list containing a description of each modification was prepared and independently evaluated by two manufacturers. These independent judges were asked to rate each of the user modifications, using a 100-point scale, on questions relating to ‘novelty of information contained in the user innovation’ and ‘how much value does the user innovation bring to the system’. The survey also collected information on the size of the organization, time of adoption of the OPAC system, customization by self and supplier during installation, receipt of others’ innovations, and experience with other technologies. These were used as covariates.

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