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Employees as User Innovators: An Empirical Investigation of an Idea Management System

Leid Zejnilovic

Carnegie Mellon University - Engineering and Public Policy

CATÓLICA-LISBON School of Business and Economics

Instituto Superior Técnico - Lisbon

Pedro Oliveira

CATÓLICA-LISBON School of Business and Economics

Francisco Veloso

Carnegie Mellon University - Engineering and Public Policy

CATÓLICA-LISBON School of Business and Economics

Acknowledgment. This project was funded by the Portuguese Science and Technology Foundation (FCT)

and the CMU Portugal Program through project CMU-PT/OUT/0014/2009.

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Employees as User Innovators:

An Empirical Investigation of an Idea Management System

Abstract

While the user-innovation literature has considered firms as potential user-innovators, little is known

about the individual contributions by employees of producer-firms, who innovate to address their own

specific needs within their firm, and which we refer to as employee-users. We investigate the innovation

proposals made by employee-user to an idea management system and theorize that their proposals are

more likely to challenge existing processes and structures when compared with other employee proposals.

We contend that employee-user proposals are more likely than other proposals to be turned into the firm’s

broader practice, regardless of their level of development. Furthermore, we find that the origin of the

proposals is a better predictor of the likelihood of adoption than their level of development. We also find

that employee-users contribute beyond continuous process improvement initiatives with proposals for

new products and services. We discuss the implications for theory and practice and suggest ways for

companies to increase the success rate of the ideas submitted by the broad base of employees to idea

management systems and to bring more radical ideas to these systems. To the best of our knowledge, this

study is the first to bridge the idea management and user innovation literatures and to directly examine

broader contribution of employees as potential user-innovators, introducing the term employee-user

innovations to emphasize conceptual difference from other types of user-innovators.

Keywords: employee-user innovation; idea management systems; innovation management

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1. Introduction

The user-innovation literature defines a user-innovator as an individual or a firm that innovates for the

benefit of use (von Hippel, 1986, 1988). These user-innovators are typically seen either as end-consumers

of goods, services, and designs, or more broadly as those who use them as input for further production in

the case of intermediary firms (Bogers et al. 2010). Although prior work has focused on user-firms as

innovators in products they acquire from their suppliers, and in that context mentioned the role of

particular employees (de Jong & Von Hippel, 2009; Enos, 1962; Morrison, Roberts, & von Hippel, 2000;

Riggs & von Hippel, 1994), to date there has been no direct examination of the contributions made by

employees as users in the context of their own employing firm. We theorize that employees may exhibit

behaviors typical for user-innovators, albeit inside their own firms, by modifying or creating processes,

products, or services.

We investigate innovation proposals submitted by employees of a large high-tech firm to an idea

management system (IMS), a tool for gathering and managing ideas from employees. To apply the user-

innovation perspective to our analysis, we assess the submitted proposals and inquire if some describe the

employees’ own projects, developed for some short-term personal benefits of use. We then explore if and

how our approach can contribute to address the challenges of innovating with employees inside their firm,

turning their ideas into practice, and to the role of idea management systems in facilitating the process.

There are at least two important trends that we believe make our research increasingly relevant. The first

is the growing role of digital technology in innovation (Yoo et al. 2012). Technological development and

the enhanced technical abilities of individuals and groups enable them to increasingly act directly upon

their ideas and needs, often as user innovators (Baldwin and von Hippel 2011). This is seen in, for

example, in the growing number of employees who experiment and develop workarounds of their own,

sometimes ignoring explicit company rules, to complete their job (Brown and Duguid 1991). An apt case

is the mushrooming of small databases and programs known as “shadow IT” developed under employers’

radar by impatient employees (Dyche 2012). These developments make it valuable to investigate what

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employee-users are doing and how firms may learn from internal user-innovation to design better

policies, especially ones that can ensure effective innovation by their more innovative employees.

The second trend is the growing importance of idea management systems as instruments to encourage

employee involvement in innovation (Carrier 1998, Fenn 2011). Despite the long history of use and

research related to IMS, or their predecessor suggestion systems (Axtell et al. 2000, Björk et al. 2010,

Carrier 1998, Dickinson 1932, Frese and Fay 2001, Oldham and Cummings 1996, van Dijk and van den

Ende 2002, Verworn 2009), evidence on the performance of IMS is mixed (Hastings 2011). Different

approaches to the implementation of IMS have brought forward a variety of challenges related to this

innovation approach, from the number of contributions and idea quality (Björk et al. 2011, Girotra et al.

2010, van Dijk and van den Ende 2002), to evaluation costs (Toubia 2006), idea implementation (Axtell

et al. 2000, Kijkuit and Ende 2010, van Dijk and van den Ende 2002), or the tension between incremental

and radical innovation (Sandstrom and Bjork 2010). We explore whether and how the user-innovation

theory can help addressing these challenges.

Our work contributes to several strands of the innovation literature. To the best of our knowledge, this is

the first study to empirically investigate an idea management system by considering the possibility that

employees may behave as user-innovators. Whereas earlier work on user-innovation identifies individuals

outside firms and intermediary firms as user-innovators (see reviews by Bogers et al., 2010, and von

Hippel, 2010), our work focuses on employees as users within a firm and the types of contributions they

make as innovators. The perspective on employees as user-innovators emphasizes their role beyond

participation in what are known as continuous improvement initiatives (Anand et al. 2009, Bhuiyan and

Baghel 2005), and more as a valuable source of internal opportunities for innovation. Also, our work

contributes to the literature on innovation management and idea management systems, showing the

facilitating role that idea management systems can have in dealing with the non-spread of innovations

across large firms (Ferlie et al. 2005). In addition, we contribute to the literature that discusses turning

ideas into practice (Baer 2012, Van de Ven 1986, Yuan and Woodman 2010) by establishing the

relationship between the innovation’s expected benefit of use and its adoption by the firm.

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2. Theory and Hypotheses

Over a 40-year period, extensive scholarly work has both demonstrated the importance of user-

innovations and explored the conditions for their emergence and evolution (see reviews by von Hippel

2005, 2010, or Bogers, Afuah and Bastian 2010). There is evidence of users modifying processes (Enos,

1962; Hollander, 1965, de Jong and von Hippel 2009, Schaan and Uhrbach 2007), modifying or creating

physical goods (von Hippel 1976, 1986) and services (Oliveira and von Hippel 2011), with significance

even at the national level (von Hippel et al. 2012).

User-innovation scholars have considered employees only when their employer firm is a user-firm which

modifies or creates processes and tools for the benefit of using them (Herstatt & von Hippel, 1992;

Morrison et al., 2000; Morrison, Roberts, & Midgley, 2004; Schaan & Uhrbach, 2007; Urban & von

Hippel, 1988). For example, de Jong & von Hippel (2009) find that 54% of 498 small high-technology

Dutch firms report that their employees innovate around the process equipment or software they acquire

from vendors to satisfy in-house needs. Morrison, Roberts, & von Hippel (2000) find that 26% of the

library information system users that they surveyed modified the system one or more times after

installation. Furthermore, Shah and Tripsas (2007) introduce the concept of professional-users. These

authors investigate user entrepreneurship, which they define as the commercialization of a new product or

service by individuals who are also their users. The authors distinguish between two categories:

professional-users and end-users, where professional-user entrepreneurs are those user entrepreneurs that

are embedded in an organization and employ a product in their professional life. They experience a need

for improvement and leave their firm in order to develop and commercialize a solution. Yet, these studies

usually sidestep the processes by which the innovation takes place inside the observed user-firms, limiting

our understanding of the role that individual employees can play. Similarly, by looking at individual

innovation cases in a given respondent firm, studies are likely to miss other contributions that employees

acting as user-innovators can have for their employer, and not only in user-firms.

To understand the broader role of individual employees as potential user-innovators, consider the

observation by Galbraith (1982) of a field engineer who worked for a manufacturer of electronic text

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editors and generated a major product improvement for his own benefit, rather than with a commercial

intent. The engineer modified both hardware and software on his own, and managed to adapt the device

so that it could be programmed to track his inventory and customer orders, before the corporate R&D

found a viable solution for it. His innovation reached the market by chance, when other employees passed

the message of the engineer’s achievement to the corporate president. The president made the innovation

of the improved text editor a basis for a commercial offer of the firm. In this example, the employee

introduced a new feature to his employer’s core product by acting as a user-innovator.

Another example is a study of sources of innovation, where Hunter (1991) considered a possibility of

internal customers initiating innovations. The context was internal organizations within New Jersey Bell

(NJB) system active in Switching Operations interacting with other organizational units within NJB. He

found that internal customers proposing improvements to services initiated 5 out of the 20 sampled

innovations. Their aim was to benefit from using these innovations.

We coin the term “employee-user innovator” to refer to employees who attempt to materialize their ideas

inside their firm and on their own, in expectation of a short-term use benefit, and not with the objective of

having their employer broadly adopting or commercializing the innovations. We refer to the proposals

developed with the objective of own use as “employee-user proposals” (EUP), and to all other proposals

as “employee-non-user proposals” (ENUP).

Our aim is to investigate the contributions by employee-users to their employers by observing the

innovation proposals in the IMS, and systematically assessing the relative success of these proposals in

terms of being turned into organizational practice, as well as their impact on existing structures,

processes, and products.

2.1. Impact of employee-user proposals on the existing processes and products

Employees have long been seen as instrumental in exploring and applying new ways of doing work and

contributing to firm’s growth (Arrow 1962, Kline and Rosenberg 1986, Smith 1776, Teece 2007,

Womack et al. 2007, Yasuda 1991). This perception has motivated many firms to develop and apply

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different methods to involve employees at all levels in systematic activities of process improvements –

commonly known as continuous improvement initiatives (Anand et al. 2009, Bhuiyan and Baghel 2005).

These methods are based on a strategic orientation towards process improvements, and evidence of their

success is often overshadowed by concerns about if and to what extent the application of these initiatives

hinders innovation (Anand et al. 2009).

To study the impact of EUP on existing processes, products, and services, it is important to observe the

type of innovation proposals the IMS may capture. The continuous improvement literature frames

suggestion systems as a part of a broader continuous improvement strategy, emphasizing their role in

employee motivation and training, where individuals submit ideas and leave it to experts to implement

them (Berger 1997, Bhuiyan and Baghel 2005). Thus, IMSs are often seen as tools for small and

incremental innovation (Carrier 1998). Yet, research has also shown that the type and quality of the

submitted suggestions is influenced by the advertised role of the systems in the organization (van Dijk &

van den Ende, 2002). In fact, recent work by Sandstrom & Bjork (2010) suggests that idea management

systems may even offer a strategic departure from local searches and small and incremental innovations,

fostering discontinuous innovation.

Existing work on user innovation leads us to believe that considering user-innovation by employees can

help us better understand the differences in the expected change that the innovation proposals in the IMS

may introduce to the existing structures, processes and products, and how that expectation influences the

evaluation outcome. User-innovators tend to think and experiment in a way that differs from established

trends – which, as some scholars argue, makes it valuable for firms to have user-innovators involved in

the new product ideation phase (e.g., Franke et al. 2006; von Hippel 1986). An equivalent reasoning led

U.S. manufacturing company 3M and Norwegian IT system integration firm Cinet to experiment with the

lead user method, with the outcomes suggesting that the majority of the lead user ideas were embedded in

new, often “breakthrough” products (Lilien et al. 2002, Olson and Bakke 2001).

Relying on internal users may be especially valuable for firms whose external users have very limited

knowledge of a technology (Magnusson 2009), when employees have a good grasp of technology (or at

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least easy access to those with knowledge and expertise), rich (internal and/or external) use experience,

and relevant need-based thinking patterns. When internal users experiment to indulge their curiosity or

address a keen need they experience on their own, often ignoring job constraints (technology used,

organizational structure, supervisors opinion, etc.), they may exhibit the same unusual thinking patterns

that external users have consistently produced, leading to innovations that significantly change existing

practices, products, and services. Thus, attracting more employee-user ideas and solutions would then

increase the value of running the IMS and their impact on their firm’s innovativeness.

In this context, we explore the extent to which bigger changes to existing products, services, and

processes can be expected from employee-user proposal, when compared to other innovation proposals.

H1: Employee-user proposal (EUPs) submitted to the IMS are, on average, more likely to introduce

bigger changes to existing processes or products than employee-non-user proposals (ENUPs).

2.2. The impact of EUPs on proposal adoption

Idea management is a process that includes generating, capturing, and evaluating ideas (Vandenbosch et

al. 2006), leading up to a firm's decision to implement them. Despite mixed results in using idea

management systems (Hastings 2011), their growing popularity (Fenn 2011) may be attributed to the fact

that, for successful IMSs, the running costs are usually lower than the benefits from the collected ideas

(Frese et al. 1999, van Dijk and van den Ende 2002, Yasuda 1991).

The main challenge in running the IMS is getting successful ideas. Searching for large number of ideas is

important to increase the likelihood of getting the best idea (Girotra et al. 2010). Yet, once they are in the

system, there is a costly evaluation process and outcomes are often disappointing given all the effort

involved (Toubia 2006, van Dijk and van den Ende 2002). Therefore, there is a strong incentive to find

ways of capturing ideas that are more likely to bring value to the firm.

Academic research in innovation has thoroughly explored contextual and personality predictors of

employees generating and submitting ideas to suggestion systems (Björk et al. 2011, Frese et al. 1999,

Ohly et al. 2006, Oldham and Cummings 1996), as well as predictors of idea implementation (Axtell et al.

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2000, Baer 2012, Kijkuit and Ende 2010, van Dijk and van den Ende 2002, Yuan and Woodman 2010).

However, we have very limited knowledge about the contributions by employees to an IMS from a

perspective of employee-user proposals, and how these could help externalize employees’ knowledge and

enhance conversion of employees’ ideas into a firm’s practice.

Generally, users act upon a need they experience (von Hippel, 1988), and are intrinsically motivated to

respond to that need (Franke, Schreier, & Kaiser, 2010; Lakhani, & von Hippel, 2003; Lakhani & Wolf,

2005). When employees are driven by intrinsic motives, such as enjoyment of problem solving (Lakhani

& Wolf, 2005), seeking reward and peer recognition (Riggs and von Hippel 1996), or the feeling of pride

in themselves for their own design (Franke et al., 2010), they may associate very different values to the

benefit from the process of ideation, elaboration, and final outcome, when compared to those with profit

in their mind (Franke et al., 2010). These differences may explain why employees act upon some ideas on

their own and invest their time and efforts in pursuing the ideas, even if they are not prompted by their

employer to do so. In extreme cases employees pursue their ideas even after being explicitly asked not to

do so (Mainemelis 2010).

When we consider the issue of turning ideas into practice, there is no obvious relationship between the

attribute of a proposal coming from an employee-user and its adoption within the firm. Namely, we may

assume that intrinsically motivated employee-users may try to respond to their unmet need on their own

and elaborate on their ideas, or even develop solutions. The existence of prior experience with the idea by

the employee-user (e.g., functional or design specification of a solution, a functional prototype or even a

part of it) may contribute to a better articulation of the need experience and the desired solution (Lilien et

al. 2002, Urban and von Hippel 1988) prior to its submission to the system and also reduce uncertainty

associated with new ideas by showing their viability (Baer 2012, van Dijk and van den Ende 2002).

This line of argumentation would lead us to hypothesize a positive relationship between EUPs and the

likelihood of their adoption by the firm. Nevertheless, there are also factors that may lead the firm to

abandon or reject even good ideas. Among them are the misalignment of the employee’s ideas and the

firm’s strategic path, the lack of resources or competences for pursuing the solution, or a challenge to the

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core competencies, structures, roles or hierarchies within the firm (Damanpour 1991, Sandstrom and

Bjork 2010, Sandström et al. 2009). But, individual promoter of ideas can use a variety of mitigating

actions to increase the likelihood that their firm adopts an idea (Baer 2012), especially when they are

intrinsically motivated as in the case of employee-users. Even in the case of strategically misaligned or

structurally challenging proposal, a firm may still choose to engage in a small-scale experimenting,

implementing a proposal to find alternatives in competence building or creating controlled challenges to

the existing structure. Hence, our second hypothesis:

H2a: Employee-user proposals (EUPs) submitted to the IMS are, on average, more likely to be

adopted by the firm when compared to employee non-user proposals (ENUPs).

It is not unusual for users to move beyond their advanced ideas, attempt experimentation and design or

develop some type of a solution for their own use (von Hippel, 1977, 1988). The level of initial

elaboration on the idea, or “idea processing” (van Dijk and van den Ende 2002, p. 391), is often seen as

instrumental for the implementation of the idea (Robinson and Stern 1998, van Dijk and van den Ende

2002). Typically, processing comes after the author is stimulated to engage in the process by visibly

accepting the idea (van Dijk and van den Ende 2002). But authors may also have motives to elaborate

ideas prior to proposing them in the IMS. In case a solution specification or a prototype exists, there is

less uncertainty with respect to the idea’s viability and expected outcome and a greater likelihood that the

tacit component associated with the idea can be transferred from the author to the firm (von Hippel 2010).

We test whether the existence of a solution specification, a prototype, or already used solution prior to

submitting the innovation proposal influences likelihood of a proposal’s adoption.

H2b: The level of idea development prior to submitting an innovation proposal is positively associated

with the likelihood of the proposal’s adoption by the firm.

3. Research Context and Methods

3.1. Research context

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We test our hypotheses using a dataset of innovation proposals submitted to the IMS of a large ICT firm,

a subsidiary of a global ICT corporation. The firm is involved in the design of telecommunication

equipment for its parent corporation, pursues regional sales and provides global maintenance of the

equipment. In addition, it has a portfolio of complex information system products, many of which are

designed, developed, and implemented independently from the parent corporation. They are a business-

to-business firm with both government and private enterprise clients.

The innovation process in the firm is well specified and designed to rely extensively on the IMS, which

makes it particularly appealing as a context for our study. All of the firm’s 1600 employees can submit

their proposals to the IMS, which upon submission go through a four-step evaluation procedure. The

procedure is designed to ensure that the evaluators’ potential bias, such as the field of application of the

proposal or the authors’ background, is reduced to a minimum. This is achieved involving, on average, 15

people from different hierarchical levels and backgrounds to look the proposal and participate in shaping

the final decision for each proposal.

Proposal submission is a voluntary effort by employees, and for the period observed the incentives were

to symbolically reward all submitted ideas, and to provide similar financial prizes for the successful

proposals. Evaluators have no time constraints for completing the evaluations, but in practice, authors

generally receive feedback on their proposals within a month of submission.

3.2. Data collection and coding

We initiated our data collection with a series of exploratory open-ended semi-structured interviews, with

an aim to understand the strategic role and utilization of the idea management system as perceived by

different hierarchical levels, and learn about the types of proposals in the system and their evaluation. We

interviewed five individuals: the coordinator of idea management related activities and R&D manager,

one of the innovation managers who help authors submitting proposals, two successful innovators with a

record of activities on the idea management system, and an employee who had never submitted a proposal

to the idea management system. We also explored how to apply the employee-user innovation

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perspective, in order to capture the motivation of employees and discriminate from other types of

proposals in the idea management system. We asked for cases of innovation by employees that did not

follow a usual path of development, from a submitted innovation proposal (idea) through to

implementation, and variations, to help us design our questionnaire, and test our classification algorithm.

Members of the coding team used these cases to learn how to code, and their coding of the cases was used

to double-check the algorithm.

An advantage of working with the IMS is the existence of structured data-- a database with records of the

submitted innovation proposals-- as our primary source of data. For reasons of confidentiality and to

avoid bias, a group of knowledgeable coders from the firm with access to the innovation proposals

independently coded the proposals. Seven coders used a questionnaire that was tested for consistency and

data validity by the firm’s innovation manager. The application comprises 18 questions on various aspects

of the proposals, such as to which of the business lines the proposal applies, whether it is related to a

product, service, or business process, and the extent of change the idea introduces to the existing

processes, products, and services. The coders read through the proposals and answered the 18 questions

that the application asked for each proposal. The coders were not aware of our hypotheses in terms of the

impact of user-innovation, and among the questions, none explicitly asked the coders to determine

whether a proposal is an employee-user innovation, as this task was to be completed by the research team

using a predefined algorithm (Figure 1) further explained below. The research team maintained contact

only through the coders’ supervisor, who was partially informed about the research objectives.

Although the records of innovation proposals submitted begin from 2000, we considered only those

submitted between January 2009 and October 2010. We chose this limited period in agreement with the

supervisor to make sure the results are meaningful as the recent proposals are likely to be better known

and can be coded with greater accuracy. Furthermore, in this period there were no changes in incentive

schemes and in organizational structure.

Upon our request, the firm’s Human Resource Management (HRM) staff provided information about the

authors of the proposals (age, tenure, education, gender) at the date of the proposal submission. They also

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helped code information from their database, to avoid revealing private information to the research team,

and helped match the data with the records from the IMS.

To validate our understanding of firm practices and our interpretation of the data, we conducted three

additional series of interviews: during the coding and data analysis period with the coordinator of the idea

management, after the results emerged with the coders and innovation managers, and with a sample of 10

individuals who submitted the innovations. Data analysis and coding required repeated interaction with

the innovation managers’ supervisor. We validated the coding through a detailed examination of 5% of

randomly selected ideas. When the data became available and the first results of our analysis emerged,

one of the authors of this paper spent a week in the firm conducting individual interviews with the

coders/innovation managers, the manager of the most successful department in terms of participation in

the IMS, and the supervisor who coordinates all IMS-related activities. The third series of interviews

validated the coding process, and verified the results. The data collected for the quantitative analysis and

the corresponding coding are presented in table 1. The descriptive statistics are presented in table 2

Insert Tables 1 and 2 about here

The last set of face-to-face semi-structured interviews was arranged with a sample of randomly selected

10 employees. Of these, 4 submitted employee-user proposals, and 6 were from the other group. Our

contact person from the firm made the selection. We describe selected cases in the findings section.

3.3. Explanatory variables – employee-user proposal (EUP)

To categorize the proposals, we used the independent coding method. The coding team was assigned by

the firm, which assembled several innovation managers familiar with the innovation proposals and

associated with different business lines. Instructions and other material for coding, developed by the

research team, were delivered to the coding team by the coders’ coordinator. To identify employee-user

proposals, we used the algorithm presented in Figure 1. With the exception of the question about the level

of development of the proposal, all the questions in the algorithm were part of the questionnaire designed

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for coding the innovation proposals.

Insert Figure 1 about here

The key variable for testing hypotheses H1 and H2a is the indicator of the employee-user proposal (table

1, variable: EUP). It communicates information about the origin of the proposal and the motivational

factors. To test the hypotheses, we first identify the employee-user proposals and quantify their share of

the observed population of innovation proposals. To test the importance of the employee-user proposals

and their general impact, as stated in hypotheses 1 and 2a, we develop a set of multivariate regression

models. Finally, to test the hypothesis 2b, i.e. the influence of the reported level of a proposal’s

development on its adoption, we asked the coder’s coordinator to assess the level of development of each

of the proposals in two ways: by searching for keywords for the mention of implementation (solution,

development, created, implemented, prototype, specification, diagram, code, and their written

alternatives), and by examining the text for all the ideas after the automated search.

3.4. Dependent variables

For testing hypothesis 1 we use as the dependent variable the impact of the proposals, a subjective

measure (on a seven-point scale) of the extent of change as perceived by the innovation managers.

Initially, we used an ordinal logistic regression model to estimate how our explanatory variables, in

particular employee-user innovation, influenced the probability of the various levels for the extent of

change. We also considered generalized ordered logistic and multinomial logistic models as alternatives.

To test hypotheses 2a and 2b, we use an adoption metric where the status of the proposal being

implemented indicates that it is adopted. We compare the regression results using two different dependent

variables, with the proposal status “implemented” as a benchmark and, as a robustness check, with the

status “accepted.” When a proposal reaches status “accepted,” this indicates that an innovation owner is

assigned with an indirect permission to start using resources for implementation. The budget is, however,

subject to further consideration by the firm. It is also possible that an accepted proposal fails and never

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reaches implemented status. Since both variables (implemented and accepted) are binary indicators (see

table 1), we develop and test multivariate logistic regression models.

3.5. Control variables

To identify the effect of the employee-user innovation, we control a number of variables, including the

influences related to the author, the nature of the proposal, and the organizational environment. In this

sub-section we provide a brief discussion of the control variables, mainly focusing on variables that do

not have a straightforward interpretation. To control for a bias due to strategic orientation and the

evaluation committee’s propensity for innovation proposals that enhance only the current portfolio, we

control for whether the proposal is an improvement, a new product or service, internal tool, or something

else. This information also informs us about the type of contributions by employee-users. Extant literature

suggests that teamwork enhances learning in the work context (Amabile et al. 1996) and the value of

innovation at work (Naveh and Erez 2004). In the context of the IMS, more than one author may be

interpreted as a sign of a well-thought-out proposal. We thus control for the effect of teamwork using an

indicator variable (see table 1).

To control for the author-related effects, in addition to the demographics, we include variables related to

the knowledge and skills the employees may have acquired: academic education, formal industrial

education (number of trainings), and informal acquisition of knowledge of the particular circumstances of

time and place (tenure). We also control for the specific type of knowledge acquired on the position

(hierarchical) in the firm, and for cross-departmental experiences – a proxy for strength of the employee’s

contact network and the diversity of knowledge about the firm an individual has acquired.

During the period considered in our study, the firm had a general goal to stimulate as many proposals as

possible with a symbolic reward for every submission. Although the firm expected this incentive would

attract good proposals, it may also attract lower quality proposals from individuals who are more sensitive

to incentives. We control for the effects of this incentive scheme by including information on whether the

author is a “serial idea generator.”

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The literature suggests supervisory support may have an important influence on individuals to submit

proposals (Ohly et al. 2006) (Oldham and Cummings 1996), and job complexity may lead to more

attempts to improve and innovate business processes (Frese et al. 1996). The work environment, and in

particular freedom in performing duties or balanced job control, is positively related to innovative

behavior (Amabile 1988, Ohly et al. 2006). With department and business lines controls, we approximate

the effects of different management styles and job complexity, but also differences in implementing the

corporate strategy within the firm.

Time controls address unaccounted-for firm-level changes in the system in calendar years 2009 and 2010.

Additionally, changes in market demand may influence authors to propose more, and evaluators to change

preferences and favor proposals that could be applied in areas with higher expected market activity. We

use the expected client for the proposal’s outcome as a proxy to control for the market demand fixed

effects, and control for the variation in adoption that arises with the influence of the otherwise unobserved

market changes.

3.6. Empirical issues

Due to our research design and data collection approach, we analyze innovation proposals that are

captured by the IMS, which can raise concerns about a potential bias due to self-selection. Self-selection

for better or more valuable proposals may occur, for example, as a consequence of a cost associated with

the process of submitting a proposal (writing it, reviewing it, going through the evaluation process) (Frese

et al.,1999), or it may result from the expectations that employees associate with its outcome (Baer 2012,

Yuan and Woodman 2010). Likewise, the existence of a solution and its diffusion within a group of

colleagues or a community of practice (Brown and Duguid 1991) may lead towards a community

evaluation and selection of ideas.

In our study, the cost-related concern is to some extent addressed by the incentive system in place at the

time of the study, with the firm paying a symbolic reward for each submitted proposal regardless of its

outcome. While this may influence some incentive-sensitive employees to step forward with ideas of low

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and high quality, the remaining concern is whether the self-selection due to the cost or image expectations

influences both groups equally. Such a possibility would represent an obstacle to our comparison of the

proposals’ importance between the groups. To argue about this concern, it may be useful to extend the

consideration of the drivers of self-selection.

One may also argue that employees would not share their best ideas if they feel they own them, in

particular, those ideas with the highest prospects of success (Hannah 2004). This would bias the observed

proposal population towards a lower average quality. The same effect could produce a fear of

deteriorating the relationship with co-workers, when proposals are challenging the status quo (Baer 2012).

All these effects may be particularly salient in the context of employee-user proposals. Doing things on

their own and primarily considering individually experienced needs may increase the feeling of

ownership. On the other side, the firm has a set of mechanisms to enforce certain behavior or influence

the sharing of innovations.

As we found a set of employees that proposed both EUP and ENUP, we compare the success and change

impact of their proposals, and find comparable proposals from both EUP and ENUP to have positive and

negative outcome of evaluation. Also, we interviewed ten employees from different departments and their

supervisors for the effects mentioned above, using scales developed in the literature (e.g., Baer 2012,

Yuan and Woodman 2010) and explicitly asking them about this concern. We found no evidence that

would point to any type of bias that would influence group comparison of the innovation proposals.

4. Findings

In this section we summarize the findings of our study. In short we find support to our first hypothesis

(H1) and conclude that employee-user proposals are likely to challenge existing processes and structures

than other proposals. We also contend that employee-user proposals are more likely to be turned into the

firm’s broader practice than other proposals are, which confirms our second hypothesis (H2a). This is true

regardless of their level of development. We find mixed results for the third hypothesis (H2b), and find

18

that the origin of the proposals is a better predictor of the likelihood of adoption, than its level of

development.

4.1. Incidence and type of EUP

In total, 759 innovation proposals were submitted to the IMS during the observed period, and afterwards

coded by the firm’s coders. Using the algorithm described in Figure 1, 46 (6%) of the proposals are

classified as EUP (Table 3). Only 245 out of 1600 employee (i.e. one sixth of the subsidiary workforce

with access to the IMS) submitted the 759 proposals (some employees submitted more than one). The

share of employees who exhibit user-innovator behavior within those who submit proposals is 14% (33

out of 245). These numbers do not represent all the employee-user innovation activity in the firm, as we

only consider those ideas and solutions that are captured by the IMS.

Although previous work has considered user-innovations inside firms only as process improvements in

user-firms (e.g. (de Jong and von Hippel 2009, Morrison et al. 2000), the observed distribution of

proposals suggests that contributions by employee-users go beyond process and product improvements

and also include new revenue generators (see table 3). In fact, some innovation proposals eventually

become firm products or services to be offered internally (within the global corporation) or to the market.

Insert Table 3 about here

A large number of employee-user proposals are related to business lines characterized by two

technologies – telecom platforms and mobile networks. Taken together, these two technologies account

for 31 out of 46 EUPs. Both are proprietary technologies developed mostly by the parent firm and

constitute a major share of the earnings. We asked three managers to identify key reasons for having such

a large share of EUPs in these two technologies. The reasons identified include ownership of technology,

decision structure, and supervisory encouragement of creativity in the workplace and of sharing ideas and

solutions through the IMS. For the technology owned by the global firm, where the decision to implement

is often governed by levels above the subsidiary, employees tend to develop solutions for their own use

and share them through the IMS. The supervisory encouragement was emphasized as the most important,

19

as many good solutions that employees apply “never get to the IMS and often do not move away from a

small group of people” (the supervisor in the business line).

4.2. Employee-user innovation cases

Four employee-user innovation cases provide a richer picture of different types of contributions by

employee-users. The information presented is from interviews with the innovators, innovation managers

in their department, and their supervisors. Our contact person in the firm selected the cases, namely a

new product, a new service, a new internal tool and a process improvement, described below.

4.2.1. New product - software for mobile network health check

A telecom engineer developed software for base-station performance data analysis, coding his tacit

knowledge in performing mobile network “health” check for telecom operators. He spent a significant

amount of his leisure time over a 2-year period writing software scripts to analyze recorded base-station

data logs. Although he had some interactions with his superiors about the topic, his idea did not attract

any substantial interest. Meanwhile, the engineer’s set of scripts evolved to a software solution that

allowed him to perform the network analysis in a few hours instead of spending a few weeks gathering

input from customers and processing the data. During the interview, he stated that the most important

reason for him to engage in developing the software was dissatisfaction with the existing solutions

applied in work. He felt that there was a way to do better, and his knowledge of technology and

experiences were telling him so; personal challenge also played a role. When he finally submitted an IMS

proposal that described the innovation, the software tool he proposed quickly became officially

recognized at the global level. The software even changed the way the mobile telecom network health

check was delivered to telecom operators. Instead of charging for analysis as a separate service, the

analysis output of the software became a complimentary service for network equipment sales. Answering

a question about submitting to the IMS when he had the product and not the idea, the engineer stated that

he did not see value in sharing it until it reached a level where the relevance was obvious.

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4.2.2. A new product and service – a course for telecom-platform testers

The parent corporation has a complex telecom-platform that acts as a giant router for different

communication traffic types and serves as a basis for a number of products. An engineer responsible for

telecom-platform products testing developed a modular course for high level testers (like him) working on

the platform. He was collecting a set of common mistakes and failures that telecom operators were

experiencing, and realized that it would be helpful to organize them as modular materials that would help

promote better troubleshooting and testing. Several other colleagues who picked up his idea contributed

in developing the material. The engineers developed a modular course for platform-testers and used

internally in their own department. Eventually, they decided to submit a proposal in the IMS to make their

work available to other similar departments across the globe. The innovator’s supervisor stated that the

key reason for coming up with the course was the engineer’s dissatisfaction with testing performance, and

a feeling that there was a lack of a systematic approach for this problem. ªIt was an idea they played with,

just collecting the problems and testing them, and they were convinced that they could really benefit from

it by raising the quality of their work. When I saw how useful it was, I encouraged the author to share our

excitement through the IMS, and now he is getting calls from all over the world to share his knowledge.”

(the author’s supervisor). The course quickly attracted significant interest across the global corporation,

and the firm generated additional revenue by selling the training service to internal customers worldwide.

4.2.3. New internal tool – test equipment monitor

One of the engineers in the firm’s laboratory for testing platform-based-products systematized his

frequent search activities through the available documentation to create a multi-criteria search engine, a

web page that made it easier to search for different testing boards in the lab and to assess compliance of

the platform-nodes used with the target hardware baseline. The author felt frustrated with his searches and

thought that it would be very convenient if there were a good search tool. He used and improved the tool

for and by himself, and after some time reported its existence to the IMS. After the proposal was

21

accepted, the project received approval for further improvement and inclusion as a part of the official lab

support. When asked about the reasons for not submitting it to the IMS prior to actually doing it, the

author responded that he did not even think about it, he just needed it and did it. Going through the

evaluation for the IMS for him seemed to be a time consuming process with an uncertain outcome.

4.2.4. Process improvement – automation of hardware diagram updates

An engineer, upon being assigned to a new job position, felt uneasy with the existing routines. During the

interview she said: “I simply felt that there was no way I could do the job as it was being done. I simply

could not.” Aware of her lack of programming skills, she analyzed the process of making updates of

changes to diagrams of different hardware versions based on client requests and specified the design of a

software tool that would automate the dull and error prone process. Yet, without programming skills, she

had to persuade her superior to allow her to use a programmer who worked in the department to code the

design, which indeed authorized. But her superior also insisted that she should submit the proposal to the

IMS and describe the innovation, which she did. When asked why she did not propose the idea before

developing it, she responded: “I didn’t think about it, and if I did, I wouldn’t have waited for the

evaluation to be completed. My supervisor is often suggesting that I should first submit the ideas there

(IMS), but why wait? If I already know how, I would rather just solve the problem.” The improved

process, with some additional work on it, significantly changed the existing routine by reducing its length

and improving its accuracy. As a result, it was elevated to a standard operating procedure for the global

corporation.

These examples of innovation, together with 42 other EUPs, became visible to a wider population in the

firm when the authors decided to describe and propose innovations to the firm as innovation proposals in

the IMS. It also demonstrates the facilitating role that an IMS can have in diffusing innovations within a

firm. In all the cases we observe a high level need caused by dissatisfaction –with the tools they are using,

the knowledge resources, the products available, or processes. Among many who worked in a similar job

position and with similar tasks, these individuals experienced higher level of need and acted upon it

22

successfully. These cases also signal an inefficiency in implementing IMSs, as some employees have

more trust in themselves than starting ideation and idea processing early through the IMS.

4.3. Impact of EUPs

For the second hypothesis, we develop an ordinal logistic regression model to test the relationship

between EUPs and the extent of change the proposals introduce. The dependent variable contains

information provided by the coders, who estimated the extent of change of each innovation proposal on a

scale from 1 to 7. We modified the original variable to have three levels (low, medium, and high extent of

change)1 to reduce the differences among coders in categorizing innovation proposals and relative

differences among categories. Although the results obtained from the ordinal regression concurred with

our initial assumption that EUPs on average introduce a higher extent of change than other proposals

submitted to the system, the model did not pass the Brant test for parallel regression assumption (Long

and Freese 2006). Our generalized ordered logistic model did not converge to interpretable results, and

we thus opted to use the multinomial logistic model. We build three multinomial regression models by

successively introducing different blocks of variables (see table 4 for the results).

In the first model, we introduce the EUP variable accompanied by author-related variables only, and find

than switching from an ENUP to a EUP increases the odds of introducing bigger change (relative to

medium change) by a factor of 2.27 (raw coefficient of 0.82, p < 0.05). Adding proposal-related variables

in the second model increased the effect of the EUP, suggesting the increase of odds of a proposal

introducing a higher change (relative to medium change) by a factor of 2.58 (p < 0.1). In the third model,

we include system-related variables. As information about the coder coincides with the information about

departments and business lines, these two controls can also be interpreted as a proxy for the coder-fixed-

effects controls. The effect of EUP reached a peak value in this model, with a raw coefficient of 1.15,

1 Since we observe a highly disproportional frequency distribution of proposals across the categories of the variable “Extent of change,” with a peak in category 4, we have modified the original variable with seven categories and created a new categorical variable with only three categories: Low Extent of Change (original categories 1, 2, and 3), Medium Extent of Change (original category 4), and High Extent of Change (original categories 5, 6, and 7).

23

which corresponds to a factor change of odds of 3.16. The EUP’s coefficient in both models 2 and 3 is at

a minimum level of statistical significance (p < 0.1). Overall, our results provide support to H1.

Insert Table 4 about here

4.4. Relevance of EUPs for the success of the IMS

In this subsection, we present the results for analyzing the effects of EUPs on the performance of the IMS,

using the multivariate logistic regression model, and variables presented in table 1. We interpret an

increase in the odds of the proposal being implemented as an improvement in the IMS success rate. The

frequency distribution of adopted proposals (see table 5) immediately suggests that a EUP is likely to

have a positive influence on the IMS success rate. Of all the implemented proposals, 19% are EUPs; their

share among not-implemented innovation proposals is less than 1%.

Insert Table 5 about here

To explore further patterns indicated by the frequency distribution in table 5, we implement four

regression models with the (latent) dependent variable the adoption by the firm and the critical

explanatory variable - the nature of the proposal. The results are presented in table 6, with each of the four

models representing the inclusion of additional blocks of control variables. A decision in favor of model

(1-4) is supported based on Bayesian information criterion (Long and Freese 2006) .

Insert Table 6 about here

We find a positive and statistically significant (at 1% level) relationship between EUP and the probability

of implementation. The odds of an innovation proposal being adopted are increased by a factor of 68 for

the proposals that describe EUPs. The results suggest no statistically significant relationship between our

proxy for the quality of a proposal and the probability of its implementation, and the same is concluded

for teamwork. When we consider the type of the proposals, the estimated coefficients suggest that the

general perception of IMS being a predominantly incremental innovation channel (Carrier 1998) is also

likely to be true in this case. Both coefficients next to new service and new product proposal indicators

24

are negative. However, only the new service proposal coefficient is significant at 1% level of significance.

Compared to the product improvement baseline, the odds of a proposal being implemented decrease by a

factor of 0.2 for a proposal of a new service, everything else held constant.

The tenure may be interpreted as technological or procedural expertise (Leonardi 2007) related to the

areas that need improvement, and interests and modus operandi related to proposal evaluation process.

We modeled the author’s age and tenure at the firm to allow for diminishing effects after a certain point in

time. However, the result is that only the linear part of the tenure is significant (p<0.05) and positive, with

an increase in the odds of a proposal being implemented for a factor of 1.2 for every additional year of the

employees’ tenure, everything else equal. This means that the proposals from experienced individuals are

likely to be adopted and therefore valuable for the firm, but in comparison to the experience EUPs are

even more important. The evidence offered supports hypothesis 2a, but does not support hypothesis 2b.

The results suggest that the association of the reported extent of development of an innovation proposal

and its implementation is positive, but not statistically significant. As a robustness check, we compare the

results of the regression of the probability of the proposal being accepted (table 6, model 2-4).

One reason to perform such a check is that, over time, the status of some accepted proposals might have

changed to implemented, altering the influence of the EUP variable. The robustness check confirms

support for hypothesis 2a, with significance at the 1% level for both adoption measures. But, the extent of

development is now positively (p<0.05) associated with the likelihood of an innovation proposal being

accepted, increasing the odds of acceptance by a factor of 3.3 if the proposal describes an already

developed solution as compared to a description of only an idea, everything else held constant. This

mixed result suggests that further exploration is needed in relation to hypothesis 2b.

5. Discussion

5.1. Theoretical contributions

This research contributes to the user-innovation literature primarily by systematically assessing

contributions by internal users (employees) within a large ICT firm. Previous research has investigated

25

user-firms as a source of innovation (e.g., Herstatt & von Hippel, 1992; Morrison et al., 2000; Morrison et

al., 2004; Schaan & Uhrbach, 2007; Urban & von Hippel, 1988). Yet, while innovation by user-firms is

an interesting phenomenon in itself, our focus is on employee-user innovation, when the employee is

inside a producer-firm, which is a fundamentally different dynamic from that of user-firms. Our approach

is also different from the one applied by Shah & Tripsas, (2007), who investigate professional-user

entrepreneurs, as those user entrepreneurs that are embedded in an organization and employ a product in

their professional life. They experience a need for improvement and leave their firm in order to develop

and commercialize a solution. In the context of our analysis, employees innovate within the employing

firm and we don’t find evidence of employees leaving the firm to purse the commercialization of the

solution. We have two possible justification for that: 1) due to intellectual property restrictions, it is

harder for employees of this firm to independently pursue these business opportunities outside the scope

of the firm, in particular after they decided to submit them to the corporate idea management system; 2)

contrary to Shah and Tripsas’ cases, in the industry we focus on, it makes more business sense to pursue

the possible commercialization of the technology within this large firm, as there is not much room for

starting new ventures based on innovations that are very specific to the industry or line of business.

Furthermore, we find that users contribute beyond improving processes (Anand et al. 2009). They

innovate with the objective of benefiting from using the outcome, but often end-up generating new

revenue sources for the firm in the form of new products and services. In all four in-depth cases of

employee-user innovations we presented, the prevailing motive was using the outcome. The innovators’

dissatisfaction with the existing situation and the existing processes, and the lack of tools, products, or

services was the trigger, and the fact that they enjoy the challenge and learning benefits was helping them

pursue the ideas further. This concurs with findings about motivation from the user-innovation literature

where innovators are outside the firm (Lakhani & Wolf, 2005; von Hippel, 2010), but also with findings

from the literature on creativity, where job dissatisfaction is found to lead to creative behavior (Zhou and

George 2001), and where playful behavior is associated with higher creativity and innovation

(Mainemelis and Ronson 2006).

26

The results of our study have several important contributions to the idea management literature. The first

is to bring forward internal users as a valuable source for innovation, beyond their participation in what is

known as continuous improvement initiatives (Anand et al. 2009, Bhuiyan and Baghel 2005). Involving

users can have an important impact on (i) the nature of the ideas submitted, as well as on (ii) the success

rate of those ideas and consequently the quantity of ideas required to get a minimum amount of valuable

ideas to keep the system running. With respect to (i) the nature of ideas, IMS are typically seen as a

limited tool, in the sense that it mostly captures small and incremental innovations (e.g., Carrier, 1998).

However, our analysis suggests that this could be modified by further involving user innovators as the

contributors to the system. Since users are often credited as having the potential of coming up with

radically new breakthrough innovations (e.g., Lilien et al. 2002), by using IMS and motivating employees

to contribute with user innovations, firms get a tool that allows them to capture products and services that

are of a more radical nature. With respect to (ii) the quantity of ideas, one challenge in running IMS is the

low success rate of the ideas. As a result, searching for large number of ideas becomes important to

increase the likelihood of getting the best ideas (e.g., Girotra et al. 2010). By focusing on user

innovations, a firm can increase the success rate and reduce the overall search and assessment costs.

The second contribution is to establish the relationship between the expected benefit of the outcome of the

EUP and likelihood of implementing the innovation in the firm. We show the importance of the proposals

rooted in a use experience, either as a self-developed solution or its functional description, by finding that

the odds of a proposal’s implementation increase by a factor of 68 when a proposal configures a EUP.

Although the overall share of EUPs in our sample is relatively small (6%), there are notable differences

between EUPs and ENUPs. This is the most obvious when adoption of the innovation proposals is taken

into consideration, a challenge that is long present in the innovation management literature (Baer 2012,

Van de Ven 1986, Yuan and Woodman 2010). Also, our work contributes to the literature on innovation

management and idea management systems, by showing an important, yet less explored facilitating role

that IMS can play in large firms (Ferlie et al. 2005). What we see is that IMS can also be a powerful tool

to allow the surfacing of local innovations and bolstering their diffusion across the global firm.

27

None of these dimensions have been explored in prior IMS literature. Also, to the best of our knowledge,

we are the first to look at employees or a firm as potential user-innovators and to bridge the IMS and user

innovation literatures. This contribution seems to be of high relevance for both streams and to the broad

innovation literature.

5.2. Managerial Implications

The main challenge in running the IMS is getting good ideas and the results of this research have

important managerial implications for idea management and more specifically for firms that have adopted

IMSs. We find that innovation proposals that describe employees’ projects undertaken on their own and

with expectation of a personal benefit of use-- referred to as employee-user proposals-- are more likely to

challenge existing processes and structures than other proposals. Thus, our results suggest that identifying

innovations by employees who are users may help improve the overall performance of corporate IMSs.

Additionally, by using IMS and motivating employees to contribute with their user innovations, firms get

a tool that allows them to capture products and services of a more radical nature. Moreover, by focusing

on users to increase the success rate of the submitted proposals firm may ensure sustainable IMS and

justify the cost for running it. Given the mixed evidence on the performance of IMSs, our suggestions can

be of high value to companies.

5.3 Limitations and Suggestions for Future Research

This study is focused on the IMS of a single large firm with skilled employees, limiting generalizability

of the conclusions. Skilled employees in ICT have an abundance of tools within the firm that they can use

to exploit and experiment with their ideas. This sort of resources does not necessarily exist in other

industries or smaller firms. Additionally, our research design our categorization algorithm is such to

classify as ENUP those innovation proposals that are rooted in some use experience, but are either a part

of a paid job or an idea proposed for commercialization rather than the use benefit. Further micro-level

exploration of motives may help in understanding differences in motives within the firm and establish

28

more precise classification methods of employee-user innovation activities inside firms.

Another limitation of this research is the lack of a social dimension of the idea generation and

implementation process. Theoretical arguments (Kijkuit and van den Ende 2007) and empirical evidence

(Björk, Vincenzo, Magnusson, & Mascia, 2011) suggest a positive relationship between a size of network

of employees surrounding an idea and the quality of ideas generated. Also, the lack of networking

abilities and strong ties significantly reduces the likelihood of an idea’s implementation (Baer, 2012). A

possible research path is to explore if there is any systematic difference in the utilization of social capital

for ideation and implementation if an idea is developed for the benefit of employees’ own use, for

commercializing or broader implementation in the firm, or as part of a paid job.

6. Conclusions

The key contribution of this paper is to extend the user-innovation perspective, which typically focuses on

users outside the firm, to a context in which users are inside the innovating firm as its employees. At the

same time we contribute to the idea management literature by clearly suggesting ways to increase the

success rate of the ideas submitted by the broad base of employees and to improve the nature of those

ideas, from incremental to more radical. To the best of our knowledge, this study is the first to directly

examine broader contribution of employees as potential user-innovators, introducing the term employee-

user proposal to emphasize conceptual difference from other types of user-innovators. Our results suggest

that employees can be seen as users and can be an important source of innovation. The employee-users

contribute beyond continuous process improvement initiatives with proposals for new products and

services. This perspective has been ignored by the vast literature on user-innovation.

Furthermore, the study design presented here allows for replication in different industries and settings

inside firms, which can provide more insight and further contribute to the generalization of our results.

Leveraging the knowledge from user-innovation literature may help address the challenge of knowledge

externalization, turning ideas into practice, and also solve issues related to the operation of idea

management systems inside firms.

29

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7. Tables and figures

Table 1 Description of variables, types and coding

Related to: Nr. Variable Measure type Description/expected relationship

Dependent Variables

D1/2 Probability of idea accepted/ implemented

Binary

Adoption of the innovation proposal; We experiment with two measures, one is the status of the innovation proposal “accepted”, and the other the status “implemented”; 0 – not accepted/implemented, 1 – accepted/implemented

D3 Extent Of Change

Categorical (3 levels)

A variable with 3 levels, indicators of data coder’s subjective perception of the extent of change the proposed idea represents to the existing service, product, process, or the other. 1 (low impact); 2 (medium impact) and 3 (high impact; significant changes, up to a new service, product, or a process).

Author / Personal

1) Gender Binary 0 – Male; 1 – Female

2) /3) Age / Age squared

Continuous Age of the author (in years)/ Squared age of the author

4) /5) Years of Tenure/ Years of Tenure Squared

Continuous Number of years of tenure the author spent in the firm / Squared Years of tenure. Relates to the firm specific experience, technology and firm-specific political know-how.

6) Managerial Level Categorical (3 categories)

Three hierarchical levels: 1. Non-supervisor; 2. Supervisor / coordinator / technical expert; 3. Manager /director

7) Academic title Categorical (4 categories)

Academic Education, the highest degree: 1 - without a university degree; 2 – Bachelor degree; 3 – Master degree, 4 – PhD degree

8) Combined Industrial Training

Continuous

An indicator of the investment in an individual, which also may indicate higher expectations in performance (not necessarily in IMS). It is a sum of counts of the following trainings: 1. External, web based training (Employees receive industrial training offered online by

educators from outside; nr of trainings/courses.) 2. External, instructor led training (Employees receive industrial training delivered by an

instructor. Nr. of trainings/courses.) 3. Internal training (nr of certificates/courses) (Employees receive training delivered by

educators employed in the firm. Nr. of trainings/courses.)

9)

Number of Conferences Attended

Continuous

Number of scientific conferences attended by the author.

10) Cross Specialty Training

Binary An indicator that is switched to one if the author had cross specialty training. Coding: 0 - no cross-specialty trainings (external or internal); 1 - employee attended trainings in at least two different specialties;

11) Serial idea generator

Categorical (3 categories)

Authors with multiple proposals over the observed period are called serial idea generators. 0 – one innovation proposal only; 1 - low serial IG with less than 4 proposals over the period; 2 – moderate, with 4 and more submissions

Environment

12) Department Categorical (6 categories)

One category for each of the departments where the innovation proposal originates

13) Business Line Categorical (16 categories)

Each of the categories represents one of the activities from the portfolio; some examples are: eHealth solutions, fixed telephony switching centers, internal tools, and other. Activities are particular business lines. Matrix of frequencies of innovation proposals by activities and departments is not a diagonal matrix.

14) Employee-user proposal (EUP)

Binary An indicator of whether the proposal is an employee-user proposal or not: 0 – Employee-non-user proposal (ENUP); 1 – Employee-user proposal (EUP);

15) Level of Development

Categorical (3 categories)

Three categories: 1 – a description of an idea ; 2 – specification of a problem and possible solutions; 3 – already developed solution with instruction for use, or further use.

Innovation proposal

16) Word Count Continuous Number of words used to describe the proposal in the electronic submission

17) Teamwork Binary An indicator variable switched to one when the proposal is a result of teamwork. Coding: 0 – Single author of the proposal; 1 – One author and one or more co-authors of the proposal

18) Client (control variable)

Categorical (5 categories)

Each of the categories represent expected client of the proposed solution/idea. 1 – Internal use or Global Corporation; 2 – Telecom Operator; 3 – Government; 4 – Enterprise; 5 – Other.

19) Proposal’s type (control variable)

Categorical (7 categories)

Each of the 7 categories determines the nature of the innovation proposal; 1 – product improvement; 2 – new product proposal; 3 – service improvement; 4 – new service proposal; 5 – internal process, method or tool; 6 – working environment related ideas

Time (control )

20) Year dummy Binary A control for the year specific changes; 0 – year 2009; 1 – year 2010

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Table 2 Descriptive statistics and correlations, (N=759); *indicates statistical significance at the 5% level

Mean S.D. D1 D2 D3 1) 2) 3) 4) 5) 6) 7) 8) 9) 10) 11) 12) 13) 14) 15) 16) 17) 18) 19) 20)

D1 Extent of change 0,93 0,73 1

D2 Implemented 0,31 0,46 0,16* 1

D3 Accepted 0,47 0,5 0.18* 0.71* 1

1) Gender 0,06 0,25 -0.1* 0.08* 0.03 1

2) Age 34,04 6 -0.04 -0.1* -0.1* -0.04 1

3) (Age)2 1194,77 452,39 -0.04 -0.1* -0.1* -0.03 0.99* 1

4) Years of Tenure 6,36 4,96 -0.03 -0.05 -0.1* 0.06 0.71* 0.72* 1

5) (Years of Tenure)2 64,97 126,06 -0.04 -0.1* -0.1* 0.09* 0.67* 0.72* 0.92* 1

6) Hierarchical level 1,25 0,6 0.04 -0.02 -0.02 -0.1* 0.20* 0.2* 0.32* 0.24* 1

7) Academic title 1,17 0,5 0.03 -0.1* -0.1* 0.02 0.31* 0.29* 0.16* 0 0.15* 1

8) Combined training 3,99 3,38 0 -0.1* -0.1* -0.1* -0.2* -0.2* -0.2* -0.2* 0.1* -0.1* 1

9) Nr.of conferences 0,49 1,22 0.03 -0.1* -0.03 -0.1* 0.29* 0.32* 0.1* 0.08* -0.02 0.5* -0.2* 1

10) Cross-specialty

training 0,92 0,28 0.16* 0.02 0.03 -0.4* -0.03 -0.04 -0.1* -0.2* 0.11* 0.03 0.35* 0.12* 1

11) Serial idea generator 0,94 0,84 -0.1* 0.1* 0.02 -0.06 0.01 -0.01 -0.1* -0.1* -0.06 0.17* -0.1* 0.22* -0.01 1

12) Department 4,81 1,33 0.08* 0 0.10* -0.02 -0.1* -0.1* 0.03 -0.02 0.13* 0.01 0.13* -

0.12* -0.07 0.01 1

13) Business Line 6,83 4 -0.06 -0.2* -0.2* -0.03 0.11* 0.12* 0.01 0.07* -0.1* 0.03 -0.01 0.07 0.05 0.01 -0.4* 1

14) Employee-user

proposal 0,06 0,24 0.21* 0.35* 0.25* 0 -0.07 -0.07 0 -0.02 0.08* -0.03 -0.04 -0.1* 0.02 -0.07 0.15*

-

0.15* 1

15) Level of

development 1,79 0,51 0.08* 0.21* 0.21* -0.02 -0.1* -0.1* -0.1* -0.07 0.01 -0.05 -0.02 -0.06 0 -0.01 0.11* -0.3* 0.27* 1

16) WordCount 199,39 187,17 0.02 -0.1* 0.01 0.04 0 0.01 -0.02 -0.01 0.07 0.22* 0.15* 0.12* 0.09* -0.1* 0.23* -0.1* 0.07 0.07 1

17) Teamwork 0,11 0,31 0.05 0 0.03 -0.04 -0.1* -0.1* -0.01 -0.01 0.05 -0.06 0.04 0 0.06 0.07 -0.1* 0 -0.02 0 0 1

18) Client 1,74 1,24 -0.05 -0.1* -0.1* -0.1* 0.08* 0.08* 0.01 0 0.01 0.11* -0.02 0.25* 0.07* 0.13* -0.01 -0.06 -0.1* -0.1* 0.12* -0.0 1

19) Proposal's type 3,92 1,75 0 -0.1* -0.1* 0.09* 0.03 0.03 0.02 0.04 -0.01 -0.03 0.19* -0.04 -0.06 0 0 0.35* -0.1* -0.1* -0.05 -0.0 -

0.2* 1

20) Year 0,6 0,49 -0.1* 0.07* 0.03 0.09* -0.04 -0.05 -0.05 -0.06 0.01 0.04 0.14* 0.03 -0.01 0.28* 0.02 -0.03 -0.1* 0.04 -0.1* 0.02 -

0.1* 0.07 1

36

Figure 1 Algorithm for classifying innovation proposals into: employee-user proposal (EUP), and employee-non-user proposal (ENUP)

Employee-user Proposal (EUP)

Employee-Non-User proposal (ENUP)

YES YES YES

Is it a description of the author’s own project (as

opposed to a project set by the company); author

proposes something which he/she designed (or wants

to be designed) for the benefit of using the

outcome?

For the benefit of using the outcome?

The innovation proposal is describing

something beyond normal job expectancy?

(It was not expected from the author(s) to propose/

make what was proposed to earn his/her salary)

Beyond normal job expectancy?

Was there any other type of payment to the author to

work on the idea?

Was the author paid to do it?

NO

NO

YES

Did the author try to materialize the idea (in a

form of a solution specification, developing a

part of a solution, or the whole solution) prior to

submitting the proposal to the system?

Was there any attempt of materialization?

NONO

Table 3 Innovation proposal type by employee-user innovation; ENUP: employee-non-user proposal; EUP: employee-user proposal;

Nature of the proposed ideas ENUP EUP Row total:

Product improvement 136 17 153

New product proposal 35 4 39

Service improvement 35 6 41

New service proposal 92 5 97

Internal process, method or tool 349 14 363

Working environment related idea 43 0 43

Other 23 0 23

Column total: 713 46 759

Column percentage of 759: 92.89% 6.06% 100%

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Table 4 Multinomial Logit estimates of likelihood of the Extent of Change

Model (1) Model (2) Model (3)

Outcomes Low-impact

High-impact

Low-impact

High-impact

Low-impact

High-impact

Independent variables

Innovation proposal related

variables

Employee User Proposal -2.76** 0.82** -2.48** 0.96* -2.17* 1.15*

Word Count, Team Work, and Market demand fixed effects

Yes Yes Yes Yes

Idea Type 2 (New product proposal) (product improvement is the reference)

-0.59 2.80*** -0.69 2.95***

Idea Type 3 (Service improvement) 1.04**

-0.68*

2.62***

2.35***

0.35

-0.76

1.19

2.35*** Idea Type 4 (New service proposal)

Idea Type 5 (Internal process, method or tool) 0.76** 1.51*** 0.17 1.80***

Idea Type 6 ( working environment related idea) 1.75*** 2.95*** 1.22 2.94**

Idea Type 7 (other) 0.67 2.11*** -0.13 1.58

Extent of development – specification of a solution (omitted is the idea only)

-0.63*** -0.43* -0.19 0.14

Extent of development –prototype developed -1.03 -0.16 -0.16 0.17

Author, Organization,

and time related

variables

Years of Tenure 0.06 -0.02 0.09 0.00 0.10 0.07

Years of Tenure Squared -0.00 0.00 -0.00 0.00 -0.00 -0.00

Gender, Age, Academic education, Cross Specialty Training, Number of Conferences attended, Managerial level, Serial idea generator (controls)

Yes Yes Yes Yes Yes Yes

System Department, business lines. Year fixed effects,

No No No No Yes Yes

Constant

-0.92

-3.94 -2.44 -5.65 -23.50 -4.98

Observations 759 759 759 759 759 759

McFadden's pseudo R2

0.116 0.116

0.197

0.197

0.443

0.443

Log-likelihood -711.1 -711.1 -645.5 -645.5 -448.1 -448.1

Raw factor shown; * p<0.1; ** p<0.05; *** p<0.01

Table 5 Innovation proposal status (implemented) by employee-user innovation; ENUP: employee-non-user proposal; EUP: employee-user proposal

ENUP EUP Row Total

Implemented 190

(81.5%)

43

(18.5%)

233

(100%)

Not implemented 523

(99.4 %)

3

(0.6%)

526

(100%)

38

Table 6 Logit estimates of likelihood of a proposal reaching status implemented a b

Group of variables related to

(1-1) Base

(1-2) Proposal

(1-3) Proposal &

Author

(1-4) Prop., Author

& System

(2-4) Prop., Author

& System

Dependent variable; Probability of idea being: implemented accepted

Innovation proposal

Employee User Proposal 39.45*** 40.46*** 49.19*** 68.05*** 21.07***

Word Count, Teamwork, Market demand fixed-effects 1.00*** 1.00* 1.00 1.00

Proposal Type 2 (New product proposal) (product improvement is the reference)

0.76 0.55 0.62 1.34

P. Type 3 (Service improvement) 1.07 1.32 2.13 1.92

P. Type 4 (New service proposal) 0.24*** 0.19*** 0.18*** 0.52*

P. Type 5 (Internal process, method or tool)

0.68 0.69 0.92 0.84

P. Type 6 ( working environment related idea)

0.31** 0.32** 0.69 0.91

P. Type 7 (other) 0.09** 0.10** 0.21 0.34

Extent of development – specification of solution (omitted is the idea only)

1.76** 1.77** 1.42 1.57**

Extent of development – with developed solution (prototype)

4.32*** 4.29*** 2.38 3.31**

Author

Years of Tenure 1.17* 1.26** 1.06

Years of Tenure Squared 0.99 0.99 1.00

Managerial level – supervisor / tech expert (non-supervisor is omitted category)

0.72 0.80 1.25

Managerial level – manager

0.99 1.33 1.56

Gender, Age and Age Squared, Academic education, Industrial and Cross Specialty Training, Number of Conferences attended, Serial idea generator (controls),

Yes Yes Yes

Moderate serial idea generator (regular idea generator is omitted category)

1.10 1.37 0.89

High serial idea generator 1.92*** 1.22 0.90

System Department, Business lines, and Year fixed effects Yes Yes

Constant 0.36*** 0.63 0.00 0.03 0.00

Observations 759 759 759 759 759

McFadden’s pseudo R2 0.09 0.17 0.24 0.32 0.21

Log-likelihood -424.4 -386.0 -357.4 -318.3 -414.9

BIC 862.1 878.1 920.4 948.3 1168.1 a Odds ratios shown; b Regression performed with robust variance estimator; *** p<0.01, ** p<0.05, * p<0.1