org innovation and its effects

Industrial and Corporate Change, Volume 21, Number 5, pp. 1283–1305

doi:10.1093/icc/dts023

Advance Access published August 11, 2012

Organizational innovation and its effects

Koson Sapprasert*,**,z and Tommy Høyvarde Clauseny

Organizational innovation, its persistence, its relationship with technological in-

novation, and their influence on firm performance remain under-researched.

These issues are investigated using an integrated firm-level data set obtained

from two recent waves of the Norwegian Community Innovation Survey (CIS 3

and 4) and firms’ financial accounts. A Heckman two-step estimation, to ensure

against potential sample selection bias, was used to demonstrate that between

1999 and 2004 a number of Norwegian firms were persistent in organizational

innovation, and this persistence raised the (positive) effects of organizational in-

novation on their performance. The results also reveal that many firms undertook,

and benefited from, different types of organizational innovation, and such bene-

fits were increased by the combinative effect of organizational and technological

innovation. The study also found that older and larger firms are more inclined to

attempt organizational change, while smaller firms are more able to benefit.

JEL classification: L25, O21, O39.

1. Introduction

Recent decades have seen a remarkable increase in scholarly attention devoted to

innovation (Fagerberg, 2004; Fagerberg and Verspagen, 2009; Fagerberg and

Sapprasert, 2010). Most research has focused on understanding product and process

innovation (Damanpour et al., 2009). By contrast, research on organizational innov-

ation has been limited (Schmidt and Rammer, 2005; Damanpour et al., 2009) be-

cause of a lack of empirical data, established definitions and measurement constructs

(Lam, 2004).

*Koson Sapprasert, Centre for Technology, Innovation and Culture, University of Oslo, Postbox

1108, Blindern, N-0317 Oslo, Norway. e-mail: [email protected]

**Koson Sapprasert, School of Entrepreneurship and Management, Bangkok University, 10110,

Bangkok, Thailand. e-mail: [email protected]

yTommy Høyvarde Clausen, Nordland Research Institute, 8049 Bodø, Norway.

e-mail: [email protected]

z Main author for correspondence.

� The Author 2012. Published by Oxford University Press on behalf of Associazione ICC. All rights reserved.

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This potentially constrains our understanding of the impact of (different types of)

innovation on economic growth and performance at the firm and country levels,

particularly as technological and non-technological change are complementary and

co-evolve (Nelson, 1991; Freeman, 1995). Organizational innovation can enable and

even enhance the effect of technological innovation on firm performance (Chandler,

1962; Lam, 2004). As Wischnevsky et al. (2011: 133) argue, “administrative processes

are key in enabling organizations to launch new products and services and to manage

change”.1

By taking advantage of a new data set on organizational innovation, this article

aims to improve our understanding of organizational innovation and its perform-

ance effects by analyzing: (i) the influence of prior organizational innovation

on current organizational innovation, (ii) the influence of prior organizational

innovation on the performance effects of current organizational innovation, and

(iii) the combined effect of technological and organizational innovation on firm

performance.

In doing so, the article moves from previous qualitative studies to undertake a

quantitative study that contributes to the literature in four ways. First, the article uses

a definition and measurement construct of organizational innovation that has the

potential to standardize the different definitions and measurement constructs used in

previous research (see, Wolfe, 1994; Lam, 2004). Second, the article extends prior

cross-sectional studies (e.g. Schmidt and Rammer, 2005) by adopting a more longi-

tudinal approach as suggested by Damanpour et al. (2009). Third, the article includes

a preliminary analysis of the persistence of organizational innovation and its effects

on performance extending prior works on technological innovation (Geroski et al.,

1997; Peters, 2009; Clausen et al., 2012) and prior studies that have used

industry-specific samples (e.g. Wischnevsky et al., 2011). Fourth, the article examines

the combined effects of technological and organizational innovation on firm per-

formance (Damanpour et al., 2009).

The remainder of the article is organized as follows. Section 2 explores how or-

ganizational innovation has been understood in the literature, and how it can be

measured in applied empirical work. Section 3 provides the theoretical background

and hypotheses. Section 4 presents data and methods. Section 5 discusses descriptive

statistics and empirical findings from the econometric analysis, while Section 6 pro-

vides a discussion and conclusion.

1The authors defined change in administrative processes as change in the social system, related

mainly to the organization’s administrative domain. This includes shifts in structure, policies,

reward systems, labor relations, control systems, and coordinating mechanisms of the organization,

which affect the knowledge and procedures used in performing the work of management.

1284 K. Sapprasert and T. H. Clausen

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2. A note on organizational innovation

Despite its acknowledged importance, organizational innovation has generally

received less attention in the literature than technological innovation. When looking

at the scholarly contributions to innovation studies (see Fagerberg, 2004; Martin,

2008; Fagerberg and Sapprasert, 2010), few prominent studies consider the import-

ance of organizational innovation.

One reason for this is that “the relationship between organization and innovation

is complex [. . .and]” there is no single coherent theoretical framework for under-

standing the phenomenon of “organizational innovation” (Lam, 2004: 31–32). This

is partly due to the ambiguity surrounding the term “organizational innovation

[. . .as] no consensus definition of the term organizational innovation [exists]”

(Lam, 2004: 31–32).

A second related reason is that the literature has lacked a clear way of operatio-

nalizing “organizational innovation” as a variable (Wolfe, 1994; Lam, 2004). This has

generated a series of inconsistent research findings and constrained the cumulative

development of knowledge on the topic. As Wolfe (1994: 405) highlights: “the most

consistent theme found in the organizational innovation literature is that its research

results have been inconsistent”. Thirdly, the lack of statistics and data has hindered

research on organizational innovation. Unlike technological innovation which is

widely examined using patent and R&D data, organizational innovation is less tan-

gible in character, and is not measured in a consistent manner across industries and

countries.

To address these problems, this article uses the definition and empirical opera-

tionalization of “organizational innovation” in the Community Innovation Survey

and Oslo Manual (OECD, 2005), which have become established standards for in-

novation measurement (See Smith, 2004 for a review). The third revision of the Oslo

Manual (OECD, 2005) as implemented in the fourth Community Innovation Survey

(for Norway), defines “organizational innovation” as “a new or significantly im-

proved knowledge management system implemented to better use or exchange in-

formation, knowledge, and skills within the firm” and/or “a major change to the

organization of work within the firm, such as change in the management structure or

the integration of different departments or activities” and/or “a new or significant

change in the firm’s relationships with other firms or public institutions, such as

through alliances, partnerships, outsourcing or sub-contracting”.

This definition differs from the one used in Damanpour (1991) and Sorensen and

Stuart (2000), and is rather narrowly defined as innovative change in a non, or rather

less, technological manner to a firm’s nature, structure, arrangement, practices, be-

liefs, rules or norms (see also Pettigrew and Fenton, 2000), which may be subsumed

under Schumpeter’s “new ways of organizing business”. This is worth noting because

different lines of research apply this term in different ways (Lam, 2004; Sapprasert,

2009). For example, organizational innovation is often more broadly defined in

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management/organization studies as an adoption of “any” novelty in an organization

(see, for example, Evan, 1966; Daft, 1978; Teece, 1980; Kimberly and Evanisko, 1981;

Damanpour, 1987, 1991).2 More specifically, Edquist et al. (2001) distinguish be-

tween “technical” and “organizational” process innovation. However, “our defin-

ition” has the advantage of being increasingly standardized.

3. Theoretical background and hypotheses

The evolutionary economics perspective highlights how “organizational routines”—a

“general term for all regular and predictable behavioral patterns of firms” (Nelson

and Winter, 1982: 14)—define the fundamental ways things are done in firms. As

time passes, some of the prevailing routines may become less effective (Dosi and

Nelson, 1994) requiring firms to change them to survive, be competitive and grow

(Romanelli and Tushman, 1994; Wischnevsky et al., 2011). Organizational change is

enabled through higher–order search routines which alter, add, and delete “ordinary

routines” within the firm (Nelson and Winter, 1982: 128).

Given the role of innovation in firms’ adaptation, a great deal of research has been

undertaken on firms’ innovativeness and the antecedents of their technological prod-

uct and process innovations (Damanpour et al., 2009). Although research on product

and process innovation is important (see Hauser et al., forthcoming; van der Panne

et al., 2003; Becheikh et al., 2006 for reviews), research on the role of organizational

innovation has been largely neglected. As Damanpour et al. (2009: 651) argue:

“Historically, research on innovation types has followed a technological impera-

tive. . . [and has]. . .focused on a narrow definition of product and process innov-

ations associated with the R&D function in manufacturing organizations [. . .]

Studies of organizational or administrative innovations have been relatively scarce”.

This can be seen in Nelson and Winter’s (1982) seminal work where they write:

“we also are very strongly sympathetic with the proposition that firm organization is

an important variable for analysis in its own right. There are strong connections both

between the techniques commanded by a firm and its organization. Largely in the

interests of establishing an understandable linkage between individual firm behavior

and industry behavior, our formal models in this book suppress considerations of

internal structure and organizational change. But in principle, an evolutionary theory

can treat organizational innovation just as it treats technical innovation” (37–38,

emphasis added).

However, there has been limited research on the antecedents and performance im-

plications of organizational innovation, or on the interactions between organizational

and technological innovation (Damanpour et al., 2009; Wischnevsky et al., 2011).

2The term “administrative innovation” is used as opposed to “technical innovation” in this line of

research.


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Recent studies have started to address these issues. Boer and During (2003), for

example, compare and contrast findings from three empirical studies on product,

process, and organizational innovation (measured as the implementation of Total

Quality Management), in different industries in the Netherlands, UK, and Belgium

to show that “the differences between product, process, and organizational innov-

ation processes are surprisingly few” (103).

Similarly, a large scale quantitative study of German manufacturing and service

sectors by Schmidt and Rammer (2005) found that technological and

non-technological innovation were often linked to each other, and had similar de-

terminants, suggesting that the decision to innovate was driven by similar factors.

They also found that firms that combined product and process innovations with

marketing and organizational innovations performed better in terms of innovation

sales and process innovation driven cost reductions (conditional upon the adoption

of both organizational and marketing innovations). A combination of product with

organizational innovation had a significant and positive effect on firm profitability,

although the firms that reported the highest effects on profit margins only intro-

duced technological innovations.

Overall the study by Schmidt and Rammer (2005) suggests that firms tend to

adopt both technological and non-technological innovation and their determinants

are roughly the same, but the performance implications of adopting different types of

innovation are more complex. Some of this complexity may be due to the

cross-sectional research design as the introduction of one innovation at a single

point in time may not matter for firm performance as much as the innovative history

of the firm (Roberts and Amit, 2003; Damanpour et al., 2009), underscoring the need

to study firms over time (Damanpour et al., 2009).

Adopting a longitudinal approach, Wischnevsky et al. (2011) found in a study of

bank holding companies that changes in products are followed by changes in both

technological and administrative processes and that the three types of change

(change in products, change in technological process, and change in administrative

process) exhibit momentum, as firms were more likely to implement changes similar

to those recently undertaken. This is consistent with recent studies on the persistence

of technological innovations (for example, Flaig and Stadler, 1994; Crepon and

Duguet, 1997; Peters, 2009)3 which explore a firm’s probability to innovate over

time (Clausen et al., 2012). Based more or less implicitly on a linear view of innov-

ation, innovation persistence can be seen as a result of sunk costs (Sutton, 1991).

This does not necessarily contradict the evolutionary view (Nelson and Winter, 1982;

Dosi, 1988) as a firm can persist in innovating by learning and collecting knowledge

3To the authors’ knowledge, the present study is probably one of the first research attempts which,

in part, looks at the topic of persistence of innovation in an organizational aspect. See, for instance,

Raymond et al. (2006) and Clausen et al. (2012) for detailed discussions of research on the topic of

persistence of technological innovation.


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that furthers its innovative capability. Because of the cumulative nature of learning

itself (Rosenberg, 1976), a firm might be able to extend and use this capability to

develop new products or processes (Raymond et al., 2006), as well as to improve its

organizational routines, at decreasing marginal costs (Amburgey et al., 1993). As

Amburgey and Miner (1992) and Kelly and Amburgey (1991) argue, organizational

change may then become a self-reinforcing process. We therefore put forth the fol-

lowing hypothesis:

H1: Past attempts at organizational innovation increase the probability of(new) attempts at organizational innovation

However, research has also demonstrated that factors which may enable innovation at

some stages in the innovation process may act as a deterrent at other stages (Wolfe,

1994) as changes in organizational routines can disrupt performance (Hannan and

Freeman, 1984). Persistent organizational innovation may therefore be disadvantageous

and decrease a firm’s performance, particularly for firms which change frequently and

do not fix the problems which arise from disruption (Amburgey et al., 1993).

An alternative perspective understands innovative persistence as a process of “cre-

ative accumulation” (Schumpeter, 1942) whereby knowledge obtained from past

innovation(s) supports new innovations. Firms learn (to change) by changing, as

in conformity with “learning by doing” (see, for example, Arrow, 1962; Nelson and

Winter, 1982; Dosi, 1988). This also means that having changed, firms may be more

able to routinize change (Kelly and Amburgey, 1991) by developing a “modification

routine” (Nelson and Winter, 1982; Aldrich, 1999) and adapt to their changing

environments. Hence, persistent organizational innovators may be more capable of

effectively reorganizing repeatedly, and may benefit more from doing so. Malerba

and Orsenigo (1999), for example, show that firms which persistently innovate pos-

sess an advantage in consistently improving their performance.

Much of this research has focused on firms’ persistent ability to develop techno-

logical innovations and its performance effects. Our knowledge about the perform-

ance effects of organizational innovation, including the role of prior organizational

innovation, remains scarce (Damanpour et al., 2009; Wischnevsky et al., 2011). Prior,

mainly cross-sectional, research has suggested that technological and non-

technological innovation share many of the same determinants and driving forces

(Boer and During, 2003; Schmidt and Rammer, 2005). Based on the literature on

persistence of technological innovation and its performance effects we thus put forth

the following hypothesis:

H2: Persistent organizational innovation increases the effects of (current)

organizational innovation on firm performance

As was argued earlier, technological and organizational innovation have complemen-

tary effects on firm performance (Chandler, 1962; Nelson, 1991; Sapprasert, 2007; see


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Bruland and Mowery, 2004 for historical evidence on the steam engine). This joint

contribution still seems to be important as firms reorganize their businesses to ex-

ploit the introduction and diffusion of Information and Communication Technology

(ICT) (Brynjolfsson and Hitt, 2000, 2003; Bresnahan et al., 2002; Brynjolfsson et al.,

2002). Because information processing and transfer can be significantly improved by

ICT, it allows more decentralization and task delegation (Brynjolfsson and

Mendelson, 1993).

Quantitative research on the combinative effects of different types of innovations

on firm performance remains scarce (Damanpour et al., 2009). In one of the few

studies conducted, Damanpour et al. (2009) analyzed organizational performance

from adopting compositions of innovations (service, technological process, and ad-

ministrative process) over time among public service organizations. Their findings

showed that divergence from the industry norm in the adoption of innovation types

could be beneficial to organizational performance. Extending this line of research

within the context of for-profit firms in the manufacturing and service sector, we put

forth the following hypothesis in relation to the combinative effect of technological

and organizational innovation on firm performance:

H3: Technological and Organizational innovation have a complementary effecton firm performance

4. Data, method, and variables

To test these hypotheses we use a unique firm-level data set that integrates annual

financial accounts (1999–2004) and two Norwegian Community Innovation Surveys,

CIS3 (1999–2001) and CIS4 (2002–2004) which include information on organiza-

tional innovation and marketing innovation, building on the third revision of the

Oslo manual (OECD, 2005), and include items/questions for their measurement (see

Schmidt and Rammer, 2005 for a general discussion).

This data can be used to study organizational innovation at the firm-, industry- or

country-level, but cannot be broken down to analyze at the plant- or project-level.

This may introduce bias, for example, in relation to firm size where larger firms may

have a higher probability to report that they are (organizational) innovators. The

reader should be aware of this issue, although the relationship between firm size and

organizational innovation is not the primary interest in this article.

Statistics Norway prepared and supplied the CIS and financial data sources. The

CIS3 questionnaire was distributed to a representative set of firms registered in

Norway with at least 10 employees. A total of 3899 firms completed and returned

the questionnaire, which constituted a high response rate of 93%. This survey was

followed 3 years later by the CIS4, which had a 95% response rate (from 4655 firms

with 10 employees or more). Information on the financial accounts of firms in

Norway is collected annually and is available for a large share of these respondents.


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The three sources were then combined, and the resulting data set contains around

1700 respondent firms in the manufacturing, service, and other industries (see

Table 1). Since this number of firms refers to an overlap of 430% of firms from

the three sources, the data set seems to be reasonably representative.

In order to examine the determinants and effects of organizational innovation on

the basis of this integrated data set, the following two-step model was constructed:

ORG ¼ PASTORGþ PASTPERFþHAMPiþ SIZEþ AGEþ IND ð1Þ

EFORG ¼ PASTORGþ INCOMPþ SIZEþ AGEþ IND ð2Þ

ORG ¼ Dummy for the attempt at organizational innovation (2002–2004)

EFORG ¼ Factor score for six types of effects of organizational innovation

ð2005; see more description belowÞ

PASTORG ¼ Dummy for the past attempt at organizational

change (1999–2001)

Table 1 Firms’ age, size, sector, and organizational innovation (2002–2004)

No. of

firms

Organizational

innovator

ORGSYS ORGSTR ORGREL 1 type of

change

2 types of

change

3 types of

change

Sector

Manufacturing 947 0.35 0.18 0.28 0.12 0.16 0.15 0.03

Services 580 0.37 0.20 0.28 0.15 0.17 0.13 0.07

Others 210 0.29 0.17 0.22 0.09 0.14 0.12 0.03

Age

Age1 557 0.41 0.22 0.32 0.16 0.18 0.16 0.07

Age2 591 0.32 0.14 0.25 0.11 0.17 0.12 0.03

Age3 589 0.33 0.19 0.25 0.12 0.14 0.16 0.03

Size

Emp1 611 0.27 0.14 0.20 0.09 0.13 0.10 0.03

Emp2 477 0.32 0.17 0.23 0.10 0.17 0.10 0.04

Emp3 649 0.46 0.23 0.37 0.18 0.19 0.21 0.06

Turn1 585 0.28 0.14 0.20 0.09 0.14 0.11 0.03

Turn2 589 0.33 0.16 0.25 0.10 0.18 0.11 0.04

Turn3 563 0.46 0.25 0.37 0.19 0.17 0.22 0.07

Total 1737 0.35 0.18 0.27 0.13 0.16 0.14 0.04


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PASTPERF ¼ Past performance in terms of profitability growth (1999–2001)

HAMPi ¼ Hampering factors (2002–2004; see more description below)

INCOMP ¼ Dummy for the joint contribution of technological and

organizational innovation (2002–2004; see explanation belowÞ

SIZE ¼ Firm size in terms of employment (LogEmp) and turnover (LogTurn)

AGE ¼ Firm age (LogAge)

IND ¼ Dummy for industrial classifications (NACE)

Because only those firms which reported to the CIS 4 that they had undertaken

organizational innovation between 2002 and 2004 were allowed to answer the ques-

tion about its effects, i.e. since only organizational innovators are included in (2), it is

important to inspect for the potential of sample selection bias when analyzing this

data. Thus, two-step estimate, which can indicate the existence/significance of this

bias, is employed (see for example, Heckman’s (1979); Zucker et al., 1998; Hall, 2002;

Catozzella and Vivarelli, 2007).4 Based on this estimate, the selection equation

explains whether, and the extent to which, the independent variables included in

Stage 1 affect firms’ decisions to undertake organizational innovation (ORG), while

the outcome equation examines the influence of the independent variables included

in Stage 2 on the outcome of such an undertaking (EFORG).

The variables of interest in this Heckman two-step procedure are organizational

innovation (ORG), its effects (EFORG), past/persistent organizational change

(PASTORG), past performance (PASTPERF), hampering factors (HAMP), the com-

plementarity of organizational and technological innovation (INCOMP), firm size

(SIZE), firm age (AGE), and industry dummies (IND). The measure of organiza-

tional innovation (ORG), employed as a dependent variable in the selection equation

(Stage 1), is obtained from the answers to the question in the CIS4 which asks

whether or not, between 2002 and 2004, the firm introduced organizational innov-

ation, defined as being a new or significant change in the firm’s structure or man-

agement methods seeking to improve the firm’s use of knowledge, quality of goods

or services, or workflow efficiency. The three types of organizational innovation

concerned in the survey are: (i) a new or significantly improved knowledge man-

agement system implemented to better use or exchange information, knowledge, and

skills within the firm (ORGSYS); (ii) a major change to the organization of work

within the firm, such as change in the management structure or the integration of

different departments or activities (ORGSTR); and (iii) a new or significant change

in the firm’s relationships with other firms or public institutions, such as through

4Since the Heckman results show no sign of selection bias, the OLS (Ordinary Least Square) esti-

mation is also used in the second stage experiment. Three types of organizational innovation

(ORGSYS, ORGSTR, and ORGREL) are added, in order to examine their potentially differential

impacts. See below.


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alliances, partnerships, outsourcing or sub-contracting (ORGREL). It is useful to

have details of the contents of change within the firm, as well as linkages between

the firm and external actors.5 Based on the three measures, a dependent variable

ORG for Stage 1 (Probit) is constructed.6 ORG equals one if the firm has a positive

answer for at least one of the three types of organizational innovation, and zero

otherwise.

The variable used to assess the impact of these three types of organizational

innovation is based on the next question in CIS4, which inquired (in 2005) about

the effects of such innovation.7 As mentioned earlier, only the firms which carried

out organizational innovation, i.e. for which ORG¼ 1, shall respond to the question

about its effects. This question asks the firm to rate (from 0 to 3) the importance of

six types of effects: (i) reduced response time to customer needs; (ii) improved

quality of goods or services; (iii) reduced costs per unit output; (iv) improved em-

ployee satisfaction and/or reduced employee turnover; (v) increased enterprise cap-

acity; and (vi) higher enterprise profitability. This information is deemed suitable for

use in investigating the effects of organizational change, as it seems to meet the two

criteria suggested by Barnett and Carroll (1995), i.e. it captures the effects at the firm

level and is broadly applicable (for example, not specific to one or only a few

industries or business categories). A factor analysis was conducted for the six meas-

ures (see Table A1). One factor was retained from this, and the factor score for each

firm is used as a dependent variable (EFORG) in the outcome equation, which

examines how the effects of organizational innovation are influenced by the pre-

dictors included in Stage 2.

Several explanatory variables are employed in the selection and outcome equation.

It should be noted that some, but not all,8 of them are taken into account in both

stages. These include PASTORG, used to determine the influence of prior organiza-

tional change (between 1999 and 2001) on the probability of another attempt at

organizational change by the firm between 2002 and 2004 (ORG) in Stage 1 (testing

5See Barnett and Carroll (1995) for a good discussion on the process and content of organizational

change.

6ORG is applied because this Heckman estimation can have only one dependent variable in a binary

format (0 or 1) in the selection equation (Stage 1). This means that such a variable (ORG in this

case) cannot be a measure of the “scale” of organizational innovation and, thus, does not (to a great

extent) explain its heterogeneity.

7It is important to emphasize that, although the information on organizational innovation and its

effects both come from the CIS4 (2002–2004) which may seem to provide somewhat little time for

the effects to be realized and thus have a “causality” problem, the question on the effects of

organizational innovation was designed to be rather explicit by asking the respondent firms to

evaluate in 2005 “the effects of organizational innovation introduced” between 2002 and 2004.

The Norwegian CIS4 questionnaire was sent out �6 months after the year of reference (2004).

8This is because of a requirement associated with this regression technique (Heckman, 1976, 1979).


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H1). As explained earlier, since only the organizational innovators between 2002 and

2004 (ORG¼ 1) are included Stage 2, PASTORG is used also in the outcome equa-

tion to assess the extent to which the combined prior and current efforts at orga-

nizational change (between 1999 and 2001 and between 2002 and 2004, i.e.

persistence of change) increased the effects of organizational innovation felt in

2005, EFORG (testing H2). In other words, this variable, employed in both equa-

tions, helps to answer two questions: to what extent were the sampled firms persist-

ent in organizational innovation? And to what extent did those who were benefit

more from being so? PASTORG, constructed on the basis of the CIS3 data, has a

value equal to one if the firm has introduced change between 1999 and 2001 in at

least one of the following types related to reorganization: corporate strategies, man-

agement techniques, and organizational structures.

The age and size of a firm are also taken into account in both equations as

important control variables, since older firms are not necessarily larger than younger

firms, and vice versa (Penrose, 1959).9 Based on the information from the financial

accounts, the explanatory variables for firm age and size are created and included in

both Stages 1 and 2. Firm age (LogAge) is calculated as the log value of the time

period between the year the firm was established and 2001 (the last year before

entering the period of main interest, i.e. 2002–2004). Firm size is measured on the

basis of information about the number of employees (LogEmp) and the firm’s total

turnover (LogTurn) in 2001.10 Also, industrial classification dummies (IND), con-

structed from the CIS3 information, are employed in both stages to control for the

influence of industry heterogeneity on the firm’s propensity to innovate, as well as on

its effects. IND equals one if the firm belongs to the respective industry (classification

based on the standard NACE code), and zero otherwise.

PASTPERF and HAMP are included in the selection equation (Stage 1).

PASTPERF, measured based on the financial accounts data as firm growth in prof-

itability (profit per employee) between 1999 and 2001, captures a recent change in

the firm’s economic performance since performance variation usually induces the

firm to change (Cyert and March, 1963; Greve, 2003). HAMP represents three types

of obstacles to organizational change perceived by the sampled firms between 2002

and 2004. These include high innovation costs (HCOST), a lack of funds (HFUND),

and a lack of qualified personnel (HPER), which are often regarded as factors which

9See Table A2 for a simple correlation test between firm age and size (in terms of both total

turnover and number of employees).

10Having both of these proxies is advantageous since they possibly explain the size of the firm in

different dimensions. That is, while LogEmp is deemed to relate more to the scale of human

resource, and may thus better depict a degree of complexity/hierarchy of the firm’s structure,

LogTurn represents the size of the firm in terms of financial capacity. A simple correlation test

conducted shows that turnover does not necessarily very strongly correlate with the number of

employees (see Table A2).


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affect innovation in the literature (see for example, Kline and Rosenberg, 1986; Galia

and Legros, 2004). Using information from the CIS4, the three proxies are con-

structed from the firm’s rating (from 0 to 3) of the importance of these three im-

pediments to innovation.11

Finally, since all the firms included in Stage 2 were organizational innovators

between 2002 and 2004 (firms with ORG¼ 1), a dummy for technological innov-

ation in terms of new or significantly improved product(s) or process(es)

(INCOMP) between 2002 and 2004 is simply used to measure the joint contribution

of technological and organizational innovation in Stage 2 (testing H3), i.e. INCOMP

is equivalent to the result of multiplying itself by ORG (which always equals one in

this stage). This variable, applied to examine their combinative effect on firm per-

formance (EFORG), is extracted from the CIS4 data on technological innovation,

and equals one if the firm introduced at least one product or process innovation

between 2002 and 2004. Table A2 provides a correlation matrix for the explanatory

variables employed, with no indication of a multicollinearity problem.

5. Analysis

The descriptive statistics in Table 1 demonstrate that more than one-third of the

firms in the sample are organizational innovators (having introduced at least one

type of organizational innovation between 2002 and 2004). In terms of the descrip-

tive picture of heterogeneity of organizational innovation (the three measures of

organizational innovation obtained from the CIS4), change in the firm’s structure

(ORGSTR) is the most common, followed by change in the firm’s knowledge man-

agement systems (ORGSYS) and change in the firm’s external relations (ORGREL),

respectively, regardless of the firm’s age, size, and sector. The results from Table 1

also show that only a small share of firms undertook all of the changes considered.

Table 2 contains the descriptive statistics of other variables in the data set. The

results demonstrate that 450% of the firms had carried out organizational change

between 1999 and 2001, and many of these had made another attempt at organiza-

tional change between 2002 and 2004 (supporting H1). Contrary to, for example

Geroski et al. (1997) and Cefis and Orsenigo (2001), who found a rather low per-

sistence of technological innovation using patent information, almost one-quarter of

the sampled Norwegian firms were persistent in organizational innovation between

1999 and 2004. However, the present study finds that technological innovation

(product/process) was more common than organizational innovation within the

11These three variables were selected on the basis of their relevance to organizational innovation

(those related only to technological innovation were excluded, for example, a lack of information on

technology and an uncertain demand for innovative goods and services), their significance during

models tests, and their uniqueness reported in the results of the factor analysis (not reported here;

available upon request).


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sample between 2002 and 2004 (47 percent of the firms reported undertaking

technological innovation, compared with the 35 percent which adopted organiza-

tional innovation). When comparing across sectors, it can be seen that a greater share

of manufacturing firms engaged in technological innovation, while a greater share of

service firms were active in organizational innovation between 2002 and 2004, which

is consistent with previous findings.12

The results of the econometric analysis are displayed in Table 3. First, considering

the lower part of the first two columns (Model I with LogEmp and Model II with

LogTurn), the Heckman Stage 1 (with ORG as a dependent variable) results provide

some evidence of persistence of organizational innovation in line with the descriptive

statistics in Table 2 and recent studies, such as Crepon and Duguet (1997) and Peters

(2009) in the context of technological innovation. Prior organizational change be-

tween 1999 and 2001 influences the probability of another attempt at organizational

Table 2 Firms’ age, size, sector, organizational, and technological innovation

No. of

firms

Organizational

innovator

(2002–2004)

Past

organizational

change

(1999–2001)

Organizational

innovation persistence

(1999–2001 and

2002–2004)

Technological

innovation

(2002–2004)

Sector

Manufacturing 947 0.35 0.50 0.23 0.54

Services 580 0.37 0.55 0.24 0.42

Others 210 0.29 0.50 0.19 0.26

Age

Age1 557 0.41 0.57 0.28 0.49

Age2 591 0.32 0.48 0.19 0.46

Age3 589 0.33 0.50 0.22 0.45

Size

Emp1 611 0.27 0.46 0.16 0.41

Emp2 477 0.32 0.46 0.21 0.48

Emp3 649 0.46 0.61 0.31 0.51

Turn1 585 0.28 0.44 0.17 0.42

Turn2 589 0.33 0.49 0.20 0.47

Turn3 563 0.46 0.63 0.32 0.51

Total 1737 0.35 0.52 0.23 0.47

12As usually argued in the literature on service innovation (for example, Evangelista, 2000; Miles,

2004; Sapprasert, 2007), non-technological and intangible characteristics of services are very sig-

nificant and particularly linked to organizational change.


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innovation by firms between 2002 and 2004 (ORG), which supports H1. This can be

seen from the significant positive coefficients of PASTORG (Past Organizational

Change) in Models I and II (0.832 and 0.794, respectively, both significant at the

5% level).

Further, the results in Table 3 shed light on how the effects of organizational

innovation (EFORG) can be explained by several determinants. Since there is no

clear evidence of selection bias (insignificant Mills ratios in both Heckman Models I

and II), the results of both the Heckman outcome equation (Stage 2—the upper part

of the results for Models I and II) and OLS (Ordinary Least Square) estimations

(Models III and IV in the last two columns), which are quite comparable, are re-

ported and discussed. First, the results of the Heckman outcome equation (coeffi-

cients of 0.129 and 0.132, both significant at the 10% level in Models I and II,

respectively)13 indicate the existence of a positive relationship between persistence

of organizational innovation (PASTORG) and firm performance (EFORG). This

supports H2 and prior research such as that undertaken by Malerba and Orsenigo

(1999), suggesting that innovation persistency is conducive to the consistent im-

provement of firm performance within the context of technological innovation.

Next, the results of all models in Table 3 confirm H3 in terms of the combinative

effect. The coefficients of INCOMP, measuring the complementarity and joint effect

of organizational and technological innovation, are positive and statistically signifi-

cant at the 10% level in Model I (coefficient of 0.146) and at the 5% level in Models

II, III, and IV (coefficients of 0.154, 0.159, and 0.169, respectively), supporting the

claim that this combined presence is associated with improved firm performance

(Chandler, 1962; Nelson, 1991; Damanpour et al., 2009).

With regard to the influence of important control variables, most notably size and

age, our results suggest that larger and older firms are more inclined to make an

attempt at organizational innovation, while, in terms of outcomes, smaller firms are

more able to benefit from such an attempt. In addition, the OLS results demonstrate

that all three types of organizational innovation are strongly associated with im-

proved firm performance.14 The Norwegian firms would seem to have benefited

substantially from changes in firms’ structure (ORGSTR), and to a lesser extent,

from changes in knowledge management systems (ORGSYS) and changes in external

relationships (ORGREL).15

13Nonetheless, the same signs are found in the OLS estimations (Models III and IV).

14The results (not reported here; available upon request) of a detailed analysis of different effects (six

types of effects as dependent variables, one at a time) of these three types of change also go along

similar lines as the evidence discussed here using factor score (EFORG) as a dependent variable.

15This finding somewhat conflicts with the basic view of organizational ecologists, that change in an

organization’s structural core, which naturally impinges on, or even disrupts, some of its existing

major routines (i.e. reduces reliability and accountability), hinders its performance.


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Tab

le3

Fac

tors

exp

lain

ing

org

aniz

atio

nal

inn

ova

tio

nan

dit

sef

fect

s

EFO

RG

(Hec

kman

2-s

tag

e)EFO

RG

(OLS

esti

mat

ion

)

(I)

Log

Em

p(II)

Log

Turn

(III)

Log

Em

p(IV

)Lo

gTu

rn

Co

nst

ant

�0

.23

5(0

.87

6)

0.0

07

(0.8

99

)�

1.3

87

(0.8

60

)�

1.0

38

(0.8

78

)

Pers

iste

nt

org

aniz

atio

nal

chan

ge

(PA

STO

RG

)0

.12

9(0

.07

8)*

0.1

32

(0.0

78

)*0

.09

5(0

.07

5)

0.0

99

(0.0

75

)

Co

mp

lem

enta

rity

(IN

CO

MP)

0.1

46

(0.0

80

)*0

.15

4(0

.08

0)*

*0

.15

9(0

.07

9)*

*0

.16

9(0

.07

9)*

*

Firm

size

Nu

mb

ero

fem

plo

yees

(Lo

gEm

p)

�0

.02

8(0

.03

0)

–�

0.0

59

(0.0

30

)**

–

Tota

ltu

rno

ver

(Lo

gTu

rn)

–�

0.0

35

(0.0

23

)–

�0

.05

6(0

.02

3)*

**

Firm

age

(Lo

gA

ge)

�0

.00

9(0

.05

5)

�0

.00

4(0

.05

4)

�0

.01

0(0

.05

1)

�0

.00

4(0

.05

1)

Ind

ust

ryd

um

mie

s(IN

D)

Incl

ud

edIn

clu

ded

Incl

ud

edIn

clu

ded

Org

aniz

atio

nal

inn

ova

tio

n(in

OLS

on

ly)

ORG

SYS

––

0.3

95

(0.0

74

)***

0.3

97

(0.0

74

)***

ORG

STR

––

0.7

11

(0.0

88

)***

0.7

12

(0.0

88

)***

ORG

REL

––

0.1

99

(0.0

74

)***

0.1

99

(0.0

74

)***

Sele

ctio

neq

uat

ion

—H

eckm

anSt

age

1(d

epen

den

tva

riab

le¼

ORG

)

Past

org

aniz

atio

nal

chan

ge

(PA

STO

RG

)0

.83

2(0

.37

5)*

*0

.79

4(0

.38

0)*

*–

–

Pro

fita

bili

tyg

row

th(P

AST

PERF)

�1

.51

3(0

.79

2)*

�1

.48

8(0

.79

8)*

––

Ham

per

ing

fact

ors

(HA

MP)

Hig

hin

no

vati

on

cost

s(H

CO

ST)

�0

.49

3(0

.25

8)*

�0

.48

2(0

.25

6)*

––

Lack

of

fun

ds

(HFU

ND

)0

.36

4(0

.23

2)

0.3

74

(0.2

34

)–

–

Lack

of

qu

alifie

dp

erso

nn

el(H

PER)

�0

.14

5(0

.21

2)

�0

.17

4(0

.21

5)

––

Firm

size

Nu

mb

ero

fem

plo

yees

(Lo

gEm

p)

�0

.02

5(0

.13

8)

––

–

Tota

ltu

rno

ver

(Lo

gTu

rn)

–0

.06

7(0

.10

6)

––

Firm

age

(Lo

gA

ge)

0.5

81

(0.3

24

)**

0.5

85

(0.3

26

)*–

–

Ind

ust

ryd

um

mie

s(IN

D)

Incl

ud

edIn

clu

ded

––

Mill

sra

tio

0.2

93

(0.5

67

)0

.27

7(0

.56

3)

––

Wal

d-t

est

59

1.5

2**

*4

29

.58

***

––

R2

––

0.1

80

0.1

84

Nu

mb

ero

fo

bse

rvat

ion

s1

73

71

73

75

97

59

7

Un

cen

sore

d5

97

59

7–

–

*,**

,***

Den

ote

sign

ific

ance

atth

e1

0,

5,

and

1%

leve

l,re

spec

tive

ly.

SE

sin

bra

cket

s.


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6. Concluding discussion

Although the importance of organizational innovation has been widely recognized, it

has been subject to limited research (Lam, 2004; Damanpour et al., 2009), even

though historical research has demonstrated its importance for technological and

economic change (Chandler, 1962; Bruland and Mowery, 2004). In this article, we

have undertaken an initial attempt to improve our understanding of organizational

innovation and its effect on firm performance, paying particular attention to its

interrelationship with technological innovation and the impact of persistence.

Three hypotheses were formulated and empirically tested using two waves of CIS

data for Norway and a Heckman regression that tests for selection bias.

Extending recent research on determinants of organizational innovation (e.g.

Schmidt and Rammer, 2005) and the persistence of technological innovation (e.g.

Peters, 2009; Clausen et al., 2012), our article has shown that past organizational

innovation is a positive and significant predictor of current organizational innov-

ation (H1) and that the effects from undertaking current organizational innovation

are enhanced by prior experience with organizational innovation (H2).

The influence of diversity of organizational change on firm performance has also

been partially assessed, and the evidence shows that the three types of change con-

sidered influence firm performance to different degrees. We also find, in line with the

argument that the complementarity of organizational and technological innovation

is part-and-parcel of economic change (Nelson, 1991), that the combined effect of

undertaking both types of innovation on firm performance is positive and significant

(H3). Put differently, firms can better reap the rewards of reorganization by jointly

reorganizing with technological innovation.

Most papers have shortcomings however, and ours in no exception. Since the

Norwegian CIS4 was conducted around the middle of 2005, there was only a short

time for the respondent organizational innovators to realize the effects of organiza-

tional innovation introduced between 2002 and 2004. Therefore, the analysis could

only show how the firms benefited from organizational change in the near term. This

limitation relates to the cross-sectional nature of data from the CIS, which may also

lead to a simultaneity problem in some cases, because certain variables (which refer

to the same, or an overlapping, time period) included in an estimate may be jointly

determined. Furthermore, the relationships between some of the variables included

in the analysis in the present study may have been influenced by common method

bias, because they were extracted from the CIS questions which used similar scale

format and/or anchors.16 This bias may have been the case, since these questions

16Strong correlations between such variables may have been, in part, due to this reason. Criscuolo

et al. (2007) explain that, in order to attempt to avoid this bias, the CIS questionnaire was designed

to incorporate a mixture of Likert scales and questions which required responses in a binary (yes/

no) or numerical format (absolute numbers, percentages), so that the respondents needed to answer

the questions in different parts in different ways. For example, the variables used to measure


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were answered based, in part, on the (same) respondents’ (subjective) evaluation.

The reliance on the respondents’ subjective knowledge or perception may also have

led to subjective indicators in the estimate, such as in the case of the CIS questions

about obstacles to innovation (Clausen, 2008).

Furthermore, it can be argued that the data on organizational innovation made

available by the CIS4 is not very detailed. The CIS4 provides only three measures

with no scaling of the magnitude of organizational innovation, and, as discussed

earlier, these measures are at the firm level (but not plant- or project-level).

Therefore, the heterogeneity of organizational innovation within and among firms

could not be taken into account in great detail in this study. Moreover, there may be

other interesting “organizational” issues to be investigated on the basis of the CIS

data (arguably, the most detailed large-scale survey data currently available for in-

novation research). For example, it is possible to look further into the differential and

complementary effects of different types of organizational innovation (such as by

means of a multivariate analysis), or of different combinations of technological and

organizational innovation. The relationship between knowledge or skilled workers

and organizational change also remains to be explored.17 These are examples of

important future research topics which, nonetheless, go beyond the scope of this

study.

Another issue owing to the scope of the study is that we have not had the op-

portunity to analyze in detail the role of the industrial environment for organiza-

tional innovation and its effects. Although we have controlled for industry fixed

effects in our analysis, the influence of these on organizational innovation and its

effects needs to be better understood. For example, our results show that firms in

some industries are more inclined to report an organizational innovation when

compared to firms in other industries but that no industry fixed effect is significant

when attempting to explain the effects of organizational innovation. In this article,

we have focused on the firm level, but an analysis of how and why organizational

innovation differs across industries would add to the existing research within innov-

ation studies on the inter-industrial heterogeneity of (technological) innovation (e.g.

Pavitt, 1984; Marsili and Verspagen, 2002). Lack of emphasis on this issue is a

weakness of the present article but suggests an interesting venue for further research.

organizational innovation and its effects in this analysis were extracted from two (consecutive)

question sets which were associated with yes/no and Likert-scale items. As described earlier, the

variables for (the three types of) organizational innovation are binary, while the variables for (the

six types of) its effects have a scale of 0–3.

17For instance, Leiponen (2000, 2005) empirically analyzes the relationship between firms’ innov-

ation and their employees’ skills/competencies, and suggests that this relationship is complemen-

tary. However, her analyses concern innovation in a rather technological sense, e.g. R&D and

product/process innovation.


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Lastly, our longitudinal approach consists of data about firms at only two points

in time (i.e. CIS 3 and CIS 4), which has then been analyzed within a cross-sectional

framework. Results from cross-sectional analyses mainly show associations between

variables, and there is always the possibility that associations between variables are

partly driven by omitted variables. Although we test and control for the presence of

selection bias in our analysis (in addition to the fact that some variables are measured

using the CIS 3 data, while organizational innovation and its effects are measured

using the CIS 4 data), we may not claim causality in this article. Despite this short-

coming, we believe that our results are still interesting because there is a lack of

empirical studies on the under-researched but important topic of organizational

innovation. We acknowledge however that our article is only a small step in this

direction and that more research on this topic needs to be undertaken, especially on

the longer-term performance effects of organizational innovation.

Acknowledgements

The authors are grateful to Jan Fagerberg, David Mowery, Bart Verspagen, Fulvio

Castellacci, Glenn Carroll, Ed Steinmueller, Paul Nightingale, and Martin Srholec for

their advice on this work. Many helpful comments received from anonymous ref-

erees at Industrial and Corporate Change are also greatly acknowledged.

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Appendix

Table A1 Principal components analysis for the effects of organizational innovation

Effects of organizational innovation Factor loadings

EFORG

Reduced response time to customer needs 0.639

Improved quality of goods or services 0.699

Reduced costs per unit output 0.639

Improved employee satisfaction and/or reduced employee turnover 0.600

Increased enterprise’s capacity 0.772

Higher enterprise’s profitability 0.734

One factor with eigenvalue41 detected, which explains 47% of total variance.

Table A2 Correlation matrix for the explanatory variables employed in the model

Age Emp Turn PASTORG PASTPERF HCOST HFUND HPER INCOMP ORGSYS ORGSTR

Age 1.000

Emp 0.118 1.000

Turn 0.050 0.595 1.000

PASTORG 0.006 0.115 0.051 1.000

PASTPERF �0.102 �0.008 �0.050 0.004 1.000

HCOST �0.086 0.001 �0.011 0.149 0.016 1.000

HFUND �0.096 0.022 �0.006 0.135 0.034 0.762 1.000

HPER �0.052 0.054 0.036 0.122 �0.002 0.556 0.555 1.000

INCOMP �0.030 0.084 0.039 0.230 �0.002 0.387 0.355 0.330 1.000

ORGSYS 0.001 0.132 0.080 0.129 �0.024 0.126 0.142 0.138 0.250 1.000

ORGSTR �0.009 0.160 0.063 0.181 0.014 0.186 0.200 0.181 0.228 0.434 1.000

ORGREL �0.013 0.144 0.020 0.123 0.043 0.177 0.163 0.125 0.142 0.257 0.400

Age, Emp (Number of employees), Turn (Total Turnover) and PASTORG (Past/Persistent

Organizational Change) are included in Heckman-Stage 1 and 2 and OLS estimation.

INCOMP (Complementarity) is included in Heckman-Stage 2 and OLS estimation.

PASTPERF (Productivity Growth), HCOST (High Innovation Costs), HFUND (Lack of

Funds), and HPER (Lack of Qualified Personnel) are included in Heckman-Stage 1.

ORGSYS, ORGSTR, and ORGREL are included in OLS estimation.


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org innovation and its effects

Documents

organizational innovation

firm performance chandler

university of oslo

number of norwegian

larger firms

bangkok university

smaller firms

new data set