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Master Thesis 2012
Faculty of Business
Administration & Center
for Innovation Studies
RADBOUDUNIVERSITY
NIJMEGEN
The Technology
O i i Di d
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O i i Di d
Preface
This master thesis is written in the context of completing my Masters degree in Business
Administration. The choice for the topic was easily made because I have always been very
interested in innovation. The innovation topic is very suitable for the specialization of my
master, which is Strategy. In my opinion, innovation is the most important way for
organizations to make a difference and to ensure their competitive advantage. Therefore, it
is really important that research focuses on innovation and the way this concept exactly
works. In this specific research the emphasis lays on determining the optimal configuration
of technological innovations and organizational innovations with regards to better
organizational efficiency. Though the whole process took more time than was expected, this
made sure that I really learned a lot the past year, not only about the specific subject but
also about how to manage a project like this. Although I am very proud on this final product I
definitely can say that it is not about the destination but all about the journey.
On this journey I was accompanied and helped by the following people. Without them I
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Abstract
Purpose This research seeks to explore the effects of temporal sequential configurations
of organizational and technological innovations on the process efficiency of manufacturing
firms.
Design/methodology/approach The effects of the organizational innovation first and
the technological innovation first perspective on organizational process efficiency were
examined by the use of a quantitative analysis of the EMS-2009 survey.
Findings The results of this study revealed that when examining the process efficiency of
manufacturing firms no significant evidence was found in support of any general temporal
sequential configurations of organizational and technological innovations. So in contrast to
what the literature review suggested no strong effects were found. However additional
analyses revealed that the timeframe in which both types of innovations are introduced
seems to have effect on some of the efficiency indicators.
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Content
1. Introduction .................................................................................................................. 7
1.1. General introduction ....................................................................................................... 7
1.2. Structure of the research ................................................................................................ 9
1.3. Problem statement ......................................................................................................... 9
1.3.1. Research problem .................................................................................................. 10
1.3.2. Research questions ................................................................................................ 11
2. Theoretical framework & Conceptual model ............................................................... 12
2.1. Technological innovation & performance ..................................................................... 12
2.2. Organizational innovation & performance ................................................................... 14
2.3. Interrelatedness technological and organizational innovations ................................... 16
2 4 S f 19
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3.3.6. Absolute number of technological and organizational innovations ...................... 31
3.3.7. Production lead time .............................................................................................. 31
3.3.8. Sales growth ........................................................................................................... 31
3.4 Operationalization .......................................................................................................... 31
3.5. Methods of analysis ...................................................................................................... 36
3.6 Conceptual model .......................................................................................................... 38
4. Analysis & results ...................................................................................................... 38
4.1. Descriptive analysis ....................................................................................................... 39
4.1.1. EMS-2009 and participating countries ................................................................... 39
4.1.2. Organizational performance .................................................................................. 40
4.1.3. Technological Process Innovations ........................................................................ 41
4.1.4. Organizational Innovations .................................................................................... 42
4.1.3. Sequential analysis ................................................................................................. 43
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Appendices ..................................................................................................................... 69
Appendix 1 ..................................................................................................................... 70
1.1 General Information EMS-2009 ..................................................................................... 70
1.2 EMS Survey ..................................................................................................................... 71
Appendix 2: Analyses of EMS-2009 data .......................................................................... 72
2.1. Reliability analysis Technological Innovations .............................................................. 72
2.2. Reliability analysis Organizational Innovations ............................................................. 73
2.3. Number of innovations introduced ............................................................................... 74
2.4. Descriptives metric variables ........................................................................................ 75
2.5. Descriptives non-metric variables ................................................................................. 77
2.6. Regression Analyses ................................................................................................. 78
2.6.1. Analysis A - Production Lead Time (PLT) .................................................................... 78
2.6.2. Analysis B - Manufacturing Lead Time (MLT) ............................................................. 86
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1. Introduction
1.1. General introduction
Modern organizations are operating in an increasingly complex and dynamic environment.
Due to environmental factors like globalization and fast moving technological development
firms face increased competition. Besides that manufacturing organizations are confronted
with the upcoming problems of scarce resources which force them to increase the efficiency
of their production processes. In this respect innovation enables organizations to increase
efficiency with regard to the use of raw materials and energy. Given the previous mentioned
developments and environmental factors the need for organizations to innovate is essential
for their growth and survival in the short and long term. Organizations have to develop and
implement new innovations in order to become more sustainable and profitable. Therefore
the study of innovation hardly needs justification as scholars, policy makers, business
executives, and public administrators maintain that innovation is a primary source of
economic growth, industrial change, competitive advantage, and public service (Borins,
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fact that these types of innovations are occurring in almost all manufacturing industries
worldwide since the start of the industrial revolution.
While technological innovations have proven their worth organizations can cope with
environmental changes and uncertainties not only by applying- new technology, but also by
successfully integrating technical or administrative changes into their organizational
structure that improve the level of achievement of their goals (Rosner, 1968; Damanpour &
Evan, 1984, p. 393). This concept of organizational innovation refers to the non-
technological innovations that contribute to the firms performance by for example changing
the organizational structure. The upcoming phenomenon of the multidivisional form can be
seen as a good example of an organizational innovation. Armour and Teece (1978) found
that the adaption of a major administrative innovation (the multidivisional structure) in
petroleum firms increased the rate of return of owners equity (Damanpour & Evan, 1984, p.
395). Non-technological innovations have proved to be of great influence in the success of
multiple organizations. Therefore it can be stated that these organizational innovations
which take place in the social system of an organization, are also important precedents for
i ti l d f
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try whether these temporal sequential configurations are significant indicators for
organizational efficiency, and if so which specific sequence is preferred. Further it isimportant to look to certain factors that also could have an influence on the way
organizations try to increase their performance by introducing new innovations. These
conditional factors could for example be the size of the firm and the specific industry the
firm is operating in. This research will try to answer all these questions by the use of
quantitative research of the data retrieved from the European Manufacturing Survey 2009,
which encompasses data from manufacturing firms from nine European countries with more
than 10 employees.
1.2. Structure of the research
This thesis starts with the problem statement and the corresponding research goal followed
by the presentation of the research questions. The following chapter discusses the existing
theories and findings of previous research conducted about this topic. This theoretical
review led to the conceptual model used within this research. From the results of this
theoretical framework a set of hypotheses is established. Chapter three provides the
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1.3.1. Research problem
The goal of this research is to make a valid contribution to the existing literature about
innovations. Theory development and empirical studies of innovation types have thus far
focused on their antecedents; namely, environmental and organizational conditions that
enhance or hamper the process of generation or adoption of each type (Jansen et al., 2006;
Kimberly and Evanisko, 1981; Tornatzky and Fleischer, 1990; Damanpour, Walker &
Avellaneda, 2009, p.2).However, research about the relation between the interrelatedness
of different types of innovations on the one hand and business efficiency on the other hand
has received considerably less attention. Existing literature describes the role and
importance of both technological developments and organizational concepts regarding their
influence on performance. Yet, important information about how to integrate the use ofboth types of innovations and especially in which temporal sequence and at which specific
conditions is a topic that deserves more attention in the field of innovation research.
Organizations can benefit from a better understanding of the interrelatedness of different
innovations. A deeper understanding helps them to adjust their innovations strategy in such
it t ib t b t t it l Th t l f diff t i ti i t
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1.3.2. Research questions
Main research question:
What is the influence of the different temporal sequential configurations of technological
and organizational innovations on the organizational process efficiency of manufacturing
firms?
Sub research questions:
- What is the effect of the organizational innovation first strategy on the organizational
efficiency of manufacturing firms?
- What is the effect of the technological innovation first strategy on the organizational
efficiency of manufacturing firms?
Additional analysis question:
Wh t i th i fl f th ti f i hi h i ti l d t h l i l
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2. Theoretical framework & Conceptual model
In this chapter the theoretical background underlying this research is presented. To start this
literature review this chapter first elaborates on the concepts of technological and
organizational innovations were the focus lays on their independent influence on
organizational performance. The second part will focus on the interrelatedness and
alignment of these different types of innovation. In this part the existing literature about the
influence of both innovations have on each other will be discussed. Consequently the
different forms of sequence will be presented. These sequences contain the Organizational
Innovation First and the Technological Innovation First perspectives.
2.1. Technological innovation & performance
In the introduction of this thesis the concept of technological innovations has been shortly
explained. In this part the relation between technological innovations and organizational
performance/efficiency is further elaborated on. Technological innovations can be defined
asthe implementation or adoption of technologically new or significantly improved
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can contribute to the performance of an organization can be explained by the so called
rational perspective. Damanpour (2012) explains that technological innovations contributeto the economy of the organization by using the following chain of events: R&D investment
development of new technology introduction of new products or services
performance outcome, which is referred to as the technology push model1. Yet technological
innovations do not only contribute to new products but also within the production process
they can enhance organizational performance in the form of higher production efficiency.
Maybe the most famous technological process innovation is the invention of the assembly
line by Henry Ford2
which had a major effect on labour productivity and quality of the T-Ford
automobile at that time. In figure 1 the different ways an organization can innovate are
clearly displayed.
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Bacharach and French (1980) found that over a ten-year period the average number of
technical innovations proposed in city governments was nearly three times the averagenumber of administrative innovations
3(Damanpour & Evan, 1984, p. 394). The question is
why do a majority of organizations put so much emphasis on these technological
developments? Technical innovations [] are perceived to influence performance more
readily than administrative innovations (Damanpour & Evan, 1984, p. 405). Not only the
business performance effects are accounted for this dominance of technological- over non-
technological innovations. Technical innovations are more observable, have higher
trialability, and are perceived to be relatively more advantageous than administrative
innovations, while administrative innovations are perceived to be more complex than
technical innovations to implement (Damanpour, 1983, p. 45-47; Damanpour & Evan, 1984,
p. 394). Further the findings from Baldwin, Diverty & Sabourin (1995) substantiated the
relation between the use of (new) technology and organizational performance. Because the
relation between innovation and organizational performance is the essence of this research
we will use the following definition for organizational performance, from a systems
perspective, performance is the ability of an organization to cope with all four systematic
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observed in the way innovation is studied. For example the Oslo Manual (2005) started to
refer to organizational innovation and marketing innovation next to technologicalinnovation. Because many researchers use this manual the importance to view innovation
within an organizational context is increasingly recognized in business literature. Because
organizational innovation encompasses multiple possible organizational changes it is
important to define it in a clear way. An organizational innovation is defined as the
implementation of an internally generated or a borrowed idea whether pertaining to a
product, device, system, process, policy, program, or service that was new to the
organization at the time of adoption (Thompson, 1965; Zaltman, Duncan, and Holbek, 1973;
Damanpour & Evan, 1984, p. 393). Yet this definition encompasses a wide range of
innovations. Damanpour & Evan (1984, p. 394) give the following examples: An
administrative innovation can be the implementation of a new way to recruit personnel,
allocate resources, and structure tasks, authority, and rewards (Evan, 1966). It comprises
innovations in organizational structure and in the management of people (Knight, 1967). All
these specific innovations which stand apart from pure technological changes contribute to a
firms performance in a lot of ways. For instance, from the perspective of institution
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to be introduced), and are cognitively less complex (are easier to understand by users)
(Damanpour & Aravind, 2011; Wolfe, 1994; Damanpour, 2012, p.11). These insights providelogical explanations that describe that organizational innovation can positively contribute to
organizational performance. This positive effect can be explained by a rational perspective
for example tested in the study of Evangelista and Vezzani (2010) which shows that the
adoption of new managerial practices has a positive effect on sales growth. Also the findings
of Birkinshaw and Mol (2009) which show that managerial innovations have a positive effect
on productivity indicate the importance and opportunities of these specific non-
technological innovations.
While technological innovations take place in the technical system of an organization
administrative innovations are defined as those that occur in the social system of an
organization (Damanpour & Evan, 1984; pp 394). The social system refers to the
relationships among people who interact to accomplish a particular goal or task (Cummings
and Srivastva, 1977). It also includes those rules, roles, procedures, and structures that are
related to the communication and exchange among people and between the environment
d l (C i d S i 19 & 1984 394) id
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that encompass the use of both organizational and technological innovations in order to
successfully increase performance. The critical aspects of aligning the investments in
technical aspects and investments in the manufacturing infrastructure such as planning,
control, quality assurance and organizational policies is described by multiple conceptual
studies (Meredith, 1987c,d; Ettlie, 1988, Zuboff, 1988; Parthasarthy and Sehti, 1992; Twigg
et al., 1992;Boyer et al., 1997). While these articles stress the importance and the
opportunities of a combined use of technological and non-technological innovations,
organizations still do not take this to a full advantage. The reason for this tendency could be
that the alignment of these concepts is a difficult process because the interaction effects of
diverse innovations differ. So there might be some combinations that enhance performance
but also negative effects by this interaction could be possible. For example, technological
process innovations are expected to have a direct effect on profit margins in the short term.
Schmidt & Rammer (2007 pp. 0) state: What is more, the highest innovation effects on
profit margins are to be found for firms introducing technological innovations without non-
technological ones, indicating that comprehensive innovation activities involving both types
are likely to raise costs stronger than returns. Yet these findings portray the effect on
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higher efficiency. Herbst (1974) describes from a socio-technical systems theory point of
view: From a theoretical standpoint, the sociotechnical systems framework emphasizes the
role of both technical and social systems operating jointly for the effective operation of an
organization, and its suggested that concentration on either the technical or the social
system without due regard to the other would result in low organizational performance and
growth. Damanpour & Evan (1984, p. 407) add to this by stating A balanced
implementation of administrative and technical innovations would help to maintain the
equilibrium between the social and technical systems, which in turn would lead to high
performance. The idea of a social technical system stems from about half a century ago
(Trist et al., 1963) were it was used to describe the influence new technologies had on jobs
and the new organizational forms that had to be developed in order to effectively use these
systems and simultaneously offer employees a quality job. While some argue that this
perspective is dated, the new technological developments in the last years have disrupted
the way employees work. For example, the new communication technologies but also other
technological developments like for example new ICT related innovations in the
manufacturing industry caused an irrevocable change in organizational structures and the
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innovations should be aligned by organizational innovations in order to increase efficiency
levels. The relation between those two distinct forms of innovation is of complementary
nature, and adopting both forms will lead to the highest returns (Armbruster et al., 2006;
Arnal et al,.2001; Evangalista and Vezzani, 2010). Brynjolfsson & Hitt (1998) describe that
these technological innovations have proven to be an essential component of a broader
system of organizational changes which do increase productivity. They found a consistent
positive relationship between the use of technology and a set of work practices that include
for example the use of self-directed work teams, greater levels of individual decision
authority increased investments in training and screening for education and incentive
systems that reward and encourage high team performance (Brynjolfsson & Hitt, 1998, p.
53).
Conclusion can be drawn that a technological innovation necessarily needs organizational
changes to become successful. It has to be acknowledged that these organizational changes
often need a substantial period of time and financial investments in order to be established.
However there are contrasting views about whether they have to be developed or
d d b f h h l l d h h h b
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2.4.1. Organizational innovation first
Although the importance of organizational innovations has been proven, the use of
organizational innovations for preparing organizations that face the implementation of new
technologies should gain more attention. Therefore it is of great importance to further
investigate the role of organizational innovations as driver for the implementation and
success of technological innovations. Earlier research describes the effect of product and
process innovations on non-technological innovation activities, but did not investigate the
opposite direction (Schmidt & Rammer, 2007 pp. 32). The organizational perspective
argues that rather than the adoption of technology, organizational change is fundamental to
the process of innovation and growth (Van de Ven et al., 1999; Vaessen et al., 2012). There
are some arguments that support this view. First we can refer to the role of organizational
innovations as a trigger or even a necessity for the development or acquiring of new
technologies. Damanpour & Evan (1984) find in their study of libraries that organizations
who implement a higher rate of administrative innovations end up with a higher number of
technological innovations in the subsequent period. These results can be explained as
f ll h h l d d ll d b h l
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eventually contribute to new technological changes but also their independent possible
effects on performance within the social system. Damanpour and Evan (1984, 2012, pp.25-
26) relied on a socio-technical perspective and related the introduction of managerial and
technical innovations as a means of introducing change in the organizations social and
technical systems, respectively. Their findings explained that managerial innovations trigger
the introduction of technical innovations more than the other way around.
Further it can be argued that organizational innovations should precede technological ones
because they have an important role in the success of new technology. Damanpour & Evan
state If the social system is not prepared, it cannot adjust to the demands created by the
technical system; therefore, the required match between the two systems for high
performance of the organization will not be achieved (Damanpour & Evan, 1984, p. 396).
Burns, Acar & Datta, (2010) provide a framework that could be used to describe the
influence of this specific temporal sequence. In their exploration of entrepreneurial
knowledge transfers they mentioned two distinct approaches: learning-before-doing and
learning-by-doing in which learning could be seen as an organizational innovation and the
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implemented. An a priori explanation is that learning-before-doing renders the recipient
more capable of learning from experience; in addition, by increasing the credibility of the
knowledge source, it enables smooth knowledge transfer to other recipients in an
entrepreneurial firm (Churchman, 1968, Nass , 1994, Burns, Acar & Datta, 2010).
From these findings it can be concluded that the organizational innovation first perspective
offers many logical opportunities for organizations to enhance their performance. However
not much quantitative analysis is performed within this field. Further they elaborate on
diverse performance indicators and also on product development instead of process
innovations. For that reason the following hypothesis was made.
Hypothesis 1: Generally speaking, manufacturing firms that let organizational innovations
precede technological innovations will have higher organizational efficiency
then organizations that followed the technological innovation first
perspective.
2.4.2. Technological innovation first
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processes) not only depends upon the product and process innovation itself, but also on
accompanying adjustment in the organisation of a firm and - with respect to product
innovation - in adjustments in marketing methods (Schmidt & Rammer, 2007, p. 30). This
quote refers to the idea that organizations use organizational innovations to increase the
chance of success of their technological product and process innovations. These
organizational innovations like for example a new organizational structure, rewarding
systems, HRM and marketing innovations increase the sales for new products and can lead
to cost reductions in the production process. This idea is supported by the findings of
Schmidt and Rammer (2007, p.32) which suggest that firms have an incentive to undertake
non-technological innovation activities if they introduce technological innovations. However
this does not automatically imply that in time these technological innovations precede
organizational ones.
However this specific sequential configuration can be described by the concept of learning-
by-doing (Acar & Datta, 2010). This idea suggest that an organization could choose to learn
from experience, and adjust the organization were needed after the introduction of the
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From the viewpoint of the technological perspective the following hypothesis can be
formulated.
Hypothesis 2: Generally speaking, manufacturing firms that let technological innovations
precede organizational innovations will have higher organizational
efficiency then organizations that followed the organizational innovation
first perspective.
2.5. Conceptual model
In the previous paragraphs the different existing theories about the Technology-
Organization-Discord are described. The examination of different academically writings led
to two hypotheses. All hypotheses can be placed within the conceptual framework that iscentral in this research. At first we have the dependent variable which is organizational
efficiency. This variable is measured by five dependent variables (see chapter 3 Table 1). The
independent variable in this research is the sequence of occurrence which encompasses the
technological innovations (TI) and organizational innovations (OI). The sequence of these
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3. Research Methodology
In this chapter the research methodology is described. In order to examine the relation
between different sequences of innovations and organizational efficiency, quantitative
research will be used. In order to correctly execute this research first the specific type of
research will be described accompanied by a description of the used European
Manufacturing Survey 2009 (EMS-2009). Further the research design will be presented in
which the scope and focus of this research will be treated. Subsequently the variables which
are used in this research and their specific indicators within the EMS-dataset will be
presented. Finally this chapter will present the specific statistical methods that will be used
to test the hypotheses.
3.1. Research type and design
To test the specific hypotheses of this research quantitative research will be used. The data
which is used comes from the European Manufacturing Survey (EMS) from the year 2009.
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of 3204 completed questionnaires. The EMS dataset provides a very comprehensive and
correct measurement of the used concepts and further its entails a high internal validity.
As mentioned above, this research will use quantitative analysing methods. In research three
types of design are differentiated: exploratory, descriptive and explanatory research.
(Churchill et al., 2010) Because this research tests existing theories by an examination of
data we perform explanatory research. Explanatory research is very useful for testing the
hypotheses that are derived from the theories presented in chapter two. By quantitative
research these hypotheses can be validated or rejected and in that way contribute to the
existing knowledge about the use of technological and non-technological in manufacturing
firms in order to increase performance.
3.2. Measurement of concepts
As discussed in chapter two the main concepts which will be measured are the sequence of
occurrence of two distinct forms of innovation namely, technological process innovations
and organizational innovation. The dependent variable in this research is organizational
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non-technological process innovations. The variables technological innovation (TI) and
organizational innovations (OI) therefore only represent technological process innovations
and organizational innovations. (See figure 3)
3.3. Control variables
Within this part the different control variables which could be of influence within this
research are treated. These are in following order: organizational size, type of manufacturing
subsector, product and production characteristics, research & development, product
Figure 3: Focus research: Process innovations:
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more easily [] and employ more professional and skilled workers (Damanpour, 1992).
Others state the opposite (Hage 1980; Utterback, 1974). They argue that large organizations
due to their inflexibility and lower ability to adapt and improve dont innovate as well as
smaller organizations. They further argue that the implementation and acceptance is easier
in smaller organizations. According to Trott (2012, pg. 103) it can be stated that Size is a
proxy variable for more meaning full dimensions such as economic and organizational
resources, including number of employees and scale of operation. The variable
organizational size is therefore expected to influence the process efficiency of manufacturing
firms and for that reason was used as control variable.
3.3.2. Type of manufacturing subsector
This research focusses on the manufacturing industry within eight countries. The
manufacturing industry is an enormous industry which encompasses many different types of
organizations. Because organizations within the manufacturing industry vary in many ways a
closer examination of manufacturing subsectors is needed. It is expected that by using this
variable within this research model a visible difference between different sectors can be
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3.3.3.1. Product development
An organization has to adjust its product development strategy according to the specifics of
the product and its organizational capabilities. For example some manufacturers of very
basic and simple products, like for example manufacturers of paperclips do not have to
develop their product regularly. In the other way manufacturers in for example the car
industry, have to develop their products according to the specific wishes of the customer
and new regulations. Between those extremes there are other ways in which a firm can
organize its product development. Organizations that take an active role in product
development and therefore often have to rearrange the production process rely on other
organizational resources then organizations that do not need this type of innovation.
Therefore it is very plausible product development also has an effect on the efficiency
indicators and therefore it was used as control variable.
3.3.3.2. Production & Assembly
According to the product and the wishes from the customer the way an organization
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3.3.3.4. Product complexity
The topic of high versus low complex products has been explained in the previous three
paragraphs in the form of product development, assembly and batch size. However these
characteristics mostly explain the alignment of the production process according to the
wishes of the customer, the features of the product itself are also important. For very
complex and specific products different results for the efficiency indicators are expected
than for simple products. Therefore product complexity is used as control variable explaining
process efficiency.
3.3.4. Research & Development.
When talking about innovation, many often refer to Research & Development. According to
many studies R&D is a necessity or at least an important precedent of new innovations, on
both technological & non-technological side. Especially in the case of product innovation
R&D plays an essential role. In the case of this specific research it could be possible that R&D
efforts are very closely related to new process innovations, which could have a strong
influence on the process efficiency. Therefore it is importance to use the R&D variable within
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3.3.6. Absolute number of technological and organizational innovationsWithin this research it is important to take the absolute number of innovations into account.
When calculating whether a firm followed a specific sequence, the average year of
implementation is used. However this could lead to wrong conclusions because the
efficiency indicators might be stronger affected by the number of implemented innovations.
Therefore it is important to control our main relations for the absolute number of
innovations, respectively on the technological and organizational side.
3.3.7. Production lead time
Because of the fact that the production lead time is an important part of the manufacturing
lead time (delivery time) it is important to use the variable PLT an additional control variable
in the regression model which analyses the manufacturing lead time.
3.3.8. Sales growth
In the calculation of capital utilization and labour productivity the total amount of sales is an
important predictor. For that reason it is important for those efficiency indicators that are
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value added per employee. These indicators represent the process efficiency of
manufacturing firms within this research.
In order to measure these concepts the following variables will be used (see table 1).
Organizational efficiency
Production efficiency indicators
Indicators Measurement
Production Lead Time (PLT) Average production time Measured in hours (metric)
Manufacturing Lead Time (MLT) Time from order to ready for
shipping (delivery time)
Measured in hours
(metric)
Scrap rate (Scrap) Average percentage of
products that have to be
scrapped or removed after
quality control
Measured in % of total production
(metric)
Capital Utilization Degree of capacity utilization Measured in % of total capacity
(metric)
Labour Productivity Value added per employee Measured in thousands of Euros
(metric)
Table 1: Organizational efficiency indicators within EMS dataset.
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Technological Process
Innovations concepts
Indicators Measurement
Integration of automation - Digital product development
- Industrial robots/ handling systems
- Process integrated quality control
systems
- Radio Frequency Identification (RFID)
- Warehouse management systems(WMS)
Nominal variables
(Y/N)
With starting year
(Interval variable)
Processing and production
techniques
- Use of Laser
- Dry operations
- Rapid prototyping/ Rapid tooling
- Bio-/ gene technological processes /
catalysts
- Processing of new materials
Nominal variables
(Y/N)
With starting year
(Interval variable)
Digital factory / IT networks - Digital exchange production planningwith supply change management
systems of suppliers/customers
- Manufacturing Execution Systems
(MES)
- Virtual Reality of 3D-simulation for
product design
Nominal variables(Y/N)
With starting year
(Interval variable)
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Organizational Innovation
Concepts
Indicators Measurement
Organization of work - Autonomous production task groups
- Integration of Tasks (integration,
planning, executing and control)
- Temporary cross-functional teams
Nominal variables
(Y/N)
With starting year
(Interval variable)
Organization of production - Client/product specific production
units
- Internal Zero-buffer-principle
(Kanban)
- Total Cost of Ownership (TCO)
Nominal variables
(Y/N)
With starting year
(Interval variable)
Standards and audits - Quality circles
- Knowledge Matrix
- Quality management on basis of ISO-
9000 series
Nominal variables
(Y/N)
With starting year
(Interval variable)
Work hours & rewarding systems - Collective regulations for flexibility
work
- Rewarding systems with bonuses for
Nominal variables
(Y/N)
With starting year
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calculated. This metric variable is subsequently recoded into a dichotomous variable in
which the value 1 represents the organizational innovation first perspective and the value 0
represent the technological innovation first perspective.5
This research further uses different control variables. These are: organizational size, Type of
manufacturing subsector, Production characteristics, Research & Development, Product
Innovations, the absolute numbers of respectively technological and organization
innovations, production lead time and sales growth. These different conditional variables
will be measured by using the following variables (see Table 4).
Control Variables Indicators Measurement
Organizational Size Number of employees in 2008 Metric variable
Manufacturing subsector Measured by eight sectors
- Food, Beverages and Tobacco
- Textiles, Leather, Paper and Board
- Construction and Furniture
- Chemistry (energy & non-energy)
Categorical variable
(nominal variable)
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Absolute number of Innovations - Absolute number of
technological innovations
- Absolute number of
organizational innovations
Metric variables
Production Lead Time - Average production time Metric variables measured
in hours.
Sales growth - Growth in sales from 2006-
2008
Measured in percentages
Table 4: Control variables (For more information about the specific questions used see appendix 2 & 3)
3.5. Methods of analysis
In order to start the quantitative analysis all used variables were examined by the use of
descriptive analysis. By providing detailed information about the variables and the use of
different tables and graphs they can be tested for their usability, reliability and validity. Also
the results can tell us more about the dataset and its possible opportunities and limitations.
After performing descriptive analysis, multiple regression analysis will be performed. Given
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Dependent variables
production
time (PT)
Manufacturing
Lead Time
(MLT)
Scrap rate
(Scrap)
Capital
Utilization (CU)
labour
productivity
(LP)
Control variables
Size + + + + +
Manufacturing subsector + + + + +
Product development + + + + +
Production/assembly + + + + +
Batch size + + + + +
Product complexity + + + + +
Research & Development + + + + +
Product innovation + + + + +
Number of implemented TI + + + + +
Number of implemented OI + + + + +
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H1
H2
3.6 Conceptual model
As earlier described this research will test conceptual model that is depicted in figure 2. In
the figure here under (see Figure 4) the same conceptual model is depicted with the
accompanying operationalization and control variables.
Organizational
Innovations FirstAverage year ofintroduction TI
-
Average year of introduction OI
> 0
Organizational
EfficiencyProduction-efficiency
indicators:
- PT,
- MLT,
- SCRAP,
- CU
- LPTechnological
Innovations FirstAverage year ofintroduction TI
-
Average year of introduction OI
< 0
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4. Analysis & results
In this chapter the analysis and results of this research are presented. First it starts with the
descriptive analysis which provides a clear overview of the used dataset. These descriptives
present the variables and discuss their usability within the conceptual model by the use of
multiple tables and graphs. Further the reliability of the measured constructs was tested.
Hair (2005, pp. 3) refers to reliability as the extent to which a variable or set of variables is
consistent in what it is intended to measure. The variables used within this research are
grouped under the constructs of organizational process efficiency, sequence of occurrence
and the different control variables. We will start this paragraph by describing the general
EMS-2009 dataset followed by a description of the dependent variables. Subsequently the
independent and control variables will be further elaborated on. To conclude this descriptive
analysis the results from the correlational analysis are presented.
In the second part of this chapter the analysis of the conceptual model will be performed. By
the use of multiple regression analysis the hypotheses within the model are tested. These
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4.1.2. Organizational performance
In the table below, general descriptive findings are presented for all organizational efficiency
indicators.
0
200400600800
1000120014001600
Frequency of completed questionaires
among participating countries
Figure 5: Absolute number of EMS respondents according to country
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product which is assumed to be correlating with the batch size and complexity. The average
percentage of products that are accounted as scrap or rework is 3,14 % with an standard
deviation of 5,77%. Concerning the Capital Utilization (CU) it can be stated that within the
dataset the mean percentage of CU is 86,64 which implies that on average 86,64% of the
companys assets are used. The standard deviation if CU is 14,92%. The mean added value of
an employee among the different organizations was 102,05 thousand Euros with an SD of
77,78. The minimum of this variable was -291,67 which displays that there are organizationswith negative labour productivity results among the respondents.
4.1.3. Technological Process Innovations
In the following bar chart an overall view of all possible technological innovations is
displayed. In this chart the percentage of each specific technological innovation is displayed.
45,00%50,00%
ns
Technological Innovations introduced
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and Bio- /gene technological processes (+ use of catalysts) are by far the least introduced
technological innovations within this dataset.
Because these different types of technological innovations are recoded into one
variable/construct for technological innovations it is important to test whether these
indicators combined are a reliable measure of the concept of technological innovation. To
test this Cronbachs Alpa () is used. Hair (2005, pp. 102) states that the values of 0.60 to
0.70 are the lower limit of acceptability. The Cronbachs alfa for the construct of
technological innovations is 0,6856
which implies that all indicators used taken together are
above the prescribed border of 0,60. The Cronbachs alfa of this construct can be increased
to 0,687 by removing the variable application of bio-/gene-technology. Because this is a very
small increase all the predefined variables are used in our analysis of technological
innovations.
Finally, the descriptive analysis provides some other important results. The average number
of used technological innovations is 2,60 (from a total of 13 predefined innovations) .This
implies that of all thirteen measured indicators on average manufacturing organization used
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These 13 indicators are combined to one specific variable. Cronbachs alfa is used to test if
these indicators are representative measured for the construct organizational innovations.
0,00%
10,00%
20,00%
30,00%
40,00%
50,00%
60,00%
70,00%
%oforganizations
Organizational Innovations
Figure 7: Frequencies Organizational innovations
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into technological processes innovations and organizational innovations. Both constructs are
measured by 13 indicators as was earlier explained. Yet with these indicators there are
multiple ways an organization can choose to innovate. First are there the organizations that
dont use any of the technological and organizational innovations, those that either only use
technological on the one hand or organizational innovations on the other hand and those
firms that use both organizational and technological innovations. In table 8 it can be seen
that a large majority of respondents both introduced technological and organizationalinnovations (75%). While on the one hand the percentage of organizations that only used
organizational innovations is (21%) on the other hand the number of firms that only
introduced technological innovations (2%) or even did not even introduced any process
innovation is small (3%).
40,00%50,00%60,00%70,00%80,00%
anizations
Innovation Configurations
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precede technological ones. From the 868 organizations 861 completed the questions about
the years of implementation.
4.1.4. Descriptive analysis metric and non-metric variables
0
10
20
30
4050
60
70
80
90
100
1950
1953
1956
1959
1962
1965
1968
1971
1974
1977
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
Percentagein
novations
Average year of introduction
Technological Innovations Organizational Innovations
Figure 9: Sequence of occurrence (cumulative frequencies)
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Independent variables control variables Sig. correlates > 0,4 or < -,04
with variable number
Manufacturing subsector1. Metal Not present
2. Textile Not present
3. Construction Not present
4. Chemistry Not present
5. Machinery Not present
6. Electronics Not present
7. Transport Not present8. Organizational size (# of employees 2008) Not present
9. Share employees R&D Not present
10. Share turnover new products Not present
Product development
11.Customer unique 12.(-.,620**)
12.Semi Unique 11.(-,620**)
13.Standard program Not present14.No product development at site Not present
Production/Assembly
15.Make to order 16.(-,566**), 17.(-,600**)
16.Assemble to order 15.(-,566**)
17.Make to stock 15.(-,600**)
18.No production/assembly at site Not present
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4.2. Regression analysis.
In the previous paragraphs a clear overview about the EMS dataset and the used variables
was made. The conclusions were that the data revealed that a majority of organizations let
organizational innovations precede technological ones. However this research focuses on
the question which temporal sequence is better from the viewpoint of organizational
process efficiency. Therefore further analysis is needed. Within this paragraph the results
from multiple regression analysis is used to check which temporal sequence of innovation
types is more effective for gaining better organizational efficiency, which is measured by five
indicators; Production Lead time (PLT), Manufacturing Lead Time (MLT), Rework/Scrap Rate
(Scrap), Capital Utilization (CU) and Labour Productivity (LP). Because these variables were
not all normally distributed some of them were log transferred in order to make regression
analysis possible. For each of them multiple regression analysis was conducted and in the
following table the main results of this analysis are presented in order present and discuss
the findings for all separate efficiency indicators. Subsequently the findings from these
predefined models led to the need for additional analysis. Therefore next to the main
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(log
)
Pro
duction
lea
dlea
dtim
e
(log
)
Manu
facturing
Lea
dTime
)
li
r(L
)
(log
)
Scraprate
(Scrap
)
Capita
l
Uti
lisation
(CU
)
(log
)
La
bour
pro
ductivity
Control Variables
Manufacturing subsector
(reference: Metal)
Food
TextileConstruction
Chemistry
Machinery
Electronics
Transport
(log) Organizational Size
(log)Research & Development
(log) Share of turnover New products
Product development
(reference: no product development at site)
Customer unique
Semi unique
Standard program
Production/Assembly
(reference: Make to order)
Assemble to order
-,123***
-,025-,122***
-,165***
,170***
-,117**
-,026
,107**
,037
-,012
-,039
-,037
-,053
-,078*
,036
,022,067**
-,025
,126***
,058
,037
-,025
-,032
,029
-,046
,003
-,114**
-,108***
-,073
-,017,096*
-,012
-,044
-,038
,015
,058
-,015
,022
-,100
-,086
-,107
-,047
-,105*
-,067-,010
-,106
,173**
,062
-,019
,102
-,070
,034
,217
,217
,110
-,027
,048
-,022-,059
,078
,012
-,017
-,030
,125*
,080
,012
-,069
,018
,003
-,082
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4.3.1. Production Lead Time (PLT)
The first efficiency indicator is the production lead time (PLT). The summarization of this
regression model is as follows [F=12,722, p
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The results showed that concerning the production lead time the temporal sequence of
technological and organizational was only significant at a 90% confidence level for the TI first
perspective when occurring within a small timeframe (1 year). Therefore it is very doubtful
that the temporal sequential configuration is important for achieving lower production lead
times. However the additional analyses revealed an interesting finding. It seemed that
instead of a specific temporal sequence the timeframe in which they are jointly introduced
has an effect on PLT. From the additional models it seems that the smaller this timeframe isthe lower the PLT will be, which can be viewed as a positive sign of process efficiency.
4.3.2. Manufacturing Lead Time (MLT)
The summarization of the regression model for the Manufacturing Lead Time is as follows
[F=42,039, p
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lead time the results found for PLT have possibly an indirect effect in the MLT of
manufacturing firms but this was not confirmed by the findings. When all findings were
taken into consideration it had to be concluded that no evidence for any influence of
temporal sequence of OI and TI on manufacturing lead time was found.
4.3.3. Rework/Scrap rate (Scrap)
After examining the regression analysis of the model representing the efficiency indicator
Rework/Scrap rate it had to be concluded that this model had significant explanatory power.
The summarization of this model is as follows [F=1,185 P>0,10] which correspondents with a
low adjusted R square of 0,014, which implies that this model only can explain 1,4% of the
variance in the variable Rework/Scrap rate. Further no significant beta coefficients are
found. It can be noted that the beta values for OI first are negative and for TI first positive,
which might point to positive efficiency effects of the OI first perspective, however these
findings are not significant and are therefore very doubtful. Also the model with the metric
variable sequence of innovation was not significant when placed into this model as
independent variable. Based on these findings we can conclude that the used models are not
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level, besides the corresponding beta values are very low (,005 & -,005). As explained in
appendix 2.6.4. this could imply two things 1) there is no effect of temporal sequence of OI
& TI on PLT, or 2) there is a significant effect of the temporal sequence of innovations
however only when they are implemented within a short timeframe ( 1 year or 0,5 year).
The results of these additional analyses which examined both perspectives within a one year
timeframe did revealed a significant beta value (-,091**) for the TI first perspective (1
year). From these results it could be concluded that the TI first perspective results in a lowercapital utilization than when occurring in a longer timeframe (>1 year =0,55) this negative
beta within the one year timeframe is a negative sign of efficiency. In combination with the
non-significant beta values for OI first 1 year (-,039) and OI first > 1 year (,032) this
indicates that a small timeframe in which TI and OI are implemented seems to have negative
effects for capital utilization.
Given these results additional analyses were conducted in which specific small timeframes
were tested. These findings revealed that when OI and TI are jointly implemented within one
year this resulted in a significant beta of (-,106**) and when occurring within a 0,5 year
timeframe (-,109**). These findings substantiate the earlier thoughts that when both TI and
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in a significant beta11
in favour of the technological innovation first perspective. To check
this finding additional regression analyses were executed in which the sequence of
occurrence as categorical variable was tested by the use of two dummy variables
representing both OI first and TI first. When examining the beta coefficients it was found
that both temporal sequences were not significant at any reasonable confidence level. As
explained in appendix 2.6.5. this could mean two things 1) there is no effect of temporal
sequence of OI & TI on labour productivity 2) there is a significant effect of the temporalsequence of innovations however only when they are implemented within a short timeframe
( one year or 0,5 year). From these additional analyses it became clear that only
organizational innovation first ( 1 year) provided a significant positive beta (,114**) in
contrast to the negative beta (-,116**) for the OI first (> 1 year) variable. Although the beta
for TI first ( 1 year) was not significant it also provided a positive beta value (,051). From
these results it could be concluded that the positive effect of the organizational innovation
first perspective is only valid when it occurs within a timeframe of maximal 1 year. These
findings are in contrast to the earlier findings from the analysis with the metric variable
sequence of innovation
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forms of innovations interrelate with each other. Subsequently it was described in which
specific temporal configurations these innovations could be introduced. This led to a
detailed description of the organizational innovation first andthe technological innovation
first perspectives. The literature review made clear that there is stronger evidence in
support of the organizational innovation first perspective regarding its influence on
organizational process efficiency. This led to the following hypothesis
Hypothesis 1: Generally speaking, manufacturing firms that let organizational innovations
precede technological innovations will have higher organizational efficiency
then organizations that followed the technological innovation first
perspective.
As described in the literature review much is written about the importance of the
organizational first perspective. By letting organizational innovations proceed technological
ones organizations should be possible to gain higher performance which in this research was
measured by process efficiency. By the use of all tested models the following conclusions
can be made in order to give a substantiated answer to this hypothesis.
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descriptive analyses it became clear that a majority of the manufacturing firms within the
dataset followed this organizational innovation first perspective no strong evidence for
positive effects on process efficiency was found. In conclusion it can be stated that although
the theory review offered many logical reasons for the positive effects of the organizational
innovations first perspective, the hypothesis in support of this literature has to be rejected.
Besides the organizational innovation first perspective the other hypothesis focussed on its
counterpart, the technological innovation first perspective, which was tested by thefollowing hypothesis.
Hypothesis 2: Generally speaking, manufacturing firms that let technological innovations
precede organizational innovations will have higher organizational
efficiency then organizations that followed the organizational innovations
first perspective.
In the literature the concept of technological determinism is widely discussed. Organizations
that focus on technological innovations subsequently followed by organizational changes
could achieve higher process efficiency. By the use of all tested models the following
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these contrasting findings it is not possible to state that the technological innovation first
perspective within a one year timeframe is positive from the viewpoint of process efficiency.
And in comparison to the previous described organizational innovation first perspective no
evidence is found that the technological innovation first perspective if more desirable within
the context of this research. In conclusion it can be stated that although the theory review
offered logical grounds for the positive effects of the organizational innovations first
perspective, the hypothesis in support of this literature has to be rejected in this case.
Additional hypothesis: Generally speaking, organization that jointly introduce
technological and organization innovations within a one year
timeframe will have higher process efficiency than
organizations who jointly introduce these innovations within a
larger timeframe.
In the models representing both the technological innovation first and organizational
innovations first perspective no strong evidence was found in support of efficiency effects of
a specific temporal sequential configuration. However the results from these analyses
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specific temporal sequence which is an interesting finding. However it is important that
more research is performed to substantiate these findings and answer the question why this
time span is of importance. Logical reasons could be that organizations who introduce
technological and organizational innovations in a longer timeframe are the organizations
who manage the configuration by themselves while the organizations implementing them
within a smaller timeframe are those that used external expertise. However this has to be
tested in additional research by investigating the role of corporation with external
organizations. On the other hand the role of these external parties could also be the reason
why no strong evidence was found in favour of one of the temporal sequential
configurations. Because the role of these external partners is not reflected within the used
dataset, because this data assumed that all innovations are executed by the firms
themselves. This is not realistically because with the implementation of for example new
computer- and software systems firms often use external organizations, like for example
consultant companies or the suppliers of the systems. However based on the results of the
tested models it can be concluded that there is some weak evidence in support of this
additional hypothesis.
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5. Discussion & Conclusion
In the previous chapter the results of the quantitative analysis of the EMS dataset were
presented. Within this chapter these findings are used to discuss the central problem
statement within this research and subsequently answer the main research questions. The
conceptual model which guided the corresponding hypotheses will be discussed and
conclusions are drawn. Finally the limitations of this research are discussed and suggestions
for further research will be provided.
5.1. Temporal sequential innovation configurations
The literature study revealed that innovation research has gained much ground in business
literature over the last decade. A huge base of literature describes the innovations that have
changed the way we practice business these days from multiple perspectives. Traditional
research focussed on technological innovations. This technological determinism points to
the importance of new technologies and explains the mechanisms on how new inventions
contributed to organizational performance in the past. On the other side the research about
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from the perspective of organizational learning and knowledge transfer it became clear that
the organizational first perspective strongly relates to the concept of acquisitive learning,
which can be explained as a learning-before-doing approach. By creating a strong knowledge
base and good knowledge transfer organizations can more effectively introduce new
technological changes, and it further also contributes to the effective implementation of
additional organizational changes after the introduction of the a new technology. On
contrary, literature revealed some logical explanations in favour of the technological
innovation first perspective. Based on the theories of technological determinism it could
be argued that new technological developments are the driving force of new organizational
changes. In some cases it would be more effective to implement organizational adjustments
only after a new technology is introduced. When examining this idea from the perspective of
organizational learning and knowledge transfer it could be related to the concept of
experimental learning (experimenting within the production environment). By the use of a
learning-by-doing approach organizations focus on developing their procedural knowledge
(Burns, Acar & Datta, 2010, p. 272) and in that way enhance organizational performance.
This specific approach is especially useful when organizations lack theoretical and practical
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because it suggests that these organizations should possibly reconsider their innovations
strategies in order to achieve better results.
In the examination of the general technological innovations first perspective also no
significant effects on any of the efficiency indicators were found. This implies that there is no
significant evidence that by introducing technological innovations before organizational ones
a higher efficiency can be gained. However when this specific sequential configuration was
examined within a one year timeframe it became clear that there was a prevalent effect on
the production lead time. It seemed that when this configuration was occurring within this
short time span, production lead time was decreased. Further a negative efficiency effect on
the capital utilization was witnessed which implies that in comparison to the positive effect
on production lead time the capital utilization was lower within this specific temporal
sequence.
So when taking all these findings into consideration it can be stated that no strong
unambiguous evidence was found in support of the literature describing the possible effects
on process efficiency. The temporal sequential configuration of innovation efforts did not
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When combining the findings from the literature review and the results from the descriptive
analysis and multiple regression analysis it became clear that in comparison to the theory
which describe possible performance effects for both the organizational innovation first
and the technological innovation first perspectives it became clear that these effects were
not significantly unambiguously supported within the EMS-2009 data.
Yet it can be stated that this research revealed that there are strong opinions about how
organizations should configure their innovation efforts. However the mechanisms that
explain how specific sequences can positively or negatively contribute to organizational
efficiency are very complex, and simply cannot be described by one general success formula.
This research showed that a majority of organizational firms follow the organizational
innovation first strategy, although this did not resulted in efficiency effects within the
specific indicators measured. Further it was found that organizations that pursue an
innovation strategy in which technological innovation precedes organizational adjustment
also did not have unambiguous effects on these indicators. The dynamics of specific
technological and organizational innovations is a complex web of many different relations
which clearly cannot be described by one dominant view or theory. So may some
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investigate how organizations could benefit from an alignment of different innovations. A
better understanding of the mechanisms underlying this alignment between technological
and organizational innovations will eventually provide specific best practices for
organizations in the future.
5.2. Limitations and suggestions for further research
Within this research the Technological-Organization Discords has been explained by thereview of existing literature and new quantitative research. Although this research tried to
encompass all existing perspectives and correctly examine the statistical data of the EMS-
2009 dataset some limitations have to be admitted. In the literature review it became clear
that although the concept of innovation is a hot topic within business literature the last
decades the total base of academically works about the interrelatedness of organizational
and technological innovations is not that extensive as for many other subjects. Many articles
stress the importance of specific innovations, describe the different state-of-the-arts
innovations of the last century and highlight the possible benefits they have to offer, not
that much is written about the strategic choices an organization has to make concerning the
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of technological and organizational innovation encompass a much wider variety of specific
innovations that might provide other results. Therefore the generalisation of results is only
possible to a certain extend. In future research is would therefore be very interesting to
focus on the alignment of specific organizational and technological innovations.
Concluding there are some limitations of the EMS-2009 dataset. Although this dataset is very
substantial and encompasses many organizations within different European countries there
are some points that need to be addresses. First the response rate of the EMS survey is quite
low which may lead to sampling bias. Further the EMS-data only contains information about
organizations within the manufacturing industry, which affect the generalizability of the
findings over other industries. At least some questions were only present in the German
surveys which make the findings less generalizable over the other countries.
Given the previous mentioned limitations and the findings of this research some general
suggestions for further research can be addressed. This research has focussed on the
temporal sequence of both technological and organizational innovations. From the results is
became clear that no strong evidence was found in favour of a specific temporal sequential
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other results are found when examining other indicators of performance like form example:
differentiation indicators or employee satisfaction.
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Appendices
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Appendix 1
1.1 General Information EMS-2009
European Manufacturing Survey Partners 2009
Weblink:
http://www.european-manufacturing-survey.eu
Partners:
Germany: Fraunhofer Institute System and Innovation Research
Austria: Division Technology Policy; ARC Systems Research
France: BETA, Universit Louis Pasteur Strasbourg
Switzerland: Institut fr Betriebs- und Regionalkonomie, Hochschule fr Wirtschaft, Luzern
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1.2 EMS Survey
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Appendix 2: Analyses of EMS-2009 data
2.1 Reliability analysis Technological Innovations
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2.2 Reliability analysis Organizational Innovations
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2.3 Number of innovations introduced
2.3.1 Number of technological innovations introduced
0,00%
5,00%
10,00%
15,00%
20,00%
25,00%
%oforganizations
Total number of innovations introduced
Number of used technological Innovations
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2.4 Descriptives metric variables
3
Above the general descriptives of the metric variables within this research are presented.
These findings are important in order to check whether the distributions of the selected
variables are subject to skweness and kurtosis. Within the orange rectangles the variables
that have a certain amount of skewness and kurtosis are depicted. Within this research the
Descriptive Statistics
N Minimum Maximum Mean Std. Deviation Skewness Kurtosis
Statistic Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Std. Error
number of technologies used in your factory 2961 ,00 13,00 2,5947 2,26755 1,016 ,045 1,105 ,090
number of organisational concepts used inyour factory
1236 ,00 13,00 4,7071 2,76590 ,404 ,070 -,350 ,139
Av Year Implementation TI - Av YearImplementation OI
861 -41,00000 48,33333 2,5689559 7,15090870 ,991 ,083 7,367 ,166
Production lead time (main product) [workdays at 8 hours]
2846 ,0 10000,0 33,159 202,4129 42,821 ,046 2075,380 ,092
Delivery time (main product) [calendar days] 2920 0 900 40,33 67,970 4,305 ,045 27,905 ,091
Rework/ scrap (main product) [%] 2871 ,0 70,0 3,141 5,7658 5,999 ,046 50,497 ,091
Degree of capacity utilisation 2008 [%] 2344 10,00 150,00 86,6404 14,92081 -,801 ,051 2,825 ,101
Value added (turnover - input per employee[Thsd. Euros])
2322 -291,67 1085,07 102,0512 77,78138 4,095 ,051 32,672 ,102
Number of employees in 2008 3204 10 44000 226,45 1326,149 20,857 ,043 544,104 ,086
Share of personnel: Research anddevelopment [%]
2926 0 90 4,80 7,180 3,804 ,045 24,128 ,090
Share of turnover generated by newproducts [% - only innovators]
1758 0 100 17,18 16,565 2,163 ,058 5,780 ,117
Sales growth 2006-2008 in % 2822 -99,88899 26566,66667 31,2818926 5,01428019E2 52,576 ,046 2783,044 ,092
Valid N (listwise) 313
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Syntax Log transformations
COMPUTE LOG1=LN(V19A+1).VARIABLE LABELS LOG1 'Log Production Lead Time (ln+1)'.EXECUTE.COMPUTE LOG2=LN(V19B+1).VARIABLE LABELS LOG2 'Log Delivery Time (ln+1)'.EXECUTE.COMPUTE LOG3=LN(V19D+1).VARIABLE LABELS LOG3 'Log Rework/Scrap (ln+1)'.
EXECUTE.COMPUTE LOG5=LN(V21B1).VARIABLE LABELS LOG5 'Log Number of employees 2008'.EXECUTE.COMPUTE LOG6=LN(V16B1+1).VARIABLE LABELS LOG6 'Log Share of personal R&D (ln+1)'.EXECUTE.COMPUTE LOG7=LN(v06b+1).VARIABLE LABELS LOG7 'Log Share of turnover new products (ln+1)'.
EXECUTE.COMPUTE LOG10=LN(RCV3+100).VARIABLE LABELS LOG10 'Log sales growth 2006-2008 (ln+100)'.EXECUTE.
Results log transformations
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2.5 Descriptives non-metric variables
Dependent variables