Proceedings of the 3rd European Conference on Intellectual Capital

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Maurizio Massaro et al. crucial factors for increasing <strong>the</strong> learning process in order to supplementing <strong>the</strong> effect <strong>of</strong> absorptive capacity (Teece, 1987). Our study centered on <strong>the</strong> role <strong>of</strong> KM during innovation strategies and using previous literature, we tried to analyse <strong>the</strong> role <strong>of</strong> Management and Control Systems (MCS) as a blend <strong>of</strong> tools that support <strong>the</strong> KM during innovation strategies processes within and across organizations. The connection between Innovation and MCS is not quite clear (Bisbe, Otley, 2004) but seems to be related more to <strong>the</strong> use <strong>of</strong> MCS than to <strong>the</strong> characteristics <strong>of</strong> a single tool (Henry, 2006). Indeed, <strong>the</strong> outcome <strong>of</strong> our literature review shows that <strong>the</strong>re is more than one definition <strong>of</strong> MCS. Many textbooks adopt <strong>the</strong> narrow definition <strong>of</strong> control: management activities are categorised separately as planning, control and decision-making. Emmanuel et al. (1990) reject this approach and adopt a wider definition <strong>of</strong> control which encompasses both planning and ensuring that plans are implemented for achieving objectives. Thus, planning, control and decision-making are inextricably linked within <strong>the</strong> wider conception <strong>of</strong> management control. Indeed <strong>the</strong> literature recognises that MCS could have two complementary and interdependent roles (Ahrens and Chapman, 2004; Simons, 1994): i) <strong>the</strong>y can be used to extend control over <strong>the</strong> attainment <strong>of</strong> organisational goals; ii) <strong>the</strong>y can also be used to enable managers and employees to search for opportunities and solve problems. Scholars recognise that this blend <strong>of</strong> aims requires a combination <strong>of</strong> multiple control systems that work toge<strong>the</strong>r (Otley, 1980), and different levers <strong>of</strong> control (Simons, 1994). Numerous prior studies have highlighted <strong>the</strong> importance <strong>of</strong> achieving a fit between a firm’s strategy and its MCS (Chenhall, 2003; Langfield-Smith, 1997). Traditionally, <strong>the</strong> relationship between strategy and MCS has been viewed as a passive one, suggesting that <strong>the</strong> MCS is an outcome <strong>of</strong> a firm’s strategy. However, Hopwood (1987) and Dent (1990) recognised that <strong>the</strong> MCS might have a proactive role in influencing strategy. Thus, <strong>the</strong> role <strong>of</strong> MCS and its impact on innovation processes has emerged as an important research question (Shields, 1997) but few empirical studies address <strong>the</strong> relationship among <strong>the</strong>se variables, especially considering SMEs. By focusing on this line <strong>of</strong> research, we adopted an exploratory study that aims to investigate <strong>the</strong> relationships between <strong>the</strong> KM processes adopted during innovation strategies and <strong>the</strong> use <strong>of</strong> MCS. Thus, our research questions are: RQ1: How do enterprises manage knowledge in <strong>the</strong> specific process <strong>of</strong> innovation strategy? RQ2: How do enterprises design and use MCSs in <strong>the</strong> specific process <strong>of</strong> innovation strategy? RQ3: Is <strong>the</strong>re a link between KM processes adopted by <strong>the</strong> enterprises <strong>of</strong> <strong>the</strong> sample in <strong>the</strong> specific process <strong>of</strong> innovation strategies and <strong>the</strong> use <strong>of</strong> MCSs?. 2. Methodology A sample <strong>of</strong> 40 SMEs located in north-eastern Italy was drawn from a list <strong>of</strong> 200 manufacturing firms supplied by local SMEs associations, which provided company names and contacts. The overall selection criterion for creating <strong>the</strong> sample was <strong>the</strong> introduction <strong>of</strong> at least one new product by <strong>the</strong> firm during <strong>the</strong> last three years and with expected costs for innovation strategies projects above 100.000 Euros. This enabled <strong>the</strong> selection <strong>of</strong> companies with an history <strong>of</strong> innovation and with relevant innovation strategy projects. Our research was conducted under <strong>the</strong> form <strong>of</strong> a knowledge audit where owner-managers were interviewed in <strong>the</strong>ir role as “experts” (Ketchen and Shook, 1996). We adopt a multi-method, multicase field research design to capture, in depth, <strong>the</strong> richness needed to investigate patterns <strong>of</strong> KM and MCS-use within innovation strategies and <strong>the</strong> link between <strong>the</strong>m. Our multi-method design employs qualitative and quantitative data collected through four main information sources: public data, company reports and one semi-structured interview and a structured questionnaire. This questionnaire was created based on <strong>the</strong> interviews and <strong>the</strong> o<strong>the</strong>r materials and sent to <strong>the</strong> CEO, <strong>the</strong> CFO and <strong>the</strong> Research and Innovation Manager asking <strong>the</strong>m to rate <strong>the</strong>ir answers on a 1-7 Likert scale. To identify <strong>the</strong> KM processes involved in innovation strategies we refer to <strong>the</strong> knowledge model <strong>of</strong> Holsapple and Jones (2004). Thus, <strong>the</strong> KM processes taken into consideration were: i) knowledge acquisition: acquiring knowledge from external sources and making it suitable for subsequent use; ii) knowledge generation: producing knowledge by ei<strong>the</strong>r discovery or derivation from existing knowledge and making it suitable for subsequent use; iii) knowledge selection: selecting knowledge generated and making it suitable for subsequent use. The variables we chose to characterise KM were: i) knowledge origin or “where”: repositories <strong>of</strong> acquired, generated and selected knowledge; ii) 232
Maurizio Massaro et al. knowledge creation process or “how”: ways <strong>of</strong> knowledge acquisition, generation and selection. “Where” and ”How” were <strong>the</strong>n connected to acquisition, generation and selection for determining our clusters. “Where” and ”How” were also used to categorise <strong>the</strong> answers from <strong>the</strong> interviews as follows: i) 15 different ways <strong>of</strong> acquiring knowledge (7 “where” + 8 “how”); ii) 8 different ways <strong>of</strong> generating knowledge (3 “where” + 5 “how”); iii) 8 different ways <strong>of</strong> selecting knowledge (3 “where” + 5 “how”). To identify <strong>the</strong> use <strong>of</strong> MCS we refer to classifications suggested by previous research (Vandenbosch, 1999, Davila et al, 2009). The variable chosen to characterise <strong>the</strong> use <strong>of</strong> management control process was <strong>the</strong> information use or “how”: ways <strong>of</strong> use <strong>of</strong> information drawn from MCS in <strong>the</strong> specific process <strong>of</strong> innovation strategic decision-making. The open answers supplied by <strong>the</strong> owner-managers that we interviewed were categorised by overlapping <strong>the</strong>se classifications recognizing 9 different uses <strong>of</strong> MCS. Cluster analyses have been used to classify firms into different groups based on <strong>the</strong>ir KM processes and MCS-use. Some techniques based on <strong>the</strong> agglomeration coefficients have guided <strong>the</strong> selection <strong>of</strong> final cluster solution: dendogram analysis; average silhouette index analysis; Tukey’s pair wise comparison analysis. These approaches have been adopted in management control and in strategic management field research (Ketchen and Shook, 1996; Gerdin and Greve, 2004; Henri, 2008). We subsequently tested our results using k-means approaches and p.a.m. approaches (Kaufman and Rousseeuw, 1990). To investigate <strong>the</strong> existence <strong>of</strong> a “coherent” link between KM processes and use <strong>of</strong> MCS we use a Chi-Square Test (χ 2 ). To provide reassurance that <strong>the</strong> observed associations were statistically robust, some control variables have been used. In particular we compare our results considering: (i) environment uncertain; (iii) technology uncertain; (iiii) organization structure; iv) organization size; v) strategy characteristics. Those variables have been selected since <strong>the</strong>y represent common factors used in contingency-based management accounting research (Chenhall, 2003). In order to carry out <strong>the</strong>se tests One-Way-ANOVA-models have been employed for each control variable. 3. Results The cluster analysis led to <strong>the</strong> identification <strong>of</strong> distinct groups at <strong>the</strong> levels <strong>of</strong> KM processes and <strong>of</strong> MCS-use, and <strong>the</strong> semi-structured interviews showed that distinct sources <strong>of</strong> knowledge and ways <strong>of</strong> applying <strong>the</strong> knowledge are employed. The following paragraphs illustrate <strong>the</strong> results <strong>of</strong> our analysis. 3.1 Knowledge acquisition The results <strong>of</strong> our analyses (see Table 1) reflect <strong>the</strong> presence <strong>of</strong> two groups that could be related to <strong>the</strong> dimensions <strong>of</strong> <strong>the</strong> knowledge acquisition model discussed by Jordan and Jones (1997), appropriately adapted to our sample companies. The groups are labeled as follows: Cluster 1 “Focused”: Enterprises <strong>of</strong> this cluster use structured approaches as <strong>the</strong>ir knowledge acquisition process. Great importance is given to pr<strong>of</strong>essional providers <strong>of</strong> knowledge such as research centers and consultants as <strong>the</strong> main sources <strong>of</strong> acquisition. Suppliers <strong>of</strong> goods and services are also important, even though less importance is given to <strong>the</strong>m than by <strong>the</strong> o<strong>the</strong>r cluster. Preference is given to collaborative approaches, temporary management and access to existing knowledge databases. Cluster 2 “Opportunist”: Enterprises <strong>of</strong> this cluster mainly use economic actors <strong>of</strong> <strong>the</strong>ir value chain (mostly suppliers, customers and sales network) in <strong>the</strong>ir knowledge acquisition process. Techniques used to acquire knowledge are sparsely structured. A fair number <strong>of</strong> enterprises recognizes <strong>the</strong> importance <strong>of</strong> <strong>the</strong> information given by competitors and gained from exhibitions, fairs, meetings. Ample importance is given to <strong>the</strong> access <strong>of</strong> existing information available in markets. 3.2 Knowledge generation Our results (see Table 2) reflect <strong>the</strong> presence <strong>of</strong> two groups that could be related to <strong>the</strong> KM strategies first identified by Zack (1999), adequately adapted to our sample companies. They are labeled as follows: Cluster1 “Aggressive Planners”: Enterprises <strong>of</strong> this cluster create specific cross-functional groups to develop <strong>the</strong>ir research projects. A few <strong>of</strong> <strong>the</strong>m also feature an R&D group, which is usually not 233
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Proceedings <stron
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Contents Paper Title Author(s) Page
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Paper Title Author(s) Page No. Hier
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Preface These proceedings represent
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educating through ‘hands on’ co
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Ibrahim Elbeltagi is a Senior lectu
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Theodora Ngosi has a PhD in Compute
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José Vale is an invited assistant
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Social Knowledge: Are you Ready? 1
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John Girard don’t know.” (Benne
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John Girard The real question becom
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John Girard our belief that many le
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John Girard organizations to gain k
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Intellectual Capital Accounting - H
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Academic Research Papers 15
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Khodayar Abili and Mahyar Abili The
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Khodayar Abili and Mahyar Abili Nah
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Khodayar Abili and Mahyar Abili Tim
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Knowledge Transfer in Romanian Univ
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Simona Agoston et al. Table 1: Know
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Simona Agoston et al. homoscedastic
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Simona Agoston et al. included in <
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A Modeling Approach to Intellectual
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Eckhard Ammann (referring to <stron
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Eckhard Ammann Figure 2: The IC spa
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Eckhard Ammann section 2. They are
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Eckhard Ammann From an overall pers
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Sorin Anagnoste and Gabriela Dumitr
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Gabriela Atanasiu and Florin Leon T
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Gabriela Atanasiu and Florin Leon B
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Gabriela Atanasiu and Florin Leon a
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Developing and Implementing Strateg
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2. Technology, faculty, and staff B
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Learning programs Individual poten
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Bob Barrett facts with the<
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Intellectual Capital Dynamics withi
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Ruxandra Bejinaru and Stefan Iordac
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Education and Training Practice Str
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Andrea Bencsik et al. We wanted to
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Andrea Bencsik et al. In this sampl
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Table 4: Types of
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References Andrea Bencsik et al. An
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Ayşen Berberoğlu and Emine Ünar
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Ayşen Berberoğlu and Emine Ünar
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Table 7: Descriptive statistics Ay
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Knowledge Dynamics Modeling Using A
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Constantin Bratianu et al. will hav
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Constantin Bratianu et al. 5. a) Gi
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Constantin Bratianu et al. 5. Data
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Constantin Bratianu et al. Nissen,
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2. Technology and infrastructure Sh
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Sheryl Buckley and Apostolos Gianna
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Donley Carrington and Mike Tayles t
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Donley Carrington and Mike Tayles i
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Donley Carrington and Mike Tayles G
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John Dumay and Jim Rooney on a more
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John Dumay and Jim Rooney Taking <s
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John Dumay and Jim Rooney “For ex
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John Dumay and Jim Rooney Mouritsen
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Marziye Ehrami et al. 1. Creation
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Marziye Ehrami et al. Note: In 2005
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Earnings Quality and Othe</
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Magdi El-Bannany resource-based per
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Magdi El-Bannany Based on this argu
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Magdi El-Bannany Table 2: Descripti
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Magdi El-Bannany variable equal to
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Magdi El-Bannany Edvinsson, L. & Ma
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Ahmed Elsetouhi and Ibrahim Elbelta
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Albrecht Fritzsche and Rebecca Geig
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Table 4: Applicability of</
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S&P 500 Market Cap ($ billions) 14,
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Tatiana Garanina and Yana Pavlova T
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Tatiana Garanina and Yana Pavlova I
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Impact of Investme
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Lidia García-Zambrano et al. 2003;
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Lidia García-Zambrano et al. 4.2 P
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Maria Cristina Morariu Tobin’s q
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Maria Cristina Morariu for determin
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The Structural Model of</st
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Fattah Nazem Ackerley (2006) reveal
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Fattah Nazem The research instrumen
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Fattah Nazem intellectual capital m
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Fattah Nazem Johnson, M.J. (1986) T
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Andrei Stefan Nestian organization.
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Andrei Stefan Nestian Chaordic syst
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Andrei Stefan Nestian environment,
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Increasing Knowledge Management Mat
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Theodora Ngosi et al. 2.2 Dominatin
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Theodora Ngosi et al. Figure 1: Del
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Theodora Ngosi et al. IT systems, a
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5. Discussion and summary Theodora
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Theodora Ngosi et al. Weill, P., an
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Bongani Ngwenya late adopters respo
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Bongani Ngwenya This paper extends
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Bongani Ngwenya argument, t
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Bongani Ngwenya DiMaggio, P. J., an
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The Quality of Kno
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5.2 5 4.8 4.6 4.4 4.2 4 3.8 Compete
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Corina Pelau et al. In order to imp
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Hierarchy and Tacit Knowledge in <s
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Ulrica Pettersson and James Nyce in
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Ulrica Pettersson and James Nyce mi
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Katja Pook and Campbell Warden time
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Katja Pook and Campbell Warden futu
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5. Commonalities of</strong
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Katja Pook and Campbell Warden <str
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Katja Pook and Campbell Warden Lazl
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Nicolae Al. Pop et al. satisfaction
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Nicolae Al. Pop et al. the<
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Nicolae Al. Pop et al. satisfaction
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Knowledge Cities: A Portuguese Case
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Katia Rodrigues and Eduardo Tomé
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3. The empirical study 3.1 Methodol
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Table 3: Smart cities - application
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4.3 Suggestions to future work Kati
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Anna Romiti and Daria Sarti 2. Orga
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Anna Romiti and Daria Sarti Governa
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Anna Romiti and Daria Sarti Valkoka
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Kent Rondeau and Terry Wagar planne
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2.1 Study measures Kent Rondeau and
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Kent Rondeau and Terry Wagar contri
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Kent Rondeau and Terry Wagar Our re
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Intellectual Capital and a Firm’s
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Figure 1: Hypothes
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Helena Santos-Rodrigues et al. Norm
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Helena Santos-Rodrigues et al. To s
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Helena Santos-Rodrigues et al. Our
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Factors Influencing the</st
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Thanaletchumi Sathasivam et al. 2.3
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Thanaletchumi Sathasivam et al. gro
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Thanaletchumi Sathasivam et al. 11.
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Enhancing IC Formation by Evoking H
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Klaus Bruno Schebesch to portray <s
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Klaus Bruno Schebesch 4. Leaning an
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Klaus Bruno Schebesch We argue in f
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Klaus Bruno Schebesch come
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In Search of key F
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Karen Smits et al. contribution, wi
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Karen Smits et al. Table 1: Correla
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Karen Smits et al. staff feel that
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Marta-Christina Suciu et al. This p
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Marta-Christina Suciu et al. The qu
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Information and Communication Techn
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Jukka Surakka et al. The participan
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Jukka Surakka et al. people. Commun
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Personal Knowledge Management (PKM)
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Marzena Świgoń provide th
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Marzena Świgoń (number and type <
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Marzena Świgoń measure self-perce
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Eduardo Tomé what we consider to b
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Eduardo Tomé IC. The intervention
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Eduardo Tomé In the</stro
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Eduardo Tomé to stress that <stron
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Eleni Magdalini Vasileiadou et al.
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José María Viedma Marti products
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Sustainable competitive advantage T
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José María Viedma Marti knowledge
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José María Viedma Marti pr<strong
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José María Viedma Marti All <stro
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ICBS I CBS José María Viedma Mart
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José María Viedma Marti Sullivan,
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2. Balanced scorecard framework Ang
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Angelos Vouldis and Angelica Kokkin
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Unclear benefits of</strong
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Angelos Vouldis and Angelica Kokkin
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9. Discussion and conclusions Angel
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The Global Position of</str
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Piotr Wisniewski safeguarding IC vi
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Piotr Wisniewski Figure 2: Ten larg
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Piotr Wisniewski suspected
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Piotr Wisniewski 2) Slow growth in
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Piotr Wisniewski US 3.8 3.8 3.9 4.3
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2. Hypothesis deve
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3.2 Control variables Deborah Brans
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Deborah Branswijck and Patricia Eve
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Deborah Branswijck and Patricia Eve
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Korosh Gholami et al. Durkheim beli
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Korosh Gholami et al. The effect <s
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Measure the Unmeas
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Knowledge Capital 5. Efforts for de
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Exclusive use of L
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Emmanouil Gkinoglou from th
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Project Assets Need for synergies Q
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Table 5: Sustainability checklist E
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Emmanouil Gkinoglou The next challe
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Kai Mertins and Ronald Orth generat
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Kai Mertins and Ronald Orth Never<s
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Kai Mertins and Ronald Orth <strong
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Ad 2 Structure of
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Kai Mertins and Ronald Orth Hauff,
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Maria Simonova “Concept o
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Maria Simonova The concept regardin
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Maria Simonova Becker, G. S. (1983)
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2. The aim of appl
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Nellija Titova W+I+DP+D+T+M+R (equa
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Nellija Titova performing better. I
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5. Conclusions and furthe</
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Nellija Titova Annex 2: Summary on
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Meaning in the tab
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Krishanthi Ganga Vithana et al. for
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Krishanthi Ganga Vithana et al. sti
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5. Conclusion Krishanthi Ganga Vith
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The Tobing Knowledge Management Arc
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Paul Lumbantobing et al. user, know
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Paul Lumbantobing et al. people con
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Paul Lumbantobing et al. influence
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Develop KM Strategy Description Dev
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Paul Lumbantobing et al. In <strong
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Yuli Purwanti et al. Figure 1: Imag
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Yuli Purwanti et al. Table below is
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Andreea Feraru and Nicoleta Ciucesc
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Roxana Mironescu This could be <str
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Roxana Mironescu Daft, R.L. (2002)
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Paloma Sánchez and Oihana Basilio
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Evaluating Intellectual Capital Usi
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Stelian Stancu and Anca Domnica Lup
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José Vale et al. However, it is im
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José Vale et al. Pöyhönen, A. an
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Proceedings of the 3rd European Conference on Intellectual Capital

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