Intelligence Capital: A Capability Maturity Model for a Software ...

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Keywords – Business Intelligence, Intellectual Capital, Knowledge .... When an entrepreneur acquires intelligence input or hires a market research, he/she is.
Intelligence Capital: A Capability Maturity Model for a Software Development Centre Abstract Knowledge assets have become relevant to every organization and society since they are the most valuable product and production means in the knowledge economy. But measuring intangible assets still remains a challenge (Stone, 2008). The emerging knowledge-based view is still far from being a widely accepted theory (Grant, 1996). Researchers in this field are faced with knowledge-based value dynamics, changing environments and uncertainty. Knowledge assets are embedded capabilities in individuals, organizations or societies (Malhotra, 2003). This research is focused on the design of artifacts that enhance Intelligence Capital. Intelligence, understood in a comprehensive manner, is a knowledge asset that leverages adaptive capabilities through information gathering, sense-making, and adaptation. From this approach, developing Intelligence Capital means to increase adaptive capabilities to make better decisions supported on internal and external knowledge (Olavarrieta, 2010). The purpose of this research is to apply the Intelligence Capital framework to a Technology Development Centre (CEDETEC) at the Tecnologico de Monterrey. It is expected that through this intervention, Intelligence Capital capabilities of this Centre can be assessed and managed. This model is an innovating proposal that deals with Capital Systems (Carrillo, 2002), adaptive capabilities, and complexity. Keywords – Business Intelligence, Intellectual Capital, Knowledge Management Strategy, Performance Management, Knowledge Management Tools.

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Introduction In last four decades, both academics and practitioners have witnessed the transition

from an industrial to a knowledge-based economy. Drucker (1969) was one of the visionary managers who foresaw a discontinuity in production means and the increase of knowledge-based production. This knowledge-based economy challenges people at all levels: workers, managers, entrepreneurs, policy makers, among others, it is a genuine paradigm shift. The manufacturing process represents industrial economy, but which process could the knowledge-based economy be modeled after? Technology innovation and development could be an example, more specifically, the case of software development centers (SDC). SDC represent the knowledge-based economy because their final product is software, which is intangible and knowledge-based (Drucker, 1969).

The SDC production

‘machinery’ include programmers, business analysts, testers, user educators, project managers, all knowledge workers who use intangibles as inputs, increase their value, and deliver them to the value chain. Within this context, individual and organizational capabilities constitute knowledge assets (Malhotra, 2003).

The challenge is how to

manage, assess and develop these. Current accounting theoretical frameworks, for instance, do not convey new knowledge-based dynamics and do not support related decision making (Stone, 2008). How are managers and entrepreneurs making decisions? Managing a SDC implies being able to manage knowledge assets. It is not like building a factory and buying machinery, because the emphasis is on hiring the right people, training them and implementing the right processes. Accounting reports, again, do not supply information about these knowledge assets (Malhotra, 2003).

SDC decision makers really need

information about the way to organize knowledge assets: how to assess and develop them and what is their impact in key performance indicators (KPI) (Grant, 1996). This should lead us to a new production function that provides a better understanding about SDC output as a result of short and long run decisions related to resources. The research we continue to carry out aims to study a SDC at Tecnologico de Monterrey and to propose a

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knowledge-based framework to assess and develop its knowledge assets. Intelligence Capital is the main knowledge asset to be studied and a Capability Maturity Model approach is defined to assess and develop it.

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Capital Systems and Production Function Knowledge assets demand new frameworks that are suitable for knowledge-based

dynamics. Knowledge-based organizations, such as SDC, create value in a very different manner than traditional manufacturing. Instead of factories, warehouses, machines and operators, different kinds of assets such as employees, procedures, practices, and methodologies are used to create value. These knowledge-based organizations act more like living organisms than machines (Nonaka, 1991). The Capital Systems framework (Carrillo, 2002) provides a comprehensive taxonomy of knowledge assets that in fact recognizes organizations as living entities.

This framework, thus, constitutes an

Intellectual Capital extended model. The Production Function (PF) is a function that expresses the output of a firm based on the combination of inputs (Solow, 1956). A general expression could be as follows:

Q = f (X1, X2, X3…., Xn) where Q = quantity of output and Xn= quantity of factor n inputs (such as capital, labour, land, etc.) For knowledge-based organizations, the value creation process is not comprised by the traditional factors of production. There is a need of deconstructing the production function “black box” into more elemental components and interactions (Spender, 1996). For instance, a SDC does not need land or traditional labour to create value; instead, it needs specialized workers and methodologies to develop software applications.

In

concordance with our findings, the Capital Systems framework is proposed to identify new factors of production or knowledge assets. Capital Systems recognizes six major types of knowledge assets (Identity Capital, Intelligence Capital, Relational Capital,

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Financial Capital, Instrumental Capital and Human Capital). The following production function for a SDC is proposed in accordance to this context:

Q = f (C1, C2,C3…., Cn) where Q = quantity of output and Cn= level of capital n inputs (such as identity, intelligence, financial, human, etc.)

Fig. 1 – Capital Systems

Within the Capital Systems framework, intelligence belongs into the wider category of Referential Capital, whose distinctive value is to provide alignment to all other capitals within the system (Carrillo, 2002). This knowledge asset has a strategic nature, insofar it conveys the extent of the organization’s capability of adapting to its environment (Olavarrieta, 2010). The SDC case study aims to understand how this center capitalizes knowledge about itself and its environment in order to make better decisions and increase its output. The analysis could be as follows: Δ Intelligence Capital

=

Marginal Product

Δ Q Whenever both Intelligence Capital and output increase, a positive marginal contribution to productivity should follow. If SDC manages to invest in Intelligence Capital and increase productivity, then it is optimizing one of its knowledge assets. Intelligence Capital as such cannot be bought, however, SDC may perhaps buy some pieces of intelligence.

Nevertheless, completing the whole process requires other

resources and capabilities. This research aims to delimit these elements in order to advance a framework to assess Intelligence Capital.

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Intelligence Capital The field of Intelligence Capital involves a multidisciplinary approach that studies

intelligence as a strategic knowledge asset which aligns the rest of the capitals such as Human, Identity, Financial, etc. (Carrillo & Olavarrieta, 2002) (Carrillo & Olavarrieta, 2009) (Olavarrieta, 2010). In a broader sense, it focuses on adaptive capabilities within organizations. Intelligence has different dimensions and components, and this is the reason why some perspectives and definitions seem to be partial and incomplete. To define Intelligence in a holistic matter, a multidisciplinary approach is needed (Sternberg, 1995).

Our proposed model of Intelligence Capital takes into account different

perspectives and definitions of intelligence, for instance: a.

Military Intelligence: information gathering about the enemy and battlefield.

b.

Psychology: cognitive and contextual intelligence and multiple intelligences (Sternberg, 1997).

c.

Information technology:

business intelligence and data warehousing (Luhn,

1958). d.

Artificial intelligence: intelligent computing and intelligent agents (Zambonelli et. al 2003).

e.

Philosophy: understanding diverse phenomena.

f.

Economy: rational agents and behavioral economics (Simon, 1969).

g.

Management: competitive intelligence and strategic intelligence (Knip et. al, 1989).

From our perspective, Intelligence Capital is a knowledge asset: a set of adaptive capabilities of an individual, organization or society. Intelligence Capital comprises knowledge about the environment, but beyond that, it integrates as well the capabilities involved in sense-making and adaptation. In trying to attain a holistic approach, the most relevant contributions were selected amongst several disciplines related to intelligence. Hence the following Intelligence Capital components are suggested: a.

Value Alignment:

Capabilities related to value recognition, definition and

assessment. b.

Sensorial: Capabilities related to sensor design, implementation and calibration.

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c.

Experience: Capabilities related to knowledge acquisition, retrieval and validation.

d.

Decision: Capabilities related to cognition, problem-solving, modeling and choice selection.

e.

Consummation: Capabilities related to bringing to fruition former decisions and referring back to value alignment.

Fig.2 – Intelligence Capital Model

Based on the principle that knowledge-based organizations (such as SDC) create value using knowledge assets (such as Intelligence Capital), it is expected that: Δ Intelligence Capital →Δ Q This means that better adaptive capabilities increase production quantity and quality. In the SDC case, knowing more about the environment could lead to hiring the right people and using the right methodologies and technology. This, in turn, could lead to having more productive development teams and to increasing software production. From here, the next question follows: how do we assess Intelligence Capital? Also, how can we recognize Δ Intelligence Capital? Intelligence Capital is a system of components, and these components are sets of capabilities. Capability Maturity Model (CMM) has proved to be an adequate method to assess sets of capabilities (Kohlegger et al. 2009). A CMM for Intelligence Capital capabilities is recommended. Periodical Intelligence Capital assessments support managers in recognizing Δ Intelligence Capital. Intelligence Capital, like most of knowledge assets, is difficult to measure using tangible assets methods. An entrepreneur can buy a piece of land or a machine, even a patent or a brand, but how can he/she acquire Intelligence Capital? He/she may pay for an intelligence input or a market research; he/she may hire a competitive intelligence consultant, but how should these expenses or investments be managed? Cost accounting and project based accounting were created to keep track of flows and to accumulate and

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distribute cost in a manufacturing or constructing process. A knowledge asset-based accounting is suggested here to manage flows from and to different capitals. When an entrepreneur acquires intelligence input or hires a market research, he/she is developing adaptive capabilities. The cash flow involved in these transactions must be recorded and related to each Intelligence Capital component. Following the proposed model, some enhancement in sensorial and experience component could be identified. This comprehensive model recognizes that these two components are developed to the extent that the whole system (Intelligence Capital) operates effectively. Adaptation does not solely require sensing the environment, but sense-making, as well as being able to respond adequately are fundamental to this function.

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Case Study: Software Development Center

4.1 Unit of analysis The focus of analysis of our research is CEDETEC, a software development center at Tecnologico de Monterrey (TEC), Mexico. TEC, being the largest private university system in Latin America, is home to a number of centers focused on diverse disciplines. At its origins, CEDETEC developed software for internal university use only. However, when the public and private sector’s demand for software development was spotted, CEDETC scope was widened as a response to market needs. Thirty three months of CEDETEC’s operations: April 2008 through January 2011 are being analyzed in our work.

Table 1 – Tecnologico de Monterrey and CEDETEC

This research aims to solve the problem of managing knowledge assets and specifically Intelligence Capital within CEDETEC.

The research question that was

addressedwas: how can we operationalize Intelligence Capital in a knowledge-based organization? Following literature overview, a multidisciplinary approach on Intelligence based on a comprehensive five components model was proposed.

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For assessing

Intelligence Capital, a CMM model based on a set of capabilities for each Intelligence Capital component is proposed. 4.2 Method This research aims to design an artifact for knowledge assets assessment and development from a normative perspective (Simon 1969).

Instead of gathering

information for descriptive purposes, a set of research tools were applied to gain understanding about the ideal state of the organization. This research is following design science stages and outputs (March, 1995). Four stages (Build, Evaluate, Theorize and Justify) are included, as well as four outputs per stage (Constructs, Model, Method and Instantiation). For the building stage, a literature overview about knowledge assets and intelligence was conducted to define research constructs as a first step. Secondly, ontology was used to model CEDETEC as a knowledge organization, using knowledge assets to create value. Then, we designed seven different templates that were applied to CEDETEC employees in order to collect quantitative and qualitative data. Finally, the model was instantiated based on the information we had gathered and according to the defined constructs. For the evaluation stage, an Agent Based Simulation (ABS) was used to assess the outputs of the building stage. Quantitative data was collected through an accounting system database and PSP/TSP documentation. PSP/TSP is a structured software development process (Humphrey, 1996) useful for such type of measurements. The accounting system provided data about incomes by project and expenses by concept (payroll, hardware, software, transportation, communications, etc.) for from April 2008 through January 2011. These data was used to validate the outputs of the building stage as well as the ABS. 4.3 Research Results The results we are putting forward are part of a doctoral dissertation, which it focuses on the first stage of design science. While the case study was conducted, the evaluation and theorizing stage and plan were prospected for the following months. CEDETEC was

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modeled as a knowledge-based organization in which the knowledge assets were identified using the Capital Systems framework. CEDETEC ontology recognizes at the first level of the organization as a set of capitals interacting with the environment (see Fig. 3). These in term can be described as a set of relevant entities (customers, suppliers, competitors, etc.).

Inside CEDETEC, three main capital categories are defined:

Referential Capital (Identity Capital + Intelligence Capital), Productive Capital (Human Capital + Instrumental Capital + Product Capital), and Articulating Capital (Relational Capital + Financial Capital).

Intelligence Capital is modeled as a five component

knowledge asset (Value Alignment + Sensorial + Experience + Decision + Consummation).

Fig.3 –CEDETEC Ontology

One of the first objectives of our project was to corroborate the hypothesis that traditional accounting systems do not convey knowledge-based organizations assets. It was found that over 85% of CEDETEC expenses were recorded just as payroll and the only acknowledged assets were accounts that were receivable due to sold projects. Our concern was: how to make decisions based on that information? What is CEDETEC’s current value? Has it increased or decreased over time? CEDETEC employees were surveyed about value process creation and how decisions were made at different levels. The findings were incorporated into our evaluation. The starting point was defining Value Alignment Component based on CEDETEC mission: “Incorporate organizations into the knowledge economy by supplying high tech information systems to increase their competitiveness…” CEDETEC aims to increase its customer’s competitiveness, but how will CEDETEC accomplish this goal? CEDETEC does not count on traditional assets such as land or machinery. IT infrastructure accounts for less than 10% of total expenses. Servers and computers are not owned by CEDETEC, as these are in fact leased to Tecnologico de Monterrey. CEDETEC’s most relevant assets are people and processes. CEDETEC’s ontology shows different knowledge asset categories as well as the relationships among these (see Fig. 3). The thread of this study was Intelligence Capital as a knowledge asset

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of the organization, zeroing on how does CEDETEC create value using this knowledge asset, and how the decision making process increases or decreases CEDETEC productivity. Based on the information delivered by CEDETEC employees, the 10 most important themes within the organization were indentified. Each employee prioritized the value elements and then a global ranking was calculated. These 10 elements, in turn, constitute the Value Alignment Component for CEDETEC.

Equalizing value creation to the

production function based on capitals, as stated above, followed:

Q = f (C1, C2,C3…., Cn) = Value Alignment Value Alignment = f (V1, V2,V3…., Vn) where and Vn= quantity and quality of value element n

Table 2 –CEDETEC Value Alignment Component

CEDETEC is looking for an efficiency and quality improvement on its production processes, but it is also trying to maximize some other variables. Therefore, a production function based on all the relevant value elements in order to manage the organization in a more systemic way is suggested to optimize the workings of the system.

It is

recommended thus, that CEDETEC decision makers should seek value creation based on knowledge assets development.

A manufacturer increases productivity by acquiring

tangible assets such as warehouses, machinery or land, while an intelligence-based system like CEDETEC creates more value developing assets such as Human, Instrumental or Intelligence Capital. Δ Knowledge Capital → Δ Value Alignment For the purposes of our research, CEDETEC employees were surveyed about the capabilities required to create value at different levels and processes within the organization. Each capability was carefully defined and mapped against CEDETEC’s ontology. As a result of this exercise, the most relevant capabilities in the organization

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were established.

These capabilities constitute knowledge assets that were assessed

through a four-level maturity model.

Table 3 –CEDETEC Capability Mapping

A four-level maturity model was designed for capability assessment, and then CEDETEC employees were surveyed about the maturity level of each capability. Two assessments were carried out. The first one was on January 2010 (“Initial Assessment”) and a second on January 2011 (“Subsequent Assessment”). The four maturity levels used for both assessments were: a.

Initial Level:

Capability at embryonic level.

b.

Development:

Capability being deliberately developed.

c.

Management:

Capability being operational and controlled.

d.

Optimization:

Capability being fully functional and systematically improved.

Fig.4 –Intelligence Capital Assessment

Capabilities were grouped in components, which in turn were associated to capitals. Through capabilities assessment, both Intelligence Capital and Components maturity levels were determined.

The assessment allowed a better understanding regarding

knowledge assets level of development as well as their corresponding impact in value creation.

Decision-making was also supported in the data since maturity level

improvements were able to be correlated to value creation improvements: Δ Maturity Levels → Δ Intelligence Capital → Δ Value Creation Challenges faced by decision-makers when dealing with knowledge organizations and knowledge assets were mentioned above. An entrepreneur may not be able to buy Intelligence Capital in the market, but he/she can surely develop adaptive capabilities within the organization and thus create value out of those capabilities. Financial flows

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invested to develop these capabilities must be recorded as investments in knowledge assets, not just as operation expenses. In the CEDETEC case, investments in Intelligence Capital were indentified and compared to maturity level assessment in order to calculate their capitalization.

So far these flows represent knowledge asset development or

Intelligence Capital development.

Following the financial flows, a last step was

calculating the impact of such development in value creation, i.e., to more clearly note how much value was created due to the efforts of this Intelligence Capital development. Furthermore, in order to look for correlations in knowledge assets development and value creation, an Agent Based Simulation (ABS) was built. ABS has proven adequate for knowledge based organizations modeling (Axelrod, 2003). CEDETEC was modeled as an intelligent agent, trying to maximize value, enabled to have tools to sense its environment, and promoting a decision-making process from a set of choices rigorously acknowledged as part of the reasoned way of functioning. In order for this to work out, each employee was modeled as an intelligent agent, with different expected value formulas based on their role at CEDETEC.

The impact of Intelligence Capital

development on value elements was therefore simulated. Literature on historical results on possible scenarios was surveyed and compared to CEDETEC’s model. The following findings have attained: a.

Intelligence Capital improvements were accomplished mainly as a result of CEDETEC PSP/TSP certification. PSP/TSP is a software development technique for increasing productivity. Nevertheless, these new capabilities were not entered as a CEDETEC asset; instead these were registered as direct expenses in the accounting system. From the managers’ perspective, CEDETEC did not have any relevant assets.

b.

Recording certification costs as direct expenses has a negative effect on CEDETEC financial statements. For decision-makers, CEDETEC profit is close to zero, giving an impression of operating at breakeven point. Market value is estimated near zero because IT infrastructure used by CEDETEC is owned by Tecnologico de Monterrey.

c.

PSP/TSP certification contributed to improving internal communication as well as the software development process.

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Nevertheless, some other Intelligence

Capital components such as sensorial and experience were not enhanced. A significant area of opportunity related to these adaptive capabilities was thus found. CEDETEC certainly needed to improve its internal development process. However, it also needed to develop capabilities to gaining a better understanding of its environment: customers, market, competitors, and above all, decisionmakers from the Tecnologico de Monterrey itself.

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Conclusions CEDETEC, as a software development center, is an example of a knowledge-based

organization whose production is based on knowledge assets. The CEDETEC case shows Intelligence Capital operationalization feasibility. We aimed in this research project to assess, develop, and manage knowledge assets and more specifically Intelligence Capital within organizations.

The proposed constructs and ontology were validated against

quantitative and qualitative data gathered from CEDETEC employees.

A Capital

Systems framework was useful to identify and establish different knowledge assets categories as well as the relationships between them and Intelligence Capital. Following design science, a set of artifacts were designed to operationalize Intelligence Capital within CEDETEC. These artifacts proved to be useful for assessment and development of the centre. Intelligence Capital model aided in detecting adaptive capabilities, as well as the relationships among them. The five Intelligence Capital components improved CEDETEC employees understanding about decision making and related capabilities.

Mapping these capabilities in different dimensions brought

meaningful insights to assess and develop CEDETEC Intelligence Capital. Finally, Agent Based Simulation proved to be adequate in modeling knowledge-based organizations.

ABS was a suitable platform to analyze CEDETEC qualitative and

quantitative data.

Financial information and operation indicators were compared to

Intelligence Capital capabilities maturity level, enabling knowledge asset assessment. Hence a subsequent theorization stage has started in order to complete design science approach.

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References Axelrod, R. (2003) “Advancing the Art of Simulation in the Social Sciences”. Japanese Journal for Management Information Systems, Special Issue on Agent-Based ModelingVol. 12, No. 3, December 2003. Carrillo, F. J. (2002) “Capital Systems: Implications for a Global Knowledge Agenda”. Journal of Knowledge Management, Vol. 6, No. 4, October, pp. 379-399. Carrillo, F. J. and Olavarrieta, G. (2002).InteligenciaExterna de Negocio 360°.(Nota TécnicaCSC2002-08). Monterrey, Tecnologico de Monterrey. Carrillo, F. J. and Olavarrieta, G. (2009) RevistaContaduríaPública,Abril 2009, pp. 34-39.

“Modelo

Integral

de

Inteligencia”.

Drucker, Peter (1966). The Effective Executive.New York: Harper Business. Drucker, Peter (1969). The Age of Discontinuity.New York: Harper & Row. Grant, R. M. (1996). “Prospering in dynamically-competitive environments: Organizational capability as knowledge integration”. Organization Science, 7, pp. 375-387. Grant, R. M. (1996). “Toward a Knowledge-Based Theory of the Firm”. Strategic Management Journal, Vol. 17, (Winter Special Issue), pp. 109-122. Haeckel, Stephan (1999). Adaptive Enterprise. Boston Massachusetts: Harvard Business School Press. Humphrey, Watts (1996).“Using a defined and measured Personal Software Process", IEEE Software, May 1996, pp. 77–88. Knip, Victor, P. Dishman, and C.S. Fleisher (1989). "Bibliography and Assessment of Key Competitive Intelligence Scholarship: Part 3.Journal of Competitive Intelligence and Management, 2003, 1(3), pp. 10-79. Kohlegger, M., Maier, R. and Thalmann, S. (2009) “Understanding Maturity Models Results of a Structured Content Analysis”. Proceedings of I-Know ’09 and I-SEMANTICS ‘09,2-4 September 2009, Graz, Austria. Luhn, H. P. (1958). “A Business Intelligence System”, IBM Journal, October 1958. Malhotra, Yogesh (2003). Measuring Knowledge Assets of a Nation: Knowledge Systems for Development. New York City, N. Y.: U. N. Headquarters . March, S. T. and G. F. Smith (1995). "Design and natural science research on information technology."Decision Support Systems 15(4): 251-266.

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Nonaka, Ikujiro (1991) “The Knowledge-Creating Company”. Harvard Business Review, Vol. 69, November-December, pp. 96-104. Olavarrieta, Gilberto (2010) Administración de Conocimiento y Desarrollo Basado en Conocimiento: Redes de Innovación.México, D.F.: Cengage Learning. Simon, Herbert (1969). The Sciences of the Artificial. Cambridge Massachusetts: The MIT Press. Solow, RobertM.(1956). "The Production Function and the Theory of Capital."The Review of Economic Studies,Vol. 23, No. 2,pp.101-108. Spender, J.C. (1996) “Making Knowledge the Basis of a Dynamic Theory of the Firm”. Strategic Management Journal, Vol. 17 Winter Special Issue, pp. 45-62. Sternberg, R. J. (1985). Beyond IQ: A Triarchic Theory of Intelligence. Cambridge: Cambridge University Press. Sternberg, R. J. (1997). A Triarchic View of Giftedness: Theory and Practice. In N. Coleangelo& G. A. Davis (Eds.), Handbook of Gifted Education (pp. 43-53). Boston, MA: Allyn and Bacon. Stone, Alexandra (2008). Rose S., LalB.,Shipp S. Measuring Innovation and Intangibles: A Business Perspective. Washington, D.C.: Institute for Defense Analyses, Science and Technology Policy Institute. Zambonelli, F., Jennings, N. and Wooldridge, M. (2003) “Developing Multiagent Systems: The Gaia Methodology”. ACM Transactions on Software Engineering and Methodology, Vol. 12, No. 3, July 2003, pp. 317–370.

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Intelligence Capital: A Capability Maturity Model for a Software Development Centre List of Figures

Fig. 1 – Capital Systems

1

Fig.2 – Intelligence Capital Model

2

Fig.3 –CEDETEC Ontology

3

Consummation

Decision

Value Alignment 2.5 2 1.5 1 0.5 0

Sensorial Initial Subsequent

Experience

Fig.4 –Intelligence Capital Assessment

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List of Tables

Tecnologico de Monterrey and CEDETEC Campuses in Mexico

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Students

Over 100,000

Faculty

Over 8,400

Programs (Undergraduate, Graduate)

Over 100

Research Centers

Over 60

CEDETEC

Software development center

CEDETEC market

Internal, public and private sector

Annual revenues

1.2 Million USD

Vision

Evolve to software innovation center

Table 1 – Tecnologico de Monterrey and CEDETEC

CEDETEC Value Alignment Component Ranking

Value Element

1

Increase revenue selling new software development project.

2

Improve efficiency in construction of products.

3

Increase customer satisfaction.

4

Gain development methodologies certifications.

5

Develop management competencies.

6

Design a new business model.

7

Improve quality in construction process.

8

Improve communication across development team.

9

Be a profitable center.

10

Avoid layoffs due to decrease in university budget.

Table 2 –CEDETEC Value Alignment Component

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CEDETEC Capability Mapping Capital

Component

Capability

Human

Organization

Development competencies.

Human

Organization

Capacity to sell new projects.

Human

Organization

Capacity to get methodology certification.

Human

Process

Capacity to increase customer satisfaction.

Instrumental

Process

Development methodology.

Intelligence

Value Alignment

Intelligence

Sensorial

Intelligence

Experience

Intelligence

Decision

Intelligence

Consummation

Efficient plan execution.

Intelligence

Consummation

Capacity to increase value.

Capacity to design new business models. Capacity to gather information from environment. Capacity to record and retrieve knowledge. Efficient resource planning.

Table 3 –CEDETEC Capability Mapping

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