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The importance of Knowledge Waste for Intellectual Capital Management and Enterprise Performance.

publicado por Helio Ferenhof

The importance of Knowledge Waste for Intellectual Capital Management and Enterprise Performance.Helio Aisenberg Ferenhof and Paulo Mauricio Selig

PPGEP, Universidade Federal de Santa Catarina, Florianópolis, Brazil

Abstract: Companies look to be ahead in its segment, or remain in the market. Therefore, it is necessary to obtain competitive advantage, going to have to continuously improve their management strategies. One of these is directly related to how innovative production processes are and how well they are managed, resulting in the launch of new portfolio of projects equal or superior to those previously produced. For this, intellectual capital and its knowledge are fundamental and its wastage management is a factor that could be determinant. Scholars sustains that the effective management of people planning must be a part of overall corporate strategy. But, how about the knowledge waste? In this context, the research highlight the problem associated with the performance of intellectual capital, may be impacted, or not by the use of knowledge in part or in whole process, the loss and / or forgetfulness, or use knowledge that does not add value for the company, its customers consequently. This work researched the databases: EBSCO; Emerald; Compendex; Scopus; ISI; and Wiley that occasioned in 506 articles which were systematically analysed, resulting in this theoretical article that presents the main references on the subject and a scientific essay regarding the importance of managing the intellectual capital knowledge waste. We concluded that managing the forms of knowledge waste, it is expected an improvement of performance and competitive advantage.

Keywords: Intellectual Capital, Intangible assets, Knowledge Waste, Waste of Knowledge, Knowledge Management, Portfolio Management.

1. Introduction

In the new era of knowledge-based companies, if ones don’t manage intellectual capital (IC) and the competitors do, then they will be beaten because the latter will get better results, being more competitive (Elias & Scarbrough, 2004). Intangible resources are more likely than tangible resources to produce a competitive advantage (John & Suresh, 2011). The recognition that much of the added value created by companies is becoming more and more conditional on intangible assets other than physical capital has stimulated a vast literature resulted by researches in the area of intellectual capital, human capital and intangible assets, highlighting the importance of IC management to add value and competitive advantage (Bontis, 2001; Bontis & Fitz-Enz, 2002; Carson et al., 2004; Edvinsson, 2000; Edvinsson & Malone, 1997; Elias & Scarbrough, 2004; Roos & Roos, 1997; Stewart & Ruckdeschel, 1998; Sveiby, 1997; Wright & Kehoe, 2008).

Companies look to be ahead in its segment, or remain in the market. Therefore, it is necessary to obtain competitive advantage, going to have to continuously improve their management strategies (Ahuja & Ahuja, 2012; Bassey & Tapang, 2012; Campbell et al., 2012; Chadee & Raman, 2012). One of these is directly related to how innovative production processes are and how well they are managed (Hsu & Sabherwal, 2012), resulting in the launch of portfolios of projects that develop goods and/or services with time and cost savings and quality equal or superior to those previously produced. For this, IC and its knowledge are fundamental (Ahmed et al., 2004; Ahuja & Ahuja, 2012; Andrzej & Marian, 2009; Carla et al., 2011; Davis & Walker, 2009; DeCarolis & Deeds, 1999) and its wastage management is a factor that could be determinant (Bauch, 2004; Ferenhof, 2011; Locher, 2008; Ward, 2007). Scholars sustains that the effective management of people means that manpower planning must be a part of overall corporate strategy, for corporate goals determines the tasks that must be performed. But, how about the knowledge waste?

In this context, the research highlight the problem associated with the performance of IC, may be impacted, or not by the use of knowledge in part or in whole process, the loss and / or forgetfulness, or use knowledge that does not add value for the company, its customers consequently. The purpose of this paper as such is to develop a model to measure the knowledge waste in order to improve the performance of enterprises. First it provides a background on the aspects related with IC, wastes and enterprise performance. Second it explains more deeply the concept of knowledge waste. Third the main results and gaps identified by a systematic literature review. Forth the conceptual model to mitigate knowledge wastes. Fifth the research approach to test the conceptual model. Finally the conclusions are drawn and further research recommendations are made.

2. The context – Portfolio, Intangible Asset, Knowledge Waste and Enterprise Performance

IC can be the most powerful asset of an enterprise in promoting value and competitive advantage, being the most important organizational resource (Kaplan & Norton, 1996; Moon & Kym, 2006). IC is classified by Stewart (1997) as a composition of: Human Capital, Structural Capital and Customer Capital. The most accepted and spread classification is the one by Sveiby (1997) that breakdown IC into three categories: Human Capital, Relational Capital and Structural Capital. He defines human capital as “the capacity to act in a wide variety of situations to create both tangible and intangible assets”; structural capital as “patents, concepts, models, and computer and administrative systems”; and relational capital as “relationships with customers and suppliers”.

But what is IC? Stewart (1997) defines IC as: […] the sum of everything everybody in a company knows that gives it a competitive edge […] IC is intellectual material, knowledge, experience, intellectual property, information […] that can be put to use to create wealth.

Knowledge is an intangible asset of IC, and now a day is the most important asset to be management to archive competitive advantage (Bueno et al., 2003). The Human Capital consists of tacit and explicit knowledge, and this intangible assets can me measure by individual competence. At the same time, Structural Capital comprises knowledge: explicit, systematic and internalized measure by structural competence, on the other hand Relational Capital consists of the value of the relationship of the entire corporation with the external ambient, as can be seen at Figure 1. Thus, IC is the possession of knowledge, applied experience, organizational technology, customer relationships, and professional skill that provide a competitive edge in the market (Edvinsson and Malone, 1997). IC also captures both stocks and flows of an organization’s overall knowledge base (Bontis, 1999; Bontis et al., 2002). The indicators shown here reflects the IC that already exists on the enterprises and their portfolios, but not the lost of knowledge on It.


Figure 1 – From Components to Indicators

Figure 1 – From Components to Indicators
Source: Bueno, Arrien & Rodríguez (2003).

It can be seen then that the existing IC models deal with the measurement of the elements that build directly competitive advantage. But how-to measure the knowledge waste?

To understand the issues related to the knowledge waste and the impact into the competitive advantage, this study was based in the portfolio management perspective. The relationship between IC and performance in project management perspective was presented by Demartini and Paoloni (2011). They sustains that project managers’ knowledge and experience are considered important in determining project outcomes. The PMI (2013) explains Portfolio management as: the coordinated management of one or more portfolios to achieve organizational strategies and objectives. Portfolio management produces valuable information to support or alter organizational strategies and investment decisions. And a portfolio transforms the strategic objectives into actions (PMI, 2013). The portfolio then, will be our unit of analysis.

3. Knowledge Waste

The meaning of knowledge waste is defined by Ferenhof (2011) as any failure in the process of knowledge conversion, better known as spiral of knowledge creation of Nonaka and Takeuchi (1997), which the waste is presented in the following ways: reinvention, lack of system discipline, underutilized people, scatter, hand-off, wishful thinking (Ferenhof, 2011), as detailed below.

Reinvention is a type of waste that happens if the organization does not reuse the designed solutions, components, projects, experiences or knowledge acquired previously. Many companies and employees do not pay attention to the fact that instead of creating a new project from scratch, uncertainly, they can increase the chances of success by reusing previous knowledge, ie, parts or whole projects and/or process already designed, tested and approved, as well as their experiences throughout their conception (Bauch, 2004; Ferenhof, 2011).

Lack of system discipline covers a number of factors related to clarity of objectives outlined in the system: goals and objectives obscure; unclear rights, roles and responsibilities; obscure rules; poor discipline of schedule; insufficient willingness to cooperate and; incompetence or lack of training (Bauch, 2004; Ferenhof, 2011).

Underutilized people, employees are not using their skills and expertise completely. Often are given very limited roles and responsibilities to them, when in reality, they could assume much more if the process was designed effectively, not wasting knowledge (Locher, 2008; Ferenhof, 2011).

Scatter, are the actions that make knowledge become ineffective by flow disturbance, basically disrupting the interaction required for teamwork. This category has two sub-categories: communication barriers and poor tools.

  1. Communication barriers directly prevent knowledge flow occurrence. They include: a) physical barriers such as distance, computational incompatible formats, etc.; B) social barriers such as the corporate class systems and management behaviour that prevent the flow of communication and knowledge c) skill barriers: people who do not transform data into usable knowledge (Ward, 2007).
  2. Poor tools refer to the fact that the tools should support for the flow of knowledge and not stifle the process, as users assume that these tools are the only solution. These developers seek to take shortcuts, copying unsuitable operating modes, causing failures by forcing the use of tools without proper analysing their relevance. Due to the insistence of using poor tools, the process ends up in a death spiral, the more one tries to improve the process are the worst failures (Ward, 2007; Ferenhof, 2011).

Hand-off occurs when one separates knowledge, responsibility, action and feedback. It results in decisions made by people who do not have enough knowledge to make the decision effectively or do not have the opportunity to accomplish it. As subcategories we have: useless information and waiting.

  1. Information is useless if they do not help to understand the customer or other aspects of integration. They do not: add value to the flow; innovate; provide meaningful data for decision-making; and are usually created only to fulfil someone’s desire. (Ward, 2007).
  2. Waits, normally occurs by establishing a standard conventional sequencing of activities, which creates a batch processing, causing: slow processes, a single path instead of multiple streams of information, a large variation of work causing the waste of scatter, spread (Ward, 2007).

Wishful Thinking means to follow the subject’s own reasoning, based on desires rather than on facts or rationality, or the decision making based on one’s own desires for reality. For Ward (2007) this means operating in the dark, blindly making decisions without consistent data. It can be divided in: testing to specifications and discarded knowledge.

  1. Testing to specifications is a practical conventional pattern. These cannot highlight whether a good or service is ready for commercialization, it is statistically impossible to test enough to be confident that they will have zero defects (Ward, 2007).
  2. Discarded knowledge happens for a number of reasons: teams and superior focus on the product or service launch, leaving aside the capture of knowledge; tests with the specifications do not say much so it can be used next time and, above all, few know how to turn data into usable knowledge (Ward, 2007).

Ferenhof (2011) elucidates that by seeking to eliminate knowledge waste, it is expected that this flow deliver benefits and results to stakeholders in a more efficient and effective, with a focus on value considering the system as a whole: processes, people and technology.

In Figure 2, we expose the view of the knowledge waste relating to the conversion knowledge process, called spiral of knowledge waste.

Figure 2 - Spiral of Knowledge Waste

Figure 2 – Spiral of Knowledge Waste
Source: Ferenhof (2011).

4. Literature review

Bibliometric and systematic review proceeding was followed. It makes viable to organize the set of publications of a specific field. The resulting bibliographic portfolio allows to identify authors, their relationships and trends; it is one of its major contributions to science (Spinak, 1996).

The bibliographic research will comprise three different stages: data collection, analysis and synthesis of results.

These three stages will implicate the following procedures:

  1. Criteria for choice and database fields;
  2. Terms used in the research;
  3. Manage and treat the references collected;
  4. Criteria for articles selection: in addition with the readings of title, abstract, and location of keywords in the body of the text, including selection by reading to get the articles that have addressed the terms concerning: measurement of performance or competitive advantage of intellectual capital;
  5. Criteria for the systematic analysis: establish according to the model by Bardin (2011).

The data analysis and synthesis results were executed beginning with the creation of tables and graphics, presented below.

The research resulted in 506 articles, 491 of them without duplicates, which were systematically analysed. The exact numbers of publications that was returned by each database can be seen in Figure 3.

Figure 3 - Database Results.

Figure 3 – Database Results.
Source: Authors.

Only 160 of the 491 articles were aligned to our main research topic by reading the title, keywords and abstract. 24 of the 160 weren’t available the full text download. Resulting in 136 articles for full text reading analysis. After the analysis, 14 were not aligned with the theme, and were discarded from the portfolio, resulting in 122 relevant articles. Figure 4 shows the years of publications of the resulting portfolio.


Figure 4 - Distribution of Portfolio Articles per Year

Figure 4 – Distribution of Portfolio Articles per Year
Source: Authors.

4.1 Meta-Analisys Conclusions:

The main results of articles analyzed can be summarized as:

  1. It has significant correlations among the relational capital and competitive strategy (Acquaah, 2007).
  2. The quality of IC components directly affects companies’ financial performance, profit growth rate and competitive position (Andrzej & Marian, 2009; Campbell et al., 2012; DeCarolis & Deeds, 1999; Hsu & Sabherwal, 2012).
  3. Human Capital has positive influence on portfolio, program and project performance, and so into the company performance as a hole (Brown et al., 2007; Demartini & Paoloni, 2011).
  4. Social capital can be built through delivering a project using a relationship-based approach and how this may be achieved using the suggested CMM template and protocols (Davis & Walker, 2009).
  5. Value creating knowledge, intangible assets, should lead to competitive advantage (Campbell et al., 2012; Carla et al., 2011; Carson et al., 2004; Davis & Walker, 2009; DeCarolis & Deeds, 1999; Elias & Scarbrough, 2004; Eric, 2010; John & Suresh, 2011; Lavie, 2007; Moon & Kym, 2006; O’Donnell et al., 2009).
  6. Companies that fail to recognize IC as a factor are prone to failure in business and in competition (Dorweiler & Yakhou, 2005).
  7. You need to have indicators that send signals about the effectiveness and profitability of the institution and in its assessment indicating whether she will remain competitive. In the construction of indicators is required to keep in mind the customer’s perspective and competition. (Estrada Munoz et al., 2010).
  8. IC is difficult for competitors to imitate even when employees are hired away because the knowledge is specific to the original work environment and therefore cannot add similar value in a different work environment (Hatch & Dyer, 2004).

What can be extracted is that the competitive advantage and the performance success of one enterprise are direct rated how well your IC is managed. It indicates an opportunity for knowledge management to eliminate or mitigate the knowledge wastes. As can be seen by the recommendations for future research from the bibliographic portfolio.


AuthorGaps for research
Abeysekera (2012)Examine how corporate governance attributes predict each human capital resource item separately. Also could examine the governance attributes in this study with firms that place less reliance on independent directors. The communication of human capital can comprise narrative, visual and numerical types of disclosure.
Acs et al. (2007)Sort out the consequences of these results for intelligent policies to enhance new-firm survival, encourage more successful new-firm formation, and to overcome any important factors discouraging local growth.
Ahmed et al. (2004)Empirically test the developed hypotheses, which linked IC indicators to organizational performance.Collect similar data longitudinally so that hypotheses can be tested by accounting for time lags in changes among IC metrics and organizational performance measures.
Antonelli et al. (2010)A more exhaustive and robust study of human capital measurement conceived in a labour demand perspective.
Bassey and Tapang (2012)Researchers should address the following research questions: 1. How does one educate the users of financial statements about the usefulness of measuring human resource value and create more awareness? 2. Who should drive the measurement of human resource value? 3. Which model for the recording of human resource value is most suited to the Nigerian organizations?
Chadee and Raman (2012)Extend the enquiry to a broader sample of firms from different sectors, including the manufacturing sector, to gain further insights into the effects of talent management and external knowledge on organisational performance, within a cross-country framework.
Cormier et al. (2009)Voluntary disclosure should take into account attributes of disclosure and not only its extent.
DeCarolis and Deeds (1999)Empirical research needs to be completed to enhance our understanding of the relationship between organizational knowledge and firm performance.
Iñaki (2002)Examine the influence of ICs on business start-up performance. In this study, we have intangible asset examined the relationship, not the causality, between IC elements and firm performance on a sample with limited size and characteristics.
Juma and Payne (2004)Relating IC to performance should attempt to develop new, more adequate or complete measures of IC. Although both practitioners and scholars would desire a quantifiable and readily available measurement of IC, the EVA and MVA variables do not seem to differentiate themselves enough from normal measures of performance to be used as a predictive indicator. The use of more qualitative sources seems to be more appropriate to determine such “un-quantifiable” and intangible variables.
Moon and Kym (2006)Modify and extend our framework making it more comprehensive or more suited to specific industries. Corporate reporting and internal management systems must therefore be more holistic allowing investors and managers to evaluate the performance of the total value creation system which includes its production factors, assets, processes, and procedures.
Mura and Longo (2012)Continue to analyse the relation between IC and organizational performance, for example exploring the mediating effect of individual performance in the IC organizational performance relationship. Development the basis for a standardized internally generated intangible assets measurement tool.
Wright and McMahan (2011)While the concept of human capital may be 40 years old, its treatment in organisational research is in an infant stage. Needs to study the role of human capital in firm competitive advantage.

Table 1 – Recommendations for future research indicated by the bibliographic portfolio.

Source: Authors.

Reviewing and analysing the models of IC, was identified a gap that none of them (Bontis, 2001; Bontis & Fitz-Enz, 2002; Bueno et al., 2003; Edvinsson, 2000; Edvinsson & Malone, 1997; Kaplan & Norton, 1996; Roos & Roos, 1997; Stewart & Ruckdeschel, 1998; Sveiby, 1997) presented indicators directed related to manage the knowledge waste. Regarding that, we propose a conceptual model that deals with this gap.

5. Conceptual Model

The conceptual model was based in three propositions: 1) in the perception that the knowledge is the most important resource of any enterprise (Choo, 1996; Davenport & Prusak, 1998; Nonaka et al., 2000); 2) is by knowledge that the IC add value to the enterprises, generating and supporting competitive advantage (Ahmed et al., 2004; Bontis, 2001; Bueno et al., 2003; Carson et al., 2004; Davis & Walker, 2009; Edvinsson & Malone, 1997; Kaplan & Norton, 1996; Roos & Roos, 1997; Stewart & Ruckdeschel, 1998; Sveiby, 1997) and; 3) the knowledge waste, wasn’t measured by any of the previous (intangible assets / intellectual capital) models.

For each of the three categories of IC in Figure 1, we associated the knowledge wastes proposed by Ferenhof (2011) Figure 2. By this association (Figure 5), the listed knowledge waste may be occurring:

  1. Human Capital: All of the six categories of knowledge waste.
  2. Structural Capital: Reinvention, lack of system discipline, scatter, and hand-off.
  3. Relational Capital: Lack of system discipline, underutilized people, scatter, and hand-off.

Key Performance Indicators (Knowledge Wastes Indicators), associated with each knowledge waste at each IC category must be created to the context that will be managed. As it’s exposed in Figure 5, Generic Model.

Figure 5 – Generic Conceptual Model - Knowledge Wastes Indicators Path

Figure 5 – Generic Conceptual Model – Knowledge Wastes Indicators Path
Source: Authors.

6. Research Approach to test the conceptual model

For testing the conceptual model presented in the previous section, it adopts a qualitative approach because it involves understanding of an event in their natural environment; fieldwork and results in a product description (Merriam, 1998).

We posit in-depth case studies into portfolio management of intensive knowledge organizations, which will permit the researchers to investigate in detail the interactions between the phenomenon under study and the contextual factors (Yin, 2009). Case study research would hence serve the following purposes:

  • Help validate and modify the constructs with clear conceptual definitions and the relationships between these constructs.
  • Help validate and modify the contextual organizational and other attributes, and uncover their influence in the specific context.

The researchers have identified a few organizations in Brazil and England for carrying out the case studies.

7. Final Thoughts

The competitive advantage and the performance success of one enterprise are direct rated how well your IC is managed. This work has sought to develop a generic model to measure and manage the knowledge wastes. We highlight the importance of managing them, by doing that it is expected an improvement of performance and competitive advantage.

The adoption of the model can impact the efficiency of the entire enterprise in terms of capturing and sharing pertinent data about the knowledge wastes to the decision maker. The use of Key Performance Indicators associated with each knowledge waste at each IC category will help the decision maker bringing quality information to decide more precisely, allocating the IC resources in an effective way. It is expected that with continued use of the model to generate historical information related to the indicators of knowledge waste, tend to be more assertive the decisions.

Hence, this article expects to promote future researches related with knowledge waste and IC performance.


Abeysekera, Indra. (2012). Role of remuneration committee in narrative human capital disclosure. Accounting & Finance, 52, 1-23. doi: 10.1111/j.1467-629X.2011.00441.x

Acquaah, Moses. (2007). Managerial social capital, strategic orientation, and organizational performance in an emerging economy. Strategic Management Journal, 28(12), 1235-1255. doi: 10.1002/smj.632

Acs, Zoltan J., Armington, Catherine, & Zhang, Ting. (2007). The determinants of new-firm survival across regional economies: The role of human capital stock and knowledge spillover*. Papers in Regional Science, 86(3), 367-391. doi: 10.1111/j.1435-5957.2007.00129.x

Ahmed, Seleim, Ahmed, Ashour, & Nick, Bontis. (2004). Intellectual capital in Egyptian software firms. Learning Organization, The, 11(4), 332-346. doi: 10.1108/09696470410538233

Ahuja, Bhavna Ranjan, & Ahuja, Narender L. (2012). Intellectual Capital Approach to Performance Evaluation: A Case Study of the Banking Sector in India. International Research Journal of Finance & Economics(93), 110-122.

Andrzej, Buszko, & Marian, Mroziewski. (2009). The intellectual capital impact on Polish construction companies during the transformation period. Journal of Human Resource Costing & Accounting, 13(3), 206-220. doi: 10.1108/14013380910995502

Antonelli, Gilberto, Antonietti, Roberto, & Guidetti, Giovanni. (2010). Organizational change, skill formation, human capital measurement: evidence from italian manufacturing firms. Journal of Economic Surveys, 24(2), 206-247. doi: 10.1111/j.1467-6419.2009.00615.x

Bardin, Laurence. (2011). Análise de conteúdo: Edições 70, São Paulo.

Bassey, Bassey Eyo, & Tapang, Arzizeh Tiesieh. (2012). Capitalized Human Resources Cost and Its Influence on Corporate Productivity: A Study of Selected Companies in Nigeria. International Journal of Financial Research, 3(2), 48-59. doi: 10.5430/ijfr.v3n2p48

Bauch, C. (2004). Lean product development: making waste transparent. Cambridge: Massachusetts Institute of Technology.

Bontis, Nick. (2001). Assessing knowledge assets: a review of the models used to measure intellectual capital. International Journal of Management Reviews, 3(1), 41-60. doi: 10.1111/1468-2370.00053

Bontis, Nick, & Fitz-Enz, Jac. (2002). Intellectual capital ROI: a causal map of human capital antecedents and consequents. Journal of Intellectual Capital, 3(3), 223-247.

Brown, A. W., Adams, J. D., & Amjad, A. A. (2007). The relationship between human capital and time performance in project management: A path analysis. International Journal of Project Management, 25(1), 77-89. doi: 10.1016/j.ijproman.2006.07.011

Bueno, Eduardo, Arrien, María, & Rodríguez, Oscar. (2003). Modelo Intellectus: medición y gestión del capital intelectual. Documentos Intellectus(5).

Campbell, Benjamin A., Ganco, Martin, Franco, April M., & Agarwal, Rajshree. (2012). Who leaves, where to, and why worry? employee mobility, entrepreneurship and effects on source firm performance. Strategic Management Journal, 33(1), 65-87. doi: 10.1002/smj.943

Carla, Curado, Lopes, Henriques, & Nick, Bontis. (2011). Intellectual capital disclosure payback. Management Decision, 49(7), 1080-1098. doi: 10.1108/00251741111151154

Carson, E., Ranzijn, R., Winefield, A., & Marsden, H. (2004). Intellectual capital: Mapping employee and work group attributes. Journal of Intellectual Capital, 5(3), 443-463. doi: 10.1108/14691930410550390

Chadee, Doren, & Raman, Revti. (2012). External knowledge and performance of offshore IT service providers in India: the mediating role of talent management. Asia Pacific Journal of Human Resources, 50(4), 459-482. doi: 10.1111/j.1744-7941.2012.00039.x

Choo, C. W. (1996). The knowing organization: How organizations use information to construct meaning, create knowledge and make decisions. International Journal of Information Management, 16(5), 329-340. doi: doi: DOI: 10.1016/0268-4012(96)00020-5

Cormier, Denis, Aerts, Walter, Ledoux, Marie-Josée, & Magnan, Michel. (2009). Attributes of social and human capital disclosure and information asymmetry between managers and investors. Canadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l’Administration, 26(1), 71-88. doi: 10.1002/cjas.89

Davenport, Thomas H;, & Prusak, Laurence. (1998). Conhecimento Empresarial. Rio de Janeiro: Editora Campus.

Davis, P. R., & Walker, D. H. T. (2009). Building capability in construction projects: A relationship-based approach. Engineering, Construction and Architectural Management, 16(5), 475-489. doi: 10.1108/09699980910988375

DeCarolis, Donna Marie, & Deeds, David L. (1999). The impact of stocks and flows of organizational knowledge on firm performance: an empirical investigation of the biotechnology industry. Strategic Management Journal, 20(10), 953-968. doi: 10.1002/(SICI)1097-0266(199910)20:10<953::AID-SMJ59>3.0.CO;2-3

Demartini, P., & Paoloni, P. (2011). Assessing human capital in knowledge intensive business services. Measuring Business Excellence, 15(4), 16-26. doi: 10.1108/13683041111184071

Dorweiler, Vernon P., & Yakhou, Mehenna. (2005). A Scorecard on Intellectual Capital Performance in the Economy. Journal of American Academy of Business, Cambridge, 7(1), 322-326.

Edvinsson, Leif. (2000). Some perspectives on intangibles and intellectual capital 2000. Journal of Intellectual Capital, 1(1), 12-16.

Edvinsson, Leif, & Malone, Michael S. (1997). Intellectual Capital: Realizing Your Company\’s True Value by Finding Its Hidden Brainpower.

Elias, Juanita, & Scarbrough, Harry. (2004). Evaluating human capital: an exploratory study of management practice. Human Resource Management Journal, 14(4), 21-40.

Eric, Kong. (2010). Innovation processes in social enterprises: an IC perspective. Journal of Intellectual Capital, 11(2), 158-178. doi: 10.1108/14691931011039660

Estrada Munoz, Jairo, Lopez, Guillermo, & Cuartas, Diego. (2010). The most relevant indicators of Intellectual Capital components in an engineering faculty. Paper presented at the 2010 2nd International Congress on Engineering Education: Transforming Engineering Education to Produce Quality Engineers, ICEED2010, December 8, 2010 – December 9, 2010, Kuala Lumpur, Malaysia.

Ferenhof, H. A. (2011). Uma sistemática de identificação de desperdícios de conhecimento visando à melhoria do processo de criação de novos serviços.  . (Mestrado em Engenharia e Gestão do Conhecimento), Universidade Federal de Santa Catarina, Florianópolis.

Hatch, Nile W., & Dyer, Jeffrey H. (2004). Human capital and learning as a source of sustainable competitive advantage. Strategic Management Journal, 25(12), 1155-1178. doi: 10.1002/smj.421

Hsu, I. Chieh, & Sabherwal, Rajiv. (2012). Relationship between Intellectual Capital and Knowledge Management: An Empirical Investigation. Decision Sciences, 43(3), 489-524. doi: 10.1111/j.1540-5915.2012.00357.x

Iñaki, Peña. (2002). Intellectual capital and business start-up success. Journal of Intellectual Capital, 3(2), 180-198. doi: 10.1108/14691930210424761

John, Dumay, & Suresh, Cuganesan. (2011). Making sense of intellectual capital complexity: measuring through narrative. Journal of Human Resource Costing & Accounting, 15(1), 24-49. doi: 10.1108/14013381111125305

Juma, Norma, & Payne, G. Tyge. (2004). Intellectual capital and performance of new venture high-tech firms. International Journal of Innovation Management, 8(3), 297-318.

Kaplan, Robert, & Norton, David P. (1996). The balanced scorecard: Harvard Business School Press.

Lavie, Dovev. (2007). Alliance portfolios and firm performance: A study of value creation and appropriation in the U.S. software industry. Strategic Management Journal, 28(12), 1187-1212. doi: 10.1002/smj.637

Locher, D. (2008). Value Stream Mapping for Lean Development: A How-to Guide for Streamlining Time to Market. New York, NY: Productivity Press.

Merriam, Sharan B. (1998). Qualitative Research and Case Study Applications in Education. Revised and Expanded from” Case Study Research in Education.”: ERIC.

Moon, Yun Ji, & Kym, Hyo Gun. (2006). A Model for the Value of Intellectual Capital. Canadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l’Administration, 23(3), 253-269. doi: 10.1111/j.1936-4490.2006.tb00630.x

Mura, Matteo, & Longo, Mariolina. (2012). Developing a tool for intellectual capital assessment: an individual-level perspective. Expert Systems, n/a-n/a. doi: 10.1111/j.1468-0394.2012.00650.x

Nonaka, Ikujiro, Toyama, Ryoko, & Konno, Noboru. (2000). SECI, Ba and Leadership: a Unified Model of Dynamic Knowledge Creation. Long Range Planning, 33(1), 5-34. doi: doi: DOI: 10.1016/S0024-6301(99)00115-6

O’Donnell, Loretta, Kramar, Robin, & Dyball, Maria Cadiz. (2009). Human capital reporting: Should it be industry specific? Asia Pacific Journal of Human Resources, 47(3), 358-373. doi: 10.1177/1038411108099293

PMI. (2013). The Standard for Portifolio Management (Vol. 3ed.). Pennsylvania: Four Campus Boulevard.

Roos, Göran, & Roos, Johan. (1997). Measuring your company’s intellectual performance. Long Range Planning, 30(3), 413-426.

Spinak, Ernesto. (1996). Diccionario enciclopédico de bibliometría, cienciometría e informática: Unesco.

Stewart, Thomas, & Ruckdeschel, Clare. (1998). Intellectual capital: The new wealth of organizations. Performance Improvement, 37(7), 56-59.

Sveiby, Karl Erik. (1997). The intangible assets monitor. Journal of Human Resource Costing & Accounting, 2(1), 73-97.

Ward, A.C. (2007). Lean product and process development: Lean Enterprises Inst Inc.

Wright, Patrick M., & Kehoe, Rebecca R. (2008). Human resource practices and organizational commitment: A deeper examination. Asia Pacific Journal of Human Resources, 46(1), 6-20. doi: 10.1177/1038411107086540

Wright, Patrick M., & McMahan, Gary C. (2011). Exploring human capital: putting ‘human’ back into strategic human resource management. Human Resource Management Journal, 21(2), 93-104. doi: 10.1111/j.1748-8583.2010.00165.x

Yin, RK. (2009). Case study research: design and methods. Applied social research methods series (5.

How to cite this paper:

FERENHOF, H. A. ; SELIG, P. M. . The importance of Knowledge Waste for Intellectual Capital Management and Enterprise Performance.. In: 10th International Conference on Intellectual Capital, Knowledge Management & Organisational Learning ICICKM 2013, 2013, Washington, DC. Proceedings ICICKM 2013, 2013.
[Crédito da Imagem: Conhecimento – ShutterStock]


Helio Ferenhof, Dr. Eng, MBA, PMP .'. ITIL Foundations Bacharel em ciência da computação pela UNESA; Mestre em Engenharia e Gestão do Conhecimento – UFSC. Doutor em Engenharia de Produção e Sistemas – UFSC. MBA em E-Business pela FGV/RJ; Professor da Pós-Graduação em Gerenciamento de Projetos e Segurança da Informação do SENAC/SC; Professor da Pós-Graduação em Gerenciamento de TI & Projetos da Universidade Estácio de Sá /SC; Diretor do IGCI empresa de consultoria em Gestão do Conhecimento, Gestão de Projetos & TI ( ; Apresenta mais de 20 anos de experiência adquirida em empresas multinacionais e consultorias de renome.

Helio Ferenhof


1 Comment

  • My name is Ronald. Am new here. Am getting a lot of help from this forum.

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