FIELD OF THE INVENTION
This invention relates to the financial performance measurement & more particularly this
invention elaborates the knowledge infrastructure and processes.
BACKGROUND OF THE INVENTION
Business was transformed by tech. Complexity and competition require organisations to
develop KM. Most firms' success depends on KM. Knowledge should trump equipment.
Globalization, innovation, technology, and shifting stakeholder expectations drive the
knowledge economy (2014). Globalize mobile and Internet. More opportunities increase staff
turnover and knowledge loss. Traditional financial performance measurement. Late 1980s
studies indicated that earlier financial indicators aren't enough to measure performance
management in the new economy (NEELY, 2002). Kaplan & Norton (1996) and BSC
recommend assessing financial performance, customer understanding, and internal company
processes. Kaplan and Norton think managers shouldn't focus on finances. Knowledge
economy requires new performance metrics, say Tabrizi, Ebrahimi, and Al-Marwai (2011).
Prior financial indicators can't measure product life cycle shortening and hypercompetition
(Neely, 2002). Linking problems. KMStats (PM). KM isn't standardised like accounting.
(2011) Knowledge management measurement is difficult. KM influences firm success and
competitiveness (JOSHI et al., 2014). Knowledge is a strategic asset in the knowledge-based
view of the organisation. Knowledge management uses organisational knowledge for
competitive benefit. RBT claims unique, nonreplaceable company resources boost
performance. Strategists prioritise knowledge over land, equipment, and raw supplies. RBV
is extended to the knowledge economy. KM might not influence finances.
SUMMARY OF THE INVENTION
Most evidence shows KM improves organisational performance. Fewer studies use wellknown financial metrics. Despite little proof, KM literature believes it boosts financial
success. Measure KM improvements without confirmation. Some findings are explained by
knowledge infrastructure and processes. Financial indicators may be affected by new
products, economic movements, etc. Most empirical conclusions are based on in-house
assessments of factors. Risky. This research succeeds. Six years have linked KM and
financial performance. This study examines the link between KMS and financial performance
during the last six years. Statistics were used to explain this link, not surveys. New: KM
awards and public financial data. The findings suggest a variety of work opportunities,
including a request for practitioners and academics to establish a large database of works
utilising KM and financial success criteria.

BRIEF DESCRIPTION OF THE INVENTION
The basic underlying assumption is that knowledge may be viewed from a unified perspective; it circulates in the organization creating knowledge assets and influences the performance of the organization. It has multifaceted characteristics, such as: state of mind, object, having access to information, or the potential for influencing future action. Clyde W. Holsapple, Jiming Wu b (2011) presents a prior relevant KM studies and explaIn: first, it appears that KM performance is an important factor for managers to consider. Second, many of the studies show a positive relationship between some aspect(s) of KM performance and some aspect(s) of firm performance. Third, most of the empirical findings are based on perceptions of independent and dependent variables by persons embedded in the firms being studied. This type of research adds risks, complications, and inefficiencies for initiatives involving knowledge creation, knowledge retention, and knowledge transfer, Fourth, few studies examine whether a firm's actual financial performance is related to its KM performance.
Similarly, the cost/benefit of investment in KM systems has been a question for a long time after some of the initial installations of big, expensive IT structures were disappointing. That consideration, along with concerns that spreading knowledge too widely might lead to greater vulnerability to competitive intelligence or other incursions by competitors, suggested that a more measured approach to sharing proprietary knowledge assets might be more prudent (ERICKSON; ROTHBERG, 2015). Cohen & Olsen, (2015) reveals that codification and human capital KM capabilities interact to influence customer service outcomes. Links between KM capabilities and performance were found to be contingent on the business strategy of the firm. Researchers have devoted much attention to empirical examination of the link between knowledge management (KM) and firm performance. Efforts have typically concentrated on the KM capabilities required for the externalization and codification of organizational knowledge, and for the development and retention of tacit knowledge embedded in human capital. Competing theoretical perspectives regarding the inter- relationship between these two KM capabilities and their implications for performance in the past researches. The effects of KM capabilities on the performance of firms in service sectors such as hospitality has received less attention.(COHEN; OLSEN, 2015)
Different constructs have been associated with organizational performance, with different levels of relevance. Yang et al., (2009) shows that organizational structure and knowledge management culture are associated with organizational performance such as innovativeness, finance, and customer service. For those authors, information technology didn’t support affects financial and customer service in shipping firms. It was noted that the shipping executives preferred to depend on their own experiences and networking relationships. Thus, a trust-based culture is the foundation for their knowledge management initiative. However, this research was limited to an evaluation of the knowledge management enablers and firm performance in liner shipping firms. D.-N. Chen & Liang (2011) defends that more and more organizations are taking advantage of external knowledge sources such as online communities (e.g., blogs and social networking websites) to enhance their competitiveness and that Knowledge could become an intangible product to be traded in electronic commerce. Their results show that different knowledge evolution strategies have affected different dimensions of organizational performance. Knowledge mutation that relies on internal creation of new knowledge has significant impacts on the improvement of internal process, while knowledge crossover that takes advantage of external knowledge sources can benefit financial and customer dimensions. It implies that when the goal of knowledge management is to improve business processes, internal innovation may be better than seeking advice from outside sources, but when the goal is to improve customer satisfaction and retention, bringing in outside expertise will be better than relying on internal knowledge. In addition, many industrial factors, such as environment variation, knowledge density, and organizational factors, including IT capability and sharing culture, are found to have moderating effects. The findings of this research will help organizations choose the right strategy for knowledge enhancement and light up new directions for further research.
According to Erickson & Rothberg, (2009, p. 159), both IC (Intellectual Capital) and KM concern themselves with identifying and better leveraging the knowledge assets of the organization but also differ somewhat in emphasis and application. KM tends to be more human resources oriented, including both the big IT systems necessary to collect, store, and distribute codified knowledge and more person-to-person applications such as communities of practice, storytelling, wikis, and related techniques. KM also tends to focus more on the details of the nature of the knowledge (e.g. tacit vs. explicit) and the motivational issues involved in getting individuals to participate in knowledge-sharing systems. IC, on the other hand, grew more out of accounting, trying to tease out the components of the intangible assets that have become so prominent in recent decades. If you can measure specific intangibles, especially those we would consider knowledge assets, we can better manage them. As the metrics and understanding get more precise, our ability to manage human capital, structural capital, and relational capital improves.
KM is an interactive process and starts with a business driver or vision of what a company wants to achieve. For effective KM implementation, organizations need to create and manage processes and systems to capture and apply knowledge sources from internal and external stakeholders. Earlier researchers have identified many key aspects in the KM processes such as acquiring, collaborating, integrating and experimenting knowledge acquisition, knowledge conversion into useful form, application and protection; acquisition, indexing, filtering, linking, distributing and application; and knowledge acquisition, knowledge sharing and knowledge distribution. Managing knowledge in organizations requires managing several processes of knowledge such as initiation, implementation, ramp-up and integration; generation (acquisition; dedicating resources; fusion; adaptation; and building knowledge networks), codification and transfer; acquisition, conversion, application and protection; acquiring, selecting, internalizing and using; acquisition, selection, generalization, assimilation and emission; creation, transfer, integration and leverage, creation, storage, sharing and evaluation ; generation, codification, transfer and; and acquisition, creation, storage and application (JOSHI et al., 2014).
KM practices, in this research, are defined according to Zack, McKeen, & Singh (2009) as observable organizational activities that are related to knowledge management. Four key dimensions of KM practice were identified from them to relate to performance: a) the ability to locate and share existing knowledge; b) the ability to experiment and create new knowledge; c) a culture that encourages knowledge creation and sharing; and d) a regard for the strategic value of knowledge and learning.
Zand, van Beers, & others (2010, p. 3) explain that exists 2 different approaches in the literature that analyze the impacts of enterprise systems in the firm performance. The first group treats supply chain management (SCM), customer relationship management (CRM) and knowledge management system (KMS) concepts as a corporate policy, management practice or organizational capability. The second group explicitly focuses on SCM, CRM, and/or KMS as IT-based enterprise systems. At this research, KMS will be considered like first group.
The objective of a Knowledge Management System (KMS) is to support construction, sharing and application of knowledge in organizations. “The strategy of utilizing a KMS to capture and distribute knowledge often requires that individuals contribute their knowledge to a system instead of keeping it to themselves or sharing it directly with known others only through conversations or written personal exchanges” (ALAVI; LEIDNER, 2001, p. 1). According to Wang et al. (2008), the better a firm is at KM, the more competitive it will be in the market and the better its performance.
Performance and performance measurement are complex constructs. To Neely, Gregory, & Platts ( 2005), “performance measurement is a topic which is often discussed but rarely defined. Literally it is the process of quantifying action, where measurement is the process of quantification and action leads to performance”. According to (BANFF; BAPUJI, 2006), several scholars have recognized the multi- dimensional nature of performance and viewed it as comprising (i) goal attainment, behavior of organizational participants, and relationship with environment, (ii) efficiency, employee morale, and effectiveness in meeting goals, (iii) financial performance, operational performance, and organizational effectiveness, and (iv) adaptive specialization and adaptive generalization.
There is a wide variety of available alternatives to classify the dimensions of performance and divides the performance measures into two main and distinct groups: operational and organizational performance. A measure is coded as operational if could be tied to a specific value chain as described by Porter but did not reflect the interactive outcome of all value chain activities. Measures that depict outcomes attributable to the interaction among all value creation activities and the organization´s environment, were treated as organizational performance measures. In our research, we consider the categories by Combs; Crook; Shook (2005) to treat financial performance and the categories proposed by Ho (HO, 2009) in article to treat KM constructs of KM Process and KM Enablers both compiled by Andersen (1999).
RESEARCH METHODOLOGY: This research was conducted like a systematic review divided in two major steps. First, a Bibliometric Study was conducted to understand the universe around this theme. After that, papers were reviewed to understand how the literature has connected Knowledge Management Systems and Financial Performance in an organizational level and identify the mainly KM practices and financial performance indicators. This research was carried out based on the guidelines presented. The procedure of systematic review includes the following steps: planning, defining research questions, searching databases, discussion of validity, data extraction, and synthesis of the results. These steps are described in the next subsections. Two researchers were involved in this research, and they are the authors of this paper. From here on, the term “authors” is used as researchers. The goal of systematic review is to find out how the authors have, in the literature, linked KM practices and financial performance in an organizational level of firms. A review protocol was developed in the beginning of the systematic review to make sure that the research is undertaken as planned and not driven by researcher expectations. The protocol includes research background, the research questions, search strategy, study selection criteria and procedures, quality assessment, data extraction, and data synthesis strategies. The research questions and article identification strategies are described in the following subsections.
RESEARCH STRATEGY AND SEARCH PROCESS
SEARCH RESOURCES: This study was planned to find relevant literature about the link between KMS and financial performance in the last years. Based on the fact that KM is a multidisciplinary topic, we search in two different electronic databases: SCOPUS and Science Direct. In the searches, were used the same research string. It was considered only papers and conferences. The results of these researches were 226 results from SCOPUS and 187 from Science Direct, totalizing 413 results. Manually researches were conducted from list references and result in one significant paper.
SEARCH PROCESS: After some tried searches, the following search string was decided on for this study: ("knowledge management system" + "financial performance"). The search string try to filter papers that treat KM systems and financial performance in the same time. It was used on the electronic databases on 28 June 2015. It was filtered only papers and conference papers and it was returned 413 researches. One more was manually added. After duplicate papers were removed by Zotero tool, 371 papers remained. After Zotero analysis, authors identified four repeated articles more, resulting in 367 unique papers. After removing the papers that are out of the inclusion criteria, 44 papers remained to be analyzed. The analyses and conclusions about the final selected papers were conducted by both authors, together. Inclusion and exclusion criteria are explained bellow.
CLASSIFICATION VALIDITY: The goal of this is to cover, as many as possible, the relevant research papers about the link between KMS and financial performance and put them into categories to make possible having a quantitative view about use of KM practices and performance indicators. Nevertheless, it is likely that some relevant papers have been missed. This can be attributed to a number of different reasons. First, even in the English language, there is some ambiguity. This means that some relevant papers that use a different terminology of the search string might not have been found. Second, some lesser-known journals and proceedings are not included in the electronic databases that were searched, and any possible papers published in these collections were therefore not included in the results. Next, some papers can also have been rejected incorrectly during the selection process from the search results to the final list of relevant papers.
DATA EXTRACTION: The data extracted from each paper were maintained through the whole review process. After identification of the relevant papers, the following data were extracted: the source (journal or conference), title, authors, publication year, financial performance indicators, method, summary of the research (including which questions were solved) and findings Summary. Based on the criteria for classifying the papers, all relevant papers were reviewed, and the corresponding data were extracted. It is not easy and, because of this, further criteria for classifying the papers were defined and discussed by the research team, based on what information was available in the papers. When needed, the categories were updated or clarified during the classification process.
DATA SYNTHESIS: When there was any uncertainty about the classification of the studies, the issue was discussed by all authors until agreement was reached. The data synthesis was specified in the review protocol from the beginning of the systematic review. Table 3 shows absolute and relative frequency of KM practices founded in selected papers and categorized according to Ho (2009). In total, it was found 161 KM practices. Practices like “KM Process” are more linked with FP than practices of “KM Enabler or Infrastructure” in the selected papers (60% x 40% respectively). IT, Intellectual Capital, Acquisition and Culture are the main practices linked with organizational performance. In total, there are 12 practices more used among the 161 found.

We Claims:

1. It has multifaceted characteristics, such as: state of mind, object, having access to information, or the potential for influencing future action.
2. For effective KM implementation, organizations need to create and manage processes and systems to capture and apply knowledge sources from internal and external stakeholders.
3. KM tends to be more human resources oriented, including both the big IT systems necessary to collect, store, and distribute codified knowledge and more person-to-person applications such as communities of practice, storytelling, wikis, and related techniques.
4. KM also tends to focus more on the details of the nature of the knowledge (e.g. tacit vs. explicit) and the motivational issues involved in getting individuals to participate in knowledge-sharing systems.
5. There is a wide variety of available alternatives to classify the dimensions of performance and divides the performance measures into two main and distinct groups: operational and organizational performance.
6. A measure is coded as operational if could be tied to a specific value chain as described by Porter but did not reflect the interactive outcome of all value chain activities.
7. Measures that depict outcomes attributable to the interaction among all value creation activities and the organization´s environment, were treated as organizational performance measures.