Proceedings of the 40th Hawaii International Conference on System Sciences - 2007
Modeling Enablers for Successful KM Implementation Vittal S. Anantatmula Western Carolina University
[email protected] Shivraj Kanungo George Washington University
[email protected]
Abstract Knowledge is recognized as a critical resource to gain and sustain competitive advantage in business. While many organizations are employing knowledge management (KM) initiatives, research studies suggest that it is difficult to establish return on investment of such efforts; however, desired results can be obtained through successful implementation. In this research study, using literature review, we identified a set of enablers and barriers of successful KM implementation. Using this set of factors, we developed a questionnaire by applying Interpretive Structural Modeling (ISM) methodology to determine underlying relations among these factors and develop strategies for successful implementation of KM initiatives. Contributions from this research effort should also support organizations in making decisions about improving organizational performance using KM initiatives, and understanding the directional relations among KM factors. Because of the number of participants in our study, applicability of our research results may have certain limitations. To address this inadequacy, as a future research effort, we intend to increase the number of respondents and participant organizations.
1. Introduction Knowledge is linked to progress practically in every aspect of our lives. Many organizations have realized that creation, transfer, and management of knowledge are critical for success today. In the current economy, advances in information technology (IT) and communications help organizations to develop, store, and transfer knowledge. Knowledge is widely recognized as a key economic resource and it is increasingly becoming obvious that organizations should have the right knowledge in the desired form and context under all circumstances to be
successful. Specifically, knowledge sharing and resultant new knowledge creation and innovation are critical for organizations to gain and remain competitive. Obviously, knowledge is considered a critical resource for sustaining competitive advantage. Managing knowledge in organizations is a challenging task because it is hard to identify, and even more difficult to value and deploy relevant knowledge to gain a competitive advantage in the market place [8]. KM is still perceived as information management by many organizations and is often associated with technological solutions such as intranets and databases [20]. However, KM is a broader concept and we should understand that the primary focus of KM is to utilize information technology and tools, business processes, best practices, and culture to develop and share knowledge within an organization, and to connect those who possess knowledge to those who need the knowledge [2]. IT plays a supporting role for effective KM implementation. While many organizations have employed KM initiatives, it remains unclear the extent to which they are successful in delivering the anticipated outcomes, and why. Several companies implementing KM rely primarily on IT tools [10] and as a result, may not achieve desired results. Recognizing the importance and value of KM, government agencies and educational institutions have joined the corporate world in managing knowledge for creating value and providing better service. Electronic government has already started delivering on the promise of influencing the interaction of citizens with their government as it provides increased access to information, enhanced efficiency, and greater access to government officials [16]. Research studies show that it is difficult to assess return on investment of knowledge management systems (KMS). Contending that the impact of KMS on the organization depends on the evolutionary stage of the KMS, Cooper (2006) suggests at the system level, completion is considered success. Likewise,
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effectiveness/efficiency of tasks, cost savings through process improvements and competitive advantage are considered indicators of success at task, process, and organizational levels respectively [7]. However, these results can be obtained only through successful KM implementation. KM success factors can be seen as facilitating factors for a KM initiative and measurement of KMS can be used as one of the means to provide an understanding of how it should be developed and implemented [14]. In addition, we consider that enablers and barriers, and understanding of their interrelations provide detailed understanding of building a successful KM initiative. The purpose of our study is to identify and model enablers and barriers of KM for its successful implementation. In this research study, we will first, using the literature review, identify both enablers and barriers of KM implementation. Using these factors, we will develop a questionnaire in Interpretive Structural Modeling (ISM) methodology that will be used to determine underlying relations among these factors. For this purpose, we use surveys, discussions, and interviews to gather information from KM professionals. We obtain results that, when understood from the final integrated perspective, resolve many counter-intuitive findings from the analysis. Our approach helps us analyze underlying relations and interactions among these elements. We will use these results to develop strategies and recommendations for successful implementation of KM.
2. Literature Review KM is considered as a complex process that is supported by enablers such as strategy, leadership, culture, measurement, and technology [22]. Past research [22] suggests that IT infrastructure also plays role in facilitating knowledge creation and transfer, specifically where technology is not readily available and mastered. This case study of six research organizations found that organizations with reasonable levels of IT infrastructure performed well in their KM efforts with potential for improvement whereas the performance of organizations with low IT infrastructure was below par. Likewise, another study investigating KM process for software engineering [26] identified leadership as the most important one among the four enablers of KM — leadership, technology, culture, and measurement. With a minor difference to the above research study findings, another study [9] considered culture, technology, infrastructure, and measurement as four key enablers of KM and maintained that each is essential and they work together to yield sustainable
success of KM. Culture promotes collaboration and sharing of knowledge; technology speeds up the knowledge transfer but creates information overload; infrastructure includes organization structure, technology, processes, and people networks to ensure knowledge flow; and measurement should focus on the impact of knowledge on organization performance [9]. Culture appears to be a common enabler of KM in several research studies. Contending that KM success is driven by KM infrastructure and process capabilities, a research study [11] proposed that technology, structure, and culture drive the infrastructure capability. This research study involving around 300 senior executives identified that an information sharing culture is critical for effective KM. KM success factors can be viewed as facilitating factors for a KM initiative and some success factors include leadership, investing in people, and developing supporting organizational conditions like technical infrastructure and secured knowledge structure [5, 14]. In an attempt to explore the relation between KM drivers and organizational KM performance, a study [28] based on 66 Korean firms, found that KM drivers such as learning orientation, knowledge sharing intention, knowledge management system quality, reward, and knowledge management team activity were significantly related to the organizational knowledge management performance— knowledge quality and user knowledge satisfaction. This study [28] identified three main enabler dimensions and nine enablers. They are: KM enabler dimension KM enabler organizational - learning orientation characteristics - communication - knowledge sharing - flexibility IT
- KMS quality - KMS functionality
managerial support
- top management support - KM reward - KM team activity
IT enables the acquisition of greater amounts of information thereby providing a greater amount of data related to organizational processes [1]. Thus, IT provides an opportunity for creating and expanding knowledge, a necessary characteristic of KM. However, most of the IT tools of KM are developed for explicit knowledge [16] and identified three critical enablers. Strategic alignment and focus
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System and data integration Security and privacy policies Another research study [12]—acknowledging that KM would help share knowledge and eliminate reinvention—proposed seven enablers of KM. They are: Strategic focus Alignment with objectives KM organization and roles Standard KM processes Culture and people engagement Content under scrutiny Technology enablement Based on the contention that much of the tacit knowledge—a greater component of organizational knowledge—is found in social interactions, and different social contexts facilitate different modes of knowledge integration, [17] suggests that social capital and social context are enablers of knowledge integration and effective knowledge integration is influenced by the characteristics of knowledge involved and the characteristics of social context in which they occur. Needless to say, social context is influenced by organization culture. In their research model that interconnects KM factors, [18] identified seven enablers, which are collaboration, trust, learning, centralization, formalization, T-shaped skills, and IT and support. Of these, trust is part of an organization’s culture and is translated into activities such as increased collaboration and communication. Trust is considered a significant factor and in the absence of trust, knowledge sharing will not take place and organizations refrain from sharing critical information across the enterprise [23]. Thus, trust fits into the roles of inhibitor and enabler. Presently, outsourcing is common to acquire quality services and expertise at a lower cost. Consequently, virtual project teams are integral to many projects in the current economy. Knowledge transfer in virtual teams for system development will have different dynamic environment than the conventional one for communications. Arguing that virtual teams may need highly skilled individuals, [24] using a research study, found that knowledge transfer in virtual teams is influenced by participating individual’s extensive participation in conversations (communication), being perceived as credible using trustworthy behavior (credibility), and having collectivist value (culture). Based on the literature reviewed thus far, we summarize the following KM enablers, which are listed as KM factors in Table 1 along with sources of reference.
Table 1: Literature review findings KM Factors
Source
Strategic focus
[22], [12], [16]
Leadership
22], [26], [14], [16]
Top management support
[18], [28], [12], [16]
Culture
[9], [11], [22], [18], [17] [26], [12], [24]
Measurement of results
[9], [22], [26], [12], [16].
Technology infrastructure
[9], [1], [22], [26], [28], [14], [12], [16]
Standard KM processes
[9], [12]
Top management involvement
[18], [28], [12], [16]
Content quality
[28], [12]
Collaboration
[9], [11], [28], [17], [23]
Formalization
[18], [16]
Communication
[28], [24].
Budgetary support
[28], [16]
While the literature findings helped to identify KM factors, understanding how these factors interact and influence each other is considered important to develop methods and strategies for successful KM implementation. Previous research studies have not addressed this concern. Specifically, our research is aimed at establishing and structuring enablers for developing strategies for KM success and showing how context-specific relationships among these factors can be developed.
3. Research Methodology To accomplish our research goals, we employ Interpretive Structural Modeling (ISM) developed by Warfield (1973). In general, this method involves structuring of goals and objectives into a hierarchical framework. In this study, after establishing a set of KM factors (Table 1), we proceeded to develop an understanding of the shared underlying mental model in which these factors operate. We have selected ISM for two reasons: first, human brains have limits in coping with complex problems of significant number of elements and relations among elements (Waller, 1975); second, input data quality is better because unlike surveys, which collect data on perceptions, ISM
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uses interactive discussion method to collect data, which forces the participant in the research study to carefully analyze links between these factors. ISM is a process that helps groups of people in structuring their collective knowledge and modeling interrelationships in a way to enhance the ability of
understanding complexity. In other words, it helps to identify structure within a system of related elements and provides opportunity to analyze it from different perspectives.
Figure 1: ISM for data collection
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Proceedings of the 40th Hawaii International Conference on System Sciences - 2007
Figure 1 was presented to the respondents and they were asked to fill out the white cells of the matrix shown in the figure with the following instructions: Enter 1 when the row influences the column Enter 2 when the column influences the row Enter 3 when there is no relation Enter 4 when row and column influence each other For example, the cell (1, 2) represents the question, “Does strategic focus lead to KM leadership or viceversa?” and the response (1, 2, 3 or 4) is entered in the cell (1, 2). The contextual relation is established based on a pair-wise assessment of all the thirteen factors as shown in Figure 1 and majority of the respondents agreeing to a specific relation between any two elements. With the use of this methodology, one can (a) identify the direct and indirect relationships between attributes of project performance and (b) show how to include softer variables in the analysis. ISM analyzes a system of elements and resolves these in a graphical representation of their directed relationships and hierarchical levels. The elements may be objectives of a policy, goals of an organization, factors of assessment, etc. The directed relationships can be in a variety of contexts (referred to as contextual relationships), such as Element (i) "is greater than"; "is achieved by"; "will help achieve"; "is more important than"; Element (j). The following is a brief description of the different steps of ISM: Identification of Elements: The elements of the system are identified and listed. This may be achieved through research, brain storming, etc. Contextual Relationship: A contextual relationship between elements is established, depending upon the objective of the modeling exercise. Structural Self Interaction Matrix (SSIM): This matrix represents the respondent’s perception of element to element directed relationship. Four Symbols are used to represent the type of the type of relationship that can exist between two elements of the system under consideration. These are: 1 .. for the relation from element Ei to Ej, but not in the reverse direction; 2 .. for the relation from Ej to Ei, but not in the reverse direction; 3 .. for an interrelation between Ei and Ej (both directions); 4 .. to represent that Ei and Ej are unrelated. Reachability Matrix (RM): A Reachability Matrix is then prepared that converts the symbolic SSIM Matrix into a binary matrix. The following conversion rules apply:
If the relation Ei to Ej = V in SSIM, then element Eij = 1 and Eji = 0 in RM If the relation Ei to Ej = A in SSIM, then element Eij = 0 and Eji = 1 in RM If the relation Ei to Ej = X in SSIM, then element Eij = 1 and Eji = 1 in RM If the relation Ei to Ej = O in SSIM, then element Eij = 0 and Eji = 0 in RM The initial RM is then modified to show all direct and indirect reachabilities, that is if Eij = 1 and Ejk = 1 then Eik = 1. Level Partitioning: Level partitioning is done in order to classify the elements into different levels of the ISM structure. For this purpose, two sets are associated with each element Ei of the system - A Reachability Set (Ri) that is a set of all elements that can be reached from the element Ei, and an Antecedent Set (Ai), that is a set of all elements that element Ei can be reached by. In the first iteration, all elements, for which Ri = RiAi, are Level I Elements. In successive iterations, the elements identified as level elements in the previous iterations are deleted, and new elements are selected for successive levels using the same rule. Accordingly, all the elements of the system are grouped into different levels. Canonical Matrix: grouping together elements in the same level develops this matrix. The resultant matrix has most of its upper triangular elements as 0, and lower triangular elements as 1. This matrix is then used to prepare a Digraph. Digraph: Digraph is a term derived from Directional Graph, and as the name suggests, is a graphical representation of the elements, their directed relationships, and hierarchical levels. The initial digraph is prepared on the basis of the canonical matrix. This is then pruned by removing all transitivities, to form a final digraph. Interpretive Structural Model: The ISM is generated by replacing all element numbers with the actual element description. The ISM therefore, gives a very clear picture of the system of elements and their flow of relationships. We have interviewed and used the survey instrument shown in Figure 1 to collect the data from the two faculty members from two universities. One is private university located in the District of Columbia and the other is a state university in North Carolina. These faculty members were actively involved in university-initiated KM efforts and KM research. Thus, we used qualitative research data as input to the ISM software to generate the model.
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4. Results and Discussion Using the software, the values of 1, 2, 3 and 4 are translated into binary values and to develop directional graph, shown in Figure 2. Computational results in detail are shown in Appendix A. These results represent the mental models of the respondents and in that sense they are subject to interpretation, hence the name interpretive structural
modeling. It can be seen that each of these relations (arrows in the diagram) are tenable. However, what is more important is the contextual development of this structure in terms of relevance to a university environment. In a corporate environment, we could have expected these thirteen elements to be configured differently. However, generic insights are also evident.
Figure 2: Model for KM enablers and barriers
4.1 Generic Insights Our results show that top management involvement, KM leadership, and the culture of the organization are the main driving factors based on which we can build a successful KM effort. With the top management involvement, KM initiatives will gain support and active participation of the senior executives of the organization. Top management involvement would also ensure that KM initiatives will have strategic focus.
4.2 Inductive Approach The greater importance of this approach from an organizational standpoint is the emergence of this
logical flow of causal influences that is not only logically consistent but is also a view that is owned and shared by the creators of this view. The contextual relevance of this approach has significant implications for practice. Our results show that in the context of universities, competent leadership of KM initiative combined with the support from the top management would lead to budgetary support for KM initiatives. Budgetary support would assist in developing technology infrastructure for sharing, and archiving knowledge. Figure 2 also shows that top management support would also lead managers involved in KM initiative to formalize KM-related functions and consequently, develop standard processes. Since, resource integration, efficient and effective use of resource
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utilization, implementation of plans to bring stability—important tenets of management—help manage complexity associated with these processes, standardization of these processes is aimed at improving efficiency and effectiveness. The next logical step would to measure results of these processes to determine the success of KM initiatives. Results show and it makes logical sense that standard process promote quality of the content that is available for knowledge transfer. Organization culture that encourages open and transparent communication among the employees of the organization would lead to increased collaboration and knowledge sharing at hierarchical levels of the organization, which leads to knowledge sharing. Increased communication with the aid of standard processes, and technology infrastructure make it easy and enhance collaboration.
4.3 Givens, Means, and Ends Figure 2 can also be interpreted in terms of givens, means and goals in a KM effort. The elements at the bottom of the Figure can be considered as the set of givens. These “givens,” from a management standpoint can be considered to be aspects that are there or not there. It is generally difficult to cultivate them in a medium or short term. In our model, KM leadership, top management support and top management involvement are considered a set of givens. “Ends” tend to be the elements at the top of the model. Collaboration, content quality, measurement of results and strategic focus are the ends in the KM effort in the context of a university. The means are the elements that can be controlled, manipulated or developed to form the link between the “givens” and the “ends.” Communication, technology infrastructure, standardized processes, culture, budgetary support and formalization of the KM effort are all aspects that can be changed, increased or decreased in order to accomplish the ends. From the standpoint of enablers and barriers, this approach allows us to understand how each of these elements can behave as an enabler as well as an inhibitor to the KM effort. For instance, in Figure 2, the weakness of an element makes it an inhibitor while the strength of that very same element makes it an enabler. As a case in point, strong and effective KM leadership leads to budgetary support and formalization of the KM effort. However, weaknesses in KM leadership will dilute the support for budgetary support and the formalization of KM processes. This approach goes to show the dual nature of elements in terms of whether they are
enablers or inhibitors in the KM effort. It also goes to show that it may not be useful to normatively classify elements as facilitators or inhibitors. These results have several implications. In order to build a successful KM initiative, universities need to secure top management involvement first. Next, the selection of a competent and committed leader is important for the initiative because the leader plays a critical role in securing funds and building technology infrastructure to accomplish KM goals and objectives. Universities must recognize that developing a culture that promotes communication and trust among the employees would facilitate accomplishing KM goals such as collaboration and knowledge sharing among employees. However, developing and nurturing a culture of openness and trust is usually a gradual process. Once a KM system is implemented, it is imperative that the system should maintain the strategic focus, and quality of the content for meaningful collaboration among the employees. Finally, instead of trying to evaluate knowledge directly, which may not be easy, we recommend assessing its contribution to business performance and processes.
5. Limitations of the study One of the limitations of this approach to creating a revealed structure is that results are not easily generalizable across organizations. As a result, we should be careful to limit our conclusions to the domain of analysis only. For instance, it would be appropriate to limit our discussions to universities only. Furthermore, the issue of number of participants remains relevant also. If we wanted to increase the validity of our results we should involve more stakeholders in developing the reachability matrix and the resulting common matrix would provide us a more robust shared mental model for a specific university of universities.
6. Suggested future research Future work along these lines is planned along the following lines. First, we plan to include more elements in developing the structural model. Second, the number of participants is going to be increased to include more participants from each organization (university) being studied. This will allow the development of a more robust structural model for each organization and finally a common model for all organizations by taking the common inputs across all the organizations. Lastly, in order to add value to this
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process, we intend to incorporate the strength of the relationships between elements by allowing for user to provide a weight for each relationship.
[10] Greenhalgh T, Robert G, MacFarlane F., Bate, P, and Kyriakidou O. (2004). Diffusion of innovations in service organizations: Systematic review and recommendations. The Milbank Quarterly, 82(4), 581-629.
7. Conclusion
[11] Gold A H., Malhotra A and Segars A H. (2001). Knowledge Management: An Organizational Capabilities Perspective. Journal of Management Information Systems. 18 (1), 185-214.
We have shown how we can capture the behavior of elements that can act as either enabler or barrier to the KM effort using ISM. In doing so we have shown that such a qualitative approach not only allows us to retain the richness of the complexity associated with the interactions among elements, it allowed us to identify elements that can act as the givens, means and goals in the KM effort. With the research results, we have identified important strategies and suggested methodologies for successful KM implementation.
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[23] Robbins S. We need a new vocabulary. Information Systems Management, Winter 2005, 22(1), 89-90. [24] Sarker S. Sarker S. Nicholson D B & Joshi K D. (2005). Knowledge Transfer in Virtual System Development Teams. IEEE Transactions on Professional Communication, 48(2), 201-218. [25] Waller, Robert J. (1975). In M. Baldwin (ed.). Application of Interpretive Structural Modeling to PrioritySetting in Urban Systems Management. Portraits of Complexity (Battelle Monograph No. 9), Battelle Memorial Institute, Columbus, OH. [26] Ward J & Aurum A. (2004). Knowledge Management in Software Engineering – Describing the Process. ASWEC 2004, IEEE Computer Society. [27] Warfield J N. (1973). Intent Structures. IEEE Transactions on Systems, Man, and Cybernetics, 3(2), March 1973. [28] Yu S, Kim Y. & Kim M. (2004). Linking Organizational Knowledge Management Drivers to Knowledge Management Performance: An Exploratory Study. HICSS37, IEEE Computer Society.
Appendix A Reachability Matrix Element Element Element Element Element Element Element Element Element Element Element Element Element
1: 2: 3: 4: 5: 6: 7: 8: 9: 10: 11: 12: 13:
1 0 1 0 0 0 0 0 0 0 0 0 0
0 1 0 0 0 0 0 0 0 0 0 0 0
0 0 1 0 0 0 0 1 0 0 0 0 0
0 0 0 1 0 0 0 0 0 0 0 0 0
0 0 0 1 1 0 1 0 0 0 1 0 0
0 1 1 0 0 1 0 0 0 0 0 0 1
0 1 0 0 0 0 1 0 0 0 1 0 0
0 0 0 0 0 0 0 1 0 0 0 0 0
0 0 0 0 0 0 1 0 1 0 0 0 0
0 0 0 1 0 1 1 0 0 1 0 1 0
0 0 1 0 0 0 0 1 0 0 1 0 0
0 0 0 1 0 0 0 0 0 0 0 1 0
0 1 1 0 0 0 0 1 0 0 0 0 1
0 0 1 0 0 0 0 1 0 0 1 0 0
0 0 0 1 0 0 0 0 0 0 0 1 0
0 1 1 0 0 0 0 1 0 0 0 0 1
Element 1 2 3 4 5
Level 1, 5, 9, 10, 6, 7, 12, 4, 11, 13, 2, 3, 8,
Canonical matrix Element Element Element Element Element Element Element Element Element Element Element Element Element
01 : Level 1: 05 : Level 1: 09 : Level 1: 10: Level 1: 06 : Level 2: 07 : Level 2: 12: Level 2: 04 : Level 3: 11: Level 3: 13: Level 3: 02 : Level 4: 03 : Level 4: 08 : Level 5:
1 0 0 0 0 0 0 0 0 0 0 1 1
0 1 0 0 0 1 0 1 1 0 1 1 1
0 0 1 0 0 1 0 0 1 0 1 0 1
0 0 0 1 1 1 1 1 1 1 1 1 1
0 0 0 0 1 0 0 0 0 1 1 1 1
0 0 0 0 0 1 0 0 1 0 1 1 1
0 0 0 0 0 0 1 1 0 0 0 0 0
0 0 0 0 0 0 0 1 0 0 0 0 0
0 0 0 0 0 0 0 0 1 0 0 1 1
0 0 0 0 0 0 0 0 0 1 1 1 1
0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 0 1 1
0 0 0 0 0 0 0 0 0 0 0 0 1
0 0 0 0 0 0 0 0 1 0 0 0 0
0 0 0 0 0 0 0 1 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 1 1 0
0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 1
0 0 0 0 0 0 0 0 0 0 0 0 0
Direct Reachability Matrix Element Element Element Element Element Element Element Element Element Element Element Element Element
01 : Level 1: 05 : Level 1: 09 : Level 1: 10: Level 1: 06 : Level 2: 07 : Level 2: 12: Level 2: 04 : Level 3: 11: Level 3: 13: Level 3: 02 : Level 4: 03 : Level 4: 08 : Level 5:
0 0 0 0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 1 0 0 0 0 0 0 0
0 0 0 0 0 1 0 0 0 0 0 0 0
0 0 0 0 1 1 1 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 1 0 0 0
Modified Reachability Matrix Element Element Element Element Element Element Element Element Element Element Element Element Element
1: 2: 3: 4: 5: 6: 7: 8: 9: 10: 11: 12: 13:
1 0 1 0 0 0 0 1 0 0 0 0 0
0 1 0 0 0 0 0 0 0 0 0 0 0
0 0 1 0 0 0 0 1 0 0 0 0 0
0 0 0 1 0 0 0 0 0 0 0 0 0
0 1 1 1 1 0 1 1 0 0 1 0 0
0 1 1 0 0 1 0 1 0 0 0 0 1
0 1 1 0 0 0 1 1 0 0 1 0 0
0 0 0 0 0 0 0 1 0 0 0 0 0
0 1 0 0 0 0 1 1 1 0 1 0 0
0 1 1 1 0 1 1 1 0 1 1 1 1
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