Organizational network analysis: A study of a university library from a network efficiency perspective

Library and Information Science Research 41 (2019) 48–57

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Organizational network analysis: A study of a university library from a network efficiency perspective

T

Anna Ujwary-Gil

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Institute of Economics, Polish Academy of Sciences, Staszic Palace; 72 Ulica Nowy Świat, Room 266, 00-330 Warsaw, Poland

ABSTRACT

A library is a particular kind of organization. It plays a valuable role and is dedicated mainly to the development and growth of society. Analyzing a library from the perspective of a network of relations and ties, which exist between social and technical network nodes, contributes to a more nuanced assessment of effectiveness. Building on social network analysis and going beyond human relations in a library, this study examines perceptions related to knowledge and skills, resources, and tasks, identified through a survey conducted at the university library in Warsaw. Overall, the analyzed library is characterized by redundancy and congruence of knowledge, resources, and tasks required at the library (organizational) level and at the particular node (employee) level. Analyzing the network efficiency of a library is a new and valuable research design which uses a unique network measurement that should attract more interest in the future. This form of analysis gives managers the tools to dynamize relations and understand the flow, use, and sharing of resources or knowledge within a library context. However, more studies in the public sector would be invaluable in order to formulate new theories or conclusions.

1. Introduction A public university library is usually open to everybody, not only to students, and users may access the library in its traditional or electronic (open access) forms. There are many institutions of this type and user choice is determined by many factors, the most important being the richness, variety, and size of resources at their disposal. These key attributes create competition among libraries, who apply for various donations and grants, including governmental ones, both in the domestic and international markets. Maintaining an advanced and modern information and communication infrastructure is crucial to a library's development, and this would not be possible without external finance. Because a library plays a vital role in the educational development of society, the way it functions is of primary importance too. As it constitutes an organizational entity, at its essence, as in the case of any other organization, is the library's socio-technical system, the complexity of which, and its corresponding business processes, make analysis interesting. This socio-technical system is composed of people (staff), whereas the technical system covers processes, tasks, knowledge and resources used by the employees in their work. So, although a library is a public organization, it fits in well with the general conditions of market competitiveness. 2. Problem statement The effectiveness of a library is analyzed in this study from the

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perspective of the network of relations between people (social system) and knowledge, resources, and tasks (technical system). The study uses a unique approach called organizational network analysis (ONA) (e.g., Liu, Moskvina, & Ouředník, 2017; Merrill et al., 2008; Novak, Rennaker, & Turner, 2011; Rizi, Barzaki, & Yarmohammadian, 2014). The organizational network in this approach is made up of knowledge networks, task networks and resource networks and their combinations. These features distinguish this approach from the traditional, and most frequently explored, one used in research, social network analysis (SNA), which constitutes a part of ONA as it particularly exposes the relations between people. The use of network analysis has become a very popular field of research, especially in an inter-organizational context (e.g., Chou & Tsai, 2013; Jones, Edwards, Bocarro, Bunds, & Smith, 2017; Mentzas, Apostolou, Kafentzis, & Georgolios, 2006; Yang & Maxwell, 2011). This network approach covers intra-organizational relations, not only between people (in this case university library staff) but also between knowledge, resources, and tasks, which are integrally connected with the way a library functions. Analyzing a library from the perspective of the network of relations and ties which exist between social and technical network elements (called nodes), contributes to a more nuanced assessment of the effectiveness of university library operations, by means of some dedicated network measures, such as:

• On a whole network level: knowledge redundancy, task redundancy and resource redundancy.

Corresponding author. E-mail address: [email protected].

https://doi.org/10.1016/j.lisr.2019.02.007 Received 13 June 2018; Received in revised form 14 January 2019; Accepted 8 February 2019 Available online 02 March 2019 0740-8188/ © 2019 The Author. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

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• On a nodes network level: knowledge (resource) needs congruence

the transfer of complex knowledge based on mutual connections. Weak relationships provide non-redundant knowledge, and strong ones provide incentives for others to share knowledge (Droege & Hoobler, 2003). In other studies, Merrill, Bakken, Rockoff, Gebbie, and Carley (2007) used organizational network analysis to support the decisionmaking process for public health managers. The analysis allowed the authors to gain an insight into organizational processes that informed public health management on solving problems and using the strengths of the network. The skills that public managers need to cope with the limitations of their roles can be improved by carefully supervising the task environment. In turn, Merrill, Keeling, and Carley (2010) used organizational network analysis to document the relations between employees, tasks, knowledge, and resources in so-called local health departments, which may exist regardless of the formal administrative structure. These studies established basic network parameters that can serve as a comparative basis for local management decisions in the areas of communication, integration and resource allocation. Organizational network analysis, with a particular emphasis on congruence and the multimodal approach to network research, has found application in the process of software development (Jiang, Carley, & Eberlein, 2012). Here, software development is based on the alignment or consistency between values, beliefs, standards, practices, skills, behaviors, knowledge, and goals of stakeholders. Li, Qian, He, and Duan (2014) extended the scope of the analysis of the organizational structure of a construction project to the knowledge that is understood as the ability to fulfill the tasks of the project, and other entities. A well-functioning project organization has a high adjustment between the organization of projects and the allocation of tasks.

and knowledge (resource) waste congruence.

These measures allow a library to be viewed from a slightly different perspective, which goes beyond such classic measures as network centrality and nodes centrality. Redundancy assesses the excess or deficit of knowledge, resources, and tasks; whereas congruence assesses the level of needs (the technical elements of the network) and the potential level of waste (the degree to which they are not used). Looking at a library (but also at any other organization) from the perspective of a relations network makes it a living organism in which relations (connections) and nodes are constantly appearing and disappearing, thus affecting the general effectiveness of the whole network (a library). Viewing the library as a network, and evaluating both whole network redundancy as well as node-level congruence, provides researchers with a unique and more sophisticated method for assessing optimal library effectiveness. The findings of this study extend the knowledge and understanding of library effectiveness; provide evidence for library managers to improve their socio-technical system; and benefit library staff by indicating how tasks and resources should be accomplished or used. 3. Literature review 3.1. Network approaches in an organizational context There are a number of theories related to the network approach which allow the interactions occurring in the organizational environment to be understood. Interactions entail the influence exerted by people (called actors in network theory) on each other or exerted by technical elements (called non-human actors in the actor-network theory). Network theory, which includes tasks and resources as nodes as well as people, is an emerging (Borgatti & Halgin, 2011), but by no means consolidated, approach to organizational research observed from the perspective of a network of ties and relations (Tichy & Fombrun, 1979; Tichy, Tushman, & Fombrun, 1979). The theory of a social network (Dunn, 1983; Ferreira & Armagan, 2011; Kilduff & Tsai, 2003; Turkat, 1980) is the dominant approach and is distinguished from other networks by the intentionality of network actors' actions. The subjects of network research are usually social relations (e.g., Abbasi, Wigand, & Hossain, 2014), network structure, and the place of nodes in the network, which can be quantitatively estimated on the basis of the complex instruments within SNA (Borgatti, Everett, & Johnson, 2018; Haythornthwaite, 1996; Wasserman & Faust, 1994). Scientists use social networks and SNA to analyze a selected fragment of organizational reality, and the organization as a whole, in which phenomena are perceived through the prism of network relations. Organizations are created by expanding relations between people and their networks, generating social capital, aligned with having greater or lesser institutionalized relations (Lin, 1999) and resources (Omri & Boujelbene, 2015). The multimodal approach to the organization as a socio-technical network and the study of its effectiveness from the perspective of information, knowledge, and task management is visible in the public health sector. Hospitals, like libraries, are mainly public and they offer their services to clients (patients). The interactions occurring in these networks are poorly understood and, generally, unmanaged. Di Vincenzo and Mascia (2017) explain the impact of the level of knowledge development on ego network redundancy in the community of hospital doctors. The level of knowledge development, and the extent to which knowledge is uniformly distributed among cooperating physicians, is related to the reduction of their advisory networks. The impact of these links on network redundancy is moderated by belonging to different professional groups. In the context of sharing knowledge, Hansen (1999) discovered that weak ties are not effective when it comes to transferring complex information. Strong ties are necessary for

3.2. Knowledge, resource, and task networks in an organizational context The knowledge network (AK) comprises relations between the actor (A) and knowledge (skills) (K) possessed by the actor, or used by them, in an organization. The ties (AKij) in the AK network indicate that the actor is connected with knowledge j if the actor possesses and/or uses knowledge in his/her work. Knowledge concerns the proper execution of organizational tasks. Knowledge networks enable one to determine the flows and bottlenecks of knowledge inside the organization from a network perspective (AlDahdouh, Osório, & Caires, 2015). Both the creation and the use of knowledge is undoubtedly a social process (Bennet & Bennet, 2014; Williams, 2014). A knowledge network is defined as a collection of individuals and teams who meet in various organizations in order to create and share knowledge, coordinate, learn, create innovations, and support individual members inside and outside the organization (Bourouni, Noori, & Jafari, 2015). In such networks, nodes are at the same time the sources and the recipients of information and knowledge, and employees are not isolated from others in the organization, except for some isolated cases. An individual may receive access to valuable information and knowledge, as every employee is a part of the network and occupies a different position in the network. This provides them with different possibilities for accessing new knowledge, which, in turn, is required to perform tasks (Wu, Yeh, & Hung, 2012). This allows one to determine what knowledge each person in an organization possesses and also how inter-personal network structures affect the degree to which knowledge is used, and means that actors share knowledge resources and relations between them in the process of knowledge use. Since AK is constantly expanded to incorporate the knowledge gained in the learning process, it should be treated as a dynamic structure combining different levels and fields of knowledge. It is essential, therefore, to facilitate the creation of networks between particular kinds of knowledge (for example individual, group, or organizational) and fields of knowledge (for example knowledge of the market, customers, or products). In the knowledge network, each actor has a subordinated domain of 49

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knowledge and also the ultimate relation—namely that the tie between the actor and the knowledge depends on whether the actor uses this specific knowledge in tasks (activities). The actors' ability to use their knowledge in relation to a task will be affected by how this knowledge is distributed among organization members. The degree of knowledge and actors' isolation will hinder the team's ability to coordinate their tasks, as it is difficult to involve everybody needed to perform the task when some actors possess knowledge which is inaccessible to others. Common knowledge helps teams since it enables them to understand expectations, explain the task to all, and plan activities in agreement with other members of the team (Espinosa & Clark, 2014). Just as in the case of the knowledge network, the resources network is also bi-modal and may determine who (A) possesses and uses what resources (R). Resources in the network constitute passive organizational elements, which are tools used for performing tasks and can be controlled by actors. The resources network (AR) is defined respectively by access to resources and their use by particular actors. The tie of ARij in an AR network indicates that actor i is tied with resource j, if actor i has access to the resource and/or uses it in his/her work. The use of knowledge is perceived as a process related to a specific task (the knowledge to perform the task network – KT), in which knowledge can be applied to the task. The relation between knowledge and task KTij in a KT network exists if knowledge i is required to perform task j. Task elements reflect resources which are organized in order to perform a specific business process. Each task and allocated resource becomes a distinguished class of node in the meta-network model (Ashworth & Carley, 2006; Carley, 1999; Tsvetovat & Carley, 2004), in which relations are modeled by means of graphs. The relation between the actor and the task ATij = v exists in an AT network if actor i is able to perform task j. Intra-organizational relations (determined by means of a matrix) must be coordinated in order to achieve the specified levels of information and knowledge sharing, and using resources or performing tasks. In order to perform a task, an actor who has knowledge of how the task should be completed is required. The actor allocated to this task may only have partial knowledge, or no knowledge, and therefore, in this case, knowledge would not be used or would only be used fragmentarily. The degree of using knowledge is thus a function of subordinating tasks to actors who possess the relevant knowledge. By understanding the complexity of the flow of information, knowledge, resources, and tasks between employees in the organization, it is possible to determine more precise ways of improving access, information use and availability, with a view to increasing productivity and creating value. Penrose (1995) brought an essential contribution to the differentiation between resources (R) and tasks (T). Resources constitute a potential for activities, creating a network of resources for performing tasks (RT) which may generate various streams of services or activities. Without subordinating particular resources (both material and nonmaterial), an activity (service) cannot be performed. The relation between resources and tasks (RTij) = v in the RT network appears when resource or resources i are required to perform task j.

congruence of needs and knowledge waste (resources) from the perspective of how this particular library operates? These research questions were reflected in two applied research methods: an interview and a questionnaire. In order to identify areas of knowledge, resources (material and non-material), and performed tasks, the interview was conducted with the director of the library, who is usually the person with the most extensive knowledge of how an organization functions (Tsai & Ghoshal, 1998). The director of the library who was responsible for its development was chosen for the interview as the person with the longest work experience in the organization. The interviewee was advised of the scope of the interview in advance and they were advised again at the beginning of the conversation. A semi-structured interview was used as it affords a less scripted method, allowing the interviewee to feel free to discuss constructively the interview goals. The interview focused on discussing the opportunities and threats to the library's external environment; discussing the business model and identifying basic business processes from the perspective of accomplishing strategic goals; and determining the required knowledge, resources, and tasks necessary to implement particular business goals. The main criteria for the selection of processes were direct influence on the library mission and vision; generating revenues and general success of the library; creating added value for the library; satisfying customers' needs (service recipients); and relying on valuable human, technological and information resources. The interview revealed a deeper understanding of the business processes, the knowledge needed and created by those processes, the resources needed and used in the processes, and the needed and performed tasks. The transcript of the interview and coding was performed using descriptive codes (Miles & Huberman, 1994) and a glossary of the library's own terms was used. Using the interview results, a category of typical knowledge and skills, resources, and tasks used in business processes was developed, and this was used as an answer option in the survey questionnaire. The following five questions and statements were used in the survey questionnaire:

• I use knowledge from this field in my work. • I use this resource in my work. • I perform this task. • Is this knowledge necessary to perform this task? • Is this resource necessary to perform this task? The answers served to build five bimodal matrixes, in which the value 1 was allocated to the existence of strong relations between actors and knowledge (matrix AK); actors and tasks (AT); actors and resources (AR); and when the knowledge or resource proved necessary to perform a given task (matrixes KT and RT). The questionnaire was pilot-tested on a sample of two employees, and the resultant effect of this pilot was, inter alia, a simplification of the survey, and the choice of a matrix construction of questions and answers (multi-grid) instead of the repeated roster. This significantly shortened the time needed to complete the questionnaire and decreased the respondents' involvement, as recommended by Borgatti, Everett, and Johnson (2013, p. 50). All library employees, both librarians and full-time administrative employees, were invited to take part in the survey. 24% of employees have work experience of up to 5 years, 20% up to 10 years, 13% up to 15 years, 13% up to 20 years, and 30% of employees have an internship of over 20 years in this library. The staff were invited (via e-mail) to participate in the questionnaire, which was available online from 7th November to 4th December 2015. In the first week, 76 employees filled in the questionnaire, after which a reminder was sent out and a further 6 employees filled in the questionnaire. This meant that, in total, the survey was conducted on 82 library employees, of whom 88% were women and 12% were men, and gave a response rate of 93% from this

4. Methodology 4.1. Research questions and instruments The study looks at the university library in Warsaw from the perspective of the knowledge network, the task network and the resources network, and the effectiveness of their use. In particular, this study focuses on the following questions:

• What are the basic areas of knowledge (skills), tasks, and resources of the analyzed library? • What is the level of redundancy in the distribution of knowledge (skills) and resources in the analyzed library? • What significance is attached to the analysis of the level of 50

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target population, which can be considered highly satisfactory for network research (Ferrin, Dirks, & Shah, 2006). In the library, apart from administrative employees who make up only 10% of the staff, the team of employees consists of librarians, junior and senior librarians, and custodians, including two persons as senior custodian. The organizational structure is hierarchical with a director, then, successively, a deputy director, executives, librarians and administrative employees. Regarding this study, it was not possible to select other observations as only the entire population (82 actors) were sampled, which automatically eliminates the use of any selection to the sample, because all library's employees were invited to take part in the study. Despite such a limited population of A (one library), the distribution of the quantities of K, R, and T assigned to each A is close to normal. Therefore, it can be concluded that, in the three categories of association, group A is the “standard”; there are no atypical A subgroups.

Let NK = AT KT = knowledge needed by actors to perform their task ; then the output value for actor i = sum(~NK(i,:).

; then the output value for actor i = sum(~NR(i,:). /sum(NR(i,:))

1) | K |],

(1)

RR

[0, (|A|

1)

|R|],

(2)

TR

[0, (|A|

1)

|T|],

(3)

5. Results Since business processes reflect the influence of people, resources, knowledge and information on each other, their identification has become the most important area for analyses (see Table 1). The identification of knowledge and skills, tasks, and resources are closely related to specific business processes indicated by the library's director and ordered by him or her in accordance with the logic of library operation. Each business process consists of knowledge, performed by the actors of tasks, and the resources used to carry out the tasks. Resources can be interchangeably called types of things or tools necessary to perform tasks. The following business processes were identified: providing access to library resources; documenting library resources; information services detailing the library's collections available to users; participating in education services; storing and protecting resources; and cooperating with the university and other libraries. Then, the required knowledge and skills, tasks, and resources necessary to implement the business processes defined in this way were attributed. For the network analysis, those processes which are of vital importance when creating value for users and for the library were chosen. Some elements needed combining (for example, marketing and promotional activities) or moving from the task area to the knowledge and skills area (for example, knowledge of gathering collections). Table 2 presents the selected and grouped elements, divided into knowledge and skills (K), tasks (T) and resources (R). In total, 24 elements of knowledge/skills (K), 31 tasks (T) and 26 resources (R) were defined. To facilitate the checking of the popularity distribution of individual K, R, and T among A, a division into 10 categories was applied (sten scores according to the formula S = 5.5 + 2∙Z, where Z is the normed value of the variable). Firstly, every K, R, and T were counted with the number A, and then for such a variable (SUM of AK, SUM of AR, SUM of AT - the sum in the columns of these matrices “01”), Z = (X was normalized) -μ)/σ (where X is the variable value, and μ and σ are its average and standard deviation, respectively) and S values (SAK, SAR, SAT) were determined based on the sten formula. In spite of the existence of low and highly popular Ks, there are no extreme outliers among them (Grubbs Test Statistic = 2.07716, pvalue = 1). Therefore, the K group is also consistent in terms of connections with A. The majority of Ks is of average popularity, and to

4.2.2. Congruence as a measure of network effectiveness Congruence denotes a relation of fit between the way in which the library is organized and its ability to perform tasks. The measure is expressed as a relation of the share of knowledge (resource) required to perform the task relative to the total knowledge (resource) of a given actor—formulas (4) and (5) below. It summarizes the necessary but unavailable knowledge. Full congruence appears when an actor possesses knowledge (resources) which is perfectly matched to the performance of the task (Jiang et al., 2012).

Let NK = AT KT = knowledge needed by actors to perform their task ~AK(i,:)) (4)

/sum(NK(i,:))

Let NR = AT RT’ = resource needed by actors to perform their task ; then the output value for actor i = sum(NR(i,:). /sum(NR(i,:))

(7)

The library is part of one of the biggest universities in Poland and, accompanied by its high ranking as a university, enjoys a great reputation in its environment, not just in its domestic education and research market. This can be attributed to many factors, inter alia: the growing importance of technical education observed in the past few years; the openness of graduates, regular users of the library, and knowledge; the strengthening of the trend combining science and technology with economy in which knowledge and its use is the main resource. Moreover, an efficient flow of information and the emerging tendency to provide full access to the results of scientific research (so-called open access), which help identify the gap in research result commercialization, are further factors promoting the institution. In addition, the location of the library in the capital of Poland encourages all kinds of cooperation and helps the library to be a leading institution, providing high-quality services.

where: |K| - the number of elements in set K (knowledge), |R| - the number of elements in set R (resources), |A| - the number of elements in set A (actor), |T| - the number of elements in set T (tasks). When computed on an actor (A) and knowledge (K); or resource (R); or task (T) network, this is called knowledge redundancy (KR), resource redundancy (RR), or task redundancy (TR) and is the average normalized number of redundant actors per K, or R, or T.

; then the output value for actor i = sum(NK(i,:).

AR(i,:))

4.3. The university library in Warsaw

4.2.1. Redundancy as a measure of network effectiveness Redundancy is expressed by the number of actors who gain access to the same resources, perform the same task, or use the same knowledge. Excess exists only when more than one actor meets the condition. Redundancy determines the distribution of knowledge, resources, and tasks within the organization, according to the formulas (1), (2) and (3) below (Carley, 2002):

[0, (|A|

(6)

Let NR = AT RT’ = resource needed by actors to perform their task

4.2. Organizational network analysis techniques

KR

AK(i,:))

/sum(NK(i,:))

~AR(i,:)) (5)

Actor knowledge (resource) waste compares the knowledge (resource) of the actor with the knowledge (resource) he or she actually needs to do his or her task. Any unused knowledge (resource) is considered wasted—formulas (6) and (7)) below, (Altman, Carley, & Reminga, 2017; Jiang et al., 2012): 51

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Table 1 Business processes in the library. Business processes

Knowledge and skills

Tasks

Resources

Providing access to library resources

Library science Information science Marketing Negotiating skills Organization of library work Managing resources IT skills

Gathering collections Recording collections Preparing collections Informing about collections Making collections available Storing

Documenting library resources

Library science Organization of library work Managing resources IT skills Educating adults (training)

Organizing editor's work Current update and correction of common data Running trainings Making reports and accounts Running the homepage

Library science Information science IT basics (creating and maintaining homepage) Organization of library work Managing resources Informing about library promotional activities Graphic skills Working with users

Creating a homepage Creating lists of purchases Creating visual information systems

Integrated library system Aleph Oracle Funds to purchase collections Premises (reading room) Server Terminals PCs Code scanners Omega-Psir Zotero Excel Word Office devices (fax, printers, scanners, etc.) PowerPoint Projectors Lecture rooms Computer laboratory Excel Binfo Aleph

Developing promotional materials Testing access and databases Informing about collections

Html files ProShow DLibra

Replies to library surveys Providing remote access to electronic collections Creating electronic version of a document Creating full-text bibliography Preparing display board exhibitions

Library science

Creating with KRK a general education program in IT education Individualizing programs to faculties' needs Running classes E-learning training Training in the methodology of research

Software for digital library Program Han Java Computer equipment Leaflets Information materials PowerPoint

Training Analysis of quotations Information services detailing the library's collections available to users

Participation in education services

Information science Pedagogy (andragogy) IT skills Graphic skills PowerPoint presentations Film presentations Quoting skills Storing and protecting resources

Cooperating with the university

History of books Library science

Protection of collections Storing collections

Logistics (moving collections)

Archiving electronic resources

Maintaining collections

Moving collections Basic maintenance procedures Selecting collections

Library science Information science Science studies Accounting and administration

Personnel matters Finance and accounting records Fixed and current asset accounts Servicing electronic mail LAN administration Coordination of gathering processes in SBI unit Running the Center of Cataloguing Library Collections at PW Central collection of electronic sources Cooperation in providing access to collections Training in using ZSB Reports on the operations of the whole system

Moodle Homepage HTML files Projectors Lecture rooms Computers, laptops Computer laboratory RefWorks Warehouse rooms Warehouses with full access to collections Separate warehouses for particularly valuable collections Servers for archived data DLibra A truck Warehouse trolleys Code readers Scanners for books SAP USOS Electronic mail Finance and accounting system Fixed asset account system ZSB HAN Statute

(continued on next page)

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Table 1 (continued) Business processes

Knowledge and skills

Tasks

Resources

Cooperating with other libraries

Library science Information science

Co-cataloguing in Nukat catalogue Creating central catalogue of conference materials Symponet Creating BazTech and BazTol Co-creating a file of model entries for formal entries KHW Entering descriptions to Nukat base Copying entries to Aleph program Verification of indexes Administering the base (creating backups) Cataloguing own conference materials in Symponet base Importing data sent from cooperating libraries (new descriptions, supplementary information) BazTech and BazTol – bibliographic descriptions with articles from science and technical journals Introducing abstracts and key words Linking to journal homepages Seeking and registering homepages in science and technology

Virtua Aleph

Basic IT skills

Yadda Excel Computer equipment Network (internet connection)

processes, specific knowledge, unique resources, and tasks which are typical for organizations of this type. The identified business processes provide a context for knowledge, resources, and tasks, and for employees involved in particular processes, even if such involvement results from the performance of a particular action or the possession of a fragment of the specified knowledge. The analysis concentrated mostly on network elements and their interactions. Without defining the basic business processes for an organization, the determination of knowledge or skills, resources, and tasks would be polarized, and it would not be known how knowledge, resources, and tasks could be incorporated into the research. In an ever-changing and stormy environment, the scope of business processes will evolve and, in time, the required knowledge, resources, and tasks will also be updated. Redundancy does exist in the library, though it is difficult to assess whether it is optimal due to the lack of research in this area. Redundancy allows the level of required specialization (related to knowledge, resources, and tasks) to be determined, and the level of knowledge specialization in the library may be higher than is actually necessary to perform tasks. With a high specialization of knowledge, resources, and tasks, each excessive relation concerning this knowledge, resource, or task, will not be beneficial. Although many library employees have the same knowledge, a smaller percentage of them perform the same tasks or use the same resources. However, from the library's perspective, this may be justifiable, taking into account the inability to outsource tasks that must be performed within the library; or maybe knowledge redundancy is necessary in order to coordinate library tasks. It should be remembered though that a redundancy of knowledge, resources, or tasks which is too high leads to similar tasks being performed and not to cooperation. In the analyzed library the level of indicators seems optimal, taking into account its size (measured by the number of employees) and given the possibility of replacing absent workers with others who have similar knowledge, use similar resources, or perform similar tasks. The profit derived from additional, excess contact (concerning the same information or knowledge) will be minimal, contrary to establishing new relations between actors. According to the concept of strong and weak ties developed by Granovetter (1973), people with whom we have weak ties have unique experience and access to new and valuable information. Therefore, it is more likely that non-redundant information will come from weak relations between actors. Non-redundancy in the library is not a feature of information (knowledge) but the fact that the exchange is very limited. A network which is composed of nonredundant ties accounts for better use of limited resources and is more efficient, especially concerning the benefits of timing and access to

maintain the reality of research, in the group there are both less and more popular Ks - of course in the minority, as it is for Ts and Rs. Despite the existence of low and highly popular Rs, there are no extreme outliers (Grubbs Test Statistic = 1.819563, p-value = 1). Therefore, the R group is consistent in terms of AR relationships. Most Rs have average popularity, and to maintain the reality of research, the R group is both less and more popular, of course in the minority. In spite of the existence of low and highly popular Ts, there are no extreme outliers (Grubbs Test Statistic = 2.22828, p-value = 1). Therefore, the T group is consistent in terms of AT connections. The majority of Ts is of medium popularity, but to maintain the reality of research, in the T group there are tasks that are both less and more popular - of course in the minority, as presented above. All three variables (SAK, SAR, SAT) are characterized by a distribution with a light right-sided asymmetry, which would indicate the predominant low values of these variables (links with less than half A). Nevertheless, in the AK, AR, AT distributions there are no atypical values (in particular too large) or extreme outliers. Yes, there are both low and high popular Ks, Rs, and Ts, but they can be considered typical for the studied population (Grubbs and boxplot tests). Having identified the areas of knowledge, resources, and tasks, the redundancy level in the library was calculated. The redundancy indicator is global (it applies to the entire network) as opposed to the congruence index, which measures this value for the individual actors. Hence the level of detail in the measurement will be different here. Knowledge redundancy (KR) = 0.430; task redundancy (TR) = 0.314, whereas resource redundancy (RR) = 0.165. Knowledge redundancy shows that 43% of the employees in the library use the same knowledge and skills in their work, 31% perform the same tasks and 16% use the same resources. Table 3 below presents the result of congruence for all the library staff. The minimal values for the congruence of knowledge needs and waste were MIN = 0, whereas the maximum values for knowledge congruence were, respectively, MAX = 1 (needs) and MAX = 0.667 (waste). The maximum values for congruence of resources are MAX = 0.759 (needs) and MAX = 0.500 (waste). The average results were, respectively, for knowledge congruence AVG = 0.348 (needs) and AVG = 0.061 (waste), and for resource congruence AVG = 0.252 (needs) and AVG = 0.177 (waste). 6. Discussion The obtained results constitute some interesting research material that enables one to look at the library through its fundamental business 53

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Table 2 (continued)

Table 2 Knowledge and skills, tasks, and resources of the library.

Knowledge and skills

Knowledge and skills K01 K02 K03 K04 K05 K06 K07 K08 K09 K10 K11 K12 K13 K14 K15 K16 K17 K18 K19 K20 K21 K22 K23 K24 Tasks T01 T02 T03 T04 T05 T06 T07 T08 T09 T10 T11 T12 T13 T14 T15 T16 T17 T18 T19 T20 T21 T22 T23 T24 T25 T26 T27 T28 T29 T30 T31

R15 R16 R17 R18 R19 R20 R21 R22

Gathering collections Preparing collections Scientific communication Providing access to collections Training needs Running trainings Accounting and administration Logistics and resource management (moving collections, selection) Marketing and promotion activities Updating databases Creating homepages Generating reports from databases Film presentations PowerPoint presentations Graphic skills Quotation making skills Negotiating skills Basic skills of using a computer and office devices Library law and procedures Copyright issues Resource management (selection, location) Creating databases Using electronic sources of information Scanning – processing digital files

R23 R24 R25 R26

Network (Internet connection) Code scanners Legislature and regulations (instructions, procedures) Office devices (fax, printers, phones, reprographic devices) Virtua Word Status Programs supporting purchasing collections, for example Dawsonera, Azymut Electronic sources of scientific information Software for creating the homepage of BG Software for digitization Software for processing graphic files

information, as suggested by Soda, Usai, and Zaheer (2004). Knowledge in dense relations is redundant, and the risk of excess (similar) knowledge in the knowledge network (AK) of the library exists, which may affect the creation of new knowledge and innovative services (see more Susskind, Miller, & Johnson, 1998). As suggested by Ronfeldt and Arquilla (2001), individual redundancy gives an organization (in this case the library) flexibility, and mitigates against risk in a small network (library) by limiting the harmful effects connected with the unavailability of an actor. Carley (1990) admits that knowledge and resources are unevenly distributed in most organizations, and this is also the case in this library. The loss of redundancy, however, may limit learning, and the adaptive and flexible reactions available to this library. Moving an actor into a network of relations brings about a significant change in risk management and requires a timely reaction to changes when strategic decisions are made by library managers. Understanding the changes occurring in a network is as equally important for a library manager as understanding the structure at a given moment. Seeing the bigger picture of the network of relations between actors, knowledge, resources, and tasks allows managers to introduce strategic interventions aimed at anticipating changes and limiting risk through, inter alia, the effective use of excess knowledge and the reidentification of key actors in the library. The role of a library manager is to identify the needs and deficits concerning knowledge and resources, and ensure access to them is provided. The congruence measure is a useful tool in this respect, as it allows the knowledge and resource needs, and the level of knowledge and resources not used by particular employees, to be assessed. The level of knowledge needs for the first ten library employees oscillates in the range 1–0.597, which may mean a high and average knowledge need, and this is higher than the resource need, which is in the range 0.759–0.448 for the first ten employees. With the measures of imbalance of knowledge and resources to workers being slightly better, and the level of unused knowledge not being high, the library can be considered efficient in this respect. A well-functioning library should have a high match between knowledge, resources, and assigned tasks (Li et al., 2014), which could create the optimal congruence without unnecessary waste of knowledge and resources or demand for knowledge, resources, and tasks in managing the knowledge network, the resource network, and the task network in the library. The experience of library employees and the positions held by them has become the context for the analysis of congruence results at the individual level. The actors A22, A62, A81 and A82 have over 20 years of tenure in this library. Actor A80 has 20 years, and actors A50 and A58 have 15 years of tenure in the institution. Two actors (A58 and A80) are in management positions, and in both cases the demand for knowledge in the implementation of tasks is high. The individual results obtained for each actor in the survey encourage a more detailed analysis to be conducted. For example, actor A80 does not have strong relations with the knowledge used but the

Ordering and registering purchases Formal preparation of collections Material preparation of collections Updating and correcting data in databases Conducting traditional training Conducting e-learning training Writing reports and accounts Website editing (and materials uploaded there, such as bulletins, Facebook, blog) Creating training programs Analysis of quotations Administering the homepage Creating the visual information system Testing databases (access, content) Informing about collections and services Replies to library surveys Providing remote access to electronic collections Creating bibliography, including full-text bibliography Preparing exhibitions Storing and protecting library materials Moving collections Selecting collections Dealing with personnel matters Finance and accounting register Fixed and current asset account Servicing e-mail Creating a glossary Administering and archiving databases Activities popularizing knowledge Servicing borrowings and readers' accounts Field information (factual, normalizing, dedicated to specific groups of users) Digitization, creating digital documents

Resources R01 Aleph R02 DLibra R03 Excel R04 HAN R05 Computers, laptops, terminals R06 Information materials/tools (homepage, leaflets, announcements) R07 Moodle R08 Omega-Psir R09 Electronic mail R10 PowerPoint R11 Projectors R12 Tools for creating bibliography, for example RefWorks, Zotero R13 SAP (work time register) R14 Servers

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Table 3 (continued)

Table 3 The results of knowledge and resource congruence. Actors

Actor knowledge needs congruence

Actor knowledge waste congruence

Actor resource needs congruence

Actor resource waste congruence

A01 A02 A03 A04 A05 A06 A07 A08 A09 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21 A22 A23 A24 A25 A26 A27 A28 A29 A30 A31 A32 A33 A34 A35 A36 A37 A38 A39 A40 A41 A42 A43 A44 A45 A46 A47 A48 A49 A50 A51 A52 A53 A54 A55 A56 A57 A58 A59 A60 A61 A62 A63 A64 A65 A66 A67 A68 A69 A70 A71

0.109 0.338 0.548 0.430 0.276 0.157 0.436 0.209 0.563 0.045 0.373 0.325 0.333 0.567 0.543 0.211 0.203 0.333 0.373 0.000 0.357 0.000 0.562 0.304 0.440 0.000 0.402 0.246 0.243 0.057 0.324 0.230 0.340 0.052 0.239 0.030 0.052 0.597 0.533 0.283 0.741 0.167 0.452 0.723 0.274 0.214 0.379 0.440 0.333 0.625 0.111 0.493 0.688 0.138 0.021 0.389 0.232 0.931 0.224 0.385 0.202 0.800 0.220 0.486 0.349 0.301 0.348 0.397 0.311 0.155 0.236

0.000 0.000 0.167 0.077 0.000 0.000 0.167 0.000 0.000 0.100 0.111 0.000 0.154 0.000 0.000 0.000 0.188 0.000 0.000 0.000 0.077 0.667 0.000 0.000 0.100 0.000 0.200 0.000 0.000 0.158 0.000 0.059 0.077 0.111 0.000 0.000 0.111 0.143 0.125 0.000 0.000 0.000 0.000 0.333 0.077 0.067 0.091 0.000 0.000 0.000 0.063 0.000 0.000 0.067 0.045 0.000 0.000 0.500 0.000 0.000 0.059 0.000 0.000 0.000 0.154 0.000 0.214 0.000 0.000 0.000 0.091

0.132 0.208 0.265 0.313 0.292 0.188 0.104 0.156 0.056 0.492 0.255 0.254 0.057 0.448 0.375 0.273 0.232 0.369 0.206 0.000 0.298 0.250 0.016 0.243 0.500 0.000 0.230 0.190 0.153 0.123 0.412 0.082 0.306 0.055 0.057 0.157 0.055 0.298 0.324 0.310 0.172 0.042 0.083 0.256 0.452 0.231 0.284 0.200 0.110 0.429 0.284 0.143 0.190 0.228 0.179 0.357 0.294 0.704 0.210 0.224 0.175 0.733 0.197 0.250 0.227 0.200 0.240 0.157 0.246 0.068 0.463

0.250 0.091 0.214 0.133 0.200 0.059 0.222 0.071 0.400 0.000 0.091 0.214 0.188 0.143 0.167 0.091 0.250 0.111 0.143 0.000 0.250 0.500 0.238 0.308 0.182 0.000 0.250 0.077 0.143 0.316 0.000 0.059 0.111 0.111 0.111 0.111 0.111 0.200 0.273 0.083 0.200 0.136 0.167 0.182 0.091 0.133 0.214 0.182 0.071 0.500 0.143 0.133 0.273 0.154 0.235 0.455 0.182 0.167 0.143 0.083 0.067 0.333 0.158 0.250 0.167 0.167 0.308 0.143 0.200 0.105 0.143

Actors

Actor knowledge needs congruence

Actor knowledge waste congruence

Actor resource needs congruence

Actor resource waste congruence

A72 A73 A74 A75 A76 A77 A78 A79 A80 A81 A82 MIN MAX M SD

0.571 0.200 0.511 0.306 0.543 0.000 0.351 0.080 1.000 0.824 0.742 0 1 0.348 0.221

0.000 0.000 0.222 0.000 0.000 0.000 0.000 0.211 0.000 0.000 0.000 0 0.667 0.061 0.110

0.294 0.340 0.325 0.084 0.406 0.500 0.364 0.000 0.141 0.722 0.759 0 0.759 0.252 0.163

0.333 0.273 0.467 0.150 0.333 0.000 0.250 0.269 0.118 0.000 0.000 0 0.500 0.177 0.112

required knowledge needs are full from the perspective of task completion. Actor 58 uses only knowledge K08 and K21 on tasks, but the required needs are much greater according to the tasks performed. Actors A58, A81, A62 also have a high degree of knowledge needs but only possess approximately 80% of the requirement. On the other hand, knowledge waste congruence is demonstrated by actors A22 and A58, who have a relatively high degree of unnecessary (excess) knowledge from the perspective of the tasks performed, at 67% and 50% respectively. In the case of resources, actor A82 uses 76% of the resources required to accomplish the tasks and a similar situation can be observed when analyzing actors A62, A81 and A58 (around 70%). The level of resource waste is relatively lower than the congruence of wasted knowledge but, nevertheless, it still amounts to 50% for actors A22 and A50. 6.1. Further research In the future, it could be worth investigating in which cases redundancy is needed due to the volume of some work areas in the library. It may also be helpful to discuss redundancy in the short, medium and long-term needs of the library. This is already done to the extent that it is mentioned that the staff may need to replace each other. One could also examine how much time employees spend on different types of tasks, especially because some jobs require the staff to perform multiple tasks in a specific domain or in many different domains. In that case, redundancy would be needed. It may also be helpful to take into account the bottlenecks that can be reflected in the network analysis using betweeness centrality. The current study design allows cross-sectional analysis but does not take into account unmet needs. Longitudinal studies would allow one to show the size of the gap in the studied areas of knowledge, resources, and tasks, and how it changes over time (growing, decreasing). On the other hand, something different and requiring further research, is the existence of dependence between the occurrence of individual Ks, Ts or Rs, as well as the correlation between the quantities of K, T, R assigned to a particular A. 6.2. Limitations The measures used in this study have made it possible to assess the efficiency of the library through the redundancy and congruence of network nodes (knowledge, resources, and tasks). These network measures are useful techniques for measuring network efficiency and their analysis provides the tools for managing the library with a view to using and sharing of resources or knowledge. At this stage, however, it 55

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is impossible to determine the required level of redundancy that defines the efficiency of the library, since the redundancy level in other libraries operating in the same sector is not known, due to a lack of research. Therefore, as the optimal level of redundancy in libraries is not known, this would suggest that the analyzed library's excess knowledge, resources and tasks is an element protecting it against the organizational risk associated with unavailability of staff. This provides the university library, however, with some flexibility in relation to excess knowledge, resources, and tasks, in the case of the loss of a given employee or employees. It is apparent that it is necessary to conduct broader, and more extensive, research to formulate any clear conclusions here.

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7. Conclusion Traditional tools for measuring an organization's efficiency are becoming inadequate and cannot grasp the dynamic nature of organizational knowledge, resources, and tasks, and associated risk, since these elements are becoming more and more potent, are sometimes hidden, and are often based on experience and interpersonal relations. This rapidly changing environment forces managers to adopt an anticipatory approach to management but, so far, the subject measures have not been widely used in organizations or network research. Nonetheless, they provide an excellent starting point for analyzing the efficiency of knowledge use, resource use, and task performance. This study constitutes a unique approach to the analysis of the efficiency of the university library through the inter-dependencies and interactions between human nodes (actors) and non-human nodes (knowledge, resources, and tasks). By using bimodal matrices, it was possible to determine the relations and ties between particular nodes. The analysis of the network of relations and the influence exerted by particular network nodes on each other give a different perspective—a network perspective—through which it is possible to visualize the interdependencies between the network elements that make up the sociotechnical system of the library. The surveys give an opportunity to identify deficits in the library, identify areas of knowledge, the tasks to be carried out, or knowledge about resources in the library which might be inadequate. Additionally, this research is a source of information for the training of librarians, and can highlight the demand for knowledge and skills in this profession and the diversity of knowledge, resources, and tasks needed in library practices. Acknowledgement This work was supported by the National Science Center in Poland [grant number DEC-2012/05/D/HS4/01338] and the Cognitione Foundation for the Dissemination of Knowledge and Science [DEC2019/02/11]. References Abbasi, A., Wigand, R. T., & Hossain, L. (2014). Measuring social capital through network analysis and its influence on individual performance. Library & Information Science Research, 36, 66–73. https://doi.org/10.1016/j.lisr.2013.08.001. AlDahdouh, A. A., Osório, A. J., & Caires, S. (2015). Understanding knowledge network, learning and connectivism. International Journal of Instructional Technology and Distance Learning, 12(10), 3–21. Altman, N., Carley, K. M., & Reminga, J. (2017). ORA User's Guide 2017. Retrieved from http://casos.cs.cmu.edu/publications/papers/CMU-ISR-17-100.pdf. Ashworth, M. J., & Carley, K. M. (2006). Who you know vs. what you know: The impact of social position and knowledge on team performance. Journal of Mathematical Sociology, 30(1), 43–75. https://doi.org/10.1080/00222500500323101. Bennet, A., & Bennet, D. (2014). Knowledge, theory and practice in knowledge management: Between associative pattering and context-rich action. Journal of Entrepreneurship, Management and Innovation, 10(1), 5–55. https://doi.org/10.7341/ 20141011. Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2013). Analyzing social networks. London, England: SAGE. Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2018). Analyzing social networks (2nd ed.). London, England: SAGE.

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A. Ujwary-Gil Organizational network analysis. Iranian Journal of Information Processing Management, 29(2), 567–590. Ronfeldt, D., & Arquilla, J. (2001). Networks, netwars and the fight for the future. First Monday, (10), 6. Retrieved from http://ojphi.org/ojs/index.php/fm/article/view/ 889. Soda, G., Usai, A., & Zaheer, A. (2004). Network memory: The influence of past and current networks on performance. Academy of Management Journal, 47(6), 893–906. Susskind, A. M., Miller, V. D., & Johnson, J. D. (1998). Downsizing and structural holes their impact on layoff survivors' perceptions of organizational chaos and openness to change. Communication Research, 25(1), 30–65. Tichy, N., & Fombrun, C. (1979). Network analysis in organizational settings. Human Relations, 32(11), 923–965. Tichy, N. M., Tushman, M. L., & Fombrun, C. (1979). Social network analysis for organizations. Academy of Management Review, 4(4), 507–519. Tsai, W., & Ghoshal, S. (1998). Social capital and value creation: The role of intrafirm networks. Academy of Management Journal, 41(4), 464–476. Tsvetovat, M., & Carley, K. M. (2004). Modeling complex socio-technical systems using multi-agent simulation methods. Kunstliche Intelligenz, 18(2), 23–28. Turkat, D. (1980). Social networks: Theory and practice. Journal of Community Psychology, 8(2), 99–109. https://doi.org/10.1002/1520-6629 (198004)8:2 < 99::AIDJCOP2290080202 > 3.0.CO;2–2. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge, England: Cambridge University Press. Williams, D. (2014). Models, metaphors and symbols for information and knowledge systems. Journal of Entrepreneurship, Management and Innovation, 10(1), 79–107.

https://doi.org/10.7341/20141013. Wu, W.-L., Yeh, R.-S., & Hung, H.-K. (2012). Knowledge sharing and work performance: A network perspective. Social Behavior and Personality, 40(7), 1113–1120. Yang, T.-M., & Maxwell, T. A. (2011). Information-sharing in public organizations: A literature review of interpersonal, intra-organizational and inter-organizational success factors. Government Information Quarterly, 28(2), 164–175. https://doi.org/10. 1016/j.giq.2010.06.008. Anna Ujwary-Gil is an associate professor at the Institute of Economics, Polish Academy of Sciences in Warsaw, Poland, where she is also a director of two MBA studies. She received her PhD in economics and management from the Warsaw School of Economics, Poland. She is a founder and editor-in-chief of Journal of Entrepreneurship, Management and Innovation. In 2010, her book “Kapitał intelektualny a wartość rynkowa przedsiębiorstwa” [Intellectual Capital and the Market Value of a Company] (CH.Beck) won the Polish Academy of Sciences monographs award. Among numerous projects, she was a project supervisor in the Sonata competition of the National Science Center, and an experienced researcher in the EU Industry-Academia Partnerships and Pathways programme. For more than 17 years, she has been the conference director and academic supervisor of annual academics' and business professionals' conferences held every June, and is founder and president of the Cognitione Foundation for the Dissemination of Knowledge and Science. Her research interests include organizational network analysis, knowledge management, intellectual capital, resource-based views, and dynamic approaches to organization and management.

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