In typical network studies, every dependent and independent variable is measured using a question concerning a relation in one network (Ibarra, 1995). It is important to bear in mind that the analysis unit in the study assumes a relationship between pairs of individuals. Hence, all variables are dyadic. These data are cross-sectional, which means that all variables were collected at the same time point. A cross-sectional study justifies the selection of ONA rather than DNA techniques, as DNA studies are longitudinal in nature. Nevertheless, elements of DNA are also present here, in particular with regard to simulating changes and positions of nodes in the network.
Analysis methods and concepts applied in SNA, ONA, and DNA are entirely different from those commonly used in traditional statistics and data analysis (Prell, 2012). The procedures of network analysis are par
ticularly problematic when they are used in statistical reasoning and hypothesis testing. Nonetheless, descriptive techniques can be used suc
cessfully, as they offer crucial information on how nodes are connected in the network. Descriptive statistics and statistical reasoning were pro
duced using networks and statistics generated by ORA and UCINET.
Beside basic quantitative statistics, such as standard deviations, means, and minimum and maximum values, the quadratic assignment procedure and multiple regression of quadratic assignment procedure were used, which are not limited to social network analysis. In the literature, scarce publication can be found using these tools in ONA based on organizational and management theories. Therefore, a more detailed analysis is justified here.
QAP is a non-parametric test measuring correlations between two vari
ables with the same matrix size, using, for example, Pearson’s correlation coefficient. The QAP test is a two-stage procedure, in which data in the form of matrices are extended into a vector of observations, and an ordi
nary multiple regression is performed on them, providing a traditional beta coefficient estimated for every independent variable. The problem is that traditional estimates of standard errors of those coefficients are very
sensitive to auto-correlation in the data, and thus may not be used as the basis for significance testing for these coefficients. Therefore, the second step involves generating a null hypothesis from the reference distribu
tion, against which the observed coefficient can be compared to verify its statistical significance. This distribution is created by multiple random permutations of the dependent variable matrix, each time calculating the regression coefficient for each independent variable, predicting the per
muted dependent variable (Hinds, Carley, Krackhardt, & Wholey, 2000).
P-values are determined based on relative frequency of a statistical value in the reference distribution obtained by permutation, which is larger than or equal to the empirically observed value (Dekker, Krackhardt, &
Snijders, 2007). The QAP technique generates significant levels of inde
pendent variables and pseudo R2, which can be interpreted similarly to R2 statistics in traditional ordinary least squares (OLS) regression.
QAP and MRQAP are used in numerous studies. Hubert and Schultz (1976) are credited with the first use of the term “quadratic assignment procedure.” Mantel (1967) is considered the author of MRQAP, first developed for identifying geographically concentrated disease clusters using distance matrices. Krackhardt (1988) developed QAP in order to solve the structural problem of auto-correlation of data in a network and to compare data at the matrix level. The author calls the critical condi
tion in correlation analysis resulting from the lack of independence of observations “structural auto-correlation.”
In the case of QAP, investigation typically concerns informal rela
tions in organizations (e.g., D’Errico, Stefani, & Torriero, 2014; Rank, 2008). MRQAP is more commonly used to analyze inter-organizational knowledge exchange networks (e.g., Diez-Vial & Montoro-Sanchez, 2014) or knowledge networks related to human resource management (Martin-Rios, 2014). MRQAP is also applied when examining intra- organizational relations and organizational management. There are also studies concerning knowledge exchange (Hsu & Tzeng, 2010), knowl
edge flow (Marouf & Doreian, 2010), knowledge sharing (Maciel &
Chaves, 2017), knowledge transfer (Kaše, Paauwe, & Zupan, 2009), product development program management (Kratzer, Leenders, & van Engelen, 2009), social capital (Nonino, 2013; Tsai & Ghoshal, 1998), or trust (Ferrin et al., 2006; Gupta, Ho, Pollack, & Lai, 2016; Rašković &
Makovec-Brenčič, 2015; Zagenczyk, Purvis, Shoss, Scott, & Cruz, 2015).
MRQAP can be treated as a statistical approach to recognizing structural dependencies between relational data. In MRQAP analysis, the depend
ent variable is a one-mode matrix, not a vector, as is usually the case in regression analysis. MRQAP is associated with a logarithmic regression or OLS model, which includes relational variables and takes into con
sideration their interrelations when assessing their statistical significance (Broekel, Balland, Burger, & van Oort, 2014). In the first stage, the regres
sion coefficient is calculated using OLS, ignoring the diagonals. Then,
the dependent variable matrix rows and columns are permuted to create a new, random matrix. OLS regression is repeated with the new, per
muted matrix, producing different beta (β) coefficients. The procedure is repeated, generating a distribution of the beta value based on the matrix permutations, which then becomes a reference distribution against which the observed coefficients are compared (Hinds et al., 2000).
In order to test the statistical correlation model, the networks were subjected to regression, with significance tests constructed using the per
mutation/randomization technique. The significance values for correla
tion and regression are based on the distribution generated in 50,000 random permutations. The selection of random permutations is signifi
cant, as it affects the accuracy of standard error estimation and stabilizes the p values and significance. The larger the number of permutations, the less the p-value variability (Borgatti et al., 2018). It is impossible to test hypotheses in which the variable takes the form of a matrix representing a relation using classical statistical regression, as the observations are not independent, which justifies the use of MRQAP to test for the existence or absence of a given relation.
3.2.3 Network Research
In ONA, there are various methods and strategies both for collecting data and for measuring relations between actors. In this study, the real
istic strategy (Laumann, Marsden, & Prensky, 1989) and the full net
work strategy were selected. In the latter, relations between all members of the networks are studied, and the scope is limited by the organiza
tion’s boundaries. Network analysis requires complete data to capture the entire network of relations in the organization. Therefore, nearly all members of the organization were studied, which made it possible to acquire comprehensive information concerning interactions and relations in the network. The main study was carried out in early 2017.
The selection of the sampling method in a study requiring peripheral network specification is, to a large extent, determined by the existence of certain actors. A full list of organization members (employees) allows for defining relations of every node with all the others, which makes it possible to present the structure (topography) and positions of individual nodes in the network (Sparrowe & Liden, 2005). This method of popu
lation selection can enhance the credibility of network data (Marsden, 1990).
The network of relations was created based on responses from the survey questionnaire; then, network visualization and statistical analysis were performed. The results were entered into ORA and UCINET for further analysis. ORA was used to understand better the dynamics exist
ing among network actors and the available knowledge, resources, and tasks. ONA was performed to determine the importance of intangible
resource management with regard to the business processes of the organi
zation. Data related to the flows of work-related information, knowledge, skills, resources, and tasks in the organization operating in the IT sector were collected. Then, network visualization was performed, to present the relations and flows of intangible resources in the organization in the form of graphs.6 Network analysis required the creation of a matrix for each question, with the use of applicable response scales. Matrix creation allowed for discovering the nature of interactions in the target popula
tion and identifying prominent (central) network nodes, which can be presented using networks and quantitative results.
The way in which data were collected significantly contributed to the elimination of the missing data issue, which is quite problematic in network research (e.g., Kossinets, 2006). Therefore, a survey (closed, password-locked) was distributed online through the “Ankieta Plus” ser
vice (www.ankietaplus.pl/) only to a selected population of employees in the given organization. Additional settings of the survey eliminated the risk of missing data, as, without all the required data, the respond
ent would not be able to continue the survey. As a data collection tool, a survey has both advantages and disadvantages (Borgatti et al., 2018).
Advantages include limiting errors related to data collection and data sensitivity in the case of direct contact between the respondent and the researcher. Incomplete data cause problems in statistical reasoning. Lack of associations between actors interferes with the ability to formulate conclusions based on the data and misguides the researcher.
Confidentiality remains an important ethical aspect of network research (Everton, 2012). Providing anonymity is difficult, as in this type of network research, relations among all participants are examined, and respondents should say with whom they communicate when performing their work. In order to maintain the confidentiality of the obtained data, each respondent was assigned a special identification number (e.g., A01), and the identity behind the number was only known to the researcher.
The respondents were assured of data confidentiality and informed that the results would be sent to an entirely external server, beyond the con
trol of managerial staff.
Table 3.4 shows a summary of study methods, techniques, and tools;
data sources used; and the obtained results.
3.3 Presentation of the Selected Case Study and the Studied Population, Broken Down by Intangible Resource Audit Model Stages
In order to exemplify the developed methodology of integrated intangi
ble resource audit, an enterprise operating in the IT sector was invited to take part in the study. Special attention was paid to illustrating the tools (metrics) of organizational network analysis. The planning, design,
Table 3.4 Research methodology Literature study Literature review Resource-based Knowledge-based Intellectual Network Actor–network Activity view view capital-based theory theory view Information Knowledge Intellectual Social network analysis capital Information audit Knowledge audit Intellectual Organizational Dynamic network capital audit network analysis analysis Data sources Web of Science; Scopus; Scimago; Google Scholar Result Integrated organizational intangible resource audit Instruments for organizational network analysis Qualitative research Methods Interviews and data coding Documentation analysis Case study Data sources Managing directors Organizational documentation Documentation, interview Result Survey development Examining the organizational context Quantitative research Data collection Survey results Data analysis Tools and techniques Survey ORA ORA, SPSS UCINET: QAP/MRQAP Result Development of matrices Interpretation of findings Correlation and regression Network research Metrics of organizational Network visualization Hypothesis testing network analysis Tools and techniques ORA ORA UCINET: QAP/MRQAP Research results Conclusions Theoretical and practical Limitations and directions for further recommendations research
and operationalization stages of intangible resource audit are briefly pre
sented, in accordance with Table 2.5. The performance stage is discussed in more detail, and its results are presented in Chapter 4. The imple
mentation stage, due to its size and complexity, including the need to develop an intangible resource management strategy, requires a separate publication.
Connecto Sp. z o.o. is a company established in 2005, localized in Silesia, Poland. At the end of 2016, the enterprise employed 47 people.
Connecto offers comprehensive IT solutions for streamlining enterprise operations in all sectors. It implements enterprise resource planning (ERP) systems by Comarch S.A., with whom they have been cooper
ating for over 12 years, and develops data management system (DMS) software, assisting in enterprise management and customer relationship management using computers and/or mobile devices. These solutions allow for constant monitoring of tasks in the enterprise and increasing the efficiency of particular departments. Connecto provides services to companies operating in production, retail, service, construction, and many other sectors. Since 2011, Connecto has been a partner of the Sile
sian Construction Chamber. Their services involve supporting customers at every stage of their project—from defining the needs, to system imple
mentation, to training and ongoing support. Connecto approaches each client individually and selects the right IT tools to help companies achieve tangible financial benefits. In 2016, the Microsoft company selected Con
necto as a “Gold Partner.”
3.3.1 The Planning Stage of the Intangible Resource Audit in Connecto
At the planning stage of the intangible resource audit, a presentation of the Connecto enterprise was made by the managing director, and the auditor was introduced to the staff. The most important steps at this stage included defining the goals and scope of intangible resource audit, as well as the research assumptions and hypotheses, in cooperation with the managing staff. Both the aims and assumptions of intangible resource audit were adjusted to the needs of Connecto. The aim of the audit was to identify and analyze intangible resources from the point of view of the network of relations. Research hypotheses (see Section 4.2) concerned correlations existing among specific information, knowledge, task, and resource networks. Techniques of organizational network analysis were presented. The study was planned to analyze the dynamics of the net
work of relations, and dependencies existing among employees, infor
mation, knowledge, tasks (actions), and resources (tools) the employees use or perform at work. Network metrics were discussed, broken down into the entire network, dyad, and individual node levels. Owing to the support of the managing director, almost all employees took part in the
study. During the study, one person left the enterprise and one was on a long-term health leave, which brought the total number of surveys to 45 (98%). Employees engaged in the novel study with eagerness and were curious about the results.
3.3.2 The Design Stage of the Intangible Resource Audit in Connecto
The stage was preparatory in nature, in the sense that its primary goal was to examine the organizational context of the Connecto enterprise.
The first step involved identifying the opportunities and risks present in the enterprise’s external environment. The last two years prove that there is increasing demand for IT products and, consequently, for implementa
tion services—both specialties of Connecto, establishing the enterprise’s competitive position in the market. The achievement of the company’s goals depends to a large extent on macroeconomic conditions, in particu
lar the level of IT investment in large and medium enterprises, the level of competition in the IT sector, and the labor market. The labor market is dynamic, and there is a high demand for qualified IT specialists. How
ever, the supply does not satisfy the demand, which creates pressure to raise salaries, resulting in increased costs. In the long term, the pressure to increase salaries in the IT sector offers an opportunity to attract quali
fied employees.
On the other hand, staff turnover and shortages of qualified employ
ees in IT entail the risk of slower growth and development of compa
nies operating in the sector, particularly since the educational market in Poland is characterized by a smaller number of students graduating from technical faculties. IT companies are sensitive to customers’ increas
ing requirements related to IT solutions. As a company operating in the B2B system, Connecto uses the latest achievements and technological advances offered by the Comarch company. The increasing importance of technology is further enhanced by the implementation of services offered by Connecto in the market. These include software for construc
tion and property development (the specialty of the studied enterprise), IT systems for production, a unique approach to Business Intelligence, software for the services and maintenance sectors, production planning and execution, mobile business solutions, and effective e-Commerce and B2B solutions.
Changes in the external environment also have a growing impact on transformation of the business model itself. Changes related to technical advances and economic development create demand for new IT systems, and the growing competition between IT companies drives down profit margins. New technologies are applied in automation and robotics—
which is the field where Connecto recognizes potential for future growth as part of the planned diversification of development. Global companies
could gradually withdraw from the IT market in Poland due to salary- related pressure and limited flexibility compared to smaller companies that operate closer to the end customer. Polish IT sector and IT sys
tems offer very high quality, hence the growing trend for international expansion into the United States, Canada, Saudi Arabia, and many other markets.
Risks are mostly related to politics and legislation, as changes in these areas can result in decreased investment, changing organizational cost structure, and higher tax costs. In this market, there are no technical barriers to globalization of activity. In Poland, the demand for IT and implementation services is high, and Connecto performs excellently in this market, for now having no plans of expanding their business geo
graphically. The risk of losing the main vendor is also low, as the coop
eration is mutually beneficial, based on a relationship with partners who generate income for the vendor.
The opportunities and risks presented above shape the strategic goals of Connecto, which include:
• increase in subject matter knowledge (on IT and new technologies),
• intra-organizational knowledge transfer,
• further professionalization,
• diversification of income,
• generation of profit and company value.
Considering these opportunities and risks associated with the exter
nal environment, which determine Connecto’s goals and directions for development, identification of the business model is essentially a response to seizing opportunities and minimizing risks in the context of value generation for customers and for Connecto. The business model is seen from the point of view of generating and delivering value for customers (Morris, Schindehutte, & Allen, 2005; Zott, Amit, &
Massa, 2011). For the purpose of this book, the definition of the busi
ness model according to Johnson, Christensen, and Kagermann (2008) was adopted. According to this definition, the business model comprises a value proposition for the customer, a profit formula, key resources, and core processes, associated with creating and delivering value for customers and the organization. An important role is played by busi
ness processes, within which one can analyze how value is created for the organization. The business model is also often defined in the con
text of conceptualization of associations between the organization and its stakeholders, in particular the customers (Baden-Fuller & Morgan, 2010), cooperation, partnership, and joint value creation, acknowledg
ing the interrelations of performed actions as the essence of the business model (Zott & Amit, 2010). In the intra-organizational view of the
business model, the information network (information flow) plays an important part, as it makes the model dynamic and affects all its com
ponents (see Zhang, Zhao, & Xu, 2016).
The components of Connecto’s business model are as follows (see Table 3.5). Key resources and key activities were not taken into consid
eration, as they will be listed in Table 3.6 in the context of core business processes.
The above elements related to the analysis of Connecto’s external envi
ronment and business model constitute the design stage of intangible resource audit, which plays an important part in identifying and defin
ing the organizational context. Business model definition is accompanied by the identification of business processes, which allowed the auditor to understand how value is created in the Connecto company. These include (Table 3.6) the commercial process (including advertising, telemarketing,
Table 3.5 The components of Connecto business model Business model Attributes
components
Value proposition - satisfying a wide range of customers’ IT and IT-related needs;
- long-lasting customer attachment through the professional character of services;
- competing on quality, not price;
- long-lasting cooperation with customers;
- handling customers’ issues that they need not be burdened with.
Customer groups - B2B,
- SMEs (small and medium enterprises), - large enterprises.
Distribution channels - website, - Internet, - e-mail,
- personal contact.
Customer relationships - personal contact, - contact by telephone.
Partners - vendors: Comarch, Microsoft, Dell, IBM, Lenovo;
- ABC Data;
- DevExpress;
- Team Viewer;
- Cristal Reports (SAP).
Sources of income - implementation process, - sale of licenses and services.
Sources of costs - remunerations (personal costs), - maintenance of infrastructure.
Sources: Author’s own work based on Osterwalder and Pigneur (2010) and the interviews performed.