An overall index of intellectual capital

An overall index of intellectual capital Livio Cricelli, Marco Greco, Michele Grimaldi University of Cassino and Southern Lazio Cricelli L., Greco M., Grimaldi M. (2014) An overall index of intellectual capital, Management

Research Review, 37 (10), pp.880-901

1 Introduction Most organizations face challenges that were hardly imaginable a few decades ago. Managers used to consider their strategies or their organizations’ distinctive physical resources as sources of competitive advantage. Today, most scholars emphasize the role that knowledge – also referred to as intangible assets (IA) or intellectual capital (IC) – plays in achieving competitive advantage (Coleman and Robb, 2010; Selçuk, 2010) and successfully completing momentous processes such as acquisitions (Riviezzo, 2013). Despite this, it may no longer be enough to understand the role of intangibles, and possibly invest in them, as has been the strategy of the most successful companies of our times. The non-recognition of IC and IA in financial statements may have negative consequences in terms of the value-relevance of financial information, resource allocation in the capital market, growth of intangible investments, and firms’ market value (Zéghal and Maaloul, 2011). As a result, managers are becoming increasingly responsive to the role that IC plays in generating profits (Tayles et al., 2007), and the ever-growing demand for capturing, assessing, and reporting IC value and performance (Marr and Chatzkel, 2004). In fact, from a resource-based perspective, IC components may enhance the organizational competitive advantage, thereby creating value for the stakeholders. A number of authors have proposed models that aim to develop and leverage IA in order to enhance firm value (e.g., Barsky and Marchant, 2000; Brooking, 1996; Edvinsson and Malone, 1997; Jacobsen et al., 2005; Kannan and Aulbur, 2004; Kaplan and Norton, 1996; Sveiby, 1997) or to calculate the success and growth of a company’s IC stock (e.g., Edvinsson and Malone, 1997; Lev and Feng, 2001; Luthy, 1998). The former group have generally proposed tecniques to assess IC components as well as their evolution, while the latter have tried to provide a financial estimation of the organizational IC. Other scholars have attempted to merge different IC measures into a single index (e.g., Chen et al. 2004; Kale, 2009 Low, 2000; Oliver and Porta, 2006; Pulic, 2000; Roos et al., 1997). Such approaches aim to improve the visualization of the firms’ value-creating processes, thereby facilitating their comprehensive management. Indeed, an index may improve the ability of managers to assess their firms’ IC situation and understand the priorities and relationships that exist between the selected indicators (Bontis, 2001). To the best of our knowledge, such indices have several shortcomings that prevent them from being universally applied (Bontis, 2001; Tan et al., 2008). In addition, none of these indices have been able to consider the interdependencies existing among the IC elements and assess their contribution to the IC overall value. The present article aims to overcome the limitations of the indices proposed in the literature. It does this by means of a holistic index, known as the AMIC index, that is representative of the organizational IC and can be effectively and successfully implemented in organizations within virtually any industry. Our index synthesizes data collected during interviews with the top managers of the organization targeted for the analysis. The interviewees make assessments regarding the impact of coherent groups of IC components on other groups and on organizational

performance. We describe the step-by-step implementation of the AMIC index in order to favour the replicability of the study and to allow scholars performing large-scale studies that may bring to the definition of sectorial benchmarks, correlations with performance indices or trend analyses. The present article includes the following three sections: 1. The theoretical background regarding the most relevant solutions proposed in the literature in recent years to quantify the IC and, more specifically, to represent it through one or more holistic indices. 2. The construction of the AMIC index. 3. The index’s implementation in an Italian real-estate company.

2 Theoretical background The first studies into IC date back two decades. The first-stage efforts were “focused on consciousness raising activities that strive to communicate the importance of recognising and understanding the potential for intellectual capital in creating […] competitive advantage” (Petty and Guthrie, 2000, p. 155). First-stage authors have proposed myriad definitions, specifications, categorizations and other details (Itami, 1991; Hall, 1992; Edvinsson and Malone, 1997; Nahapiet and Ghoshal, 1998; Sullivan, 1998; Petty and Guthrie, 2000). The extensive speculation led to the literature identifying small differences (although these have not been met with consensus among scholars) regarding terms such as IA, IC or even knowledge itself. IA represent something more than mere knowledge, as they include virtually anything that is not tangible (incuding intellectual property, network of relationships, etc.). On the other hand, IC is often considered as “the value” of IA (Baskerville and Dulipovici, 2006). Not surprisingly, therefore, some authors have estimated that IC is the difference between the book value of a company and its market value (Edvinsson and Malone, 1997). This early approach was criticized by several authors (Garcıa-Ayuso, 2003; Habersam and Piber, 2003; Andriessen, 2004), who argued that such difference could also be caused by other factors, such as market turbulence and undervalued tangible assets (Cricelli and Grimaldi, 2008). In the present paper, we have adopted the brief and generic definition provided by Edvinsson and Sullivan (1996), who stated that IC is “knowledge that can be converted into value” (p. 358), with an extensive interpretation of “knowledge” that pertains to both human resources and intellectual assets.

2.1 The IC components IC is often considered as the sum of the following three categories (see, e.g., Hall, 1992; Roos et al., 1997; Sveiby, 1997; Luthy, 1998; Andriessen, 2004; Dalkir et al., 2007): • Human capital (HC) refers to the people in an organization and describes their cumulative tacit knowledge skills. • Structural capital (SC) refers to the explicit knowledge embedded in an organization. • Relational capital (RC) represents organizational relations and knowledge exchange with the organization’s external stakeholders. As Table 1 shows, the IC components identified in the literature (Boedker et al., 2004; Choong, 2008; Petty and Guthrie, 2000; Green and Ryan, 2005; Marr, 2008; Corvello and Iazzolino, 2013) can be classified according to HC, SC and RC. Table 1 – Non-exhaustive list of IC components classified according to human, structural and relational capital.

HUMAN CAPITAL

Changeability motivation, competencies, creativity, education/training, employee competence, employee demographics, employee engagement, employee information , employee loyalty, employee satisfaction, emotional intelligence,

entrepreneurial spirit , experience, flexibility, formal relationship, human-centred assets, identity of individual, influencing behaviour , informal relationships, innovativeness, know-how, knowledge and skills, learning and development, learning capacity, loyalty, management skills, managerial work, proactive and reactive abilities, satisfaction, vocational qualification, workforce training, workrelated competencies, work-related experience, work-related knowledge Cross-functional teams, culture, enterprise intelligence, information systems, infrastructure assets (management philosophy, corporate culture, management processes, information systems), infra-structured assets, intellectual property (patents, copyrights, trademarks, data and information, codified knowledge, trade secrets), internal collaboration and projects, leveraging technology, location STRUCTURAL capital, management philosophy, management processes, organizational culture CAPITAL (corporate values, social capital, management philosophy), organizational structure, organizational learning, organizational practices, personal relationships, process product/service technology, process and routines (formal processes, tacit/informal routines, management processes), research and development technology, service/product quality, statutory-based assets, start-up capital, strategy, structural resources Advertising, alliances and partnerships, brand image, brand value, business collaborations, community relations, company names, competitors, consumer trust, contribution to licensee, contribution to spin-off , corporate reputation, customer capital, customer loyalty, customer relations, customer retention rate, RELATIONAL customer satisfaction, customers, distributing contracts, distribution agreements, CAPITAL distribution channels, favourable contracts, financial relations, franchising agreements, joint ventures, licensing agreements, licensing/franchising, market assets, networking systems, partnerships, partnerships, payments on account, royalty revenue, social networks, supplier relations, trust From a resource-based perspective, the IC components are potential sources of competitive advantage and can therefore enhance value creation, assuming that the organization is capable of using them properly (Branco and Rodrigues, 2006). The value of IC components, such as those related with corporate social responsibility (reputation, corporate culture, etc.), has also been studied through the lenses of the stakeholder theory. Indeed, the IC components can be critical in order to meet or exceed the expectations of both direct and indirect stakeholders, such as employees, clients, pressure groups and communities (Branco and Rodrigues, 2006). Good relations with stakeholders help firms to develop valuable intangible assets (resources and capabilities) and can therefore be sources of competitive advantage (Cricelli and Greco, 2013). Furthermore, the acquisition of legitimacy, defined as the perception that the actions of an entity are desirable and proper, can improve the organization’s access to resources, reputation and advantages in competitive situations (Fernández-Alles and Valle-Cabrera, 2006). Moreover, some IC components may have a stronger impact on the value creation process, depending on the organization’s peculiarities (for example, a research and development (R&D) company may benefit more from its knowledge, competences and intellectual properties, while a consultancy company may benefit more from its relational capital). In line with this assumption, several authors have suggested prioritizing IC elements in order to select those value drivers (VDs) that are critical to support the value creation process and organizational performance (Andrikopoulos, 2005; Marr, 2007; Kim and Kumar, 2009; Alcaniz et al., 2011). Structured techniques to synthesize pairwise comparisons, such as the Analytic Hierarchy Process (Saaty,

1980) and its generalization, the Analytic Network Process (Saaty, 1996), have been found to be particularly useful for processes of prioritization (Cricelli et al., 2013). Many authors have also suggested that the value creation process depends also on how IC components interact with one another (see, e.g., Diakoulakis et al., 2004; Moeller, 2009; Cricelli et al., 2013; Greco et al., 2013a). These authors have agreed that the strength of the impact that one IC component has on another may not be generalized, given that the impact is dependent on the peculiarities of the target organization. Thus, an assessment of the impact of each IC component on another can be performed by interviewing managers or experts in order to quantify the interdependences among the IC components. Studies regarding IA have also provided managers with approaches and tools to identify the IC components and the relationships among them, as well as to facilitate effective managerial interventions (Coakes and Bradburn, 2005; Dumay, 2009; Grimaldi and Cricelli, 2009; Moeller, 2009; Shih et al., 2010). Key IC components may be represented through maps or graphics in order to provide the management with an intuitive outlook of their company’s IC (Green and Ryan, 2005; Andreou et al., 2007; Marr, 2008; Jhunjhunwala, 2009; Lopes, 2010). However, Edvardsson and Oskarsson (2011) noted that none of these approaches had been universally accepted.

2.2 Evaluating IC The increasing interest in IA and IC has led to the development of methods that aim to assess or measure them. The methods can be classified according to two streams (Petty and Guthrie, 2000; Bontis, 2001; Johanson et al., 2001; M'Pherson and Pike, 2001; Cotora, 2007; Tan et al., 2008; Alcaniz et al., 2011; Grimaldi et al., 2012):  Non-dollar evaluation of IC: the IC strategic/managerial perspective, which aims to develop and leverage IA to enhance firm value.  Dollar-evaluation of IC: the IC measurement/accounting perspective, which aims to calculate the success and growth of IC stock. Most studies have provided non-dollar evaluations of IC, the best known of which are described briefly below, in chronological order. Firstly, Brooking’s “technology broker” (1996) provided a financial evaluation of four IA categories: market assets, human-centered assets, intellectual property assets and infrastructure assets. Kaplan and Norton (1996) used the “balanced scorecard” approach to evaluate IA and relate the output measures with performance indicators, although their approach has since been described as very company-specific (Tan et al., 2008). Edvinsson and Malone (1997) proposed the “Skandia business navigator”, which assigns the IA’s contribution to innovation, according to five areas of focus: financial, customer, human, process and renewal and development. Sveiby (1997) mapped organizational knowledge flows according to three IC categories: internal structure, external structure, and individual competence. Although some companies have resorted to his method, which includes three measurement indicators, they have found it difficult to assign monetary financial values to the final measure (Bontis, 2001). Barsky and Marchant (2000) listed 10 non-financial measures that can be integrated into management reporting and evaluation systems; again, however, with the exception of market share, the proposed metrics were subjective and not easily quantifiable, which made it difficult to extend them to different companies (Tan et al., 2008). Kannan and Aulbur’s (2004) proposed model for measuring IC involved three steps. They started by assessing knowledge management awareness, the knowledge criticalities and the core competences; they then measured the effectiveness of existing systems and processes; finally, they linked processes and systems to basic effectiveness standards and financial and social outcomes systems. Jacobsen et al. (2005) introduced the IC Rating approach to facilitate the analysis of IC.

Dollar evaluation of IC is consistent with the common assumption that IC is “the value of IA”. Therefore, authors frequently use the market-to-book value comparison, arguing that the value of a firm’s IC can be represented by the difference between its book value and its market value (Edvinsson and Malone, 1997). This approach could be imprecise, because a firm’s market value is influenced by a number of external factors (including turbulence, macroeconomic factors, and the bounded rationality of the investors), and its book value may vary on the basis of the accounting strategies pursued by the top management. Luthy (1998) suggested the use of Tobin’s q method to measure IC. With respect to the market-to-book ratio, the method replaces the book value of tangible assets with their cost. Although the procedure improves the market-to-book approach, it still suffers from the variability of market value. Lev and Feng (2001) recurred to a “production function”, considering the firm’s economic performance as a function of physical, financial and knowledge assets (Lev and Feng, 2001). The main shortcoming of their approach is the need to forecast earnings, which is often difficult.

2.3 IC indices Several scholars have attempted to merge different IC measures into a single index. An index can improve the visualization of the value-creating processes of the company, thereby facilitating the management of these processes. It can also enhance the assessment of the IC situation of a company and, at the same time, understanding the priorities and relationships that exist between the selected indicators (Bontis, 2001). The first well-known index, proposed by Roos et al. (1997), consolidated Skandia’s individual indicators into a single index. Their “IC index” has the following peculiarities: it focuses on monitoring the IC dynamics, it can consider performance from prior periods, it provides a different perspective with respect to the typical valuation based on an examination of physical assets, and it can be adjusted to reflect changes in the market value of the company (Roos et al., 1997). However, the IC index has also been described as context-specific and therefore limited in its universal application (Bontis, 2001; Tan et al., 2008). Low’s (2000) “Value Creation index” made it possible to measure the nine most critical intangible categories of performance that determine corporate value creation and synthesize the values through specific weights depending on their relative impact. Pulic (2000) developed the “Value Added Intellectual Coefficient”, an index that synthesizes the organizational IC on the basis of specific budget items. As noted in the previous sub-section, the limitation of this approach relates to its exclusive adoption of book values, which may be prominently influenced by the top management’s accounting policies. Chen et al. (2004) used questionnaire-based qualitative indices to assess the status quo and the tendency of the IC elements. These indices are synthesized through weights depending on the management’s perception of their importance. Oliver and Porta (2006) discussed the Intellectual Capital Cluster Index®, a weighted sum of measures and assessments of IC, although a more comprehensive practical application has been considered necessary to validate the model (Tan et al., 2008). Finally, Kale (2009) discussed a model that uses fuzzy set theory and the Analytic Hierarchy Process. As noted, IC components may enhance organizational value creation, both directly and indirectly. From a resource-based perspective, one IC component may be more critical than another, leading to the need to prioritize among them. Due to the limitations observed for accounting frameworks, we adopted a strategic/managerial approach with the aim of providing managers with unambiguous information that could support strategic decisions regarding organizational IC. We found that many authors have tried to synthesize IC indicators into overall

indices, the limitations of which have already been highlighted. Indeed, most indices have proven difficult to extend universally and, to the best of our knowledge, none of the solutions proposed in the literature have considered the performances of the IC components, the interdependencies among them, as well as their strategic contribution to the value creation process. Some solutions have focused exclusively on financial or market-dependent indicators or relied on official reports or objective measurements without taking the managers’ perceptions into consideration. The following section describes an overall index that aims to overcome the limitations of the solutions proposed in the literature to date.

3 The AMIC index This section describes a framework to improve the assessment and management of IC (AMIC) by calculating an overall index of IC (Cricelli et al., 2011). The AMIC index has been conceptualized to holistically represent a company’s IC in order to enhance the decision making process. It can therefore be classified in the “non-dollar evaluation of IC” stream of the literature and is grounded in the resource-based perspective. The framework that enables the calculation of the AMIC index considers the manager’s concept of value, the elements that constitute and influence value, and the value creation process itself. The index can support managers to improve or re-organize the value creation process. The AMIC index is defined by means of an algorithm that considers not only the current performance of each VD, but also the additional contribution generated by each VD’s influence on the other VDs. This correcting factor is based on the cumulative nature of knowledge, which can be compared with the “cumulative nature of information” as expressed by Moody and Walsh (1999). The AMIC index is implemented by interviewing the top managers of the target organization(s). The interviews consist of four steps. In the first step (sub-section 3.1), the interviewees identify critical groups of IC components that may enhance value creation. In the second, third and fourth steps (sub-sections 3.2, 3.3, 3.4), the interviewees assess the mutual direct and indirect impact that each of the identified IC groups of components has on other groups and on performance. Finally, the collected data is synthesized in an overall index (sub-section 3.5).

3.1 The Value Drivers As a first step, the framework makes it possible to identify the critical IC components for the target organization. Each organization is unique, so a framework that aims to assessing its IC should be customized in order to avoid nuisance IC components, according to the managerial perspective. Hereafter, coherent sets of IC components are defined as VDs in order to emphasize their attitude to create value. The literature suggests heterogeneous lists of VDs, depending on different targets of the authors (such as measuring IC rather than discussing IC components), different areas of implementation (for example, a case study of a multinational company rather than analysis of a sample of questionnaires) and different focus of the articles (for example, measuring IC rather than one of its components, such as relational capital). Thus, we argue that the definition of a list of VDs should be context-dependent, and by no means correct in absolute. Figure 1 presents 13 representative VDs grounded in the literature. The interviewed managers of the target organization may wish to add VDs to the list or remove or modify some of them in order to make them fit with the organization’s peculiarities.

Figure 1 - Value drivers and corresponding exemplificative sets of IC factors

Given that not all VDs will have the same relevance within the organizational context and along the value creation process, the importance of each individual asset and specific VD within the value creation process is outlined. Hence, the framework evaluates the relationships among VDs.

3.2 Assessment of the VDs’ impact In the second step of the framework, managers assess the impact of each VD, which is representative of a VD’s degree of importance (priority) compared with all other VDs, in order to achieve the company’s goals (Marr, 2008). The impact (Ii) of the i-th VD is assessed through the Analytic Hierarchy Process (AHP), which makes it possible to capture and foster IC dynamics: experts and managers are supported by the use of linguistic variables in the evaluation process of the VDs. The AHP, as well as the Analytic Network Process, has been implemented to identify the IC sources of competitive advantage (Costa and Evangelista, 2008; Grimaldi and Cricelli, 2009); to assess the relative importance of the IC components in terms of their contribution to the value creation process (Calabrese et al., 2013; Grimaldi et al., 2012; Kale, 2009); to estimate the determinants of consumer-based brand equity (Battistoni et al., 2013); to evaluate organizational performance (De Felice and Petrillo, 2013); and to develop a performance measurement model based on IC indicators (Wu et al., 2010; Yurdakul and Ic, 2005). Moreover, the processes have been used in the literature for a number of other purposes, such as choosing knowledge management systems (Greco et al., 2013b) and enhancing supply chain management (Falsini et al., 2012; Silvestri et al., 2012).

Decision makers who use the AHP make a sequence of pairwise comparisons among alternatives. In this step of the framework, the interviewed anagers make pairwise comparisons among VDs, with respect to their importance in the value creation process. More specifically, the managers need to determine whether two VDs are equally important, or whether one is moderately more important, strongly more important, very strongly more important, or extremely more important than the other. Verbal judgments are then translated into numerical values (1, 3, 5, 7 and 9, for the above comparisons, respectively, while even numbers from 2 to 8 are considered intermediate values). The overall priority of each VD is then calculated through the mathematical synthesis of the judgments. The resulting values are comprised between 0 and 1; the higher the numerical value of the impact of a VD, the higher its strategic relevance. The sum of all the values of the impact of the VDs is unitary.

3.3 Assessment of the VDs’ cross-impact In the third step of the framework, managers assess the cross-impact of each VD. In fact, as discussed in the theoretical background, IC components may influence one another (such as Cricelli et al., 2013; Diakoulakis et al., 2004; Moeller, 2009; Greco et al., 2013a). The cross-impact (CIi) of the i-th VD represents its degree of influence on the others and is used as an additional contribution to the VDs performance, deriving from indirect reinforcing cycles. For each VD, the interviewees specify whether its influence on another VD is strong, moderate or absent; the three levels of influence are expressed with the values 1, 0.5, or 0, respectively. The cross-impact of each VD is the sum of the numerical values representing its inbound influences. CIi is comprised between 0 and the cardinality of the VDs minus one (|𝑉𝐷𝑠 | − 1). The higher the CIi, the higher the impact of the i-th VD and the higher its influence in the value creation process should be considered to be.

3.4 Assessment of the VDs’ performance In the fourth step of the framework, managers assess the performance (Pi) of the i VDs. The performance of a VD represents its current qualitative stock. Managers express their opinion regarding the adequacy of stock of each VD to improve the value creation process. They identify some IC factors for each VD (as shown in Figure 1) and then express a qualitative evaluation of the performance of each IC factor by means of the following linguistic variables: “none”, “weak”, “medium”, “strong”, “excellent”. Linguistic variables are converted into quantitative values of 1, 3, 5, 7 and 9, respectively. For each VD, the resulting performance value is the average of the assessments of its IC factors.

3.5 The AMIC index Finally, the AMIC index combines impact, cross impact and performance. In order to aggregate the three vectors, they must be dimensionally coherent (Giovannini et al., 2008). To this end, given that impact is a percentage value, cross-impact and performance are normalized by means of “Min-Max” normalization with respect to the range 1–9. The AMIC index is then calculated as follows: 𝑛

𝐴𝑀𝐼𝐶𝑖𝑛𝑑𝑒𝑥 = ∑[𝐼𝑖 ∗ 𝑃̇𝑖 + 𝐼𝑖 ∗ ln(1 + 𝐶𝐼̇ 𝑖 ) ∗ 𝑃̅𝑗𝑖 ] 𝑖=1

Where:  i is the generic i-th VD, where i=1…n  𝐼𝑖 is the impact of the i-th VD

 𝐶𝐼̇ 𝑖 is the normalized cross-impact of the i-th VD  𝑃𝑖̇ is the normalized performance of the i-th VD  ji is the generic j-th VD influencing the i-th VD, where ji=1…Ji if Ji is not empty 𝑃̇  𝑃̅𝑗 = ∑𝑗 𝑗 is the average value of the normalized performance of the j VDs influencing the 𝑖

𝑖

𝐽𝑖

i-th VD. 𝑃̅𝑗𝑖 = 0 if Ji = 0. The AMIC index includes both the current performance of each VD (stocks) and the additional contribution deriving from the relationships with the other VDs (flows). The contribution of each VD to the index is determined by the sum of the two terms: the former represents the stock unit, the latter represents the flow unit. The stock unit of each VD refers to the value of its performance. This value is weighed according to its strategic importance as acknowledged by managers, and is therefore obtained from the combination of impact and performance. For each VD, when Ii or 𝑃̇𝑖 have low values, the value of the stock unit is close to 0. On the other hand, the stock unit is close to its maximum value 1 when both Ii and 𝑃𝑖̇ have values close to the maximum unitary value. The flow unit of each VD refers to the relationships among VDs, which are intangible by definition, and to their impact on organizational performance. More precisely, the VDs performances can be improved or influenced indirectly by those VDs that have relationships with them. The flow unit expresses the acknowledged value deriving from all the influences received by each VD. The contribution of the flow unit, like the stock unit, is weighted according to its strategic relevance. Consequently, the flow unit combines the impact with two entities: 𝑃̇ ln(1 + 𝐶𝐼̇ 𝑖 ) and ∑𝑗 𝑗 : 𝑖

𝐽𝑖

 The first of these entities accounts for the “cumulative nature of knowledge” and for the sub-linear modalities of growth of its performance at the growth of the number of the influence sources. The peculiar aspect of the knowledge performance can be represented by the asymptotic trend of the logarithmic curve. Since the cross-impact value is comprised between 0 and 1, the most appropriate base of the logarithm is e. In this way, the cardinality of VDs influencing each other is discounted algebraically, which makes the contribution derived from a high-performing VD higher than those deriving from many low-performing VDs.  The latter entity of the flow unit considers an added quota of the performance deriving from other VDs contributions. This quota is determined by the mean performance of the VDs influencing the generic i-th VD. By construction, the contribution of the flow unit for each VD is smaller than that of the stock unit. In fact, the value expressed by the flow unit of a VD is between 0 and ln2, being product of three values: two of them are between 0 and 1, and the third, ln(1 + 𝐶𝐼̇ 𝑖 ), is comprised between 0 and ln2 (when a VD has CIi equal to 1; that is, it is influenced by all the other VDs). The contribution of the flow unit is meant to increment the stock unit proportionally to the quantity and quality (performance) of its inbound arches. The total of flow contributions in the AMIC index is a positive value that is superiorly limited by the sum of the products between the natural logarithmic factor of the cross-impact of a generic VD and the mean of the performances of the VDs influencing it. When more than one interviewee is involved in the assessment process, the AMIC index is calculated as the average value of the indices estimated by the interviewees.

4 An implementation of the AMIC index This section contains an exemplificative implementation of the AMIC index in a real-estate company hived off from one of the biggest Italian public transport companies. The company promotes extraordinary repairs, when needed, to improve the value of its assets and to sell real estate. The company’s managers identified eight VDs and corresponding IC factors, which are shown in Figure 2. Figure 2 contains five fewer VDs than Figure 1; these were considered irrelevant exante for the following reasons:  Innovation: This VD is difficult to identify in the company, even from a “process” or “organizational” perspective.  Information: This VD is completely absent in the company (with the exception of standard information technology hardware and software, that may not be considered as a relevant source of value).  Intellectual property: The company does not own patents, copyrights, trademarks, or trade secrets.  Inter-firm relations: There are no collaborations or partnerships with any other company, with the exception of the controlling one, which cannot be considered here because it is a shareholder.  Financial relations: There are no specific financial relations that can be considered relevant sources of value.

Figure 2 – Value drivers and corresponding sets of IC factors for the company

We interviewed a sample of 14 members of the company (see Table 2), which is representative of the company’s human resources (30 employees, excluding the president and the CEO), but is not statistically significant for each sub-cluster. Table 2 - Sample of interviewed human resources

Division

Interviewed top managers 1 1

Technical Support

Interviewed senior staff 5 3

Interviewed employees 1 3

The results of the interviews were aggregated by calculating the average values and the final AMIC index was calculated as shown in Table 3. The relative impact of each VD is shown in Figure 3. Table 3 – Calculation of the company’s AMIC index

𝑰𝒊 ∗ 𝑷̇𝒊

VDs 1 2 3 4 5 6 7 8

Knowledge skills Management skills Creativity and innovativeness Intangible infrastructural assets Relations with customers Relations with suppliers Institutional relations Brand and image Total

Relations with customers

0.10 0.10 0.05 0.09 0.04 0.03 0.10 0.02 0.53 Relations with suppliers

̅𝒋 𝑰𝒊 ∗ 𝐥𝐧(𝟏 + 𝑪𝑰̇ 𝒊 ) ∗ 𝑷 𝒊

Total

0.03 0.04 0.02 0.04 0.02 0.01 0.03 0.02 0.21

0.13 0.14 0.07 0.13 0.06 0.04 0.13 0.04

0.74

Brand and image Creativity and innovativeness

Institutional relations

Management skills

Knowledge skills

Intangible infrastructural assets

Figure 3 – Contributions of the VDs to the overall AMIC index

Several considerations can be made by comparing the 𝐼𝑖 , 𝑃𝑖 and 𝐶𝐼𝑖 of each VD. The average values recorded of the three AMIC index components are 0.125, 5.15, and 4.05, respectively. Figures 4 and 5 make it possible to identify the VDs for which investments are required.

Figure 4 – Impact and Performance of the VDs

Figure 5 – Cross-impact and Performance of the VDs

Knowledge skills (VD1) have “high” impact but “weak” cross-impact and “below-average” performance. Therefore, the interviewees perceived that other VDs might not influence VD1 prominently. Nonetheless, the performance of VD1 must be improved in order to cope with its perceived importance. Management skills (VD2) and intangible infrastructural assets (VD4) are both “above average” in terms of performance and impact, which indicates they should be at least maintained at the current level of performance. Creativity and innovativeness (VD3) is considered to have “low” impact and “low” performance; accordingly, there is no need to improve its current performance, considering that it benefits from “high” cross-impact (improving other VDs will improve it as well). Relations with customers (VD5) and relations with suppliers (VD6) are considered to have a low impact on competitive advantage, and there is no need to improve their current performance. Institutional relations (VD7) are “above average” according to each of the three dimensions described in Figures 4 and 5, and should therefore be considered critical for the company and maintained at the current level of performance. Brand and image (VD8) is considered to have “low” impact and “average” performance, indicating there is no need to improve its current performance. The analysis of the results shows a primary need for improvement in human resources competences, which is to be accomplished through a new training plan, given that the current one is deemed unsatisfactory. According to the cross-impact matrices, the influence of knowledge skills is the highest among the VDs, indicating that if the management invests on knowledge skills the company will benefit from externalities and the AMIC index will grow fast. An additional analysis can be performed to understand the extent to which the interviewees agree on the characteristics of their own company. In fact, knowing that the average value of AMIC-indices calculated by means of the interviews is 0.74 is only a partial piece of information. Figure 6 shows that the AMIC index changes significantly as the interviewee changes, ranging from 0.39 to 1.13, while almost half of the interviewees assess the AMIC index in a range between 0.62 and 0.94. An in-depth comprehension of the causes of such variability is important to the management because it could imply ambiguities in employees’ perceptions of the importance of the VDs.

Figure 6 – Boxplot representing the distribution of the AMIC-indices calculated by each interviewee

Figure 7 shows that variability occurs in both the divisions, but that the range between the two quartiles is much wider in the technical division. Interestingly, the two median values are almost identical. A non-parametric U Mann-Whitney test confirms that the null hypothesis that the two medians are equal cannot be rejected. The result may be interpreted as if the two series of data come from the same distribution. Therefore, being part of one division does not seem to influence the perception of the AMIC index to a statistically significant extent.

Figure 7 – Boxplots representing the distribution of the AMIC-indices calculated by each interviewee, clustered according to their division

An analysis of the variability of the contribution of each VD to the AMIC index may explain the variability of the overall indices. Indeed, Figure 8 shows reasonable variability in most VDs contributions to the AMIC index, with the manifest exception of “institutional relations”, the importance of which is the subject of strong disagreement among “technical” employees. Top management should explore this noticeable disagreement in order to understand its causes.

Figure 8 – Boxplots representing the distribution of each VD’s contribution to the AMIC index, calculated by each interviewee and clustered according to the interviewees’ division

A different clustering of the VDs’ contribution to the AMIC index according to the role of each interviewee (top manager, senior staff, employee) shows that top managers consider “management skills” much more important than their colleagues (see Figure 9). This result may represent a communication breakdown regarding the activities promoted by the top management, or a case of top management over-confidence. In addition, top managers do not consider “institutional relations” very important, while the interviewees within the other two clusters are at variance with such a source of competitive advantage. More specifically, the variance of employees (disagreement on the VD) is higher than that of the senior staff: the greater the employee’s distance from the control room, the greater that person’s confusion regarding the role of institutional relations.

Figure 9 – Boxplots representing the distribution of each VD’s contribution to the AMIC, calculated by each interviewee and clustered according to interviewees’ role

5 Conclusions Several theoretical streams, such as the resource-based perspective, the stakeholder theory and the legitimacy theory, consider IC components to be potential enhancers of the organizational value creation process. A number of authors have proposed methodologies to assess or measure IC, adopting either a strategic/managerial or an accounting approach. Others have proposed overall IC indices in order to improve the visualization of the organizational value-creating processes, consequently facilitating their comprehensive management. Nonetheless, several limitations have been found in the existing indices. Specifically, most were found difficult to extend universally, and none allowed simultaneous consideration of the performances of the IC components, the interdependencies among them, as well as their strategic contribution to the value creation process. Others referred exclusively to financial or market-dependent indicators or relied upon official reports or objective measurements without taking the perceptions of the managers into consideration. This article has presented the AMIC index, which implies an advancement of the literature, allowing both the interdependencies among the IC elements and the managers’ perception about their strategic importance in the value creation process to be considered. Specifically, the AMIC index makes it possible to assess both the direct impact of each previously selected VD and its

indirect impact on other VDs. The index is adaptable and widely utilizable as its construction procedure can be adapted and applied to virtually any organization. From a practical perspective, the AMIC index can be very useful for an organization willing to understand the evolution of its IC over time; for example, by relating the index to other organizational indicators. Moreover, the analysis of the AMIC index components may provide timely suggestions regarding both VDs on which further investments should be recommended, and on latent disagreements among human resources with respect to “what is really important for the organization”. Corrective actions in both these areas may improve the organizational performances in the short, medium and long term. From a research perspective, the AMIC index could be implemented on a sample of homogeneous organizations (for example, those in the same industry or dimension) in order to understand its relation with organizational performances or to estimate benchmark values. We have implemented the AMIC index in an Italian real estate company by interviewing 14 of its workers. The results of our analysis are organization-specific and may not be generalized or used to draw theoretical considerations. Nonetheless, they actually helped the management find critical VDs and understand incongruities in the employees’ perceptions. Future studies should focus on the definition of a holistic index based on the analysis of quantitative data that ‘measures’ IC rather than ‘assesses’ it. Such an index could validate the AMIC index (in case of a high and significant correlation between the two indices), or lead to improvements in its definition. We believe that the definition of such index could dramatically change the branch of research in IC.

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