Intellectual capital (IC) is an important factor to push and drive organisations to achieve competitive advantages and growth. This study aims to develop and test an instrument to measure IC for employee behaviour in the context of small and medium-sized enterprises (SMEs).,The measurement instrument was designed after assessing literature reviews on IC that provided a strong theoretical support for application of a specific set of items in the SME context. Instrument validity and reliability were tested for item-objective congruence by five experts. The results ranged between 0.8 and 1.0, with a reliability coefficient of 0.950. Exploratory factor analysis and confirmatory factor analysis were used to confirm construct validity between theoretical and empirical evidence. Data were collected from 240 hairdressers employed by SMEs in Thailand.,The results revealed that IC can be classified into two groups as human capital and relational capital. Constructs between theoretical concepts and empirical evidence gave values of χ2 = 42.336, df = 35, p = 0.184, χ2/df = 1.210, GFI = 0.972, AGFI = 0.938, RMSEA = 0.030 and SRMR = 0.018.,Empirical findings were derived from a sample of 240 hairdressers. However, validation and reliability of the instruments require confirmation in various other contexts with a larger number of samples. This cross-sectional study identified the effects of IC in SMEs, thereby contributing to the ongoing debate regarding the determinants of research performance.,The IC instrument was determined as valid and fulfilled the knowledge gap concerning SMEs by facilitating future studies on boundaries with IC assessment spanning SME contexts. This instrumental support will assist researchers and academics to develop a more comprehensive understanding of IC and explore its potential in future research areas.

Intellectual capital of SMEs in Thailand: an employee behavioural measurement instrument

The main objective of this study is to investigate the relationship between intellectual capital and financial and non-financial performance of the West cement company in Kermanshah. In this regard, the main hypothesis was. formulated as follows: 1. Human capital, structural capital and relational capital on the basis of mutual influence. 2. Intellectual capital has a positive impact on organizational learning capability. 3. Intellectual capital is positive on firm performance. 4. Organizational learning capability has a positive impact on firm performance. In this study, intellectual capital and its components as independent variables and learning capability variables with its dimensions as them editor variable and firm performance variable with its dimensions as dependent variables were analyzed. The research study is descriptive - correlation. In this study, correlation coefficient and path analysis using LISREL software have been used. The statistical population of this study, all employees and managers of the West Cement Company, is approximately 700 people. In order to determine the survey sample, a simple random sampling is used. The assumptions of the study, the relationship between the stock of knowledge and financial performance, as well as the relationship between structural capital and non-financial performance were not confirmed. Other hypotheses to study the effect of variables based on assumptions and research conceptual model were confirmed to have direct or indirect impact.

https://textroad.com/pdf/JBASR/J.%20Basic.%20Appl.%20Sci.%20Res.,%203(6)427-432,%202013.pdf

The Relationship between Intellectual Capital and Financial and Non- Financial Performance in West Cement Company in Kermanshah

In an intensely competitive, rapidly evolving, and increasingly knowledge-based high-tech sector, the ability to learn is critical to the success of multinational organizations. It is now clear that building and maintaining a firm’s human capital through organizational learning represents the only sustainable source of competitive advantage. Barney (1991), for example, illustrates that the strategic resources which underpin the success of business enterprises include an organization’s physical, human, and organizational capital. Physical capital includes plant and equipment, geographic location, and access to raw materials. Human capital includes the training, experience, judgment, intelligence, relationships, and insights of managers and workers. Organizational capital includes firm structure and processes, internal and external relations, both formal and informal. It is clear, however, that human capital lies at the foundation of both physical and organizational capital (Nordhaug, 1994). This chapter argues that human capital can be enhanced through the application of IT (as physical capital) ABsTRACT

Building and Maintaining Human Capital with Learning Management Systems

Purpose: The purpose of this paper is to propose an “Integrated Performance Measurement System” (PMS) addressing the measurement and management of the financial and non-financial performance and “Intellectual Capital” (IC) for the “Small and Medium Entreprises” (SMEs). The paper relies on the “Integrated New Model” (INM) by Pirozzi and Ferulano (2016) that deals with the same task applied to a specific sector. Thus, we propose the modification of the INM model to define a “Renewed and Advanced Model” called “INM*” as integrated PMS for SMEs. 

Design/methodology approach: We used a qualitative method with an inductive and deductive approach to obtain an advanced model INM*. Thus, the SMEs characteristics are translated in our advanced INM* model according to the INM* structure and the IC framework. Moreover, we integrated in our proposed model INM* the “Strategic Management Accounting” (SMA) and the “Systems of Innovation” (SI) perspectives.

Findings: We proposed the advanced model INM* as integrated PMS. This is a model supporting entrepreneurial and innovative SME as well as a conceptual framework summarizing the interactions and the knowledge conversions that occur between the IC components within the innovation processes. Thus, the proposed model is a useful tool for SMEs organizations.

Originality/value: The renewed and advanced model INM* is useful in the academic and practical communities. It exhibits the advantages related to the innovative usage of a unique measurement system devoted to accomplish all the measurement tasks activated by SMEs. In addition, two other models are proposed and, in turn, are available for further research.

/pdf/defining-the-integrated-performance-measurement-systems-in-y4aiebny8r.pdf

Defining the Integrated Performance Measurement Systems in Small and Medium Enterprises: An Advanced Model

Data mining has been used in a variety of business applications, such as consumer buying pattern prediction and credit card default prediction, but recent research studies in accounting and finance have applied data mining techniques for classification and prediction of events such as firm bankruptcy and auditor changes. Data mining may be a useful tool for accounting and finance applications because of the huge volume of financial data that impacts business decisions. In this chapter, we summarize three published research studies in which we applied various data mining applications using accounting and other data for classification and prediction decisions, and we identify important issues to consider when applying current data mining tools. The three research studies we summarize in this chapter are Kwak, Eldridge, Shi, and Kou (2009), Kwak, Eldridge, Shi, and Kou (2011), and Kwak, Shi, and Kou (2012). In these studies, we evaluate the relative usefulness of a variety of data mining approaches primarily by comparing overall accuracy rates for predicting material weaknesses in internal controls (Kwak et al., 2009), auditor changes (Kwak et al., 2011), and bankruptcy (Kwak et al., 2012). We compare the results of multiple criteria linear programming (MCLP) with those of linear discriminant analysis and Decision Tree based See5 (DT) in Kwak et al. (2009) and with those of classification and regression tree (CART), linear logistic regression (LLR), and Bayesian network in Kwak et al. (2011). The CART, LLR, and Bayesian network applications are three of the 13 data mining models in Witten and Frank’s (2005) WEKA Data Mining Workbench, and Kwak et al. (2012) apply all 13 data mining models.

Data Mining Applications in Accounting and Finance

An Epistemology of Intellectual Capital and its Transition to a Practical Application

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An Epistemology of Intellectual Capital and its Transition to a Practical Application