Purpose The purpose of this paper is to propose an extended and modified value-added (VA) intellectual coefficient (VAIC) model, which includes intellectual capital (IC) components which were missing in the original VAIC approach. The proposed model has been used to explore the relationship between IC and firm performance for Turkish manufacturing firms on a more detailed level. Design/methodology/approach Multiple regression analysis has been employed to identify the IC components, which predict the performance of the firm and the moderating effect of some IC components on IC components–firm performance relationship. Data are required to calculate the IC components, and firm performance variables have been obtained from the financial reports of the Turkish manufacturing firms for the period 2003–2013. Findings According to the results for Turkish manufacturing sector innovation capital efficiency has a moderating effect on the relationship between structural capital efficiency (SCE) and profitability, meaning, depending on an increase in R&D expenses, the effect of SCE on profitability also increases. On the other hand, it has been found that innovation capital efficiency has a direct impact on firms’ productivity. The results also showed that IC efficiency components have a moderating role on the relationship between capital employed efficiency and profitability. Research limitations/implications There might be a time lag until the effect of R&D investments can be observed in firms’ performance. However, this lagged impact of innovation capital and also other IC components on future firm performance has not been investigated due to concerns related to sample size. Originality/value The proposed model differs from the original VAIC model in three ways: it, namely, includes two additional IC components, customer capital (CC) and innovation capital. It explores the moderating effect of innovation capital on structural capital–firm performance relationship and the moderating effect of IC components on employed capital–firm performance relationship. As the last difference, it proposes an alteration in the VA calculation due to newly added IC components, CC and innovation capital.

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The aim of this study is to determine the factors affecting blue‐collar workers’ intention to use a web‐based learning system in the preimplementation phase in the automotive industry. For that purpose an extended technology acceptance model (TAM) is proposed, which included factors such as image, perceived content quality, and perceived system quality as additions to the basic model. Data collected from 546 blue‐collar workers were used to test the proposed research model by using Linear Structural Relations software LISREL, Version 8.54. The findings of the study indicate that perceived usefulness is the strongest predictor of behavioral intention to use a web‐based learning system. In addition, a high proportion of perceived usefulness is explained by perceived content quality, and perceived ease of use is explained by perceived system quality and anxiety.

PurposeThe purpose of this paper is to examine various factors affecting users' behavioral intention to use (BIU) enterprise resource planning (ERP) systems, based on data from 75 potential end‐users of ERP systems.Design/methodology/approachA survey methodology is used to gather data. The research model is constructed based on the findings of the previous studies.FindingsThe results indicate that subjective norms, perceived usefulness (PU) and education level are determinants of BIU the system. In addition, PU affects attitude toward use, and both perceived ease of use (PEOU) and compatibility affect PU. In addition, among personal characteristics, education level has a significant effect on PEOU and behavioral intention. However, there is no significant relationship between attitude and behavioral intention.Research limitations/implicationsAs the sample is limited, the findings will require validation among other populations.Practical implicationsThis paper provides evidence that compatibility and PEOU are important for the users to perceive the system's usefulness. Also, subjective norm is very important for the BIU the ERP systems.Originality/valueThis paper combines technology acceptance model, theory of reasoned action and innovation diffusion theory with personal characteristics of gender, education level and experience to determine the factors important for the acceptance of ERP systems.

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