This paper: Obtains measures of intellectual capital performance for quoted banks in selected European countries (Czech Republic, Denmark, Finland, Germany, Italy, Norway, Poland, Spain, Sweden) during the 2004-2007 period;  Investigates empirically the relationship between (i) the efficiency of value creation and (ii) bank market valuation and financial performance;  Tests the effects of intellectual capital performance on profitability and evaluates whether or not intellectual capital can be considered a decision-making factor for investors.Using data drawn from public annual reports and Ante Pulic's Value Added Intellectual Coefficient (VAIC) as a measure of the efficiency of capital employed and intellectual capital, the study uses regression models to examine the relationship between corporate value-creation efficiency and a firms' market-to-book value ratios and corporate value-creation efficiency.Some important findings include:  The determination of correlation, if any, between the financial performance of the banks and their VAIC. The determination of the Italian bank efficiency in the use of intellectual capital in relation to some other European competitors.  The validation of the assumption as to whether or not investors place higher value on firms with greater intellectual capital.The research limitations/implications include:  The failure of the study to consider all banks operating in the countries analyzed (due to insufficient data, mainly for unlisted banks) and the limited time period of three years.Practical implications of the analysis include:  The results can assist the managers of the respective banks in benchmarking their positions regarding intellectual capital. The study might also assist policymakers in formulating and implementing policy regarding intellectual capital development, while it may also aid investors in modifying investment strategies.

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Purpose This paper aims to analyze the impact of DARQ technologies (distributed ledger, artificial intelligence, extended reality, quantum computing) in the financial sector, focusing on artificial intelligence (AI) applications in personalized banking, which consists of treating every customer as a segment of one. The research has two main goals. First, providing a complete and organic analysis of the DARQ technologies framework currently missing in the literature. Because this research focuses on the financial sector, more attention is dedicated to DARQ technologies in this industry. Second, studying applications of one of the DARQ technologies, AI, in personalized banking, where it appears to have a great potential impact. Design/methodology/approach The research analyses both the supply side, collecting secondary data from documentation, reports and research studies to study the major trends and results obtained by leading banks, and on the demand side, collecting primary data through a dedicated survey and elaborating opinions and preferences of potential customers. Using this information, a detailed go-to-market plan based on the framework elaborated by Bain and Co. in 2012 is developed, considering the hypothesis of a well-known universal bank, operating globally, with an established brand and access to modern AI technologies, which decides to invest in this field as a priority. Findings In addition to giving a detailed overview of DARQ technologies from a technical and a business perspective, the results related to the hypothetical case of the study help to understand which would be the most suitable target for the launch phase, which value proposition should be offered and how to deliver it, but also how to evolve the project to attract more customers and strengthen the relationship with the existing ones. Nevertheless, this research could be a starting point for future studies and updates, considering related evolutions, investigating more representative demand samples or analyzing how the combination of more DARQ technologies could be applied to the financial sector. Research limitations/implications Some limitations affect this work. First, the topics studied are evolving rapidly and partially dependent on other innovations under development; therefore, they may become obsolete and less significant in the next years. As regard the data collection, the supply-side analysis involves strategical information kept private by companies; therefore, the collected data probably miss some useful details. As concerns primary data, the sample could have been larger and more heterogeneous and biases and misinterpretations could have affected the answers. A compromise has been found between the time and resources available and the qualitative and quantitative characteristics of the sample. Practical implications This research could be a tool for financial companies interested in investing in AI for personalized banking, but it also provides useful insights about the whole DARQ framework, which could be interesting for all the financial and nonfinancial firms. Applying AI effectively and efficiently could offer great benefits, both economic and noneconomic, to financial firms but also to their customers, who could benefit from hyper-tailored services at a reasonable and affordable price, whereas in the past, they were reserved only for very important person clients. This win-win situation could lead the way to further investments and consequent innovations in the future. Social implications Some issues still exist, mainly about data security and privacy, but also the social risk linked to the labor market due to the AI substitution for some tasks and the related shift in professionals required by employers, which could negatively affect the salary gap among workers with different levels of educations, tightening up existing inequality problems. An effort by public and private subjects will be required to make this transition inside the labor market smoother. Despite this, the research shows that AI applications in personalized m-banking could mutually benefit both the demand and the supply of the market. Originality/value Apart from the organic overview offered on DARQ technologies and their related business applications, currently missing in the literature, which could be useful for a better comprehension of the topic and could also give interesting insights to firms, this research presents an original and concrete roadmap to follow for financial companies interested in delivering a personalized mobile banking service leveraging on AI. Every step presented in the output of this work is based on an in-depth analysis of past, and present actions carried out and result obtained by competitive firms on the market and on needs and preferences observed among potential customers.

The purpose of this paper is to provide a description of the present role of intellectual capital (IC) in the Italian Banking Sector, giving as output a ranking representation of the banks involved in the analysis in terms of their efficient use of tangible and intangible assets in the creation of corporate value. The paper will investigate the components of IC and the impact that these components had on performance during the period [2003][2004][2005][2006][2007]. A multiple regression analysis is used to test the relationship between IC performance and certain independent variables. A model of analysis will be adopted from the literature in order to approximate the efficiency of banks in their use of intangible assets for the creation of value (VAIC Analysis). The results show that investors may place different values on each of the three components of value creation efficiency (physical capital, human capital, and structural capital).