This paper describes an approach to measure the entrepreneurship orientation of online P2P lending platforms. The limitations of existing methods for calculating entropy of interval-valued intuitionistic fuzzy sets (IVIFSs) are significantly improved by a new entropy measure of IVIFS considered in this paper, and then the essential properties of the proposed entropy are introduced. Moreover, an evaluation procedure is proposed to measure entrepreneurship orientation of online P2P lending platforms. Finally, a case is used to demonstrate the effectiveness of this method. 1. Introduction With the enormous popularity of online communities, a new way of loan origination has entered the credit market: online peer-to-peer (P2P) lending [1]. Online P2P lending platforms are financial institutions operating without the participation of traditional financial intermediaries. Although online P2P lending is a relatively young field of research, an increasing amount of scientific contributions has been published in recent years. The main research is focused on stakeholders (Herzenstein et al., 2008 [2]; Klafft, 2008 [3]; Galloway, 2009 [4]), funding success factors (Kumar, 2007 [5]; Lin, 2009 [6]; Larrimore et al., 2009 [7]; Duarte et al., 2012 [8]; Burtch et al., 2013 [9]), determinants of interest rates factors (Pope and Sydnor, 2011 [10]; Iyer et al., 2009 [11]; Collier and Hampshire, 2010 [12]; Brandes et al., 2011 [13]), and lenders’ behavior (Shen et al., 2010 [14]; Yum et al., 2012 [15]; Dezs and Loewenstein, 2012 [16]; Zhang and Liu [17]). The online P2P lending platforms are firms, and they need to acquire entrepreneurial competences to survive. The processes of strategy-making and the styles of firms engaging in entrepreneurial activities are together referred to as “entrepreneurship orientation” (EO) [18]. Several studies have found a positive relationship between EO and firm performance (e.g., [19, 20]). Consequently, measuring EO of online P2P lending platforms is of special importance for firms and for organizations, such as venture capitalists, business angels, and governments. Variations in the weights often influence the rankings of the alternatives [21]. The weights can be classified into subjective weights and objective weights depending on the information source. The most well-known method of generating objective weights is the entropy method [22]. Entropy has been the main tool for measuring uncertain information since information theory was conceived in the work of Shannon [23] more than sixty years ago. Fuzziness, a feature of imperfect
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