Intelligent impact assessment of HRM to the shareholder value George Xirogiannis a, * , Panagiotis Chytas b,1 , Michael Glykas c,2 , George Valiris b,1 a University of Piraeus, Department of Informatics, 80, Karaoli and Dimitriou Street, 185 34 Piraeus, Greece b University of Aegean, Department of Business Administration, 8, Michalon Street, Chios 82 100, Greece c University of Aegean, Department of Financial and Management Engineering, 31, Fostini Street, Chios 82 100, Greece Abstract Despite the extensive research in human capital management and performance measurement, intelligent treasoning mechanisms, which integrate human resource (HR) practices into strategic-level shareholder decisions, are still emerging. This paper discusses a novel approach of designing a decision-modeling tool, which assesses the impact of contemporary human resource management (HRM) prac- tices to the shareholder value and satisfaction. The underlying research addresses the problem of establishing HRM interrelationships in order to drive the overall business performance from the shareholder value perspective. The proposed methodology tool utilizes the fuzzy causal characteristics of fuzzy cognitive maps (FCMs) to generate a hierarchical and dynamic network of interconnected HR perfor- mance drivers. The intelligent computing characteristics of FCMs are also employed to establish a dynamic feedback and bi-directional alignment of HRM practices and strategic improvement. Finally, this research provides a practical insight on the applicability of soft approaches in capturing and illustrating the effect of HRM practices. Ó 2007 Elsevier Ltd. All rights reserved. Keywords: Fuzzy cognitive maps; HR performance; Decision support; Strategic planning 1. Introduction Enterprises are evolving in turbulent and equivocal envi- ronments (e.g. Drucker, 1993; Grove, 1999; Kellys, 1998). This requires enterprises to be alert and watchful for the detection of weak signals (e.g. Ansoff, 1975) or discontinu- ities of emerging threats and to initiate further probing based on such detection (Walls et al., 1992). Enterprises today face critical business challenges (Ulrich, 1998) like globalization, profitability through growth, technology integration, intellectual capital management, continuous change, etc. Such challenges require organizations to build new capabilities, but it is not always apparent who should be responsible for developing those capabilities. Perhaps, everyone and no one, but in any case this is a unique HR’s opportunity to play a leadership role in enabling organizations to meet such competitive challenges. Ensur- ing that human resource (HR) strategies are in place to deal with these challenges is increasingly recognized as critical to success (Leopold et al., 1999). Human resource management (HRM) in the literature has been considered a second- or third-order strategy, lar- gely related to implementation rather than shareholder level decision-making. The process of HR strategy formu- lation and evaluation had not been widely conceptualized until recently. Moreover, the impact of HRM practices to the shareholder strategic value is not modeled adequately, despite the utilization of sophisticated performance evalua- tion mechanisms at the employee level. The evidence that HR issues are fundamental to business is compelling at the level of unit labor costs, but whether they are funda- mental to the strategy process remained highly question- able until recent years (Ritson, 1999). This can be attributed to the fact that contemporary performance eval- uation mechanisms focus on analyzing the operational 0957-4174/$ - see front matter Ó 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2007.08.103 * Corresponding author. Tel.: +30 210 4142000; fax: +30 210 4142328. E-mail addresses: georgex@unipi.gr (G. Xirogiannis), p.chytas@chios. aegean.gr (P. Chytas), mglikas@aegean.gr (M. Glykas), gval@aegean.gr (G. Valiris). 1 Tel.: +30 22710 35000; fax: +30 22710 35099. 2 Tel.: +30 22710 35400; fax: +30 22710 35499. www.elsevier.com/locate/eswa Available online at www.sciencedirect.com Expert Systems with Applications 35 (2008) 2017–2031 Expert Systems with Applications