Intelligent impact assessment of HRM to the shareholder value

Available online at www.sciencedirect.com Expert Systems with Applications Expert Systems with Applications 35 (2008) 2017–2031 www.elsevier.com/locate/eswa 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 University of Aegean, Department of Business Administration, 8, Michalon Street, Chios 82 100, Greece University of Aegean, Department of Financial and Management Engineering, 31, Fostini Street, Chios 82 100, Greece b c 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) practices 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 performance 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.  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 environments (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 discontinuities 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, * Corresponding author. Tel.: +30 210 4142000; fax: +30 210 4142328. E-mail addresses: [email protected] (G. Xirogiannis), p.chytas@chios. aegean.gr (P. Chytas), [email protected] (M. Glykas), [email protected] (G. Valiris). 1 Tel.: +30 22710 35000; fax: +30 22710 35099. 2 Tel.: +30 22710 35400; fax: +30 22710 35499. 0957-4174/$ - see front matter  2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2007.08.103 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. Ensuring 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, largely related to implementation rather than shareholder level decision-making. The process of HR strategy formulation 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 evaluation 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 fundamental to the strategy process remained highly questionable until recent years (Ritson, 1999). This can be attributed to the fact that contemporary performance evaluation mechanisms focus on analyzing the operational 2018 G. Xirogiannis et al. / Expert Systems with Applications 35 (2008) 2017–2031 effectiveness of the human capital rather than addressing the issue of strategic alignment. This paper addresses the problem of designing a novel modeling methodology tool to act as an intelligent decision support mechanism for evaluating the impact of HRM practices to the shareholder value and satisfaction. This attempt focuses on bridging the gap between the operational characteristics of HRM practices and the strategic decisions at the shareholder level. The proposed methodology tool utilizes the fuzzy causal characteristics of fuzzy cognitive maps (FCMs) to generate a hierarchical and dynamic network of interconnected HR performance drivers. By using FCMs, the proposed novel mechanism simulates the operational efficiency of HR models with imprecise relationships and quantifies the impact of HRM activities to the overall shareholder satisfaction model. This research builds on contemporary HRM experience to establish a ‘‘soft computing’’ approach on how to interrelate HRM activities and shareholder value. The FCM approach does not pose as a substitute of traditional HRM operations nor does it offer an alternative to HR performance evaluation. It presents an intelligent decision-making framework for strategic-level HRM based on scenario building and ex ante impact assessment. Primarily, the proposed model targets the principle directors and stakeholders of HRM projects (e.g. HR department, change management leaders, business strategy leaders, etc) assisting them to reason effectively about the status of strategic-level performance metrics, given the (actual or hypothetical) implementation of a set of HR practice changes. However, the holistic nature of the proposed model may couple effectively with other strategic performance evaluation systems. Given the demand for effective shareholder positioning, such a succinct mechanism of conveying the essential dynamics of HR practices is believed to be useful for anyone contemplating or undertaking a strategy formulation exercise. Nevertheless, the explanatory nature of the mechanism can prove to be useful in a wider educational setting. As far as the contribution to decision-making is concerned, the application of FCMs as an intelligent modeler of HR knowledge is believed to be novel. Moreover, this paper extends typical FCM algorithms in order to adapt to the distributed nature of typical HR activities. Also, this research adopts a new qualitative approach to interpret fuzzy linguistic variables to weight and concept values in order to support further the soft computing characteristics of the tool. It is the belief of this paper that the fuzzy reasoning capabilities enhance considerably the usefulness of the proposed mechanism while reducing the effort of identifying quantitative impact measurements. As far as the added value of this research is concerned, the proposed methodology offers an alternative approach to HRM based on shareholder value analysis and modeling. Preliminary experiments indicate that the proposed approach can be effective and realistic, without employing detailed quantitative calculations. This paper consists of six sections. Section 2 presents a short literature overview. Section 3 presents an overview of the proposed system, Section 4 discusses the new approach to HRM modeling and Section 5 discusses the major advantages of the proposed tool. Finally, Section 6 concludes this paper and briefly discuses future research activities. 2. Literature overview 2.1. Contemporary HRM As firms become increasingly aware that people are among their most valuable strategic assets, they are reappraising the way in which they manage their human capital. The emphasis is shifting from personnel management to the wider, strategic concept of human resource management in which human resource policies and activities, including training and development, are linked closely to business strategy. HR specialists who wish to develop a strategic approach to people management must establish credibility with top management as the key figures to achieve successful results (Handy et al., 1989). Strategic human resource management addresses a number of key issues. Typical examples (loosely adapted from Baker, 1999) include internal integration of personnel policies, their external integration with overall strategy, line management responsibility for HR implementation, individual rather than collective employee relations, HR commitment, HR initiatives, the managerial role of ‘‘enabler’’, ‘‘empowerer’’, and ‘‘facilitator’’, etc. Following a resource-based view of an enterprise, firms can develop sustainable competitive advantage by creating value in a way that is rare and difficult for competitors to imitate (Baker, 1999). Although traditional sources of competitive advantage such as natural resources, technology, economies of scale, value creation, and so forth, the resource-based argument is that these sources are increasingly easy to imitate, especially in comparison to a complex social structure such as an employment system. If that is so, HR strategies may be an important source of sustainable competitive advantage (Lado & Wilson, 1994). But one may ask how HR management can deliver organizational excellence and competitive advantage. According to Ulrich (1997) there are four ways to do so: • HR management could become a partner with senior and line managers in strategy execution, helping to move planning from the conference room to the marketplace. • HR management could become an expert in the way work is organized and executed, delivering administrative efficiency to ensure that costs are reduced while quality is maintained. • HR management could become a champion for employees, vigorously representing their concerns to senior management and at the same time working to increase employee contribution; that is, employees’ commitment to the organization and their ability to deliver results. G. Xirogiannis et al. / Expert Systems with Applications 35 (2008) 2017–2031 • Finally, HR management could become an agent of continuous transformation; shaping processes and a culture that together improve an organization’s capacity for change. Research (Schuler & Jackson, 1996) presents further evidences that show how HR can contribute to competitive advantage. Recent consultancy studies show that firms that manage their human resource effectively have higher levels of profitability, higher annual sales per employee (productivity), higher market value, higher earnings-per-share growth, etc. In other words they meet the needs of the stockholders and investors. Effective HRM can also meet the needs of the employees in several ways: as firms survive, expand, and increase their profitability, they provide further employment security, job opportunities, and high remuneration packages. Successful HRM serves the needs of society by elevating the standard of living, strengthening legal regulations and ethical guidelines, and controlling the effect of the firm on the surrounding community; that is, it contributes positively to society, which in turn supports a favorable corporate image in the mind of the public. However, strategic HRM will not be taken seriously unless it can be shown that it is worth the return on investment. Traditionally, the HR director could discuss abstractly and conceptually about employee morale, turnover, and commitment. To fulfill the business partner role of HR, concepts need to be replaced with evidence, ideas with results, and perceptions with impact assessments (Ulrich, 1997). 2.2. Decision tools as back end to HRM Conceptually, a central aspect of ‘‘strategic HRM’’ is the integration of the HR function with strategic decision-making (Bennett et al., 1998). Research like (Cook & Ferris, 1986; Huselid, 1995; Wright & McMahan, 1992) reason that organizations linking HRM to strategic decision-making will outperform those that do not. However, without building an information system one cannot provide individual system users with integrated information. This integration is achieved through Human Resource Information Systems (HRIS). HRIS can be defined as integrated systems, which gather, store and analyze information regarding an organization’s human resources. According to Kavanaugh et al. (1990) the first attempt to manage personnel information was bounded to employee names, addresses and brief employment history. Between 1945 and 1960, organizations became more aware of human capital issues and began to develop formal processes for employee selection and development. At the same time, organizations began to recognize the importance of employee morale on the firm’s overall effectiveness. While this period of change in the profession did not result in significant changes in HRIS, it set the pace for consequent changes. 2019 In the next 20 years (1960–1980) HR was integrated into the core business mission, and governmental and regulatory reporting requirements for employees also increased significantly. The HR department became one of the most important users of the exceptionally costly computing systems of the day, often edging out other functional areas for computer access. Although HRIS systems were computerized and grew extensively in size and scope during this period, they were merely simple record-keeping systems (Kavanaugh et al., 1990). Over the last two decades, firms have increasingly relied on the HR function to provide management solutions that increase the effectiveness of human capital. HRIS systems have evolved into complex tools designed not only to manage a rich variety of information about the firm’s human capital, but to also provide analytical tools to assist in decision-making about the management of those assets (Kavanaugh et al., 1990). Recent research activities like (Tannenbaum, 1990) decompose HRIS into of three systems, namely Expert Systems (ES), Decision Support Systems (DSS) and Executive Information Systems (EIS). 2.2.1. Expert systems (ES) Expert systems are designed to model the knowledge of domain experts in order to help users derive expert-quality solutions. ES are expected to support HR managers in analyzing and solving verbally stated problems, which require HRM expertise. They model the human HR decision-making process by applying questions and heuristics used by HR domain experts (Stefik, 1990). Hence, non-experts can use ES to produce solutions by accessing to the expert knowledge base (Lawler & Eliot, 1996). However, there exist only a few attempts to develop ES in the HR field (for example (Byun & Suh, 1994; Sturman et al., 1996). 2.2.2. Decision support systems (DSS) The important role of DSS is to provide information to users in order to analyze situations and make calculated decisions (Poe et al., 1998). Research efforts like (Mohanty & Deshmukh, 1997) discuss a comprehensive HR planning system for large multi-divisional, multi-locational petroleum companies. Such efforts adopt a resource support approach, which primarily focuses on providing the critical tools and resources needed for HRM at the tactical level to plan for manpower requirements, recruitment, placement, promotion and appraisal. Research (Bellone et al., 1995) proposes the ISPM decision support system to assist in personnel career management. The ISPM system has been designed mainly to deal with the establishment of the training a human resource should undergo before occupying a job position. Hence this system addresses mainly tactical management issues, rather than strategic level choices. Research (Niehaus, 1995) discusses the results of a multiyear effort in implementing a series of human resource planning DSS applications in the US Navy shipyard community. These applications span both corporate (integrated 2020 G. Xirogiannis et al. / Expert Systems with Applications 35 (2008) 2017–2031 management of eight shipyards Navy-wide with 78,000 employees) and local (individual shipyards with 9000 employees) perspectives. This research effort first concentrates on the development, implementation and introduction of a DSS into a large organization that is going through a personnel-downsizing process. Then, a discussion is provided of the implementation of complex human resource planning models in a DSS that can be used by a mid-level staff person rather than at strategy-level executives. 2.2.3. Executive information systems (EIS) EIS first appeared on the decision support scene in the middle of 1980s (Schendel & Hofer, 1979). The aim of EIS is to ‘‘provide executives with easy access to internal and external information that is relevant to their critical success factors’’ (Watson & Howdeshel, 1997). Several commercial applications implement HR models, however the ‘‘source mechanism’’ of such EIS is not always available to public. 2.3. FCMs as a modeling technique Fuzzy cognitive maps is an intelligent modeling methodology for complex decision systems, which originated from the combination of Fuzzy Logic (Zadeh, 1965) and Neural Networks. An FCM describes the behavior of an intelligent system in terms of concepts; each concept represents an entity, a state, a variable, or a characteristic of the system (Dickerson & Kosko, 1997). FCM nodes are named by such concepts forming the set of concepts C = {C1, C2, . . . , Cn}. Arcs (Cj, Ci) are oriented and represent causal links between concepts; that is how concept Cj causes concept Ci Arcs are elements of the set (A = {Cj, Ci)ji}  C · C. Weights of arcs are associated with a weight value matrix Wn·n, where each element of the matrix wji 2 [1, . . . , 1]  R such that if (Cj, Ci) 62 A then wji = 0 else excitation (respectively inhibition) causal link from concept Cj to concept Ci gives wji > 0 (respectively wji < 0). The proposed methodology framework assumes that [1, . . . , 1] is a fuzzy bipolar interval, bipolarity being used as a means of representing a positive or negative relationship between two concepts. In practice, the graphical illustration of an FCM is a signed graph with feedback, consisting of nodes and Weight weighted interconnections (e.g. ! ). Signed and weighted arcs (elements of the set A) connect various nodes (elements of the set C) representing the causal relationships among concepts. This graphical representation illustrates different aspects in the behavior of the system, showing its dynamics (Kosko, 1986) and allowing systematic causal propagation (forward/backward chaining). Positive or negative sign and fuzzy weights model the expert knowledge of the causal relationships (Kosko, 1991). Concept Cj causally increases Ci if the weight value wji > 0 and causally decreases Ci if wji < 0. When wji = 0, concept Cj has no causal effect on Ci. The sign of wji indicates whether the relationship between concepts is posiW W tive ðC j j;i ! C i Þ or negative ðC j j;i ! C i Þ, while the value of wji indicates how strongly concept Cj influences concept Ci. The forward or backward direction of causality indicates whether concept Cj causes concept Ci or vice versa (e.g. Fig. 1). Simple variations of FCMs mostly used in business decision-making applications may take trivalent weight values [1, 0, 1]. This paper allows FMCs to utilize fuzzy word weights like strong, medium, or weak, each of these words being a fuzzy set to provide complicated FCMs. In contrast, (Kwahk & Kim, 1999) adopted only a simple relative weight representation in the interval [1, . . . , 1]. To this extend, (Kwahk & Kim, 1999) offered reduced functionality since it does not allow fuzzy weight definitions. Generally speaking FCM concept activations take their value in an activation value set V = {0, 1} or {1, 0, 1} if in crisp mode or [d, 1] with d = 0 or 1 if in fuzzy mode. The proposed methodology framework assumes fuzzy mode with d = 1. At step t 2 N, each concept Cj is associated with an inner activation value atj 2 V , and an external activation value etaj 2 R. FCM is a dynamic system. Initialization is a0j ¼ 0. The dynamic obeys a general recurrent relation atþ1 ¼ f ðgðeta ; W T at ÞÞ, "t P 0, involving weight matrix product with inner activation, fuzzy logical operators (g) between this result and external forced activation and finally normalization (f). However, this paper assumes no external activation (hence no fuzzy logical operators), resulting to the following typical formula for calculating the values of concepts of FCM: ! n X t tþ1 ai ¼ f wji aj for i ¼ 1; . . . ; n; ð1Þ j¼1;j6¼i where is the value of concept Ci at step t + 1, atj the value of the interconnected concept Cj at step t, wji is the weighted arc from Cj to Ci and f : R ! V is a threshold function, which normalizes activations. Two normalization functions are usually used. The unipolar sigmoid function where k > 0 determines the steepness of the continuous 1 kx function f ðxÞ ¼ 1þe . When concepts can be negative (d < 0), function f(x) = tanh(x) can also be used. aitþ1 W 41 Number of customer W46 W34 W13 Sales volumes W12 W63 Product price W 35 W 23 Company profitability Product risk W56 W52 Operating cost Fig. 1. Simple FCM. 2021 G. Xirogiannis et al. / Expert Systems with Applications 35 (2008) 2017–2031 2.4. Applications of fuzzy cognitive maps 3. Fundamentals of the methodology tool Over the last 10 years, a variety of FCMs have been used for capturing–representing knowledge and intelligent information in engineering applications, for instance, geographical information systems (Liu & Satur, 1999) and fault detection (Ndouse & Okuda, 1996; Pelaez & Bowles, 1995). FCMs have been used in modeling the supervision of distributed systems (Stylioa & Georgopoulos, 1997). FCMs have also been used in operations research (Craiger et al., 1996), web data mining (Hong & Han, 2002; Lee et al., 2002), as a back end to computer-based models and medical diagnosis (e.g. Georgopoulos et al., 2002). Several research reports applying basic concepts of FCMs have also been presented in the field of business (e.g. Xirogiannis & Glykas, 2004; Xirogiannis & Glykas, 2004) and other social sciences. Research in Axelrod (1976) and Perusich (1996) have used FCM for representing tacit knowledge in political and social analysis. FCMs have been successfully applied to various fields such as decision making in complex war games (Klein & Cooper, 1982), strategic planning (Diffenbach, 1982), (Ramaprasad & Poon, 1985), information retrieval (Johnson & Briggs, 1994), distributed decision process modeling (Zhang et al., 1994), performance-driven changes (Xirogiannis & Glykas, 2004). Research like (Lee & Kim, 1997) has successfully applied FCMs to infer rich implications from stock market analysis results. Research like (Lee & Kim, 1998) also suggested a new concept of fuzzy causal relations found in FCMs and applied it to analyze and predict stock market trends. The inference power of FCMs has also been adopted to analyze the competition between two companies, which are assumed to use differential games mechanisms to set up their own strategic planning (Lee & Kwon, 1998). FCMs have been integrated with case-based reasoning technique in the field of knowledge management (Noh et al., 2000). Research (Parenthoen & Reignier, 2001) proposed the use of FCM as a tool to model emotional behavior of virtual actors improvising in free interaction within the framework of a ‘‘nouvelle vague’’ scenario. Recent research adopted FCMs to support the core activities of highly technical functions like urban design (Xirogiannis et al., 2004). Summarizing, FCMs can contribute to the construction of more intelligent systems, since the more intelligent a system becomes, the more symbolic and fuzzy representations it utilizes. In addition, a few modifications have been proposed. For example, the research in (Silva (1995)) proposed new forms of combined matrices for FCMs, the research in (Hagiwara (1992)) extended FCMs by permitting non-linear and time delay on the arcs, the research in (Schneider et al. (1995)) presented a method for automatically constructing FCMs. More recently, Liu & Satur (1999) has carried extensive research on FCMs investigating inference properties of FCMs, proposed contextual FCMs based on the object-oriented paradigm of decision support and applied contextual FCMs to geographical information systems (Liu, 2000). 3.1. An integrated framework Typical HR decision-making can be achieved through the establishment of an integrated performance flow: creating and improving HR value to enhance employee satisfaction, successful operational utilization of all HR entities and strategic effectives to achieve strategic shareholder value. Such a typical though static performance flow (Fig. 2) is usually utilized by most research attempts as presented in Section 2.2. This paper extends such static frameworks by proposing a dynamic feedback to assert and maintain employee satisfaction based on a continuous establishment of contemporary HR practices. Having said that, it is the proposal of this research that effective integration of HRM benefits and shareholder value can be realized through a bi-directional alignment: • HRM perspective (‘‘HR practices ! shareholder satisfaction’’): It is the view of this research that optimal decision-making can be achieved through the establishment of an integrated performance flow: creating and improving HR value to enhance employee satisfaction, successful operational utilization of all HR entities, strategic effectives to achieve shareholder value and feedback to assert and maintain employee satisfaction. • Shareholder value perspective (‘‘shareholder satisfaction ! HR practices’’): It is the view of this research that optimal strategic improvement can be realized through the alignment of customer-oriented service delivery with the organizational capabilities of the enterprise, as well as the alignment of HRM with the organizational characteristics. Fig. 3 illustrates the dynamic integration of the strategic HR framework with the business framework. This research groups the interrelationships between the core components into two decision modeling chains compatible with the two alignment perspectives: • The forward chain assumes that HRM caters for improved shareholder value (as far as HR is concerned). HR practices can be conceptualized as the key drivers for building organizational capabilities, enhancing Business Strategy Shareholder Satisfaction Organisational Capabilities Customer Satisfaction Human Resource Practices Employee Satisfaction Fig. 2. Static integrated model connecting strategic HR framework to key results areas. 2022 G. Xirogiannis et al. / Expert Systems with Applications 35 (2008) 2017–2031 Business Strategy Human Resource Practices Organisational Capabilities Shareholder Satisfaction cient results in smoother variation of concept values during the iterations of the FCM algorithm. The coefficient k2 expresses the ‘‘influence’’ of the interconnected concepts in the configuration of the value of the concept Ai at time t + 1. In practice, it indicates the centralized or decentralized importance of the concept Ai in comparison to other concepts of the map. Intuitively, the introduction coefficient k2 imposes three steps of analysis for establishing the ‘‘influence’’ of causal HR relationships: Customer Satisfaction Employee Satisfaction Fig. 3. Dynamic integrated model connecting strategic HR framework to key results areas. employee satisfaction, and more innovatively, shaping customer satisfaction. Organizational capabilities along with customer satisfaction and the business strategy affect the shareholder satisfaction and value. • The backward chain assumes that shareholder value drives further changes to organizational characteristics in an attempt to improve HR practices and maintain employee satisfaction. This backward chain assumes that strategic level decisions cater for proper management of human assets in return for increased shareholder satisfaction. 3.2. FCMs in decision modeling 3.2.1. Updated FCM algorithm This research extends typical FCM algorithms (as used for example by Kwahk & Kim, 1999), by proposing the following updated algorithm aiming at modeling more effectively the dispersed nature of the HR domain: ! n X  t tþ1 t Ai ¼ f k 1 Ai þ k 2 ð2Þ W ji Aj : j¼1;j6¼i This paper also assumes that k1 and k2 can be fuzzy sets, extending previous relevant research. The coefficient k1 represents the proportion of the contribution of the value of the concept Ai at time t in the computation of the value of Ai at time t + 1. In practice, this is equivalent to assume that Wii = k1. The incorporation of this coeffi- • Step 1: Estimation of the direct influence of an HR concept Cj to a shareholder value concept Ci with the actual weight wji of the relationship. In such cases both Ci and Cj belong to the same HR domain and level (e.g. organizational unit, line of service, etc). This step sets k2 = 1. • Step 2: approximation of the indirect importance of duplicate causal relationships spanning to ‘‘equivalent’’ HR domains and levels, using coefficient k2 < 1. • Step 3: approximation of the indirect importance of causal relationships spanning to different HR domains and levels using coefficient k2 < 1. The three steps introduced above are used to estimate k2 under three different ‘‘influence’’ cases. Consider for example a typical sales department of an enterprise (Fig. 4) consisting of several sales units/teams, each offering the same services over different distribution channels (e.g. internet sales, direct sales, phone sales, etc). All units and channels operate under the same strategic framework. Let ‘‘Sales & promotion expertise’’ and ‘‘profitability’’ be interrelated performance concepts. As far as the second step is concerned, from a theoretical standpoint weight w1 should appear to be the same for all duplicate ‘‘Sales & promotion expertise – profitability’’ relationships across all sales units. From a practical standpoint, sales units may serve different number of customers (or even customers with different transaction volumes). In this case, coefficient k2 models the fact that direct sales & promotion expertise can prove more useful to the sales persons of the ‘‘Direct sales unit’’ simply because such sales persons may serve more custom- Sales Department Profitability W1 Sales & promotion expertise W2 Technical background Internet sales unit W1 W2 Sales & promotion expertise Technical background W1 W2 Sales & promotion expertise Direct sales unit Fig. 4. Horizontal decomposition of HR practices. Technical background Phone sales unit G. Xirogiannis et al. / Expert Systems with Applications 35 (2008) 2017–2031 C1 Level 0 Level 1 C2 C3 W3 C4 W1 k2,(L1,L2) Level 2 2023 C5 k2 ≈ k2,(L1,L2) + k2 ,(L2,L3) W2 k2 ,(L2,L3) Level 3 C6 Fig. 5. Top–down maturity decomposition. ers on a daily basis. In contrast, the sales & promotion experience of the sales persons of the ‘‘Internet sales unit’’ may be less important simply because they serve fewer customers (or even serve them off-line), despite the fact that sales and promotion coordination is the same for all sales units. Consider now the third step of influence analysis and Fig. 5, which presents a generic organizational breakdown structure, accompanied with a sample concept hierarchy. Regardless of weight values w1, w2, w3, coefficients 1 ¼ k 2;ðL1 ;L1 Þ P k 2;ðL1 ;L2 Þ P k 2;ðL2 ;L3 Þ model the fact othat affecting HR concepts at level Li+1 (e.g. concept C5) are more important in determining the value of affected shareholder concepts at level Li (e.g. concept C3) in comparison to other affecting concepts at level Li+2 (e.g. concept C6). k2,(Li,Lj) stands for the value of coefficient k2 associated with levels i and j. Ideally, coefficient k2 could break down into two separate coefficients (say k 2 ¼ x k x2 þ y  k y2 ), where k x2 aligns indirectly the value of HR concept Ci with the nature of shareholder concept Cj, while k y2 aligns indirectly the value of concept Ci with the significance of the hierarchical positioning of concept Cj within the enterprise. Parameters x, y could present the relative importance of k x2 and k y2 in mixed interconnection problems (e.g. concepts in different HR levels participating in duplicate relationships). However, preliminary experiments showed that this separation imposed initialization overheads without increasing significantly the accuracy of the FCM algorithm. Two alternative but equally interesting interpretations of coefficient k2 follows: • If the set of identified HR concepts Cj, j 5 i, is incomplete (e.g. incomplete maps, missing concepts, etc), then the estimation of the value of shareholder concept Ci may prove imprecise. In this case coefficient k2 may indicate the sufficiency of the set of concepts Cj j 5 i, in the calculation of the value of the concept Ci. • If the information necessary to approximate the input values of HR concepts Cj, j 5 i, is incomplete (e.g. incomplete HR performance evaluation), then the estimation of the value of shareholder concept Ci may also prove imprecise. In this case coefficient k2 may indicate the completeness of information utilized in the approximation of the input values of concepts Cj during the calculation of the value of the concept Ci. 3.2.2. Sample FCMs demonstration To demonstrate intelligent performance modeling using FCMs, consider Fig. 6, which depicts a graphical example of fuzzy relationships with no feedback loops followed by sample numerical calculations using formula (3), with k1 = k2 = 1 and k = 5 as the steepness of the normalization function. Setting the input value of ‘‘training‘‘ to 0.5 (1st scenario) triggers the FCM formula. The formula then calculates the current values of all related concepts. A ‘‘zero’’ concept value indicates that the concept remains neutral, waiting for causal relationships to modify its current value. A generic interpretation of the first scenario indicates that if training increases by 50% then deployment may increase by 88.07% and the operations performance by 95.61%. In contrast, if training increases by 20% (2nd scenario), then deployment and operations performance may increase by 59.86% and 89.04% respectively. Fig. 7 presents a typical example of a feedback loop. Similarly to Fig. 6, changing the input value of ‘‘training’’ triggers the FCM formula. The feedback loop dictates that calculations stop only when an equilibrium for all affected concepts has been reached, modifying all input values accordingly. 4. Estimating the impact of HR practices 4.1. FCM categories This research effort proposes a set of generic decision maps that can supplement HRM modeling of typical 2024 G. Xirogiannis et al. / Expert Systems with Applications 35 (2008) 2017–2031 External input value (t=0) Current Value Concept Scenario A Scenario B Scenario A Scenario B Training 0.5 0,2 0.5 0,2 Deploymentt 0 0 0,8807 0,5986 Operations performance 0 0 0,9561 0,8904 Fig. 6. Sample FCM calculations with no feedback loop. Initial input (t=0) Current Value Concept Scenario A Scenario B Scenario A Scenario B Training 0.5 0,2 0,81 0,5128 Deployment 0 0 0.9623 0,8860 Operations performance 0 0 0,9666 0,9569 Fig. 7. Sample FCM calculations with feedback loop. financial sector enterprises. This intelligent modeling approach may store concepts under different decision map categories (Fig. 8), for example: • Business category: all concepts relating to core organizational and HR utilization activities. • Technical category: all infrastructure related concepts with emphasis on technology infrastructure. • Social category: all HRM related concepts, accompanied by external stimuli (e.g. external hire characteristics, etc). • Integrated category: essentially all top-most concepts (e.g. a concept Ai with no backward causality such that "j: wji = 0), or concepts which may fall under more than one main categories. This categorization is compatible both with either the ‘‘process view’’ or the ‘‘organizational view’’ (as adopted by (Kwahk & Kim, 1999)) of the enterprise to allow greater flexibility in modeling HRM drivers in contrast to other modeling discussed in Section 2.2. The hierarchical decomposition of HRM concepts generates a set of dynamically interconnected hierarchical maps. Each map analyzes further the relationships among concepts at the same hierarchical level. Currently, the mechanism integrates more than 250 concepts, forming a hierarchy of more than 15 maps. Its dynamic interface allows its users to utilize a sub-set of these 250 concepts by setting the value of the redundant concepts and/or the value of the associated weights to zero. Fig. 8. Map categories. This research effort can portray the HR performance model following either a holistic or a scalable approach. This view is analogous to seeing the shareholder satisfac- G. Xirogiannis et al. / Expert Systems with Applications 35 (2008) 2017–2031 tion and value either as a single, ‘‘big bang’’ event or as an ongoing activity of targeting successive tasks of selected sub-processes. The proposed mechanism can accommodate both approaches, extending older models presented in Section 2.2. Also the current implementation allows: • easy customization of the function f and easy re-configuration of the formula Aitþ1 to adapt to the specific characteristics of individual enterprises, • generation of alternative scenarios for the same skeleton FCM, • automatic loop simulation until a user-defined equilibrium point has been reached. Alternatively, step-by-step simulation (with graphical output of partial results) is also available to provide a justification for the partial results. The following sections exhibit sample skeleton decision maps for the social and integrated categories, which provide relevance and research interest to this paper. While the presentation of other map categories supports the completeness of the proposed methodology tool, they add little (if any at all) significance to the research activities presented by this paper. 4.2. Social metrics This research proposes a set of maps with generic HRM performance concepts. For example: • The ‘‘Deployment’’ map (Appendix, Fig. 10) essentially relates concepts, which affect the operational effectiveness of the enterprise given the utilization characteristics of its human capital. • The ‘‘Staff morale’’ map relates all employment characteristics to the level of employee satisfaction. Also it supports reasoning of the impact of knowledge management, training and employee satisfaction to the financial model. • The ‘‘Benefits’’ map (Appendix, Fig. 11) relates concepts which affect the financial and operational effectiveness given a set comprehensive rewards and remuneration practices. • The ‘‘Training’’ map (Appendix, Fig. 12) reasons on the impact of qualifications and experience enhancement to the internal operational effectiveness of the enterprise. • The ‘‘Staff performance’’ map (Appendix, Fig. 13) summarizes concepts that affect the performance assessment practices of the enterprise. 4.3. Integrated metrics The proposed integrated category consists of two different maps as follows: • The ‘‘HR practices’’ map (Appendix, Fig. 14) essentially relates concepts, which fall under more than one category, as well as top-most concepts. 2025 • The ‘‘Integrated model’’ map (Appendix, Fig. 15) provides an FCM-based implementation of the dynamic interrelationships presented in Fig. 3. Concepts denoted as ‘‘#’’ expand top–down to lower level maps. Similarly ‘‘"’’ denotes bottom–up causal propagation. 4.4. Business metrics This research also proposes maps with generic strategic performance concepts. For example, the ‘‘Differentiation strategy’’ map reasons on the impact of strategic change to the competitive identity and financial status of the enterprise, the ‘‘Customers’ satisfaction’’ map reasons on the impact of customer appreciation of the HR improvement effort and consequent financial value of the enterprise, the ‘‘Productivity’’ map reasons on the impact of internal HR efficiency and organizational adequacy on the overall profitability of the enterprise and so on. 4.5. Infrastructure metrics This research may also integrate various maps consisting of technical concepts like centralization/decentralization of IT infrastructure, IS/IT effectiveness, etc. While the presentation of such concepts supports the completeness of the proposed methodology tool, they add little (if any at all) significance to the research activities presented by this paper. All the above-mentioned maps form a generic domain of FCMs. This knowledge domain serves as the basis of the proposed approach and can be modified to comply with the requirements of specific knowledge capturing exercises. For example, further maps in the technical category could relate concepts that relate the adequacy of the production cycle with the expected financial performance of the enterprise (Valiris & Glykas, 2000; Irani et al., 2002). 4.6. Assigning linguistic variables to FCM weights and concepts In order to define weight value of the association rules in an adaptive and dynamic manner, the following methodology is proposed. HR experts are asked to describe the interconnection influence of concepts using linguistic notions. Influence of one concept over another, is interpreted as a linguistic variable in the interval [1, 1]. Its term set T(influence) = {negatively very very high, negatively very high, negatively high, negatively medium, negatively low, negatively very low, negatively very very low, zero, positively very very low, positively very low, positively low, positively medium, positively high, positively very high, positively very very high}. This paper proposes a semantic rule M to be defined at this point. The above-mentioned terms are characterized by the fuzzy sets whose membership functions l are shown in Fig. 9. 2026 G. Xirogiannis et al. / Expert Systems with Applications 35 (2008) 2017–2031 µ µnvvh µnvh µnh µnm µnl µnvl µnvvl µz µpvvl µpvl µpl µpm µps µpvs 0.1 0.35 0.5 0.65 0.8 µpvvs 1 0.5 -1 -0.9 -0.8 -0.65 -0.5 -0.35 -0.2 -0.1 0 0.2 0.9 1 influence Fig. 9. Membership functions of linguistic variable influence. • M (zero) = the fuzzy set for ‘‘an influence close to 0’’ with membership function lz • M (positively very very low) = the fuzzy set for ‘‘influence close to 10%’’ with membership function lpvvl • M (positively very low) = the fuzzy set for ‘‘influence close to 20%’’ with membership function lpvl • M (positively low) = the fuzzy set for ‘‘influence close to 35%’’ with membership function lpl • M (positively medium) = the fuzzy set for ‘‘influence close to 50%’’ with membership function lpm • M (positively high) = the fuzzy set for ‘‘influence close to 65%’’ with membership function lph • M (positively very high) = the fuzzy set for ‘‘influence close to 80%’’ with membership function lpvh • M (positively very very high) = the fuzzy set for ‘‘influence close to 90%’’ with membership function lpvvh • Similarly for membership functions with negative influence. The membership functions are not of the same size since it is desirable to have finer distinction between grades in the lower and higher end of the influence scale. As an example, three managers/HR experts proposed different linguistic weights for the interconnection Wij from concept Ci to concept Cj: (a) positively very low (b) positively strong (c) positively very strong. The three suggested linguistics are integrated using a sum combination method and then the defuzzification method of center of gravity (CoG) is used to produce a weight wij = 0.549 in the interval [1, 1]. This approach has the advantage that experts do not have to assign numerical causality weights but to describe the degree of causality. The same semantic rule and term set can be used to define coefficients k1, k2 as well as to assign values to concepts. 5. Preliminary experiments 5.1. The nature of the experiments Two informal experiments were conducted by utilizing decision metrics from actual HRM exercises in two major financial sector enterprises. For each experiment, a team of experts was engaged to: • provide linguistic variables for the causal weights, the concept values and the coefficients values to let the FCM algorithm reason about the impact of potential HR performance changes, • provide their independent expert estimates (using similar linguistic variables) of the impact of HR practices to the overall business model. In both cases, the proposed tool iterated a subset of approximately 120 concepts spread over eight sample hierarchical maps in order to calculate their equilibrium values. 5.2. Discussion 5.2.1. Theoretical value Various aspects of the proposed modeling technique are now commented on. As far as the theoretical value is concerned, the proposed mechanism extends previous research attempts by: • allowing fuzzy node and weight definitions in the cognitive maps, • introducing a specific interpretation mechanism of linguistic variables to fuzzy sets, • proposing an updated FCM algorithm to suit better the HRM domain, • supporting node linking to establish map hierarchies and dynamic map selection during simulation, • concentrating on the actual HR activity and the associated HR model, • allowing dynamic map decomposition and reconfiguration, • integrating three modes of FCM simulation, namely bivalent (with a crisp activation set {0, 1}), trivalent (with a crisp activation set {1, 0, 1}) and linear (with an activation set in the fuzzy interval [1, . . . , 1]). • supporting the user with interface windows when loops, cycles and node conflicts are identified. G. Xirogiannis et al. / Expert Systems with Applications 35 (2008) 2017–2031 5.2.2. Practical value As far as the practical value of the proposed mechanism is concerned: • When compared to the expert estimates, the mechanism does not provide fundamentally different ‘‘diagnosis’’. On the contrary, it provides reasonably good approximations of the expert estimates. • The justification of the ‘‘diagnosis’’ (essentially the metrics decomposition) proved helpful in comprehending the sequence of complex decision interactions (essentially the performance roadmap). • The concept-based decision approach did not restrict the interpretation of the impact of HRM practices to the overall business model and shareholder value. The fuzzy interpretation of concept and weight values served as indications rather than precise arithmetic calculations. • The hierarchical (or partial) traversal of performance metrics improved the distributed decision monitoring throughout the organizational levels of the enterprise and stipulated targeted communication of the associated HR performance. • The adaptive nature of this modeling technique is also worth mentioning. For instance: – the approach can portray either a holistic or a scalable view to shareholder value modeling to comply with the decision modeling approach of the enterprise, – knowledge categorization is compatible with either the ‘‘process view’’ or the ‘‘organizational view’’ of the enterprise, 2027 – hierarchical (and/or decentralized) composition/ decomposition of knowledge concepts couples effectively with the hierarchical (and/or decentralized) structures of an enterprise, – skeleton decision maps accompanied by business cases (scenarios) improve the flexibility of the tool by allowing the user to generate alternative performance-based decision roadmaps with little extra effort, – decision maps can expand/retract on demand allowing the user to utilize only the necessary subset of performance concepts, – decision maps are dynamic, further performance concepts may be added to encapsulate further of knowledge interactions. 5.2.3. Added value Having established the theoretical and practical value of the proposed mechanism, it is useful to discuss also the added value of incorporating such a mechanism into HRM exercises. It is the belief of this paper that the resulting tool provides real value to the principle beneficiaries and stakeholders of such exercises. For example: • The mechanism eases significantly the complexity of deriving performance-based decisions at a strategic level. Informal experiments indicated that the time required by experts to estimate manually the extensive impact of major changes to HRM practices to the shareholder value could pose as a considerable overhead. On the other hand the elapsed time for automated decision Fig. 10. Deployment FCMs. 2028 G. Xirogiannis et al. / Expert Systems with Applications 35 (2008) 2017–2031 support using FCM can be insignificant, once the map hierarchies have been set up. • To extend further this syllogism, realistic decision support should involve continuous argument of change options (e.g. application of best practices, alternative HR practices, etc.) until an equilibrium solution has been agreed upon. Informal discussions with the principle beneficiaries of the two financial sector enterprises revealed that the proposed support can reduce signifi- cantly the impact estimation overheads, letting the stakeholders focus on the actual HRM exercise while exploring in depth all alternatives and controlling effectively major change initiatives. • The proposed modeling mechanism can also assist performance evaluation of the enterprise on a regular basis. FCMs may serve as a back end to performance scorecards (Bourne et al., 2000; Kaplan & Norton, 1996; Kaplan & Norton, 2001) to provide holistic strategic performance Fig. 11. Benefits FCMs. Fig. 12. Training FCMs. G. Xirogiannis et al. / Expert Systems with Applications 35 (2008) 2017–2031 evaluation and management. However a detailed analysis of this extension falls out of the scope of this paper. Summarizing, preliminary experimental results showed that FCM-based management of shareholder value can be effective and realistic. This is considered to be a major contribution of the proposed modeling methodology to actual decision-making exercises. Moreover, ex ante reasoning of the impact of HRM practices to the overall shareholder model can be estimated with a moderate start-up effort. Scenario building on the other hand can be trivial once the skeleton FCMs have been established. 6. Conclusion This paper presented an intelligent decision-modeling technique, which assessed the impact of contemporary 2029 HRM practices to the shareholder value. This research addressed the problem of establishing HRM interrelationships in order to drive the overall business performance from the shareholder value perspective. The proposed methodology offered an alternative approach to HRM based on shareholder value modeling. The underlying research addressed the problem of performance capture and representation in order to provide an implementation of an integrated HRM framework. The proposed methodology utilized the fuzzy causal characteristics of FCMs to generate a hierarchical and dynamic network of interconnected HR performance decision concepts. Also, generic maps that supplemented the decision process presented a roadmap for integrating hierarchical FCMs into HR performance management techniques. The application of FCMs as an intelligent (though soft) modeler of HR knowledge is believed to be novel. Fig. 13. Performance benchmarking FCMs. Fig. 14. HR practice high-level FCM. 2030 G. Xirogiannis et al. / Expert Systems with Applications 35 (2008) 2017–2031 Fig. 15. Integrated model FCM. This paper extended typical FCM algorithms to adapt to the distributed nature of typical HR activities. Also, this research adopted a new qualitative approach to interpret fuzzy linguistic variables to weight and concept values in order to support further the soft computing characteristics of the technique. It is the belief of this paper that the intelligent reasoning capabilities enhanced considerably the usefulness of the mechanism while reducing the effort of identifying quantitative impact measurements. The proposed mechanism should not be regarded only as an effective decision modeling support tool. 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