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|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|402||2012||18 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Evaluation and Program Planning, Volume 35, Issue 3, August 2012, Pages 303–320
This study presents a structural evaluation methodology to link key performance indicators (KPIs) into a strategy map of the balanced scorecard (BSC) for banking institutions. Corresponding with the four BSC perspectives (finance, customer, internal business process, and learning and growth), the most important evaluation indicators of banking performance are synthesized from the relevant literature and screened by a committee of experts. The Decision Making Trial and Evaluation Laboratory (DEMATEL) method, a multiple criteria analysis tool, is then employed to determine the causal relationships between the KPIs, to identify the critical central and influential factors, and to establish a visualized strategy map with logical links to improve banking performance. An empirical application is provided as an example. According to the expert evaluations, the three most essential KPIs for banking performance are customer satisfaction, sales performance, and customer retention rate. The DEMATEL results demonstrate a clear road map to assist management in prioritizing the performance indicators and in focusing attention on the strategy-related activities of the crucial indicators. According to the constructed strategy map, management could better invest limited resources in the areas that need improvement most. Although these strategy maps of the BSC are not universal, the research results show that the presented approach is an objective and feasible way to construct strategy maps more justifiably. The proposed framework can be applicable to institutions in other industries as well.
As a result of the global financial crises beginning in mid-2007, international stock markets have sharply crashed, and numerous enterprises have collapsed or have been bought out (Shah, 2009). Financial institutions in particular have encountered more competitive challenges worldwide during the chain effects of the financial “tsunami.” It is therefore imperative that banking institutions enhance their competitive advantages in order to outperform the numerous competitors in the industry. These institutions must place more emphasis on improving internal operational performance (Davis and Albright, 2004 and Littler et al., 2000). Banking institutions must develop an effective way to align their strategies with corporate goals on the basis of performance analyses. The structural analysis of an evaluation model that links strategic objects as effective improvement paths becomes a critical issue for banking institutions if they are to sustain their competitive advantages. Several analysis models have been applied to organizational performance measurement for years (e.g., ratio analysis, total production analysis, regression analysis, Delphi analysis, balanced scorecard (BSC), analytic hierarchical process (AHP), and data envelopment analysis (DEA)). These approaches vary regarding their basic concepts, aims, advantages, and disadvantages (Dessler, 2000). The analytical methods or tools chosen for performance analysis by management depend on the situation and the type of organization. Nevertheless, most successful organizations have common characteristics, including specific visions, positive actions, and effective methods of performance measurement (PwC, 2009a and PwC, 2009b). Moreover, performance management is most effective when objectives beyond operational variables are incorporated logically, with an understanding of strategic effectiveness enabled by the appropriate analytical systems (Barlas and Yasarcan, 2006 and Wright and Taylor, 2001). Thus, the strategic steps aligning an organization's objectives with a corporation's specific visions are most important for organizations to achieve effective performance management (Schalock and Bonham, 2003 and Sridharan et al., 2007). Organizations can efficiently reach their goals by prioritizing their actions in order to fulfill corporate visions and by incorporating effective performance management. The BSC is an adequate evaluation methodology for achieving these goals (Davis & Albright, 2004). The BSC stresses financial and nonfinancial aspects, long-term and short-term strategies, and internal and external business measures (Kaplan & Norton, 1992). Through the BSC, management can not only communicate well with their employees but also control the progress of strategic development in order to improve organizational performance and to increase competitiveness. Because of the intangible nature of the products and services provided by banking institutions, one cannot easily measure the efficiency and competitiveness of banking products and services. Most available research has focused on gauging the productivity and efficiency of the banking industry by measuring outputs, costs, and performance (Kosmidou, Pasiouras, Doumpos, & Zopounidis, 2006). Moreover, many of the studies only use financial ratios to evaluate banking performance. Most of the traditional performance measures in banking focus on external financial reporting (Hepworth, 1998). However, focusing solely on these external reports has kept banks from long-term learning, growing, innovating, and planning (Chia and Hoon, 2000, Davis and Albright, 2004 and Ko and Lee, 2000). Furthermore, banks need to completely reassess their performance measurement in order to adapt to constantly changing customer needs and requirements. To achieve more effective performance, banks must align their goals with those of their clients’ services (Nist, 1996). Banking institutions as well as other organizations have widely applied the BSC not only as the key to achieving a successful execution of strategic plans (Frigo, Pustorino, & Krull, 2000) but also for strategic development and performance measurement (Aranda and Arellano, 2010, Banker et al., 2004 and Littler et al., 2000). A number of studies have researched the BSC implementation (Aranda and Arellano, 2010, Banker et al., 2004, Bhagwat and Sharma, 2007, Chan et al., 2002, Chen et al., 2008, Da Silva et al., 2005, Fernandes et al., 2006, Hsu, 2005, Kaplan and Norton, 1992, Kaplan and Norton, 1996a, Kaplan and Norton, 1996b, Littler et al., 2000, McNamara and Mong, 2005, Mearns and Havold, 2003, Norton et al., 1997 and Wu et al., 2009) and strategy maps (Kaplan and Norton, 2004a and Kaplan and Norton, 2004b) of the banking industry. However, most of the BSC-related studies have focused mainly on performance measures; only a few papers have examined the creation of a mechanism that distinguishes causal relationships between key performance indicators (KPIs) for the purposes of strategy implementation. To enhance banking performance, BSCs should be incorporated into performance measurement models not only through properly screening effective evaluation indicators for performance measurement but also through constructing feasible strategy maps motivated toward the development of improvement programs (Chia and Hoon, 2000, Schalock and Bonham, 2003, Sridharan et al., 2007 and Wu et al., 2011). Of the related studies, almost none purposely presents a plan for the construction of strategy maps; rather, these studies mainly focus on the generic framework of the four BSC perspectives for performance measurement (Jassbi, Mohamadnejad, & Nasrollahzadeh, 2011). Strategy mapping is the most important procedure in building a BSC system since the strategy map can be viewed as the causality of hypothesis between strategic objectives (measured by KPIs) in the main structure of a BSC system (Kaplan & Norton, 2004a). Therefore, establishing strategy maps with clearly causal/logical links leads to the establishment of strategic pathways throughout the organization (Evans, 2007). However, numerous companies dilute the efforts of their BSC systems as a result of basic mistakes in mapping (Makhijani & Creelman, 2008). In addition, there is a lack of the articulation of the cause-and-effect relationships between some of the suggested areas of measurement in the BSC (Malina et al., 2007, Malmi, 2001, Nørreklit, 2000 and Nørreklit, 2003). Although Thakkar, Deshmukh, Gupta, and Shankar (2007) have proposed an ISM model for the connection of strategic objectives, only causal directions are taken into account. Two other BSC-related studies by Tseng (2010) and Jassbi et al. (2011) use the Decision Making Trial and Evaluation Laboratory (DEMATEL) to build strategy maps, but these studies categorize performance indicators into “cause groups” and “effect groups,” with no in-depth analyses of the complex interactive relationships among indicators. As a result, our proposed model of the establishment of strategy maps, which takes into consideration the impact (including both influential directions and strengths) of KPIs, can fill the apparent gaps in the literature (Malina et al., 2007, Malmi, 2001, Nørreklit, 2000 and Nørreklit, 2003). In other words, the main theme of the current study is to propose a methodology to establish the BSC strategy map and provide profound analysis of the complicated interactive relationships (influential directions and strengths) among the KPIs. Therefore, the term “strategy” referred by the strategy map here, is specifically defined the “logical links” (causal relationships) among the KPIs, demonstrating the central KPIs and the prioritization of strategic steps linked by the KPIs. Thus, according to the four perspectives of the BSC, the DEMATEL method (Gabus and Fontela, 1972 and Gabus and Fontela, 1973) is proposed as a tool with which to scrutinize the cause-and-effect relationships between banking performance indicators in order to establish strategy maps. The purpose of this research is as follows: (1) to organize suitable KPIs for the evaluation of banking performance based on the BSC perspectives; (2) to use the DEMATEL technique to explore the complex causal relationships among KPIs and to identify the critical central indicators and effective prioritization of the strategic steps in order to construct the strategy map for banking performance improvements; and (3) to provide suggestions from the analytical results and references for the management of associated organizations as well as for future research. The remainder of this paper is organized as follows: the literature related to banking performance measurement is reviewed in Section 2. In Section 3, the concepts of BSC and strategy maps are introduced. The proposed framework of constructing a strategy map by the DEMATEL method is described in Section 4. Section 5 illustrates an empirical example of a banking strategy map, including the selection of the indicators of BSC performance measurement, the construction of the strategy map, and the resulting analyses and discussions. Finally, some of the important managerial implications and suggestions for future research are proposed in Section 6.
نتیجه گیری انگلیسی
The study has contributed to providing decision makers with a systematic approach for establishing a visual strategy map with a consideration of the intricate causal relationships among KPIs along with in-depth analyses of important strategic improvement paths. As stated previously, the four perspectives of the BSC serve merely as a template, and the relevant measures are not evident from this template alone. In other words, the specifics of BSC vary from organization to organization, and the strategic processes are situation dependent. Since each organization is unique, it should have its own way of choosing different measures (i.e., performance indicators) to sufficiently reflect its performance. Nevertheless, the basic knowledge of BSC implementation can be generic. Therefore, the BSC strategy map construction framework proposed in this research would be a useful and valuable reference for other organizations. 6.1. Managerial implications In this research, strategic analysis is performed to create logical links between the KPIs based on the content of the BSC evaluation criteria that are most appropriate for banking performance. By synthesizing the relevant experts’ opinions, the DEMATEL method was used to determine the causal relationships and strengths of influence among the KPIs to establish a strategy map. The results of this prioritization of the strategic steps indicate a path for management to better invest resources in the aspects needing improvement most. Compared with the conventional method of devising strategy maps subjectively, the DEMATEL is a more logical approach to disclose causal relationships among the KPIs. In other words, the complex cause-and-effect relationships between the KPIs displayed by the DEMATEL are more rational and provide a clearer road map to help management choose the crucial indicators (including the main cause-factors and effect-factors) and focus on the strategy-related activities. However, numerous causes exist related to any specific effect that may also have influence on others (causes or effects). The use of “cause and effect” analysis helps to identify the mix of possible improvement paths (linked directly and/or indirectly by KPIs) for the organization's performance in all of the four BSC perspectives. Therefore, the proposed strategy map with the relative influential strengths of KPIs can provide a reference of priority for management in determining strategic improvement paths. Moreover, in addition to weighing the influence among KPIs, management is advised to link up the organization's strategies with its competencies and to further transform the strategies into significant tasks and results-oriented program services and supports (Hamel and Prahalad, 1996a and Schalock and Bonham, 2003). According to our research findings, some of the important managerial implications are summarized as follows. First, based on the strategy maps systematically constructed here, the DEMATEL method can provide feasible references for the prioritization of strategic steps in practice since the causal links between all the KPIs are logically exposed. Especially under constraints of limited time and resources, focusing on the vital few indicators with stronger influential degree among these KPIs would be useful as managers’ first concern. For example, referring to the strategy map developed by the case bank, several leading indicators (performance drivers) have influence specifically on the one lagging indicator (outcome measure), namely, “Customer satisfaction,” which is a main effect-factor with a relatively lower priority. This result implies that as customer complaint, sales performance, and management performance improve, customer satisfaction increases. That is, management can take customer satisfaction as a major lagging indicator (outcome measure) for banking performance measurement. In addition, for the case bank, depending on its current core competencies (e.g., workforce, skills, and technologies), the strategy implementation (improvement path) for accomplishing objectives may consist of planning better management mechanisms (e.g., effective personal appraisal system, sound compensation system, and attractive benefit programs) in order to increase employee stability, reduce customer complaints, and achieve higher customer satisfaction. Second, management should pay more attention to the interdependences (feedback relationships) among the KPIs, since the influence of feedback relationships can be that of positive reinforcement among the KPIs. For example, in terms of cause-and-effect relationships, enhancing management performance is the first priority for a bank to improve its sales performance. Moreover, higher sales performance can drive the retention of more loyal customers, resulting in increased customer satisfaction. In addition, the results show that the first priority for a bank to improve its “Learning and growth” perspective is employee stability. It is suggested through our results that management can decrease employee turnover rate by implementing relevant policies (e.g., professional training, benefit system) to satisfy employees and increase employee retention. Consequently, with higher personnel stability, organizational competence is also enhanced. Third, the results reveal that “Customer satisfaction,” “Sales performance,” and “Customer retention rate” are the three most essential evaluation indicators of banking performance. Two out of these three critical indicators, “Customer satisfaction” and “Customer retention rate,” are effect-factors and belong to the customer perspective of the BSC. Thus, we can conclude that, instead of the financial measures generally used by the traditional BSC implementations as final outcome measures shown in the top (financial perspective) of the basic template of the strategy map illustrated by Kaplan and Norton (2004a), nonfinancial measures, in particular those within the customer's perspective, may be more effectively emphasized by the service sector as the foremost outcome measures. In other words, for banking institutions, setting up the strategic objectives of strategy maps should be driven by a customer-orientation and should take the customer-related indicators as final outcome measures. 6.2. Lessons learned In this research, since it possesses advantages over other methods, the DEMATEL was used to investigate the interactive relationships among KPIs for the constructing of the strategy map of a banking institution. In comparison with traditional SEM, the DEMATEL method uses the knowledge of experts to lay out the structural model of a system (i.e., performance measurement system) in order to determine the casual relationships among KPIs and requires no pre-hypotheses and large-data verifications. Moreover, as noted earlier, compared with the ISM approach for linking up KPIs by merely taking into account causal directions, the DEMATEL method not only helps us to visualize the cause-and-effect relationships among KPIs through causal diagrams but also indicates the strengths of influence among KPIs. Moreover, based on the DEMATEL analysis, the influential directions and strengths among the KPIs can be used to identify critical KPIs as well as to prioritize alternatives (logical links) in decision making. However, there are some limitations of the current study. First, in the DEMATEL questionnaire survey, like all the other professional questionnaires, responses are necessarily limited to human subjective consciousness because of the nature of decision making reflected in the subjective ability of human beings. Second, it is inevasible that forming an expert panel will result in a bias in the selection of indicators. Third, the subjective judgments of related experts about the different BSC perspectives may vary from each other. To reduce the variation of opinions, in this study, the results of the DEMATEL analysis, which is derived from an average of the inputs from the experts, were further confirmed by interviews with the committees of experts as part of an attempt to ensure consensus through a more complex perspective. It is advisable to use common techniques (e.g., Delphi method, focus groups, and nominal group techniques) and any other effective communication tools to help reach a consensus (Aranda and Arellano, 2010 and Thakkar et al., 2007). Fourth, the selected KPIs with respect to the BSC that were adopted by the case bank may not be suitable to other institutions because KPIs need to be derived from an organization's vision, mission, and strategic objectives, and this distinction is not the main focus of the presented approach. Therefore, necessary precautions should be taken about the applicability of the results to other sectors/situations. In addition, for the details of the important concept, architecture, and process involved in the BSC development, it is recommended to refer to relevant prior studies (e.g., Littler et al., 2000, Meyer and Markiewicz, 1997 and Thakkar et al., 2007). Furthermore, as years of debate, quantitative data or quantitative analysis is no guarantee of rigorous research. Nevertheless, the DEMATEL method was adopted in this study. This systematic approach involves quantitative data to analyze both the influential directions and strengths of the logical links among the KPIs; therefore, it is somehow more “objective” than the “subjective” measurements acquired only from an expert group that assigns preferences between the indicators by rules of thumb. In addition, a follow up qualitative study with an in-depth analysis might help to minimize bias and therefore assist in achieving reliable results. 6.3. Future research In summary, according to the conclusions and limitations of this study, the following suggestions are drawn to aid future research. First, since no one performance indicator fits all scenarios, future studies are suggested to tailor performance indicators to meet the organization's overall goals as well as the objectives of each individual unit. Second, the results reveal that the KPIs of the BSC perspectives may not be mutually exclusive. That is, there exists some degree of interdependence among the KPIs. Other analytical techniques (e.g., fuzzy integral, Analytic Network Process) can be employed to solve the interactive and feedback relationships between the indicators and to further explore the relative importance among the KPIs. Finally, more cases and empirical studies are recommended as tools to validate the usefulness of the proposed model of establishing strategy maps in depth.