رویکرد چند گانه به ایجاد نقشه های عملکرد علت و معلولی از دانش تخصصی

This paper describes a multi-method approach to building the foundations of a causal performance measurement model. Such models have received considerable attention in the management accounting literature in recent years. Conventional models, such as the balanced scorecard commence with the strategic understanding of top management which is then translated into operational measures at lower levels. In contrast, this study proposes methods of performance mapping that draw on the knowledge of experts who control core-operating tasks. Causal knowledge is elicited from individuals who through their experience and training have encoded relational or causal knowledge about complex systems; that is, they understand how things fit and work together, although they might not have articulated that knowledge. Because no single method for eliciting causal performance maps dominates the literature, the study triangulates three methods of deriving a map of causally linked key success factors (KSFs)—a computerized analysis, an ethnographic analysis and an interactive mapping by expert participants. The study's primary contribution is the development and illustration of an approach to building performance models in management control settings where expert knowledge workers perform complex processes, the outcomes of which are difficult to quantify. The study's secondary contribution is the triangulation of multiple qualitative methods to enhance the validity of performance model development. This approach demonstrates (1) the use of cognitive mapping to extract tacit knowledge from employees in knowledge-intensive organizations; (2) the extensive array of performance-relevant variables that arises from such mapping, and (3) the potential to use the resulting causal performance map as a comprehensive, articulated basis for developing a performance measurement system. The approach used in this study for developing a causal performance map is adaptable to management control of other knowledge-intensive organizations.

Performance measurement systems are an integral part of an organization's management control system (Hemmer, 1998 and Otley and Fakiolas, 2000). Kaplan and Norton (1996) were among the first to articulate the link between performance measurement and the firm's production function. The distinguishing characteristic of the balanced scorecard (BSC) is that it represents a model of performance. It articulates the links between leading inputs (human and physical), processes, and lagging outcomes and focuses on the importance of managing these components to achieve the organization's strategic priorities. Others also have described similar models in the management accounting literature (Otley, 1999, Epstein et al., 2000 and Ittner and Larcker, 2001). A key assumption in these performance measurement models is that the production process is known and can be modeled. It is also assumed that an organization's strategy can be articulated and communicated unambiguously throughout the organization. While research has examined implementations of BSCs (Malina and Selto, 2001 and Kasurinen, 2002) and assessed the causal links between leading and lagging indicators (Rucci et al., 1998 and Malina and Selto, 2004) it is silent on how key success factors (KSFs) and the relations among them are articulated. The performance measurement models reported in practice appear to be the result of (a) top-down imposition of desired KSFs and interrelations (e.g., Malina and Selto, 2001), (b) interviews of top or divisional managers (e.g., Ambrosini and Bowman, 2002), and (c) data-mining of existing archival sources (e.g., Porac et al., 2002 and Rucci et al., 1998). Clearly, all are feasible methods to gather performance-relevant data, but all are somewhat flawed. Building a performance measurement model based solely on data currently available might create gaps in KSFs. Data mining relies on conveniently available data that might be unrelated to actual drivers of system performance or what should be but has not been measured. Top-down models might not reflect know-how, routines, and capabilities that really drive performance (e.g., Huff and Jenkins, 2002). Top management might understand the organization's intended strategy and policies but might be ignorant of or unwilling to discuss actual observed system behavior (e.g., Morecroft and Sterman, 1994 and Forrester, 1994). An alternative approach is to build a performance model independent of current performance measurement practice, consisting of KSFs and their relationships with valued organizational outcomes. This method provides a more complete foundation for performance measurement by identifying all KSFs, some of which may not be currently captured by performance measurement protocols. Once KSFs are determined, then currently available performance measures can be compared with the identified KSFs. However, the question of how to identify the KSFs and the relations among them remains unclear. This paper describes a multi-method approach for the derivation of a performance model consisting of KSFs and their interrelations. We use this approach in a knowledge-based organization. In such organizations performance measurement problems can be particularly acute. Management control of organizations that compete based on knowledge is challenging as the knowledge crucial to value creation resides with experts (Albert and Bradley, 1997). Attempts to derive performance measurement models which reflect the firm's production function is problematic because knowledge of KSFs and the processes that drive organizational outcomes is expert knowledge located at core operating levels, not general knowledge known to top management (Forrester, 1994). We use three methods of cognitive mapping to capture the “map” of expert work. We allow the experts to explain what they do, the processes associated with their work, and the facets of their behavior that go unspoken in the organization. From this we learn about the relations between actions and outcomes and develop a causal performance map that represents experts’ understanding of organizational performance. This paper reports on the first stage of a longitudinal study to develop and implement a performance-based management control system in a clinical program of a large, teaching hospital. A causal performance map is a prerequisite and serves as the building block for the design of the organization's performance measurement system. The paper is structured as follows. Section 2 synthesizes the literature that forms the basis of the study. Next, three methods for eliciting causal maps are described. The research site, methodology, and the resulting causal performance maps are then described. Finally, the paper presents conclusions and future research extensions.

We use interviews as a common source of mental data but triangulate three independent approaches to the analysis of the data to enhance the validity of the causal performance map. Most qualitative studies rely on one of several available methods, but a single qualitative method might not identify all of the organization's important performance factors and causal relations. This study triangulates the results of (1) computer-assisted mapping, (2) ethnographic mapping, and (3) interactive system mapping by participants. The integration of the three qualitative approaches leads to a causal map of system performance. To our knowledge, no previous study has triangulated methods as a means of validating the causal modeling of qualitative data. None of the three methods used in this study, by itself, revealed the complexities of activities and their relations that are reflected in the composite causal performance map in Fig. 5. Each method added information and cross-validation to the mapping effort. The computer-assisted approach might offer comfort to more quantitatively oriented researchers that the ultimate map has a relatively objective core. A limitation of the computer analysis is that it uses an unweighted numerical threshold to identify the “core map” of possible causal links and does not consider expressed or perceived importance of causal relations beyond simple counting. Both the ethnographic mapping (using researchers’ perceptions of causality) and the participant mapping (using participants’ perceptions of causality) add this crucial qualitative, although more subjective, input. To ensure an objective core map, perhaps, at minimum, a computer-generated analysis should be used in addition to either the ethnographic or interactive method. The study's primary contribution is the development and illustration of an approach to building performance management models in organizational settings where expert knowledge workers perform complex processes, the outcomes of which are difficult to quantify. There has been little research addressing the complexities of performance management in such settings. Mechanisms for understanding the sources of value creation and the processes that drive organizational outcomes are a critical foundation for research in performance management in knowledge-based organizations. The approach presented here demonstrates (1) the use of cognitive mapping to extract tacit knowledge from employees in knowledge-intensive firms; (2) the extensive array of performance-relevant variables that arises from such cognitive mapping, and (3) the potential to use the resulting causal performance map as a comprehensive, articulated basis for developing a set of performance measures. While this approach for developing a causal performance map is adaptable to other knowledge-intensive organizations, the potential contribution to the hospital sector alone is significant. This is a setting in which effective performance management of health care professionals is problematic as they often do not view organizational goals as consistent with the achievement of their own professional related goals. A performance measurement system could be an effective mechanism to achieve goal congruence in this setting but these systems often fail as they do not capture the processes and outcomes relevant to core knowledge workers. This study presents a general qualitative approach to identifying a plausible and coherent causal performance map in a knowledge-intensive organization. Identifying this map is the first step in establishing an effective performance measurement system in these types of organizations. Developing the causal performance map enables management to learn about the activities and processes necessary to achieve a well functioning clinical program. It serves as a viable alternative in an organization with dispersed knowledge (e.g., Widener, 2004) to a top-down imposed performance measurement system or a data-driven system. However, Fig. 5 is a highly complex map of performance drivers. It is unlikely to be feasible or desirable to implement a performance measurement system that includes performance measures for all of the KSFs identified in Fig. 5. Sufficient data usually are not readily available and the use of excessive performance measurement can lead to information overload or selective focus on the most easily achievable measures (Feltham and Xie, 1994). The selection of critical KSFs needs to be performed with considerable care. A comprehensive performance model opens the way for top management to develop performance management protocols that effectively control key value drivers. This can be approached in several ways. First, measurable drivers are identifiable in the performance map (e.g., recruitment, patient flow). Existing data sources can be mined to test statistically the relative importance of these causal factors in driving outcomes. Second, potentially measurable drivers are identifiable. These factors are identified as key performance drivers, but are currently not measured within the organization (e.g., employee satisfaction). These are candidates for measurement depending on cost/benefit and feasibility assessment. Finally, key drivers that are not readily captured by conventional measurement protocols are also identifiable (e.g., empowerment). This prompts management to think beyond measurement as a means of achieving desired results. For example, people-focused measures might be desirable because of their salience or attention-directing qualities, but if the performance driver is empowerment then this might be best controlled by mechanisms other than measurement (e.g., Ouchi, 1977 and Simons, 2000). One needs to resist the temptation to use whatever data are at hand. Data-driven development of a performance measurement system can ignore performance drivers, such as staff empowerment and satisfaction that are critical to success but are currently immeasurable. However, given an understanding of the relations among KSF within each major component, it might be possible to select a reliable proxy for the KSF related to that construct. For example, sick leave statistics (which are relatively easily obtained from an organization's HR system) might be used to reflect staff satisfaction.