Decision Support for Strategy Control Andrzej Ceglowski* Leonid Churilov** School of Business Systems Monash University, Melbourne, Australia *Email:
[email protected] **Email:
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Abstract This paper presents a decision support approach that combines multiple criteria concepts with optimisation methods to assist with exploration of alternatives in strategic control. The contributions of this paper are the novel way in which different strategies may be characterised and objectives quantified, and the generation of “good” combinations of alternatives, rather than mere ranking of alternatives that is standard practice in most decision support methods. An illustrative example is provided. The paper describes how the approach can be used in the comparison of different strategies, identification of effective and efficient courses of action and recognition of emergent opportunities. Keywords MCDM, Multicriteria decision aid, support, strategy, strategic control, goal programming
1. INTRODUCTION Traditional multiple criteria decision analysis (MCDA) seeks to explore best courses of action by bringing them to an equitable frame of evaluation. Typically, these courses of action, or alternatives, achieve levels of preference or scores on a finite number of criteria, with the criteria forming a basis of comparison (Lootsma 1999; Belton and Stewart 2002) The preferences or scores are accumulated across criteria for each alternative in order to illuminate the most preferred. If several alternatives are required, rather than a single best alternative, a “top of the stack” approach is used, with little consideration of efficient combinations of alternatives. As a contrast, consider a real-life situation that cannot be resolved by the implementation of a single activity, but instead requires several simultaneous activities. Take for example an emergency situation of a truck turning into the road in front of a moving motor vehicle. Several alternative courses of action present themselves to the driver of the motor vehicle. Braking, swerving, accelerating and gearing-down are all possible alternatives, but the optimal solution is probably a combination of these. Maybe an accident can be avoided by swerving with simultaneous acceleration to bypass the danger, or possibly the combination of gearing-down, braking and swerving are a better course of action. Traditional multiple criteria methods would rank the individual alternatives, possibly using such criteria as “time and space available”, “state of road”, and “chracteristics of vehicle”. This analysis would neglect to explore which combinations were most effective, unless an exhaustive list of all possible permutations were supplied as alternatives. The analysis would be further complicated when extent of implementation is included. Does one brake hard while swerving sharply or are there more effective combinations? The above example shows that a single best alternative often gives insufficient information to provide the necessary decision support. A mechanism is required whereby combinations of alternatives and levels of implementation may be explored that lead most efficiently and effectively to the desired outcome. The objective of this paper to present a decision support approach that combines traditional MCDA with optimisation to yield information about “good” sets of alternatives, rather than mere ranking of alternatives. Instead of accumulating preference or score across criteria for each alternative, sets of alternatives are sought that simultaneously achieve overall objectives within criteria. The approach is presented within the context of a typical real-life problem – the strategic control of an organisation. The subsidiary objective of this paper is then “the presentation of a method for strategic control that supports determination of performance levels on operational-level actions”. Background to strategic control is provided in Section 2. This background leads directly to the framework for strategic control that is developed in Section 3. In Section 3.1, the novel concept of “strategy space” is 157
Decision Support for Strategy Control introduced. Movement through strategy space is presented in Section 3.2 and “navigation” in strategy space is discussed in Section 3.3. Despite the focus on strategic control, these sections may readily be translated in terms of other decisions situations where several alternatives are required subject to limited criteria. The method is illustrated in Section 4 with an example from strategy development done at one faculty of an Australian University. Higher education in Australia is an industry of significant size, with increasing emphasis on planning and strategy development (Hafner 1998; Cropper and Cook 2000), comparable to the commercial sector in having to satisfy numerous stakeholders and being pressured to reduce costs, so the example transfers readily to the commercial environment. The concept and method is reviewed critically in a discussion in Section 5, and conclusions drawn.
2. BACKGROUND Strategic control is the alteration of an organisation’s activities in response to a gap between actual results and planned goals (Asch 1989; Daft and Macintosh 1989). Organisations seek to measure success in following a strategy, preempt adverse outcomes and identify new opportunities. Strategic control assumes that a stable strategic path can be maintained by constantly addressing deviations from desired objectives (Hofer and Schendel 1978; Quinn 1980; Lorange et al. 1986; Asch 1989) or by modifying the original objectives in the light of current achievements (Argyris 1977). Lorange and Murphy (1984) identified a number of barriers to strategic control which they grouped into Systematic, Behavioural and Political classes. Systematic barriers arise from deficiencies in the control system or its management. Behavioural barriers result from managers’ limited ability to overcome familiar thought patterns and think in new ways. Political barriers stem from the complexity of resource allocation in a political environment. Control is an ongoing evaluation activity that informs strategic adjustments and is itself modified to cater for changes in the environment (Thompson and Strickland 2001). Determination of measures for long-term strategic control, rather than short-term operational control, is a challenging problem (Lorange and Murphy 1984; Nilsson and Rapp 1999) that has been addressed by positioning performance measures in a strategic context (Neely et al. 1995), or by relating quality programs to strategy (Ittner and Larcker 1997). Measures of operational level actions are usually aggregated within each functional area and included in management reports for monitoring purposes and the identification of interventions. Evaluations are based on management’s cognitive maps of cause and effect linkages (Huff and Jenkins 2002), rather than a formal or rigorous heuristic. Enterprise-wide computer systems have done little to extend support (Singh et al. 2002), although popular enterprise systems such as SAP and Baan now offer examination of internal and external data and automated dissemination of results (SAP AG 2001; Eichenbaum 2002). Financial measures have traditionally been used to compare actual against planned performance and thereby become the de facto strategic control mechanism (Flavel and Williams 1996). Financial measures are filtered through a hierarchy of management reports until a high level "strategic" view of the organisation is being reported (Chandler 1977; Institute of Chartered Accountants of Scotland. 1993; Maciariello and Kirby 1994). This emphasis on financial measures affects communication of non-financial aspects of strategy (Reed and Buckley 1988). Non-financial indicators have been combined with financial indicators in order to overcome this reliance on financial measures. Although such systems have been in use in the U.S. since the 1920’s (Epstein and Manzoni 1998), Kaplan and Norton of the Harvard Business School have extensively promoted the “Balanced Scorecard” (Kaplan and Norton 1992), especially in strategic planning (Calabro 2001; Kaplan and Norton 2001). The Balanced Scorecards are sets of documents reflecting financial and non-financial “Key Success Factors” for each business unit, integrated in a hierarchy (Epstein and Manzoni 1998). The Balanced Scorecard is widely used as a framework for strategy development and implementation, and as a convenient mechanism for the accumulation of measures to form a strategic view of organisational performance (Hope 2000). Numerous works have been published on the definition and selection of measures for strategic control (Kaplan and Atkinson 1989; Stewart 1991 and others; Kaplan 1994; Brignall and Modell 2000) and many formal strategic control systems exist (Maciariello and Kirby 1994; Langfield-Smith 1997). Recent approaches combine categories of control to give a more accurate judgment of the current situation of the organisation (Haas and Kleingeld 1999; Nilsson and Olve 2001). Unfortunately, there has been little extension of Snow’s work on measuring organisational strategy (Snow and Hambrick 1980), nor has there been provision of additional decision assistance for management beyond presentation of a more representative group of figures.
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Decision Support in an Uncertain and Complex World: The IFIP TC8/WG8.3 International Conference 2004 Information systems have been developed that assist with management of strategy by gathering data from multiple sources, often from within an enterprise system, and presenting them in an intuitive graphical manner (for instance Corvu Software’s Balanced Scorecard solutions for ERP systems (Balanced Scorecard Collaborative 2002), and SAP’s “Management Cockpit Room”(SAP AG 2001)). However, these systems fail to address whether the pattern of activities are contributing to intended strategic outcomes or are possibly leading to unintended outcomes. The systems merely plot actual results against objectives. Many OR/MS/DS models and tools have been proposed to support strategic decision making (See Clarke 1992; Dyson 2000 for overviews). Formal methods have been used for choice and control of policy (Brans et al. 1998), building a foundation for strategy (Keeney 1999a) and selection of portfolios during strategic planning (Saaty and Vargas 2001; Wind 2001). Colson and de Bruyn (1989) described use of multiple criteria methods for control of objectives for manpower optimisation in a managerial environment, and Brännback and Spronk (1997) described a decision support system in which critical success factors for the sector were continually monitored to assist with strategic decision making. Little decision support exists to help managers choose appropriate operational level interventions or for relating current performance to desired strategy. The method provided in this paper facilitates understanding of operational-level interactions and provides support for strategic control.
3. A STRATEGIC CONTROL DECISION SUPPORT SYSTEM Daellenbach (1994) named three components necessary for control of systems: (1) determining targets for the system to reach; (2) defining the system such that it is capable of reaching the targets; and (3) generating a means by which the system behaviour might be influenced. (Other authors have similar cycles. See, for instance, Lorange and Scott Morton 1974; Ouchi 1977; Todd 1977; Dunbar 1981). These components are expanded specifically for strategic control in the next three sections. 3.1 Target setting for strategic control Strategic planning involves the development of plans to achieve the objectives outlined in the vision (Wilson 1992) and it generally proceeds once an overall vision has been agreed for the organisation (Thompson and Strickland 2001). Functional areas decompose the strategic plans into operational-level actions with measurable targets, milestones and resource allocations (Flavel and Williams 1996). Targets may be expressed in terms of key strategic themes, as in the Balanced Scorecard (Kaplan and Norton 1996) or fundamental objectives, as in value-focused thinking (Keeney and Raiffa 1976; Keeney 1999a; Keeney 1999b). Each action will bring the organisation closer to one or more targets. It is intended that the total effect of all actions will be achievement of all targets. The targets may be considered criteria by which actions can be evaluated. If the targets can be expressed in terms of widely accepted concepts that have strategic meaning, then the strategic effect of the actions may be monitored. The financial, customer, internal activities and training perspectives common in Balanced Scorecard exercises may be used, but corporate values are also widely accepted and can form the basis of effective visions and strategies (Humble et al. 1994; Flavel and Williams 1996). Since corporate values “are seen as control devices that may be more effective than formal controls if they are the right values for the environment and if they are shared widely and deeply” (Maciariello and Kirby 1994 p viii), they provide convenient criteria for evaluating strategy. The corporate values can be weighted to reflect the preferences of the strategy team. The weighting of the corporate values can reflect the strategic vision of the organisation, as in Kaplan and Norton’s (2000) characterisation of Treacy and Wiersema’s (1993) three distinctive strategies (Table 1). Preferences or weights may be assigned to the corporate values to reflect both the current and desired strategic states. The process of agreeing and weighting corporate values for strategy definition offers a major opportunity for both “soft” and “hard” decision support. This issue will not be extensively addressed in this paper, apart from a brief reference in the illustrative example in Section 4.
Key strengths –better than competitors Skills commensurate with competitors
Customer Intimacy Service Relationships Brand
Product Leadership Time (convenience) Functionality Brand
Operational Excellence Price Time (convenience) Selection
Price Quality Time (convenience) Selection
Price Quality Service Relationships
Quality Service Relationships Brand
Table 1: Corporate values for Treacy and Wiersema’s strategies (Kaplan and Norton 2000)
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Decision Support for Strategy Control If the corporate values are viewed as axes of multi-dimensional “strategy space”, the current and desired states can be determined within this space and progress between the two states monitored. The weights associated with each corporate value define the organisation’s desired strategic direction in strategy space. The final destination in strategy space lies on this direction vector and acts as the target for the strategy control system. The determination of this target is discussed in more detail in Section 3.3, but it is first necessary to describe the control system so it is capable of reaching the target. 3.2 Defining the system capable of reaching the targets If care is taken to ensure that the corporate values are preferentially independent (Von Winterfeldt and Edwards 1986), they can comprise criteria, so the corporate values need to be reviewed to confirm that adjustment of level of preference in one will not affect the level of preference of another. Organisational activities can then be evaluated on contribution to each of the corporate values. The normalised contribution for each operational level action across criteria may be considered a direction component; and the level of achievement (performance) a magnitude component of a vector in strategy space. Since outcomes are the net effect of actions, the result of all actions may be approximated by the sum of their vectors in strategy space. The effect of actions accumulate so focusing on actions that contribute heavily to one corporate value will direct the organisation towards those criteria in strategy space. Direction and final position (cumulative magnitude) of actions can be altered by changing the actions (different actions can lead to different outcomes), or by changing levels of performance of actions. Thus the vectors of operational-level actions define a system capable of reaching any target in strategy space. Having described the system capable of reaching any target in strategy space it becomes a simple matter to formalize it. If each action is scored on contribution to corporate value criteria and the organisation's current performance on each of these actions evaluated, then the sum product of performance and contributions will result in the organisation’s position in strategy space, i.e. if there are: •
s actions for the organisation and
•
t corporate value criteria;
then joint listing of the contributions of each action on every corporate value will result in a t x s matrix, [A], with components aji where j = 1...t corporate value criteria and i = 1…s actions. If x ={x1, x2, x3… xs} are levels of the organisation's current performance on s actions (xi ≥ 0), then the organisation’s normalised state for each corporate value criterion over all actions is: s
∑
i =1
aji xi
t
/∑
k =1
s
∑
aji xi
(j=1,2,3…t)
i =1
(1)
Movement can be achieved by altering the levels of performance on actions or by altering the actions themselves. A mechanism for determining direction and extent of alterations is described in the next section. 3.3 Strategic control: Determining adjustments to the system Common strategic control decisions involve allocation of resources in response to results. Extra resources are often allocated where performance has been lower than expected, or actions are modified to overcome shortcomings in the current plan. These decisions may be viewed as assessments of performance on numerous actions. The level of complexity rapidly rises beyond our intuitive reasoning ability and some form of support for analysis is required. Goal programming and linear programming have long been used to assist with problems of this nature (Lee and Olsen 1999). Finding appropriate performance levels to (re)align the organisation with its desired strategy can be viewed as determination of efficient paths through strategy space. This may be formulated as the goal programming problem of finding levels of performance for each action that will efficiently deliver the organisation (sufficiently close) to its desired position in strategy space: •
Let ℝ+ denote the non-negative part of the real line, i.e. ℝ+ = [0;∞);
•
Let x ∊ ℝ+ be a sx1 vector of control variables to be found that determine the performance levels of actions i=1,2,3...s;
•
Let A = {aji } be the t xs matrix of scores for s actions (i=1,2,3...s) on t corporate values (j=1,2,3…t);
•
Let b ∊ ℝ+ be the t x1 vector of targets for goals for corporate value criteria (j=1,2,3...t);
s
t
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Decision Support in an Uncertain and Complex World: The IFIP TC8/WG8.3 International Conference 2004
•
t
t
With n ∊ ℝ+ negative deviations from the targets and p ∊ ℝ+ positive deviations from the targets;
the weighted goal programming (WGP) formulation for the problem becomes: Minimise
t
(2)
∑α j n j + β j p j
j =1
Such that
s ∑ a ji x i + n j − p j − b j = 0 i =1
(j=1,2,3...t)
(3)
There are three issues related to this goal programming formulation that need to be resolved: •
The values the control variables (x) may take on;
•
Targets for goals (b);
•
Values for αj and βj in the objective function (2).
The first issue is managed by restricting the control variables to the scale used for performance evaluation so that solutions may be related to current performance. The second issue, determination of targets for goals, is one of the primary difficulties in satisficing problems (Belton and Stewart 2002). Being able to set a target implies previous judgement of appropriate goals, bringing into question the need for evaluation. There is also an apparent paradox in setting lower, and easier to achieve, targets for criteria with low weights (i.e., low weighting of a criterion intuitively accords it less importance, while minimising negative deviation on goals often guarantees that these “easy” goals are overshot). This situation is entirely appropriate to the problem at hand, which seeks to accomplish all goals, not only ones associated with high weights, and over-achievement is not adverse. The reference point approach (Wierzbicki 1999), which first minimises under-achievement, then maximises over-achievement of certain goals may also be considered fitting for this problem. (Should some goals be considered much more important than others and statistically independent of them, then a lexicographic formulation to the goal programming problem may be suitable (Romero 1991)). In addressing the third issue, clarification of values for αj and βj in the objective function (2), it is important to remember that the objective is to ensure a certain minimum achievement in all corporate values, yet accept overshoot on some corporate values. Overshoot does not detract from competitive advantage (although it will consume resources that may be better directed). It is possible to weight deviations by assigning values to αj and βj to penalize deviations on certain goals. This will not be done here, since the weighting is considered inherent in setting the goal programming targets. Accordingly, αj and βj will be assigned values of one or zero only. Solutions can be explored by minimising all positive and negative deviations (the naïve formulation, where αj and βj are all 1), and then by optimising for each target in turn, while ensuring minimum achievement in all other targets (αf and βf are 1; αj and βj are zero; f ≠ j). Minimum levels of performance on actions can be ensured by constraining their control variables. This interactive exploration of decision space (Vanderpooten 1992) does not replace sensitivity analysis, but it does give insight to the susceptibility of solutions to perturbations in variables, and, consequently, insight to variation or error in setting those variables. A table of results can built up by accumulating solutions. The table will give insight to the patterns of efficient performance levels and so the interaction of activities. This will be clarified through an illustrative example.
4. ILLUSTRATIVE EXAMPLE 4.1 Target setting at the university Corporate values of Excellence, Scholarship, Innovation, Diversity, Collegiality and Community were agreed and extensively defined by the planning group at the university faculty at the start of the strategy development process (Appendix Table 1). These values may be used to characterise several strategic directions akin to those of Treacy and Wiersema (Table 2). Attempting to pursue all available strategies at once is likely to dilute efforts, stretch resources, and fail to express a competitive advantage (Wiersema and Treacy 1995 p42).
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Decision Support for Strategy Control Strategy Description
Relationship (Customer intimacy)
Innovation (Product leadership)
Lowest Price (Operational excellence)
Customer service always beats low cost
Commercialise new technologies quickly.
Deliver low cost generic products and services
Skills performed better than competitors
Excellence Diversity Community
Innovation Scholarship
Excellence Diversity Collegiality
Skills matching competitors
Scholarship Innovation Collegiality
Excellence Diversity Community Collegiality
Scholarship Innovation Community
Table 2: Three possible strategies for the faculty The “customer intimacy” strategy may be chosen as a sample strategy for the purposes of further illustration. The corporate value that seems best, relative to the objective, may be “Community”, with its focus on favourable career prospects for alumni, communication with stakeholders and community in general, and high profile with the community. The corporate value that is worst with respect to the objective is probably “Collegiality”, since its focus is more on networking within the academic environment rather than building strong links with alumni, government agencies, industry and other external stakeholders. The remaining corporate values can now be ranked and given weights using Swing Weighting (Von Winterfeldt and Edwards 1986 p286) as illustrated in Appendix Table 2. 4.2 Defining the strategy control system for the Faculty A set of strategic actions agreed by the faculty planning group during the planning process were combined with actions from benchmarks suggested by the Australian Department of Education Science and Training (McKinnon et al. 2000) in order to obtain 254 actions that define the functioning of the faculty at any time. The actions were scored according to contribution to each corporate value's using a direct rating on a five point qualitative scale (Table 3). The scale has one as its lowest value to limit problems that arise with interpreting the solution if zero is included. Should some strategic initiatives be deemed negative on certain corporate value criteria, then the scale can be adjusted so that the lower range of the scale reflects negative impact, the middle reflects neutral impact and the upper part denotes positive impact of the actions. The scores were normalised so that each action reflected a unit of movement in strategy space. Score
Description
5
Action contributes directly to all elements
4
Action contributes directly to most elements
3
Action contributes directly to about half the elements
2
Action contributes directly to less than half the elements
1
No elements are being met and some are contra-indicated by this action
Table 3: Scale for scoring strategic initiatives The faculty’s current performance was evaluated on each action by direct rating on a continuous scale between 1 (poor performance) and 5 (outstanding performance). The ratings given in this example were generated randomly, and do not reflect judgement of the faculty’s actual performance. Samples of the normalised scores and performance rating are included in Appendix Table 3. 4.3 Strategic control: Decision support for efficient adjustments to the system The goal programming targets were set by simple anchoring of the total scores across all actions to the relative weights of the corporate value criteria. If: •
Weightr is the highest weight corporate value;
•
Weightq is the normalised weight of corporate value q , and
•
Totalr is the sum of scores for the highest weight corporate value across all s actions,
then:
Targetq =[Weightq / Weightr] x Totalr
For example, the target for Community is
0.253 / 0.253 * 165 = 165 (Table 4).
162
Current state as contribution|performance sum product
Community
Collegiality
Diversity
Innovation
Scholarship
Description
Excellence
Decision Support in an Uncertain and Complex World: The IFIP TC8/WG8.3 International Conference 2004
140
153
122
128
145
165
Normalised values of current state (Equation 1)
0.164
0.179
0.143
0.151
0.170
0.193
Desired weights (Appendix Table 2)
0.190
0.101
0.177
0.228
0.051
0.253
105
61
85
116
29
165
Targets for control system
Table 4: Determination of strategic control targets Several solution scenarios were developed for the desired relationship strategy. These were programmed in LINGO and solved using the simplex linear solver in LINGO 6 (Lingo 2000): •
The naïve solution to act as a reference (minimisation of both negative and positive deviations);
•
Zero performance was allowed so actions not contributing to desired strategy could be isolated (0 ≤ xi ≤ 5);
•
Performance levels calculated with all actions retained (1 ≤ xi ≤ 5);
•
Minimum performance required in all actions (2 ≤ xi ≤ 5); (3 ≤ xi ≤ 5).
In solutions to any of the above scenarios (Appendix Table 4), overachievement on a target implies inefficient use of resources. The solutions provide information about actions that could survive lower performance levels and so have resources reduced. High performance actions could be allocated priority access to resources. Actions required high performance were examined by isolating actions where high performance was indicated in more than one solution. Actions were identified as over-resourced if they currently had a high level of performance but the solution indicated low performance. Actions that support “Diversity” and “Community” needed highest performance, as would be expected, given the definition of the “Relationship” strategy, while actions that focus on staff training were repeatedly identified as over-resourced. The above results provided insights to the interaction of activities. Actions from different functional areas (teaching and administration, for instance) were grouped so decision makers and managers could understand how better to deploy valuable resources. Decisions could be made whether lower levels of performance were acceptable on some activities, or whether other adjustments needed to be made to operations. Resources currently dedicated to achieving high levels of performance could be reallocated to more relevant issues.
5. DISCUSSION AND CONCLUSION This paper proposed a decision aid capable of showing a “big picture” that supplements existing decision support methods. An analogy might be drawn with replacing the arrow keys in a video game with a control joystick. At present, decision methods tend to support selection of best alternatives (arrow keys), whereas the method described here acts more like a joystick in that management is informed about which direction to push in order to achieve an outcome. The concept of “strategy space” introduced in this paper offers a unique, yet simple, basis for determining strategic direction. The goal programming method provides a way of navigating by determining efficient routes to the desired position in strategy space. The method has the potential for a high level of decision support, yet there are a number of sources of possible inaccuracy and difficulty. The cause and effect linkages between actions and outcomes are subject to the limitations of human perception and the scoring of alternatives is highly subjective. Inaccuracy relating to subjectivity may be addressed by careful formulation of scales and presentation of evaluation questions (Von Winterfeldt and Edwards 1986) or by building of scales linking measurable attributes to performance (Keeney and Raiffa 1976). A frequent criticism regarding the use of goal programming is the lack of consideration for the variant used (Romero 1991; Tamiz et al. 1998). The variant selected here was formulated specifically within a Simonian satisficing philosophy of minimising non-achievement of goals (Romero 2001) and to facilitate a solution of maximum efficiency (Romero 1997).
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Decision Support for Strategy Control Several assumptions need to be made in order for the method to become functional. The criteria must be preferentially independent, so corporate values need to be selected carefully. All actions are evaluated on a unitary contribution to strategy. If this is not considered appropriate then the actions may be individually weighted, but this will obviously lead to a second set of subjective decisions. The pattern of interactions has been assumed to be linear. This may be a gross approximation, yet dynamic and other systems that may account for more complex interactions will have difficulty in achieving the granularity needed. The method assists with understanding efficient courses of action, yet it can also be used to assist with the identification of emergent opportunities by quantifying alternate strategies in the manner illustrated and comparing them to organisation’s current position. This exploration may lead to the conclusion that better use of the organisation’s resources may be made if a different strategy is followed. The illustrative example gives an indication of the support gained for strategic decision making by isolating operational level actions with key strategic leverage. The solutions gave insight to overall patterns within the organisation and facilitated an understanding of good use of resources, viability of the current strategy, and cross-functional patterns of interaction. The cross-functional search for patterns of activity is more powerful than blind adjustments to meet targets within functional areas. It allows a holistic view of the enterprise so that overall strategy may be monitored. The decision support method discussed in this paper can help overcome the barriers to strategic control that were mentioned in Section 2. Systemic barriers may be reduced by having to ensure that measures adequately describe the organisation’s functioning. Using measures of operational level actions eliminates errors arising from hierarchy of measures. Encouraging management to view systemic effects, rather than being limited within functional areas addresses behavioural barriers. Decisions regarding allocation of resources and prioritization of actions are highly charged in most environments, often leading to political barriers to strategic control. This paper shows how actions can be identified by strategic contribution regardless of functional area so facilitating objective rather than political decision making.
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APPENDICES Corporate Value
Definitive elements
Excellence
Scholarship
-
Strive for pre-eminence in teaching and research Are happy and confident to be judged nationally and internationally. Support staff to improve individual and collective outputs Assure quality / be proud in all that we do.
-
Staff members acknowledged by their peers as scholars of the highest standing. Training, support and resources for staff and students to improve their skills of scholarship. Acknowledge and reward those who demonstrate scholarship.
-
-
Support programs in place to enable staff to be leaders in their research field. Research is of significant value to academic and other communities (publications, seminars, etc.). High level of industry engagement and commercialisation of research ideas. Staff to utilise new and improved teaching methods.
-
Existence of interdisciplinary exchange programs for students and staff. Diverse work activities, undertaken by staff Staff diversity at all levels. Full support for equal opportunity and affirmative action initiatives. Diverse range and delivery modes of courses.
-
Most staff participate and take shared responsibility in the handling of faculty issues Climate of listening, debate and consideration of the views of others.
-
Innovation
-
Diversity
Collegiality
-
Community
-
Community satisfaction with academic and research programs. Have staff members who are well known and respected within their communities Community is aware of the results of the faculty.
Appendix Table 1: Sample elements of the faculty’s corporate values Excellence
Scholarship
Innovation
Diversity
Collegiality
Community
Best level on all criteria
Highly rated research, teaching and administration
Excellent publication record / highly regarded by peers
High rate of commercialisation of research / introduction of new courses
Multicultural staff and student environment /diversity in courses
High degree of co-operation in all aspects of university life
Contributes positively to community
Worst level on all criteria
No research, poor teaching evaluations; complaints about administration 3 75
No peer reviewed publications / no good reputation 5 40
No commercialisation of research / stagnant course
Active negative dissent
Viewed negatively by community
4 70
Entrenched negative racial attitudes / stagnant course list 2 90
6 20
1 100
0.177
0.228
0.051
0.253
Rank % Norm. Weight
0.190
0.101
Appendix Table 2: Swing weighting of corporate values for the “stakeholder relationship” strategy
167
Decision Support for Strategy Control
0.06
2.0
0.14 0.07 0.21 0.29 0.07
0.21
3.0
Articulation between coursework and research masters degrees
0.14 0.14 0.14 0.29 0.07
0.21
4.0
Appoint Research Professors.
0.17 0.28 0.11 0.17 0.22
0.06
2.5
Introduce a Postdoctoral Fellowship scheme
0.15 0.20 0.10 0.15 0.25
0.15
3.5
Introduce a visiting scholar program
0.11 0.16 0.16 0.26 0.26
0.05
4.0
Use advisory boards that draw upon a range of backgrounds
0.14 0.14 0.19 0.19 0.14
0.19
4.2
Collegiality
0.18 0.29 0.12 0.18 0.18
Offer research master degrees in external mode
Diversity
Introduce a professional doctorate degree
Description of action
Innovation
Performance
Excellence
Community
Scholarship
Normalised scores on criteria
Solicit / act on feedback from graduates & employers
0.22 0.11 0.17 0.17 0.06
0.28
4.5
Upgrade staff teaching qualifications
0.23 0.31 0.15 0.08 0.15
0.08
3.8
Community
Collegiality
Diversity
Innovation
Excellence
Description
Scholarship
Appendix Table 3: Scores and performance for operational-level actions
Targets (Table 4)
105
61
85
116
29
165
Strategic control solution for minimisation of all deviations
141
155
123
130
148
166
Strategic control solution for 0 ≤ xi ≤ 5
105
95
85
116
73
154
Strategic control solution for 1 ≤ xi ≤ 5
105
61
85
116
29
165
Strategic control solution for 2 ≤ xi ≤ 5
105
113
91
116
105
145
Strategic control solution for 3 ≤ xi ≤ 5
125
137
110
116
131
147
Appendix Table 4: Exploration of solution space
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