ACCOUNTING FOR COMPANY REPUTATION: VARIATIONS ON THE GOLD STANDARD Developments in Business Simulation and Experiential Learning, Volume 31, 2004 ACCOUNTING FOR COMPANY REPUTATION: VARIATIONS ON THE GOLD STANDARD Hugh M. Cannon Wayne State University hugh.cannon@wayne.edu Manfred Schwaiger Munich School of Management ABSTRACT In order to address this problem, Gold suggests a systems level design based on the well-accepted economic theory of the firm. Implicit in his approach is the establishment of a standard platform – a kind of “Gold standard,” as it were -- from which future simulations may be constructed, simply by modifying the individual components of the model required to address the specific phenomena being modeled. For instance, if gamers were interested in modeling the effect of company reputation, they would only need to identify those variables within Gold’s model that would be affected by reputational considerations, and make the appropriate adjustments. A recent paper by Gold (2003) presents a system-dynamic- based approach to the design of business simulations. In it, he argues that the focus of simulation design efforts have mostly been carried out at the subsystem level, developing independent algorithms that follow inconsistent logic, and therefore do not lend themselves to integration into a single, dynamically interactive model. To address this, he draws on the economic theory of the firm to develop and test a system of interacting algorithms that gives equal emphasis to both demand and supply factors. Most important, it provides a common, theoretically anchored platform for integrating potentially conflicting functional algorithms. This paper tests the robustness of this approach by using Gold’s model as a vehicle for simulating the effects of company reputation, a phenomenon that has emerged from a totally different (management and marketing) research tradition. The purpose of this paper will be to put the “Gold standard” to the test by addressing the particular issue of company reputation. The phenomenon of company reputation is particularly interesting in itself, as suggested by Cannon and Schwaiger (2003). However, it also provides an excellent test for the “Gold standard.” First, it addresses a phenomenon that is posited to have a pervasive influence on virtually every aspect of a firm’s performance, affecting multiple, interactive aspects of the simulation model. Second, it has emerged from a totally different research tradition (management and marketing), with virtually no effort to integrate it into the economic theory of the firm. INTRODUCTION In a recent paper presented at the annual conference of the Association for Business Simulation and Experiential Learning, Gold (2003) presents a comprehensive, system-dynamic-based model for developing simulation games. In presenting his rationale, he draws on Goosen’s (1981) call for developing a less intuitive, more scientific approach to simulation design and development. He suggests that developers responded to Goosen’s challenge by focusing on issues relating to the design of subsystems rather than the overall structure and interactive structure of the game. For instance, the work published on simulation algorithms between 1982 and 1988 focused on issues of demand, marketing, and finance (Gold and Pray 2001). The focus then shifted to the supply side of the model, following the lead of Thavikulwat (1989). THE CONCEPT OF COMPANY REPUTATION While corporate reputation has grown up in the management-strategy rather than the marketing tradition (Fombrun and Shanley 1990), Cannon and Schwaiger (2003) argue that it is closely aligned to the marketing concept of brand equity (Aaker 1991, 1996). The difference is only in the entity in which the equity is invested. We will use the term company reputation to represent the more marketing-oriented notion of reputation as the equity invested in the overall name of the enterprise. Whereas corporate reputation connotes an application to large, corporate entities, company reputation suggests that the concept can be applied to any organization, regardless of its size or complexity. Gold’s thesis is that, notwithstanding the contribution made by the more systematic and scientific approach to the development of simulation algorithms, the efforts lacked a unified theoretical base. Rather, as Goosen, Jensen, and Wells (1999) point out, the efforts have reflected the individual biases and disciplinary conventions of the various researchers. This, in turn, has tended to create conflicting theories and procedures, thus inhibiting the integration of functional subsystem designs into larger systems of simulation algorithms. Company reputation represents an extension of traditional marketing theory to the supply as well as the demand elements of the firm. This is consistent with the view of marketing as the study of directed social exchange (Bagozzi 1975), or in economic terms, transactions (Williamson 1975). Indeed, the value of company reputation, or company brand equity, can be seen in its ability to facilitate marketing exchanges, or lower 300 Developments in Business Simulation and Experiential Learning, Volume 31, 2004 transaction costs with all the firm’s existing and potential stakeholders. Company reputation is especially important in today’s highly competitive marketing environment. Companies have traditionally been able to sustain high profit margins, facilitated by strategies of product differentiation and market segmentation. However, today, companies are having a hard time differentiating their products, even with large research and development budgets. Any innovation is quickly copied by the competition. Similarly, any company that is successful in identifying and exploiting underserved segments is also copied. To address this situation, marketers are turning to a strategy of relationship marketing, where they rely on lower transaction costs rather than perceived product superiority to win customer support. Cannon and Schwaiger extend this concept to transactions with a broad range of stakeholders, from customers to employees to government regulators. The concept of lower transaction costs has immediate implications for simulation design. The general design principle is simple: A strength of a company’s reputation will lower transaction costs by some percentage, the actual amount of which would depend on the industry, the nature of the transaction, and the specific characteristics of the stakeholder. From Hugh M. Cannon and Manfred Schwaiger. “Incorporating ‘Company Reputation’ into Total Enterprise Simulations,” Developments in Simulation and Experience Learning, volume 30 (2003), p. 292. Reprinted in The Bernie Keys Library, 4th edition [Available from http://ABSEL.org] Stakeholder characteristics presents a particularly important design concept in company reputation. Most discussions of corporate, or company, reputation see it as a uni-dimensional construct, derived from a number of financial, management, customer-oriented, and ethical drivers (Fombrun and Shanley 1990). However, Cannon and Schwaiger (2003) present evidence of two separate dimensions – sympathy and competence – relating to the more human aspects of a company versus its market performance. If there are two dimensions of reputation, we would expect corresponding differences in stakeholder preference, some preferring companies that show greater sympathy, and others preferring companies with greater competence. Exhibit 1 portrays this concept, suggesting five general reputational positions, the impact of which is likely to vary, depending on stakeholder preference. Exhibit 1: Classifying a Company’s Reputational Position Competence Sy m pa th y Also Rans People Companies Hard Chargers Solid Citizens True Stars We can conceive of a two-dimensional reputational index, where a company is rated from “0” to “1” on each of the two dimensions, where “1” is the highest conceivable index, and “0” the lowest. In this manner, every company can be mapped into some position of the matrix shown in Exhibit 1. The strength of the reputational effect on a given variable with respect to a given market segment would be Rijk = RCi RICjjk + RSi RISjjk (1) Where Rijk = effective reputational value of firm i for segment j and stakeholder group k RCi = perceived reputation for competence of firm I RICjjk = relative value of competence for segment j and stakeholder group k RSi = perceived reputation for sympathy of firm I RISjjk = relative value of sympathy for segment j and stakeholder group k (equal to 1- RICjjk) 301 http://absel.org/ Developments in Business Simulation and Experiential Learning, Volume 31, 2004 We will follow the common assumption in the literature that perceived reputation is constant across stakeholder types and segments, or groups, within each type. By contrast, the relative value of competence and sympathy will vary by both stakeholder type and group. For instance, we would expect that government regulators (one type of stakeholder) would tend to favor companies with a relatively high reputation for sympathy, while investors (another type of stakeholder) would tend to place more value on competence. However, among government regulators, there will be a group, or segment, of those who place relatively more value on competence. Similarly, among investors, there will be those who place relatively more value on sympathy. The combined value of the two attributes is constrained to equal 1.0, thus preserving the “0” to “1” scale for Rijk. The issue of segmentation is somewhat problematic in the context of multiple stakeholders. The economic theory of the firm only addresses consumer and labor markets. While it can be modified to accommodate a broader range of stakeholders, these stakeholders would generally not reflect the same segmentation structure as consumer markets. This suggests that a game would need a different segmentation scheme for each type of stakeholder, where the segments differ in their relative preference for sympathy versus competence in their response to company reputation. In Gold’s model, some of the variables require reference to both consumer and stakeholder segments simultaneously. Therefore, we will use the index “j” to represent consumer segments and “k” to represent segments, or groups, within other stakeholder types. (In order to avoid confusion, we will use the term “segments” to represent consumer segments and “groups” to represent segments within other stakeholder types). In the remaining sections of this paper, we will address two key issues: First, how do we model the effects of company reputation? That is, given the reputational index described in equation 1, how will it effect the various performance aspects of a simulated firm? Second, how do we derive a reputational index from company decisions? This addresses the underlying purpose of the modeling company reputation, which is to enable game- players to consider it’s effects when formulating their decision- making strategy. KEY ISSUE I: MODELING THE EFFECTS OF COMPANY REPUTATION Cannon and Schwaiger (2003) argue that company reputation benefits a company by lower transaction costs with its various stakeholders. Specifically, they list a number of areas in which this might benefit a firm’s performance, as suggested in the following list. We will address each of them areas in subsequent sections. : Lower costs for customer acquisition and retention (customer stakeholders) Lower distribution costs (distributor stakeholders) Lower supplier prices (supplier stakeholders) Lower cost of employee acquisition and retention (employee stakeholders) Lower cost of capital (investor stakeholders). Lower costs of lobbying and government relations (governmental stakeholders) More positive word-of-mouth advertising (general public) Lower cost of advertising and promotion (all stakeholders) Reduced risk of litigation (all stakeholders) CUSTOMER ACQUISITION AND RETENTION Customer stakeholders are the easiest to address. Gold suggests that a firm’s market share is a function of a firm’s price (pij), marketing expenditures (mij) and the difference between actual product attributes and the ideal for segment i (dij). The effect of reputation can be handled in two ways. One would be to treat reputation as a product attribute, incorporating it into the calculation of dij. On the surface, this would appear to make sense, especially when we view reputation in terms of a positioning map, as illustrated in Exhibit 1. However, reputation does not lend itself to an “ideal position.” The more you have, the better. To illustrate, consider two companies, one with a perfect 1.0 reputation on both sympathy and competence (a “true star” in the parlance of Exhibit 1) and another with a 1.0 reputation on sympathy and a 0.0 on competence. Now, consider a stakeholder group who places all its value on sympathy as opposed to competence. The stakeholder group does not place a negative value on competence, only a zero value. Therefore, members of the group should rate the two companies as having the same quality of reputation, even though they occupy very different positions on the reputational map, presumably corresponding to two different ideal points. The “product attribute” approach would yield a misleading result. The second, and better, way of addressing company reputation would be to use it as a kind of marketing “intensifier,” allowing it increase the effective marketing expenditures. This can be accomplished in Gold’s model by substituting effective marketing expenditures (emij) for mij, using equations (2) and (3): emij = (1 + ar RIij) mij (2) RIij = (Rij – Rj) (3) Where RIij = Effective reputational impact on segment j for firm I Rj = average effective reputational value for all firms competing in segment j ar = Scaling parameter representing the relative impact reputation can have on effective marketing expenditures 302 Developments in Business Simulation and Experiential Learning, Volume 31, 2004 Note that reputational impact (RIij) is based on the value of a company’s reputation, as compared to other companies competing in the same segment. This provides a strategic component to the game, where decision makers are rewarded for focusing on segments in which they are likely to have a reputational advantage. Thus, if the firm has positioned itself as a “people company,” it will do best to not only focus on segments that value sympathy rather than competence, but also to look for segments where the major competitors are relatively weak along the sympathy dimension. We assume that the impact of reputation will help determine market share, but not the overall demand for the industry. Therefore, it figures only in the firm’s demand algorithm. The scaling parameter (ar) will depend on the relative importance of reputation in the overall marketing effectiveness of the firm. A value of “1.0” would mean that a company whose reputation is twice the industry average would double its effective marketing budget. In practice, variations in ratings of company reputation would never yield an RIij as high as 100%, and generally not above 20%. A scaling value (ar) of 1.0 would not be unrealistic. DISTRIBUTION COSTS Gold’s model does not make any specific provision for distribution costs. As a rule, distributors would be compensated by margins taken after paying the manufacturer wholesale prices. Additional promotional incentives and sales costs would be considered marketing expenditures (mij), suggesting that the effect of company reputation on distribution stakeholders would be incorporated the estimate of effective marketing expenditures (emij). Again, a scaling value (ar) of 1.0 appears to be reasonable. However, in the case of a new product, company reputation might play an important role in winning initial distribution acceptance, in which case the value of the scaling parameter might be higher. In the absence of any empirical evidence, we would estimate that it could be as high as 1.20. SUPPLIER PRICES Presumably, company reputation would make a company more desirable to suppliers in two ways. Both are derived from the notion that suppliers would naturally pursue a client with a good reputation because of the prestige it adds to their client list. This would reduce their own marketing costs in winning other clients. The effect of reputation would be reflected, first, in lower prices, as suppliers seek to win and hold the company as a client. Second, it would be reflected in lower administrative costs, resulting from decreases in the normal “friction” that accompanies tepid supplier enthusiasm. Gold’s model contains a price-of-materials (Pm) variable. It does not include a variable representing the administrative costs associated with the purchasing process. Notwithstanding the conceptual distinction between the two effects, both can be addressed by simply adjusting the price of materials to reflect the impact of company reputation, creating an effective price (EPm), as shown in equation (4). EPm = Σ [ (Mijk/ Σ Mijk) Pm / (1 + br RIijk) ] (4) j k RIijk = (Rijk – Rjk) (5) Where Mijk = Amount of materials supplied by supplier group k for use by firm i in products for segment j RIijk = Effective reputational impact on stakeholder group k for segment j and firm i Rjk = Average effective reputational value for all firms on stakeholder group k for segment j br = scaling parameter representing the relative impact reputation can have on the price of materials To illustrate, we posit two groups of supplier stakeholders (k), each varying in their relative preference for sympathy versus competence in company reputation. Equation (4) represents a weighted average effective price of materials delivered by supplier groups (k) for use for products sold in the various segments (j). For each segment and supplier-group combination, the price of materials (Pm) is discounted, dividing it by the reputational impact (RIijk). Thus, if RIijk is 20% above the average for companies in the industry, the discounted price would be (1 / 1.2 =) 83% of the original. Developing two supplier groups provides an interesting opportunity for modelers to address the emerging trend for companies to choose business partners (clients, in this case) who reflect their own company values. For instance, a supplier might offer price concessions to a high-reputation company in an effort to be associated with a company that is known for its responsiveness to social issues. Note that reputational impact (RIijk) is based on the value of a company’s reputation compared to other companies using the same suppliers. As with consumer segments, this provides a strategic component to the game. Players are rewarded for focusing on suppliers for which they are likely to have a reputational advantage. A company should obviously seek out suppliers who share their reputational orientation. But the advantage of doing this decreases as other companies with a similar reputational orientation do the same. Even if most suppliers are looking for clients with high reputations for competence, if most of the companies using these suppliers are high along the competence dimension, a company with a strong reputation for sympathy might be able to secure better price concessions by working with the smaller number of suppliers who are positively disposed toward sympathetic clients. The scaling parameter (br) is analogous to the consumer scaling parameter (ar). It depends on the relative importance of reputation in the overall price of materials for the firm. A value 303 Developments in Business Simulation and Experiential Learning, Volume 31, 2004 of “1.0” would mean that a company whose reputation is twice the industry average would effectively cut its price in half. This would virtually never happen. We noted earlier, RIij might get as high as 20%. Generally, we would expect supplier prices to be less responsive to reputational influence than consumer sales. This is because most of the variation in industrial prices is driven by such factors as purchase volume, volume guarantees, and the overall competitiveness of the industry. A reasonable range of values for the scaling parameter (br) might be between .05 and .25. EMPLOYEE ACQUISITION AND RETENTION If company suppliers might have differing preferences for a company’s reputation regarding sympathy versus competence, this would generally be much more true for employees. A person’s employer speaks a great deal about the person. The model assumes that employee acquisition and retention can ultimately be translated into a dollar value. That is, if the wages are high enough, a company can hire and retain anyone. However, a person will make major wage concessions to work for a company whose reputation is compatible with his or her self-image, values, and lifestyle. This will be reflected in a lower cost of labor. Gold includes the cost of labor (Pl) in his cost equation. Following the same basic pattern used in addressing suppliers, we can adjust labor costs to derive an effective price of labor (EPl) as shown in equation (6). COST OF CAPITAL Gold’s cost of capital (Pk) parallels the cost of materials and labor. It too can be easily adapted to address company reputation, as suggested in equation (7). EPl = Σ [ Lijk/ Σ Lijk Pl / (1 + cr RIijk) ] (6) j k Where Lijk = Amount of labor supplied by labor group k for use by firm I in products for segment j cr = scaling parameter representing the relative impact reputation can have on the price of labor The scaling parameter (cr) is directly analogous to parameters “ar” and “br” used in equations (2) and (4). We suggest a value of 1.00. However, in cases where the simulation seeks to emphasize the role of value congruency in the labor market, the value might be set as high as 1.20. EPk = Σ [ Kijk/ Σ Kijk Pk / (1 + dr RIijk) ] (7) j k Where Kijk = Capital equipment and facilities acquired by firm i, financed by investor types k in service of products for segment j. dr = scaling parameter representing the relative impact reputation can have on the cost of capital. The scaling parameter (dr) is directly analogous to parameters “ar”, “br”, and “cr”, used in equations (2), (4), and (5). Again, we suggest a value of 1.00. As in the case of labor costs, however, in cases where the simulation seeks to emphasize the role of value congruency in the capital market, the value might be set as high as 1.20. COST OF LOBBYING AND GOVERNMENT RELATIONS While Gold’s model does not account for lobbying or government regulations, such factors would not be hard to address. For instance, we might conceive of a model where simulated events could be announced that would have a negative effect on the firm’s performance. Game players might be sent a notice that the Environmental Protection Agency was taking a hard line on waste disposal practices, and that many of the practices that had been condoned over the years were now subject to potential penalties (Ek). The likelihood of the penalties being imposed (Pk) might be dependent on any number of factors, such as investments in environmentally sound practices, updating equipment to more pollution-free models, and governmental lobbying. The expected cost of Government action (EEk), then, is the cost adjusted by the probability of occurrence. Company reputation would act to lessen the probability of a negative event happening, as suggested by equations (8) and (9). 304 Developments in Business Simulation and Experiential Learning, Volume 31, 2004 EEk = Ek EPk (8) EPk = Pk / (1 + er RIik) (9) Where Ek = cost of Government action Pk = probability of Governmental action k taking effect EPk = effective, or adjusted, probability of Government action k taking effect RIik = effective reputational impact on the probability of Governmental action k taking effect er = scaling parameter representing the relative impact reputation can have on the probability of Government action The actual financial consequences of any Governmental actions might be addressed in two ways: First, the firm might choose to set aside contingency funds, thus lowering profits by the expected value of the loss (EEk). Second, the game could impose periodic costs, based on the probability that event “k” would occur. Either approach would reduce profit by EEk, as determined by equation (8) and would utilize company reputation in the same manner. WORD-OF-MOUTH ADVERTISING Gold’s model does not make any specific provision for word-of-mouth advertising. However, in the absence of any specific algorithm to simulate word-of-mouth advertising, its effect would be incorporated in the estimate of effective marketing expenditures (emij), as discussed in equation (2). COST OF ADVERTISING AND PROMOTION The notion that company reputation might lower the cost of advertising and promotion again grows out of the logic of equation (2). Reputation acts as a kind of marketing budget multiplier, producing better results for the same expenditure of marketing funds. RISK OF LITIGATION Risk of litigation can be handled in the same manner as Government regulation, which was described in our discussion of equations (8) and (9). Litigation would constitute an “event,” just as a saw with Governmental penalties. KEY ISSUE 2: DERIVING A REPUTATIONAL INDEX FROM COMPANY DECISIONS Our discussion so far presumes that a simulated company already has a company reputation. But, for the simulation to make sense, the reputation must be earned by the simulated decisions of the company. Exhibit 2 presents a structural analysis of the survey items from which Cannon and Schwaiger (2003) developed their reputational model, indicating the actual survey items from which the various reputational characteristics (attractiveness, responsibility, quality, and performance) were derived. These reputational characteristics, in turn, are what drive a company’s reputation for sympathy and competence. From Hugh M. Cannon and Manfred Schwaiger. “Incorporating ‘Company Reputation’ into Total Enterprise Simulations,” Developments in Simulation and Experience Learning, volume 30 (2003), p. 291. Reprinted in The Bernie Keys Library, 4th edition [Available from http://ABSEL.org] Exhibit 2 suggests a number of items that might be used in designing operational indices of company reputation. In the following section, we will develop a method for addressing attractiveness, responsibility, quality, and performance through Gold-standard compatible game decisions, thus demonstrating the viability of Gold’s standardized model. This will also provide an operational model for incorporating company reputation into an enterprise simulation. For convenience, we will rate reputation on a five-point scale, with a value of three representing the average reputation for companies within the industry. We may treat the actual reputation for sympathy (RSi) and competence (RCi), introduced in equation (1), as averages of attractiveness and responsibility, quality and performance, respectively. This is show in equations (10) and (11). RSi = a (RAi + RRi)/2 + (1 – a) RSi,t-1 (10) RCi = a (RQi + RPi)/2 + (1 – a) RCi,t-1 (11) Where RAi = attractiveness rating of company i on a five-point scale RRi = responsibility rating of company i on a five-point scale RQi = quality rating of company i on a five-point scale RPi = performance rating of company i on a five-point scale a = parameter representing the lagged effect of prior reputation 305 http://absel.org/ Developments in Business Simulation and Experiential Learning, Volume 31, 2004 ATTRACTIVENESS In general, the attractiveness factor appears to be the degree to which a company provides an attractive place to work. Exhibit 2 suggests that these might be a function of such things as turnover, employee quality, and work environment. To address these items, we may create a new category of variable cost -- employee training and maintenance (TMi) – expressed as a percentage of the price of labor (Pl). This would result in a modification of equation (6), as shown in equation (12). Exhibit 2: A Structural Analysis of Company Reputation n.p. .76 .84 .70 .80 .61 .79 .76.63.58.73.79 .70 .74 .81 .79 .81 .68 .80 .68 .77 .68 .49 .71 .69 .58 .74 .64 71% 90% Responsibility services ... offers are good cust. concerns in high regard reliable partner forthright in giving information high quality products/services good value for money high-quality employees see myself working at like physical appearance fair attitude tow. compet. not only into profit socially conscious economically stable modest business risk has growth potential like more than other companies miss more than other companies recognized world-wide top competitor in its market preserves environment trustworthy company have a lot of respect for innovator, rather than imitator QualityQuality Performance AttractivenessAttractiveness very well managed clear vision about future Competence SympathySympathy identify more with differentiates itself positively .62 .85 .52 .52 -.56 -.49 n.s. n.s. EPl = (1 + TMi) Σ [ Lijk/ Σ Lijk Pl / (1 + cr RIijk) ] (12) j k Where EPl = cost of labor from equation (6), adjusted to reflect the cost of employee training and maintenance used to manipulate company attractiveness TMi = employee training and maintenance expenditure (-fr < TMi < fr) fr = parameter denoting the allowed range of TMi A reasonable percentage of price range (fr) for TMi would likely be somewhere between 0% and 10%, depending on the industry and company strategy. A negative training and maintenance means that a company would provide below average training and support, exploiting the cheapest possible labor in order to achieve lower wages. This, of course, would have a negative effect on both the effective cost of labor (EPl) and the attractiveness (RAi) of the company. Equation (12) determines the cost of implementing a program to achieve company attractiveness. However, we still need to establish the level of attractiveness the program achieves (RAi). As we have noted, a game player may actual decrease attractiveness by cutting labor costs, in order to achieve higher profits. Of course, there is a limit to how much a manager can squeeze out of labor, suggesting a lower limit for TMi. Theoretically, there is no corresponding upper limit. Added expenditures for TMi would simply reach diminishing returns as they approached a maximum effective expenditure, ceasing to increase attractivenss. In practice, a similar effect can be achieved by simply constraining TMi to an effective range (-fr < TMi < fr), such as plus or minus 10% of price. Taking this approach, the impact on sympathy can be represented adequately by equation (13). If the maximum value of TMi (i.e. the value of fr) is 10%, and the company invests 5% of labor costs on training and maintenance, the company will realize half of the possible increase in attractiveness relative to the average firm (defined as having a attractiveness of 3), or an attractiveness value (RAi) 4 out of 5. 306 Developments in Business Simulation and Experiential Learning, Volume 31, 2004 RAi = 3 + 2 (TMi / fr) (13) Where RAi = company i’s attractiveness on a five-point scale RESPONSIBILITY The responsibility characteristic appears to address issues of ethics and social responsibility, including issues such as monopolistic abuses, taking advantage opportunities for short- term profit at the expense of long-term benefit to multiple stakeholders, social consciousness (corporate philanthropy and citizenship), and environmental consciousness. Halpin and Biggs (2000) suggest that a simulation might present students with a series of incidents, providing specific response alternatives, the selection of which provides quantitative input into the actual simulation algorithm. This is illustrated in equations (14) and (15). Equation (14) represents the total effect of the company’s decisions on its reputation for responsibility (RRi), while equation (15) represents the monetized cost of these decisions. RRi = Σ RVik / n (14) ECi = Σ ECik (15) Where RRi = company i’s responsibility on a five-point scale RVk = responsibility value of incident k for company i (also on a five-point scale, but weighted so that incidents with values between “4” and “5” or “1” and “2” count as incidents) n = total number of incidents players must address, adjusted upward to account for incidents counted twice due to their responsibility values ECik = monetized cost to company i of incident k, treated as a fixed cost in Gold’s model Note that the responsibility value of an incident (RVk) is not dependent on the incident itself, but on the decision game players make to deal with the incident. This assumes that all incidents are equal in merit and significant enough to merit players’ careful consideration. (Incidents of varying importance could be developed as a means of testing players’ ability to assess the degree of environmental opportunity or threat, but we have not addressed this possibility here). The double counting of high- or low-value incident decisions reflects the assumption that incidents representing very high or very low levels of social responsibility are likely to stand out and have more influence on a company’s reputation than ones that do not. The monetized, or economic, cost of incident decisions must be established by the game designer, providing an opportunity for strategic trade-offs between responsibility and short-term profit. The amount and nature of these costs must be explained in the incident descriptions presented to the players, following the pattern illustrated by Halpern and Biggs. The amount may include intangible as well as tangible costs, including things such as risks associated with outcome uncertainty, inefficiencies created by the development of new work procedures, and confusion coming from potentially mixed cultural signals within the organization. Incidents may also involve positive benefits as well (even beyond those accruing from a better company reputation). These might include such things as long-term increases in productivity dues to healthier work procedures, the elimination of mixed cultural signals, and so forth. While these monetized costs can be associated with real monetary costs (and benefits), they are accounted for in a separate ECi variable because there is no convenient place to put them elsewhere in Gold’s model. QUALITY The quality characteristic is much easier to address than either attractiveness or responsibility. It represents a company’s tendency to act in a manner consistent with the “marketing concept” – the notion that success derives from a systematic focus on customer needs. A simulation might create an index of quality by using measures such as quality of products and services, value, customer service, reliability, forthrightness, trustworthiness and innovation. However, the easiest way to address the quality issue as we have defined linking it to the degree to which the company’s products address consumer needs – i.e. the difference between the actual and ideal product attributes for company i in segment j (dij). Rather than addressing price (Pij) as a factor in “value” (i.e. looking at dij fit relative to price), we would treat price as another attribute. This allows for the economic anomalies associated with symbolic pricing. Based on this approach, quality (RQi) is reflected in equation (16). 307 Developments in Business Simulation and Experiential Learning, Volume 31, 2004 RQi = 3+2 Σ (di – dij) / |dj - dij|max (16) Where RQi = company i’s quality value on a five-point scale dij = difference between ideal and actual product attributes for company i in segment j, as postulated in Gold’s model dj = average difference between ideal and product attributes for all brands in segment j |dj - dij|max = maximum absolute difference between dj and dij The effect of equation (16) is to adjust the average quality value of 3 up or down, depending on whether the average difference between company i’s brands and segment ideals is less or greater than the average for all brands. In order to ensure that RQi fits to a five-point scale, we compare each di – dij difference to the maximum difference found among all the companies and segments. Equation (16) awards this one a quality value (RQi) of five, or if it involves a brand that is below the industry average, a value of one. PERFORMANCE Performance represents the overall manner in which a company manages its business. Measures of this might include quality of management, sales and earnings stability, forecasting accuracy, low risk, and clear vision (as judged by the game administrator). Gold provides a host of different measures of performance in his model. We will use two indices, both based on Gold’s net income per share after tax (NIPSi). The first measure is the average NIPSi over time compared with that of other firms in the industry (RNIPSi,av). The second is the relative variability in net income per share after tax over time (RNIPSi,var). These are reflected in equations (17), (18) and (19). RPi = (RNIPSi,av + RNIPSi,var) (17) RNIPSi,av = 3+2Σ (NIPSi,av – NIPSav) / |NIPSi,av – NIPSav|max (18) RNIPSi,var = 3+2Σ (NIPSi,var – NIPSvar) / |NIPSi,var – NIPSvar|max (19) Where RNIPSi,av = company i’s average net income per share after taxes, as compared to other firms over all periods of the game RNIPSi,var = variance in company i’s average net income per share after taxes, as compared to other firms over all periods of the game |NIPSi,av - NIPSav|max = maximum absolute difference between NIPSi,av and NIPSav |NIPSi,var – NIPSvar|max = maximum absolute difference between NIPSi,var and NIPSvar SUMMARY AND CONCLUSIONS The development and basic testing of the reputational model appears to support the “Gold standard.” That is, Gold’s theory- of-the-firm simulation model does appear modifiable to accommodate the effects of company reputation. Of course, the devil is in the details. For instance, the parameters of the model must be carefully tested to ensure that the cost/benefit trade-off between actions required to build company reputation and reputational payoff are realistic. But, of course, attention to this kind of trade-off is an essential part of every simulation design. The good news is that the task is feasible, thus opening the door for integrating subsystem designs that would have otherwise been interesting, but of little use to the progress of the discipline. Following the “Gold standard” metaphor, working backward from a standard model promises to increase the efficiency of business simulation design. The second benefit of his project relates to company reputation itself. Although the major purpose of this paper was to investigate the viability of a standard, theory-of-the-firm- based simulation platform for modeling a phenomenon that arises from another discipline and associated literature, operationally incorporating company reputation into an enterprise simulation is not insignificant. The literature on simulation and gaming appears to have neglected the movement toward more relationship-oriented market transactions, having given little attention to modern concepts such as relationship marketing, brand equity, and company reputation (Cannon and Schwaiger 2003). This paper addresses the problem head-on. Finally, the process of reconciling non-economic concepts with a standard economic model inevitably points to new areas for research. Having noted that all the major needs of the company reputation model can be addressed in some component of Gold’s model, we have nevertheless pointed out a number of areas where the model can be improved. For instance, the model 308 Developments in Business Simulation and Experiential Learning, Volume 31, 2004 would lend itself to a more detailed accounting of distribution effects, word of mouth advertising, advertising and promotional costs, and costs associated with litigation risk, as well as a more rigorous accounting of the factors leading to the development of company reputation. Again, the message is twofold: First, there is room for improvement in the modeling of company reputation. Second, there is room for developing subsystem models in general, linked to an economic theory-of-the-firm model to ensure compatibility. REFERENCES Aaker, David A. (1991). Managing Brand Equity: Capitalizing on the Value of a Brand Name. New York: Free Press. Aaker, David A. (1996). Building Strong Brands. New York: Free Press. Bagozzi, Richard P. (1975), "Marketing As Exchange," Journal of Marketing, 39:3 (October), 133-139 Cannon, Hugh M. and Manfred Schwaiger. “Incorporating ‘Company Reputation’ into Total Enterprise Simulations,” Developments in Simulation and Experience Learning, volume 30 (2003), p. 288-297. Reprinted in The Bernie Keys Library, 4th edition. Gold, Steven (2003). “The Design of a Business Simulation using a System-Dynamics-Based Approach.” Developments in Business Simulation and Experiential Learning, vol. 30 (March). Reprinted in The Bernie Keys Library, 4th edition. Gold, Steven C. and Thomas F. (2001). “Historical Review of Algorithm Development for Computerized Business Simulations.” Simulation and Gaming 32:1 (March), 66-83. Goosen, Kenneth R. (1981). “A Generalized Algorithm for Designing and Developing Business Simulations.” Developments in Business Simulation and Experiential Learning, vol. 8 (February), 41-47. Reprinted in The Bernie Keys Library, 4th edition. Goosen, Kenneth R., Ron Jensen, and Robert Wells (1999). “Purpose and Learning Benefits of Business Simulations: A Design and Development Perspective.” Developments in Business Simulation and Experiential Learning, vol. 26 (March), 133-145. Reprinted in The Bernie Keys Library, 4th edition. Fombrun, Charles. and Mark Shanley (1990). “What’s in a Name? Reputation Building and Corporate Strategy.” Academy of Management Journal 33:2 (June), 233-258. Halpin, Annette L and William D. Biggs (2000). “Internationalizing the Introduction to Business Course Using an International Text and a Domestic Simulation with a Twist.” Development in Business Simulation and Experiential Learning, Vol. 27 (March), pp. 115-121. Reprinted in The Bernie Keys Library, 4th edition. Williamson, Oliver. (1975). Markets and Hierarchies: Analysis and Antitrust Implications. New York: The Free Press. 309 Table of Contents Volume 31, 2004 Controlling the Complexity and Orenting Target Groups by a Modular, Server-Based Business Game System Learning Network Demonstration: Delivering Business Education in a Distance Learning Environment Economic Evolution, Human Capital Investment, and Adult Distributed Electronic Learning: A Literature Review Designing a Globalization Simulation to Teach Corporate Social Responsibility Developing and Teaching an Online / In-Class Hybrid: A Demonstration A Model for Evaluating Online Instruction An Evaluation of a Distributed Learning Course: A Students'-Eye Perspective Blended Learning Strategy Improved Business Writing Skills How to Receive and Process Attachemnts while Greatly Reducing the Risk of Viruses and Trojans Introducing Online Components to a Class: How to Increase teh Likelihood of Success Teaching Strategic Communications Online: Using Learning Outcomes to Develop a Case-Based Course Implementing Distance Approaches to Education: A Panel Discussion for ABSEL: Las Vegas, 2004 MANDI: Learning Management Through Field Sales Experience An International Capital budgeting Experiential Exercise A Primer To Combating Terrorism: Playing It Safe While On Overseas Assignment (An Experiential Exercise) Integrating The Business Curriculum With A Comprehensive Case Study: A Prototype The Case Brief: A Model For Case Analysis, Writing And Discussion Technology Infused Pedagogy And Delivery – A Sure Bet? A Proposal For Panel Discussion Absel Conference 2004 Research Strategy And The Bkl: Getting The Most From The Absel Archives Simple But Effective: Rediscovering The Class Discussion Needle And Thread: An Activity For Examining Various Management Behaviors A Customized Excel Data Analysis System For Use In Undergraduate Marketing Research Team Leader Selection - Does It Matter? The Power Of Perspective: Reframing Your Framing Skills For Innovative Instruction In Leadership And Influence Exercise: How Should Merit Raises Be Allocated? An Online Situation For Problem-Based Learning In A Junior-Level Management Course The Eden Alternative As A Roadway For Change: A Service Learning Quality Improvement Project Avoiding Catastrophe: The Role Of Individual Accountability In Team Effectiveness Omega Systems: A Change Management Exercise The Risks And Rewards Of Providing Students A Structured Cheating Opportunity Experimentation With Assessment Techniques: A Proposal For Panel Discussion Using A 2 - Page Case To Introduce Concepts Of Business Strategy Interactive Session The Integration Of Appreciative Inquiry And Experiential Learning For Peak Performance Appreciative Inquiry Case Story: New York City Leadership Challenge Individual Achievement Versus Team Performance: An Empirical Study With Business Games Some Strategists Don't Learn Or Can't Learn Computer Simulation: A Design Architectonic On The Value Of Bugs In Simulation Environments Online Sales Forecasting With The Multiple Regression Analysis Data Matrices Package Simulation Exercises And Problem Based Learning: Is There A Fit? A Study Of Business Game Stock Price Algorithms Assessing Individual Performance In A Total Enterprise Simulation Information Use In A Business Game Determining The Value Of A Firm Unsorting Algorithms For An Ordered List And Its Application To Business Simulations Teaching Public Finance Management Through Simulation Antecedents Of Game Performance Student Expectations Of Classroom Teaching Practices In Developing And Presenting Course Information In Hong Kong Implementation And Impacts Of The Balanced Scorecard: An Experiment With Business Games Impact: Shocking The Legacy Mindset Implementation Of The Eepad Framework Of Business Processes In An Accounting Information Systems Course Are Business Games Really Delivering What Students Are Led To Believe?? Reporting Lessons Learned: What Gets Reported; Who Gains Value Teacher Expectations Of Classroom Teaching Practices In Developing And Presenting Course Information In Hong Kong Student Reactions To The Use Of A Computer-Based Simulation As An Integrating Mechanism For A Mba Curriculum A Cognitive Investigation Of The Internal Validity Of A Management Strategy Simulation Game The Casino Challenge: Making Simulation Delivery A Safe Bet! Accounting For Company Reputation: Variations On The Gold Standard Foreign Currency Hedging: A Simulation The Influence Of Variables Easily Controlled By The Instructor/Administrator On Simulation Outcomes: In Particular, The Variable, Reflection. Absel Awareness Among Business School Faculty Validating Business Simulations: Does High Market Share Lead To High Profitability? Simulation Debriefing Procedures Coaching And Business Simulations: A Formula For Success? A Seminal Inventory Of Basic Research Using Business Simulation Games The Influence Of Scorecard Evaluation On Decisions And Outcomes Award: Best Paper Award Recipient 2004 - Simulation Track