The benefit of a good reputation: an empirical analysis.

Jones, Gary H. ; Jones, Beth H. ; Little, Philip L. 等

INTRODUCTION

Like individuals, organizations are identified in part by the value of their good name. There is growing recognition in the business community that managerial considerations of reputation are no less significant than those involved with corporate operational, financial and legal decisions. Although a number of studies have addressed ways in which organizations might build a good reputation, the consequences of corporate reputation have been less well examined empirically. What is the value of a solid reputation once secured?

It has been suggested that reputation can serve as something of a reservoir of goodwill, both in the accounting sense (where reputation is assigned a dollar value when a firm is sold, note Davis, 1992; Bromley, 1993, citing Davis, p. 166), and in a public relations sense, where it is implied that communities will tend to give highly reputable firms the "benefit of the doubt" (Bostdorff & Vibbert, 1994; McGuire et al., 1988 [by implication], Patterson, 1993; Sobol et al., 1992). This study examines the increased importance of corporate reputation by empirically testing the premise that a good reputation serves as an intangible asset which can help protect the organization in times of corporate crises--in public relations terms, the "reservoir of goodwill" presumption.

RESEARCH QUESTION

For our purposes reputation is operationalized using the Fortune's "most admired corporations" survey rating. The crisis, or the event from which the buffer is to protect the company, is more difficult to select. While it would be interesting to study the impact of corporate reputation on disasters such as the Valdez spill or Tylenol tampering, these disasters are not comparable. They were unique and basically affected one company, or in the case of Alar, only a few companies. What is needed for the purposes of this analysis are instances where a general, unpredicted distress affected a large number of major corporations within a very narrow time frame. Additionally, such incidents must have occurred between 1982 and the present, which is the range of the corporate-reputation data set. Given these parameters, this study will examine three significant one-day stock market declines that have occurred in the past 15 years: October 19, 1987 (508 points, or 22 percent of value), October 13, 1989 (190 point drop, or 7 percent of value); and March 8, 1996 (171 points, 3 percent). Despite the wide range in percentages, all three events were among the top ten most significant point drops in the stock market of the past 15 years, as measured by the Dow Jones Industrial Average (Kansas, 1996). The selection of these three events will allow for determination of reputation effects across a range of decline severity.

The final measurement issue concerns the actual degree of economic harm suffered by the 250-350 companies in this study relative to their respective reputations. This will be determined by percent change in stock price. It is essential to note that the corporate reputation composite index is constructed from eight attributes (described below), only three of which are purely financial in nature (e.g., "financial soundness"). Furthermore, Fortune survey respondents consist of over 8,000 managers and analysts, who rate each company in their area of expertise according to their perception of that corporation. Therefore, as reputation is largely perceptual in nature, based on more non-financial than financial criteria, using stock prices to measure the concept of buffering while operationalizing economic shock as a general but severe decline in the market provides a meaningful degree of separation between event and effect. The question is: Can reputation serve to protect a company against short-term economic loss in the event of a major, sudden, general economic shock (in this case a major stock market crash or decline)?

LITERATURE REVIEW AND HYPOTHESES

The importance of reputation as an area of inquiry is underscored in the literature by its numerous suggested benefits. First and foremost, reputation has increasingly come to be recognized as an asset. (Bromley, 1993; Brouillard, 1983; Caminiti, 1992; Hall, 1992; Holmes, 1995; Sobol, Farrelly & Taper, 1992; Weigelt & Camerer, 1988). As an asset a solid corporate reputation has a number of potential advantages. It can signal publics how a firm's products, jobs, strategies and prospects compare with other firms. It can signal product quality, may enable premium prices, enhance access to capital markets and attract investors (Bromley, 1993; Frombrun & Shanley, 1990). A good reputation can help attract better job applicants, retain them once hired and maintain employee morale (Brouillard, 1983; Sobol et al., 1992). Building on a admirable reputation, a firm may have a more sustainable competitive advantage (Camaniti, 1992; Frombrun and Shanley, 1990), a better-protected "ecological niche" (Bromley, 1993), and a higher probability of generating desired returns on investment (Knipes, 1989; Riahi-Belkaoui and Pavlik, 1992). A reputable firm may charge higher prices for its product and services (Brouillard, 1983; Sobol et al.). Sobol, Farrelly and Taper (1992) summarized many of the advantages of a good reputation under the four categories of labor, finance, product/service sales and community (note especially pp. 58-79).

Although less commented upon in the literature, it is generally presumed that reputation can provide some protection for the organization in times of trouble; it can, theoretically, provide the company with resource "slack" in the event of adversity--a crisis or sudden economic downturn. In this regard, some have considered reputation using an inoculation metaphor (Caminiti, 1992; d'Alessandro, 1990). In her 1992 Fortune article, for example, Caminiti quotes a marketing consultant with Yankelovich that "Exxon 'never developed the kind of strong reputation that could have inoculated it against something like the Valdez spill'" (p. 77). Burgoon, Pfau and Birk (1995) find evidence that issue/advocacy advertising can inoculate against "attitude slippage" following exposure to a persuasive attack on behalf of an opposing position.

Another assertion is that corporate reputation represents a positional capability which could, along with other organizational resources, contribute to a firm's sustainable long-term competitive advantage (Hall, 1993). Unfortunately, few empirical studies have actually demonstrated hypothesized benefits of corporate reputation.

Based on the considerations outlined above, this study tests the following primary hypothesis:

[H.sub.1] The higher a firm's reputation the less relative economic loss that firm will suffer in cases of economic crisis.

The economic events studied will be the stock market crash of October, 1987, and the large one-day market declines of October, 1989 and March, 1996. A company's relative economic damage will be measured by corporate common stock price loss and reputation will be operationalized according to results of Fortune magazine's annual corporation reputation surveys. It is therefore hypothesized that companies with higher reputations will suffer significantly lower stock price declines the day of the market plunge, and will exhibit significantly less of a price decline two weeks after the initial drop (H1a and H1b, respectively). Further, in the case of the historic 1987 crash, it is hypothesized that stock prices of the more highly reputed firms will exhibit less of a decline 90 days after the market collapse ([H.sub.1c]).

There is another issue which must be addressed. Several scholars have suggested that corporate reputation as measured by the Fortune survey is a biased construct. Despite the fact that five out of the eight attributes of this reputation measure are non-financial in nature (discussed below), these researchers found evidence that considerations of a company's financial well-being may have influenced respondents overall perception of the corporations being evaluated, thus creating a confounding bias or financial "halo effect" (Brown & Perry, 1994; Fryxell & Wang, 1994). Nevertheless, there exists general consensus that reputation serves as an independent construct, even if the degree of that independence from financial measures is sometimes questioned. One way to test for this possible bias is to test for correlation between Fortune's reputation measure and a purely financial measure of corporate health in a given year. Then, if multicollinearity is not too high, include the financial measure in the model. One highly regarded and widely used measure of financial soundness is Standard and Poor's (S&P) earnings and dividend rankings for common stocks. These rankings--actually, grades--are determined by averaging performance over a ten-year period and are computed by measuring corporate earnings and dividends, then adjusting for growth, stability with long-term trend, and cyclicality (Standard & Poor's ..., 1987).

[Q.sub.1a] Are the variables corporate reputation and corporate financial rating significantly correlated?

Because past studies have indicated lack of clear relation between reputation and long-term financial performance (McGuire et al., 1988; Sobol et al., 1992), and, more importantly, because reputation does consider a number of non-financial attributes, it is suggested that, while corporate reputation and corporate financial rating might be correlated, they will have differing relationships with the dependent variable. Therefore, the following research question is raised:

[Q.sub.1b] Is there a difference between the relationship of corporate reputation with company stock price decline and corporate financial rating with stock price decline in cases of economic crises?

The second research question involves industry groups. For purposes of market reporting and analysis, corporate stocks are typically classified as belonging to one of eight major industry groupings. As listed daily in The Wall Street Journal, these groups are: basic materials, consumer cyclicals, consumer non-cyclicals, energy, financial, industrial, technology and utilities. Corporations in different industry groups exhibit differing degrees of interaction with aspects of their external environment, for example, an unexpectedly positive federal unemployment report might have immediate repercussions in a consumer-cyclical industry like automobiles, but very little effect in the utility category. So it was in the case of the 1987 stock market crash. An analysis conducted by The Wall Street Journal a week after the 1987 crash confirmed that the stocks of different industrial sectors were affected differently (Slater, 1987). This analysis, however, was by individual sector (e.g., toys), not category (e.g., cyclicals), and considered neither reputation nor financial rating as variables. This raises the following research question:

[Q.sub.2] In cases economic crisis, is there a difference in stock price decline when firms are examined by industry category?

METHODS

Variables

DEPENDENT: STOCK PRICE DROP: The dependent variable in this study is the percent change in the company's common stock price from the closing price the day before the major market decline to the closing price at the following selected points in time:

a) the day of the decline

b) two weeks after the decline

c) 90 days after the crash (for 1987 data only)

INDEPENDENT: 1. CORPORATE REPUTATION RATING: The independent variable "reputation" was measured by Fortune magazine's annual survey of corporate reputations. These surveys have been conducted every fall since 1982, with summary results published within the first quarter of the following year. In these surveys, executives, outside directors and corporate analysts were asked to rate the ten largest companies in their industry on eight attributes: quality of management; quality of products or services; long-term investment value; innovativeness; financial soundness; ability to attract, develop, and keep talented people; community and environmental responsibility; and use of corporate assets. The industry groups surveyed are the largest in the Fortune 500 and Fortune Service 500 directories of U.S. industrial and non-industrial corporations. Sample sizes for three years of survey results analyzed were 262, 263, and 370 for the 1987, 1989, and 1996 data sets, respectively.

A number of studies have used the Fortune data set (Chakravarthy, 1986; Conine and Madden, 1986; Frombrun & Shanley, 1992; McGuire, Sundgren, and Schneeweis, 1988; RiahiBelkaoui and Pavlik, 1992; Sobol et al., 1992). Frombrun and Shanley (1990) point out, however, that the approach of several prior studies has been inappropriate to the extent that they relied on single dimensions of reputation--dimensions that, together, "demonstrate considerable empirical relatedness" (p. 245). Their factor analysis of the 1990 data, by rating, extracted a single factor which accounted for 84 percent of the variance (alpha=.97). After finding similar results with the Fortune survey results from 1982, 1983, 1984, and 1986, they concluded that "the eight attributes elicited from respondents were components of an underlying and stable construct of reputation" (p. 245). For the current study, the authors performed a similar analysis on the 1987 Fortune data set, with similar results. The eight attributes' ratings were included as variables in a factor analysis; one factor emerged, with a Chronbach's alpha reliability measure of .96. Thus, the overall reputation measure is unidimensional and the eight individual attributes are not included in further analyses.

INDEPENDENT: 2. STANDARD AND POOR'S (S&P) GRADE for earnings and dividend rankings for common stocks. This variable measures the financial soundness of a company, and is probably best described by quoting directly from S&P: "In arriving at these rankings [S&P uses] a computerized scoring system based on per-share earnings and dividend records of the most recent ten years ... Basic scores are computed for earnings and dividends, then adjusted as indicated by a set of predetermined modifiers for growth, stability within long-term trend, and cyclicality. Adjusted scores for earnings and dividends are then combined to yield a final score ..." (Standard & Poor's Security Owner's Stock Guide, 1987, p. 7). The final S&P scores assigned are in the form of letter grades, which were converted to numbers for our analyses, as follows:

A+ Highest = 7 B+ Average = 4 A High = 6 B Below Average = 3 A- Above Average = 5 B- Lower

= 2

C Lowest = 2 D In Reorganization = 2 Not rated by S&P

= 1

The lowest 3 grades were combined for this analysis, giving 6 ordinal categories of the variable "S&P Grade". A seventh category, grade 1, was assigned to those companies who were not rated by S&P.

INDEPENDENT: 3. INDUSTRY CATEGORY: Fortune's reputation data set contains as many as 40 very specific industry groups. These were consolidated into more general industry categories using The Wall Street Journal classifications: Basic materials; Consumer, cyclical; Consumer, non-cyclical; Financial; Industrial/manufacturing; Technology; and Utilities.

Statistical Tests

The main objective of the statistical analysis is to determine the effects of three independent variables on percent change in stock price. Two of the independent variables in the study are qualitative (defined as categorical or ordinal). Specifically, S&P grade is ordinal and industry group is categorical. The other independent variable, reputation rating, is quantitative (defined as interval-or ratio-scaled). The dependent variable, percent change in stock price, is also quantitative. In the situations where the dependent variable is quantitative and independent variables are both quantitative and qualitative, analysis of covariance (ANCOVA) is appropriate (Kachigan, 1986; Pedhazur, 1982; Wildt & Ahtola, 1985).

ANCOVA results show whether reputation rating, industry group, and/or S&P grade has a statistically significant effect on stock price change. It also provides R-squared information which measures how much of the change in the dependent variable is being explained by the independent variables.

Where ANCOVA's F-test shows that one of the qualitative independent variables (grade or group) has a significant effect overall, a post hoc comparison test is used to determine exactly where the differences are. The test used is Duncan's Multiple Range test (Huck, Cormier, and Bounds, 1974, p. 68-9). Additional tests performed on the data include Cook's test for outliers (SPSS-X User's Guide, 1988, p. 858-9) and correlation analysis. When correlations were run, grade 7 was left out of the "grade" column and row, as it was not necessarily an ordinal measure.

These statistical tests were repeated separately for each of the three dependent variables: oneday, two-week, and 90-day (1987) drop in stock price, in each of the three years studied. The following section shows the results of the correlation analysis, by year, followed by the ANCOVA and Duncan-Wallis results. The succeeding section summarizes the findings and discusses conclusions drawn from the results.

RESULTS

Table 1 shows the results of a correlation analysis run for all dependent and independent variables in the study.

The correlation between the S&P rating ("grade") and the reputation according to the Fortune survey (Reprate) ranged between 45.9% to 51.2%. Although statistically significant, a 50% correlation demonstrates that these two variables are not identical and both can and should be included in the ANCOVA model for further analysis.

Example Analysis--Two-Week Drop, 1987

Both the company reputation and the S&P stock grade had a significant relationship with the change in stock price the day before the crash of 1987 and the price two weeks later (p<.01). The industry group was also significant at the p<.01 level. Those stocks with a better reputation suffered significantly less decline in price, as did those with a better stock grade (see Table 2). The adjusted R2 was .420.

As hypothesized, the fall in stock price was different by industry group. Below, the Duncan test shows more specifically where differences among categorical variables were.

This table (Table 3) reveals that industry groups 3 (consumer non-cyclicals) and 7 (utilities) fell significantly less in price than the others. Stocks in Group 4 (financial institutions) fell the most--a resounding 20.3%. As would be expected, S&P grades A+, A, and A- (coded 7, 6, and 5) weathered the crash better than those of lower grades.

Summary of Analysis of Covariance Results

Both Analysis of Covariance (ANCOVA) and, where differences were indicated by ANCOVA, Duncan-Wallis tests were performed on the data. ANCOVA results are summarized in Table 4 below. The "90 day" column is "not applicable" in the 1989 and 1996 analyses because by that time, in the case of both stock market crashes, the market had fully recovered. These results and Duncan-Wallis test results are discussed below.

The ANCOVA results show that group and reputation had a significant effect on the 90day stock price drop in 1987 (p<.05), but financial grade did not. Those stocks with a better reputation suffered significantly less decline in price, as did those with a better stock grade. As hypothesized, the fall in stock price was different by industry group. The price drop after 90 days of noncyclicals was still significantly less than the drop in stock prices of other industry groups (Table 5).

In 1989, as in 1987, both the company reputation and the S&P stock grade had a significant relationship with the change in stock price the day before the market decline of 1989 and the price two weeks later. Those stocks with a better reputation suffered significantly less decline in price (p<.05), as did those with a better stock grade (see Table 4). The industry group variable was significant at the .05-level. As before, the fall in stock price was also different by industry group. For the 1989 data set, three companies were eliminated from the analysis because Cook's Distance showed that they were outliers. This was justified on the basis that their extremely large drops in price must have been influenced by factors outside of this analysis. For example, one of them, Federal National Mortgage Association, had a 3-for-1 stock split during the time period under study.

The Duncan procedure results (Table 6 below), show significant differences among industry groups as they related to stock price decline. Group 4 (financial) suffered the largest loss, Group 7 (utilities) the least. Companies with a financial grade of A+ (here Grade 7), experienced significantly less stock price decline than those with grades of A- and lower (Grades 6 through 1).

One other time period, the one-day 1996 time period, bears discussing. Unlike in 1987 and 1989, both the company reputation and the industry group had a significant relationship with the change in stock price the day immediately following the market decline of 1996. Those stocks with a better reputation suffered significantly less decline in price (seeTable 4). Industry group was significant at the p<.01 level; reputation was significant at the p<.01 level; financial grade was not significant. In this analysis there were no missing cases. As before, the fall in stock price was also different by industry group. Again, the Duncan test shows more specifically where differences among categorical variables were.

The Duncan procedure (Table 7 below), shows that Industry Group 4 (financial) again suffered the largest stock price decline relative to other groups. Group 1 (basic materials) experienced a significantly lower decline than Groups 4, 2 (consumer cyclical) and 6 (technology).

DISCUSSION OF RESULTS

In this section the results of the data analyses are discussed. Each subsection discusses a different Hypothesis or Research Question. The primary set of hypotheses concerned corporate reputation and economic loss subsequent to a crisis. Recalling that economic crisis in this study was operationalized as a one-day stock market decline of at least three percent, these were as follows: Companies with higher reputations will suffer significantly lower stock price declines the day of an economic crisis (H1a); Companies with higher reputations will suffer significantly lower stock price declines two weeks after the initial crisis (H1b); Companies with higher reputations will suffer significantly lower stock price declines 90 days after the market collapse (H1c for 1987 only).

Research Question 1 concerned the relation between corporate reputation and financial rating, and their respective ability to predict loss in times of economic crisis: Are the variables of corporate reputation and corporate financial rating significantly correlated (Q1a)? Is there a difference between the relationship of corporate reputation with company stock price decline and corporate financial rating with stock price decline in cases of general and sudden negative economic events (Q1b)? Research Question 2 concerned the relation between industry group and stock price drop in times of economic crisis: In cases of general and sudden negative economic events, is there a difference in stock price decline when firms are examined by industry category (Q2)?

Reputation and Loss

The central focus of this study was the hypothesis that higher corporate reputation would predict lower economic losses in cases of sudden and general economic decline (H1). Subsequent to three large stock market declines, seven different points in time were analyzed to determine the relationship between firm reputation and stock price loss. For convenience of reference an abridged summary of results table is reproduced below.

As shown by Table 8, the reputation variable was significant in the analysis of covariance model at four out of the seven measurement points. Further, at two weeks after the 1996 decline, the market had fully recovered, meaning that for practical purposes reputation was significant in four out of six points in time in the analysis, the one-day junctures in 1987 and 1989 being the obvious exceptions. Thus in 1987 H1 was supported at the two-week and 90-day post-crash measuring points, but not at the one-day point (H1a). With exception noted, higher corporate reputation is associated with reduced economic losses in times of economic crisis. This relationship is more consistent than that of corporate financial grade and loss, as discussed below.

However, it is not always true. At the close of the day immediately following the severe stock market declines of 1987 and 1989 the reputation variable did not even approach statistical significance. Two weeks after the less precipitous market fall of 1996 reputation also failed to reach significance; however, again, the market had fully recovered. These results suggest that during and immediately after a substantial market decline investors may react irrationally--or at least less rationally than usual. This explanation is bolstered by the fact that no other variable tested in this research achieved significance at one day subsequent to the 1987 and 1989 market declines in any of the statistical analyses conducted. There were no significant Correlations between variables, and in the ANCOVA analysis the industry group variable, significant at every other meaningful measuring point, failed to approach statistical significance by a considerable margin. Likewise the financial grade variable, which was significant at some other points, was statistically insignificant here.

This argument, that the irrational behavior of market investors immediately after a substantial crash precludes patterns of meaning, gains further strength by recalling some of the language employed in contemporary editions of popular media. Time, Newsweek, and Business Week, described the crisis of 1987 with words like "massacre," "historic crash," and "blind panic," and the 1989 decline as a "crash," "nightmare," and (quoting an analyst) "total emotional and psychological chaos." In contrast, the still serious but less panic-inducing 1996 market decline was often referred to simply as a major "selloff."

Furthermore, as Business Week, Time, and The Wall Street Journal report, a major factor behind the speed of the market's descent in 1987 was the almost total computerization of the New York exchange and other markets. A large volume of trades on the day of the 1987 crash was performed automatically by computers programmed to execute trades of large portfolios of stock when prices fell to predetermined trigger points. These programs took decisions out of the hands of brokers, contributing to a scenario where " ... Wild price fluctuations bore little resemblance to the fundamental value of the venerable industries involved" (Isaacson, 1987). Safeguards were subsequently imposed on the New York and Chicago (Mercantile) exchanges, but in 1989 these automatic trading cutoffs, or "circuit breakers," while slowing the rate of decline, were not fail-safe. In Chicago some traders claimed that futures restrictions prevented the ability of some investors to hedge their losses, "forcing them to dump stocks and exacerbating the selling frenzy in Manhattan" (Greenwald, 1989). By 1996 these idiosyncrasies had been worked out of the system. Thus, at the truly meaningful measurement points in the study (one-day 1996, two-week 1989, and both two-week and 90-day 1987), the reputation variable was significant.

In conclusion, it is logical to presume that in proportion to their positive strengths, reputations do provide a reservoir of goodwill to corporations, and that this quality can serve as a buffer to help reduce environmental uncertainty by insulating companies from loss in times of economic crisis. Further, although by the standards of econometric or market prediction models selecting seven data collection points for analysis is relatively limiting, it is also logical to conclude that there tends to be a pattern of significance over time to the reputation variable which reflects the severity of the economic crisis involved.

Reputation, Financial Grade, and Loss

Standard & Poor's (S&P), the authoritative investment rating corporation, grades common stocks according to per-share earnings and dividends records over a ten year time span, yielding the most respected and widely used corporate stock assessment measure on Wall Street. Grades range from A+ to D, and were incorporated into the present analysis to determine the predictive value of this important indicator in terms of stock price loss during times of economic crisis. Furthermore, this variable was included to help determine if any functional distinction in predictive value could be made between financial grade and corporate reputation. That is, given that three of the eight attributes of the reputation variable are financial in nature, and that a suggestion has been made in the academic literature that Fortune's construct suffers from a financial "halo effect," is reputation so defined distinct from purely financial considerations?

Company reputation ratings and Standard & Poor's financial grade were highly correlated in all three years studied. The correlations were greater than .5 in 1987 and 1989, and .45 in 1996, all significant at the p<.01 level This is to be expected, as several attributes in the reputation construct were financially based. Yet the two variables do measure different qualities. As pointed out elsewhere, the reputation rating is perceptual, constructed from survey data, whereas the financial grade is based on accounting data. Second, the reputation measure incorporates a number of non-financial attributes.

The financial grade of a company, as rated by Standard & Poor's, was statistically significant in only two of the four (meaningful) time periods measured. Unlike the other two variables however, financial grade was only significant at the two-week point in 1987 and 1989, failing to attain significance at either the 90-day point in 1987, or at the one-day point following the 1996 drop (see Table 4).

The lack of significance of corporate financial grade at times when reputation is significant is remarkable. Standard & Poor's financial grades are closely monitored on Wall Street and widely regarded across the financial community. These financial ratings are based on a number of accounting factors--"hard data"--and reflect the most objective view of the financial soundness of a company available. Reputation, on the other hand, while incorporating several financial attributes and representing the collective opinion of industry experts, is nevertheless based on survey data and therefore essentially perceptual. This is most evident with the five non-financial attributes of reputation--innovativeness; ability to attract, develop and keep talented people; quality of product or services; quality of management; and community and environmental responsibility. These are impressionistic attributes, difficult to quantify. Yet it is clear good corporate reputation is an important variable when considering severity of stock price decline

Industry Group and Loss

Of the dozens of potential accounting, institutional, and market variables that might influence stock price--and therefore economic loss--in times of sudden turmoil, the business in which a firm engages is a fundamental consideration. It is generally understood that some sectors of the economy are simply more volatile that others and therefore more likely to experience more significant damage in instances of sudden market downswings. Utility companies, for example, enjoy more price stability than companies producing consumer goods which experience cyclical demand. This inherent volatility differential was taken into account in this study by categorizing companies into seven industry groups, and incorporating that variable into the analysis of covariance model.

Mirroring the results of reputation, the industry group variable was statistically significant in all cases except the one-day post-crash measurements of 1987 and 1989 (excepting again the meaningless two-week post-1996 crash; see Table 4). This lends further support to the argument that the mass hysteria which can envelope the trading floor the day of a major stock market crash can be sufficient to wash out the influence of all variables. With those exceptions--which exist, it is assumed, for similar reasons as elaborated in the previous section--Research Question 2 is answered affirmatively. In cases of economic crisis there is a difference in stock price decline when examined by industry category.

SUMMARY AND CONCLUSIONS

The primary finding of this study is that good corporate reputation can help to buffer a company against economic loss on occasions of sudden, unanticipated stock market decline--categorized as a nonviolent, unintentional crisis. It is suggested that the general mechanism by which this phenomenon occurs is the process of uncertainty reduction within the context of resource dependence theory. One means by which corporations can reduce the uncertainty of a competitive and potentially hostile environment is by cultivating a 'reservoir of goodwill'--an abstraction often employed in both the organizational communication and public relations literature, but not empirically tested. This reserve of positive reputation could act in a preventative, proactive capacity in a similar fashion to the inoculation effects of issue/advocacy advertising, a phenomenon for which some empirical evidence does exist. Unlike issue advertising, however, which is based on specific issues and involves refutational preemption of a threat, reputation is a general construct which does not involve specific preemption and can protect the organization against loss in times of crisis. Reputation is the vessel in which 'goodwill' is accumulated.

By statistically comparing Standard & Poor's financial grade with the Fortune gauge of reputation across several hundred companies in three recent years, this study has contributed to the understanding of the relationship between these two measures of corporate well-being. The former measure is based on objective accounting data, the latter, reputation, based on survey data. The reputation construct consists of a number of attributes independent of financial consideration. As was shown, these measures are very significantly correlated at about the .5 level. This finding supports the argument that, while both measures are indicators of corporate fundamental condition, they are different constructs. It may be, as has been suggested in some of the literature, that the financial condition of a company is so central to all else that it taints measures of broader concern like reputation. But the correlation statistic indicates that the overlap is not excessive. This argument is further buttressed by the finding that at two different points in the analysis, 90 days after the stock market crash of 1987 and immediately after the decline of 1996, reputation proved to be a significant predictor of lower corporate loss, whereas financial grade was not significant. Results confirmed the presumption that type of industry, as a categorical variable, would have a significant effect on the extent to which a negative economic event influenced stock price decline.

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Wildt, A. R., & Ahtola, O. T. 1985. Analysis of covariance. Sage University Papers, Series: Quantitative applications in the social sciences., 12. (Series Editors, J. L. Sullivan and R. G. Niemi. Series No. 07-012). Beverly Hills, CA: Sage.

Gary H. Jones, Truman State University

Beth H. Jones, Western Carolina University

Philip L. Little, Western Carolina University

Table 1: Correlation Analysis

1987 GRADE REPRATE ONEDAY TWOWEEK NDAY

GRADE 1.0000
REPRATE .5117 ** 1.0000
ONEDAY .0717 0.03% 1.0000
TWOWEEK .5487 ** .4331 ** 8.20% 1.0000
NDAY .1603 * .1851 ** .0842 .4104 ** 1.0000

1989 GRADE REPRATE ONEDAY TWOWEEK

GRADE 1.0000
REPRATE .5073 ** 1.0000
ONEDAY 0.1036 0.1054 1.0000
TWOWEEK .3707 ** .3426 ** .3176 ** 1.0000

1996 GRADE REPRATE ONEDAY TWOWEEK

GRADE 1.0000
REPRATE .4593 ** 1.0000
ONEDAY 0.0368 -.1040 * 1.0000
TWOWEEK 0.0066 -.0511 .1639 ** 1.0000

*--Significance LE .05

**--Significance LE .01 (2-tailed)

Table 2: Two-Weeks After 1987 Crash--Analysis of Variance

Dependent variable: two-week % change in stock price

Source of Variation Sum of DF Mean
 Squares Square

Main Effects 1.141 13.000 .088
GROUP .266 6 .044
GRADE .168 6 .028
REPRATE (Covar) .054 1 .054

2-Way Interactions
GROUP GRADE .217 29 .007

 F Sig
 of F
Source of Variation

Main Effects 16.043 .000
GROUP 8.116 .000
GRADE 5.112 .000
REPRATE (Covar) 9.950 .002

2-Way Interactions 1.365 .110
GROUP GRADE

Table 3: Differences Among Categorical Variables--1987 Two-Week

MULTIPLE RANGE TEST--DUNCAN PROCEDURE

Mean Industry Group
 Group 4 1 5 2 6 3 7

-.2035 Grp 4, Financial
-.1940 Grp 1, Materials
-.1914 Grp 5, Industrial
-.1728 Grp 2, Cyclicals
-.1711 Grp 6, Technology
-.0758 Grp 3, Noncyclicals * * * * *
-.0158 Grp 7, Utilities * * * * * *

Mean Grade
 2 1 4 3 5 6 7

-.2483 Grade 2
-.2096 Grade 1
-.1942 Grade 4 *
-.1772 Grade 3 *
-.1245 Grade 5 * * * *
-.1080 Grade 6 * * * *
-.0860 Grade 7 * * * *

* denotes pairs significantly different at the p<.05 level

Table 4: Summary of ANCOVA Results

([R.sup.2] Not Adjusted)

Year ONE DAY
 (Percent loss)
1987
October No
 Significance
 (22%)
1989
October No
 Significance
 (7%)

1996 Reputation: p<.01
March Group: p<.01
 Grade: NS
 [R.sup.2] = .16
 (3%)

Year TWO WEEK

1987 Reputation: p<.01
October Group: p<.01
 Grade: p<.01
 [R.sup.2] = .45
1989 Reputation: p<.05
October Group: p<.05
 Grade: p<.01
 [R.sup.2] = .24

1996 No
March Significance

Year 90 DAY

1987 Reputation: p<.05
October Group: p<.05
 Grade: NS
 [R.sup.2] = .11
1989
October Not
 Applicable

1996 Not
March Applicable

Table 5: Differences Among Categorical Variables--1987 90-day

MULTIPLE RANGE TEST--DUNCAN PROCEDURE

Mean Industry Group
 Group 6 2 4 5

-.2294 Grp 6, Technology
-.2259 Grp 2, Cyclicals
-.2154 Grp 4, Financial
-.1685 Grp 5, Industrial
-.1504 Grp 1, Materials
-.1248 Grp 7, Utilities
-.0989 Grp 3, Noncyclicals * * *

Mean Industry Group
 Group 1.00% 7 3

-.2294 Grp 6, Technology
-.2259 Grp 2, Cyclicals
-.2154 Grp 4, Financial
-.1685 Grp 5, Industrial
-.1504 Grp 1, Materials
-.1248 Grp 7, Utilities
-.0989 Grp 3, Noncyclicals *

Mean Industry Grade
 Group

-.2294 Grp 6, Technology "No two grades are
-.2259 Grp 2, Cyclicals significantly
-.2154 Grp 4, Financial different at the
-.1685 Grp 5, Industrial p<.05 level"
-.1504 Grp 1, Materials
-.1248 Grp 7, Utilities
-.0989 Grp 3, Noncyclicals

* Denotes pairs significantly different at the p <. 05 level

Table 6: Differences Among Categorical Variables--1989 Two Week

MULTIPLE RANGE TEST--DUNCAN PROCEDURE

Mean Industry Group
 Group 4 1 5 2
6 7

-.1312 Grp 4, Financial
-.0954 Grp 1, Materials *
-.0925 Grp 5, Industrial *
-.0903 Grp 2, Cyclicals *
-.0766 Grp 6, Technology *
-.0653 Grp 3, Noncyclicals * * *
-.0366 Grp 7, Utilities * * * *

Mean Industry Group
 Group 6 3 7

-.1312 Grp 4, Financial
-.0954 Grp 1, Materials
-.0925 Grp 5, Industrial
-.0903 Grp 2, Cyclicals
-.0766 Grp 6, Technology
-.0653 Grp 3, Noncyclicals
-.0366 Grp 7, Utilities *

Mean Grade Grade
 1 2 3 4 5

-0.1395 Grade 1
-0.1167 Grade 2
-0.1009 Grade 3 *
-0.0915 Grade 4 *
-0.0807 Grade 5 * *
-0.0627 Grade 6 * * * *
-0.0472 Grade 7 * * * * *

Mean Grade 6 7

-0.1395 Grade 1
-0.1167 Grade 2
-0.1009 Grade 3
-0.0915 Grade 4
-0.0807 Grade 5
-0.0627 Grade 6
-0.0472 Grade 7

* denotes pairs significantly different at the p<.05 level

Table 7: Differences Among Categorical Variables--1996 One Day

MULTIPLE RANGE TEST--DUNCAN PROCEDURE *

Mean Industry Group
 Group 4 2 6 3 7 5 1

-.0434 Grp 4, Financial
-.0297 Grp 2, Cyclicals *
-.0276 Grp 6, Technology *
-.0242 Grp 3, Noncyclicals *
-.0210 Grp 7, Utilities *
-.0206 Grp 5, Industrial * *
-.0184 Grp 1, Materials * * *

Mean Industry Grade
 Group

-.0434 Grp 4, Financial "No two grades are
-.0297 Grp 2, Cyclicals significantly
-.0276 Grp 6, Technology different at the
-.0242 Grp 3, Noncyclicals p<.05 level"
-.0210 Grp 7, Utilities *
-.0206 Grp 5, Industrial
-.0184 Grp 1, Materials

* denotes pairs significantly different at the p<.05 level

Table 8: Condensed Summary of Results, Reputation

Year ONE DAY TWO WEEK 90 DAY
 (Percent loss)

1987 NS Reputation: p<.01 Reputation: p<.05
 ('22%)
1989 NS Reputation: p<.05 N/A
 (7%)
1996 Reputation: p<.01 NS N/A
 (3%) (0%)