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    Improving the communication of accounting information throug Improving the communication of accounting information throug Document Transcript

    • AAAJ 9,2 Improving the communication of accounting information through cartoon graphics 68 Malcolm Smith Murdoch University, Perth, Australia, and Richard Taffler City University Business School, London, UK Introduction Scant attention has been paid by accounting academics to the question of improving the communicative ability of financial statements and their decision- support role. Libby (1981, p. 101) identifies three available options for the improvement of decision making: (1) changing the content or presentation of the available information; (2) education of the decision maker; and (3) replacing the decision maker with a model. This paper addresses the first of these options, in the context of improvements in the presentation of accounting information. Accounting data is essentially multivariate and its assessment depends on the simultaneous effect of several variables in different spheres of activity. Complex tabular presentations do not facilitate an integration of the key features of the accounts and a segmented multi-column format may leave an indication of separate aspects of performance rather than an overall assessment. An alternative means of presentation might provide a clearer and more efficient representation, complementing existing methods. Pictorial methods, especially those able to represent several dimensions simultaneously in a form that may be perceived in terms of an overall impression (a Gestalt), may potentially be useful in this regard. De Sanctis and Jarvenpaa (1989) suggest that before graphical displays become more meaningful than traditional numeric methods further studies need to demonstrate: (1) the conditions under which graphs are effective; (2) how users might be trained to use graphs; and (3) how graphs might be altered to increase their power relative to accounting data. The third of these criteria provides the focus of this paper. Accounting, Auditing & Conventional pictorial methods are extremely limited in their application. Accountability Journal, Vol. 9 No. 2, 1996, pp. 68-85. Traditional graphs and charts work well in only two or three dimensions and © MCB University Press, 0951-3574 quickly become over complicated when multivariate information is employed.
    • Working within three dimensions is extremely advantageous from a Communication communications point of view, but in many practical instances this is rarely through cartoon possible if more than a superficial overview is to be conveyed. Many alternative pictorial methods have been employed in an attempt to facilitate the graphics communication of information – ranging from the familiar bar and pie charts and pictograms to more obscure forms. The pie chart, bar chart and trend graph as detailed by Beattie and Jones (1992), have become familiar and 69 acceptable in the financial report as alternatives to the narrative and numerical form; schematic faces have yet to achieve such acceptability, perhaps because of the novelty of the approach and the emotive manner in which their accounting message is conveyed. Alternative methods of presentation, notably those involving the use of a facial format, may seem a little strange to existing users, but the test of their usefulness will be in the successful communication of financial messages. While demanding attention, such figures should also be clear enough to make interpretation possible without a detailed explanation. The complexity and familiarity of faces makes them a special class of visual input which derives from developmental changes in infants, whereby they learn quickly to respond to more differentiated forms. Schaffer (1971, p. 69), suggests that with increasing age the overriding importance of the eyes as a source of recognition and attraction is complemented by increasing attention to other facial features, facilitating the differentiation between various expressions. The similar reaction of infants to real faces, photographic representations and schematic line drawings, forms the basis of their reaction as adults to the messages provided by cartoon faces. The possibility exists that, with appropriate assignments, the facial format might be employed to communicate information on the magnitude and change in a number of variables simultaneously without the need for detailed explanation or education of users. There has been limited study to date of the effectiveness of alternative methods of presenting accounting information for financial decision purposes. Smith and Taffler (1984) recommend the further exploration of the use of schematic faces to represent accounting information, following the success of this medium in displaying multivariate data in other task environments. This paper explores empirically the usefulness of the schematic face as a communication device, in a particular decision context, compared with more conventional presentation formats, focusing on the relative usefulness of schematic faces, financial ratios and accounting statements as information formats for decision making. Literature review Financial information is both complex and multidimensional and if a complete picture is to emerge, rather than a series of financial relationships, then additional graphical methods are required which will represent adequately the multivariate nature of financial data. Canadian Institute of Chartered Accountants CICA (1993, p. 122) suggests that the ability of multivariate
    • AAAJ graphics to portray data in an understandable form may result in their 9,2 producing better decision making than corresponding tabular presentations. The impact of graphical representation is an important attribute since a forceful picture must be produced which allows new stimuli from a complex data set to be perceived while existing stimuli are being integrated. Psychologists, among them Yin (1969), Smith and Nielsen (1970), and Reed 70 (1972), argue that the acquisition and organization of information within dimensions, by decision makers, is perceived as a Gestalt so that stimuli are processed in a holistic manner. In addition to providing a Gestalt, the familiarity of faces commands attention and triggers an emotional reaction which enhances their overall impact in a way that other forms of graph do not. Garner (1978), Homa et al. (1976) and Sergent (1984) provide empirical support for the face being regarded as a spatial interrelationship of features capable of being perceived as a Gestalt, an issue central to the development of the schematic face in cognitive research. The familiarity of faces and their ease of recognition and description makes them superior to other pictorial forms of representation. Wilkinson (1982) demonstrates that face-based icons outperform alternative formats in the judgement of similarities. Haig (1984) demonstrates the incredible sensitivity of respondents to the smallest changes in facial features. Morton and Johnson (1989) note that faces are special more than by virtue of their being visible parts of the human form since they can signal their intentions. This is so even though there is no convincing evidence that they are processed any differently from other objects among which we require to discriminate. Chernoff (1971) initiated the computer-based construction of schematic “faces” whose features can be made to vary in size and shape according to the value of the assigned variable[1]. The original form of portrait has been adapted by Bruckner (1978) to provide greater variation and by Frith in Everitt (1978) and by Flury and Riedwyl (1981) to provide greater realism. Valentine (1986) views the human face as a series of vectors in multidimensional space with dimensions corresponding to significant features. Her study suggests that a matching of significant features with financial performance measures, provides the possibility of communicating multidimensional financial information in a simple, integrated and readily comprehensible form. Three major contributions to the literature in this area (Moriarity, 1979; Smith and Taffler, 1984; Stock and Watson, 1984) have applied Chernoff ’s methods in a financial environment: Moriarity (1979), in the seminal study in the area, working with financial statement data, examines the use of multidimensional graphics as a technique for describing the financial status of the firm. His innovative approach provides encouraging results which suggest that unsupported faces provide an excellent framework for decision making when produced as an alternative to information conveyed in more traditional fashion. Moriarity examines the speed and accuracy with which respondents classify companies as failed or non-failed, without knowledge of prior
    • probabilities, when presented with appropriate financial information in Communication alternative formats. through cartoon Moriarity’s respondents found the changes most easily detected in the faces, graphics which were classified faster and more accurately than either the raw accounting numbers or derived ratios. His results suggest that our familiarity with faces makes an interpretation of the portraits possible without a detailed knowledge of the information used or the facial assignments employed. However, 71 Moriarity’s sample included only ten respondents of any accounting sophistication, the remainder being first year accounting undergraduates, so the strong relative performance with the faces may be attributable to ignorance of accounting concepts. Moriarity makes no attempt to match the most salient financial and facial features nor to manipulate eyebrow slant in his schematic faces, despite the psychological evidence suggesting their importance. Stock and Watson (1984) take a similar approach but employ judgementally- determined bond ratings as their classification base in suggesting the potential usefulness of schematic faces in situations where statistical models are weak. They demonstrate the relative usefulness of faces but their findings may reflect the complexity of the task rather than the method of data presentation. In addition, both of the above studies might be criticized for failing to compare like with like, so that the superiority of presentation apparent from the faces may actually represent superiority of information. Schematic faces are constructed in a relative, not absolute manner, usually standardized relative to industry means and standard deviations. However, Moriarity provides no standard deviations for his financial ratios and Stock and Watson provide neither means nor standard deviations. No attempt is made to compare the performance of users of varying levels of accounting sophistication. Smith and Taffler (1984) in a UK environment illustrate how intertemporal performance comparisons can be made using schematic faces together with their use in facilitating the distinction between failed and non-failed companies in large datasets. They suggest that schematic faces may provide a clearer indication of financial status than is apparent from a company’s financial ratio profile. However, their study does not show whether accounting data can be analysed more quickly or more effectively in a facial format than when represented by more conventional means. None of these studies adequately reflects contributions from the psychological literature relating to feature assignment and facial construction. These are addressed below with a consideration of the features of the face, their interaction, and the use of caricatures. Goldstein and Mackenberg (1966), Grant (1970), Laughery et al. (1971) and De Soete and De Corte (1985) identify three main expressive areas of the face which link movements with particular emotions: the eyes, the eyebrows and the mouth. They emphasize the importance of the eye and mouth regions, which are more mobile than others and communicate more information, and the relative insignificance of the ears. Notably, Stock and Watson (1984) employ a feature direction the opposite of that suggested by the psychological literature and
    • AAAJ accord the nose a prominent role in the assignment of financial variables to 9,2 facial features, despite the strong psychological evidence to the contrary. McKelvie (1973) demonstrates that the perceived meaningfulness of the face is at its greatest when both the eyebrows and mouth curvature vary from an average position, suggesting that the interaction of eyebrow slant and mouth curvature provides an effective force in the communication of meaning in facial 72 expressions. The findings of these studies from the psychological domain are employed in the facial constructions in this study. Following Ryan and Schwartz (1956), Chernoff (1978) suggests that experience with caricatures and cartoons indicates that the need for realistic faces on pictures is not great. Further supporting evidence is supplied by Diamond and Carey (1986), who find respondents to prefer schematic cartoon faces to real faces in recognition exercises, and by Rhodes et al. (1987) who show that facial caricatures are recognized more quickly than line drawings – results consistent with a holistic theory of encoding. Benjamin and Pachella (1982) advise against making Chernoff faces more realistic, since the consequent introduction of irrelevant information will cause perceptual problems. They suggest that respondents will be unable to ignore irrelevant features, even when instructed to do so, so that the number of features presented in the display should be equal to the number of variables whose values are to be mapped. The suggestion is that where the facial portrait is required to communicate a message the emphasis must be placed on the mobile features. These features can be varied efficiently with the Chernoff (1978) and Bruckner (1978) formulation to facilitate the interpretation of the overall portrait, so that financial performance can be represented through appropriate assignment of financial attributes to facial characteristics. Provided that due attention is paid to the combination of facial features, without overemphasis on dominant features, it is possible that an integrative picture might emerge to give a clear indication of overall performance. This study overcomes many of the deficiencies of its predecessors and makes original contributions by incorporating relevant evidence from the psychological literature, and by extending the experimental work to skilled users. The complex issue of how financial variables are assigned to facial characteristics, however, remains an area for further study. No attempt is made here to vary the feature assignment, rather a single assignment is employed throughout; the most salient financial variables are assigned to those facial features deemed by the literature to be the most important, in a manner entirely consistent with the psychological evidence. Research method The task domain is the failed/non-failed company decision situation since there is a wealth of literature demonstrating the strong degree of environmental predictability for accounting statement-based ratio information (e.g. Altman, 1968; Taffler, 1982). A substantial literature, summarized by Foster (1986, p. 534), highlights the prediction of performance on the basis of trends supplied
    • by financial ratios and accounting statements. The ease of processing facial Communication profiles suggests that they might provide a more efficient means of making the through cartoon same analysis. This paper suggests that the relative lack of predictability of graphics information presented in the form of schematic faces in existing studies may be due to insufficient attention both to the appropriate assignment of variables to facial components and the relevant psychological literature on feature saliency. Accounting ratios suggested by Taffler and Sudarsanam (1980) are used to 73 represent the four key dimensions of accounting information[2]. These ratios are assigned to facial features in order to provide Chernoff portraits of the type shown in Figure 1. The aim is to produce faces which reflect the financial performance of the company and which can be interpreted without the need for a detailed explanation of the variables employed or the feature assignments. Figure 1. A template for failure classification: alternative outcomes from the assignment of financial variables to facial characteristics Distressed Neutral Healthy The schematic Chernoff faces employed here incorporate four variable features: mouth, eyes, eyebrows and nose. The ears remain constant throughout but the size and shape of the face may change because of the size and position of the facial features which it bounds. The precise location of each of the features is determined by the values of the assigned financial ratios relative to industry means and standard deviations. Thus, mouth (length, curvature and height) will be determined by a profit ratio; eye (separation, size and pupil direction) will be determined by financial gearing; eyebrow (angle and height) will be determined by liquidity, and nose (length and width) will be determined by working capital position. Different assignments are possible, but this particular assignment ensures that three key financial performance measures (profit, gearing and liquidity) are assigned to the mobile features of the face. Moriarity (1979), Stock and Watson (1984) and Smith and Taffler (1984) all use this type of schematic face structure to contrast “healthy” and “distressed” companies. An impression of a healthy, profitable and secure company is created by a smiling face and large eyes, while a company in financial distress has a worried frown, down-turned mouth and small eyes. The overall message created for the latter would be one of the empty, washed-out face of an impoverished enterprise.
    • AAAJ The clarity with which the foregoing figures convey their financial 9,2 messages, in the absence of a detailed narrative explanation, provides the impetus for an analysis of the relative explanatory power of alternative presentation formats. The feature assignment illustrates the way in which a visual impression of the personality of each company can be created which may even allow some 74 speculation as to its corporate identity and attributes[3]. The financial ratios are mapped precisely onto the facial features so that their positions correspond precisely relative to industry averages. For each financial ratio calculated, the industry means and standard deviations corresponding to that time period are used to convert the ratio into a numerical form expressed as “number of standard deviations above/below the industry mean”. For example, suppose that the manufacturing Company XYZ has a profitability ratio (PBIT/TA) of 0.238, where the industry mean and standard deviation for that sector in that particular year are respectively 0.07 and 0.12. Relative to the rest of the sector Company XYZ’s profitability is, therefore, 1.4 standard deviations above the mean  0.238 – 0.07   .  0.12  The mean position for the neutral face and the total range of lengths and angles feasible in the facial caricature allow the development of means and standard deviations for each facial feature. Financial ratios are mapped onto their assigned facial characteristic in terms of the number of standard deviations from the mean so that a precise correspondence of number to position is achieved. If profitability is mapped onto the mouth, say, then both the length and curvature of the mouth will be determined by the profit ratio. In the case of Company XYZ, above, both the length and the curvature will be 1.4 standard deviations above their mean position, resulting in the display of a modest smile. Use of industry relatives means that it is possible for an improved financial ratio in absolute terms to coincide with a deterioration of facial message if the ratio improvement is lower than that for the industry as a whole. The implicit use of industry statistics in constructing the facial portrait potentially improves the processing of financial messages over that with financial ratios, even when the industry statistics are made available to users. Consequent on the results of earlier studies, together with the incorporation of advances in the psychological literature, two issues arise for further consideration: (1) facial profiles might be processed significantly more quickly than either financial ratios or accounting statements;
    • (2) the classification decisions made using the facial profiles might be more Communication accurate than those made with either financial ratios or accounting through cartoon statements. graphics Together, these issues prompt a discussion of the resultant improvement in decision performance from the use of schematic faces, measured in terms of “efficiency” and “effectiveness”. With respect to the format of presentation, the most efficient form of presentation is defined by Bertin (1983) as that which 75 minimizes the effort, measured by time, which is expended to interpret the relevant aspects of the information set and provide a satisfactory answer to the given question. Effectiveness is defined by Lusk (1979), in this context, as the form of presentation which makes it easiest to generate the most accurate answer to a given set of questions. An experiment is, therefore, conducted to examine the facility of respondents of varying accounting sophistication with accounting information presented in alternative formats. Their processing time and the number of errors of classification that they make generate measures of the efficiency and effectiveness of the alternative formats. Although several previous studies (e.g. De Sanctis, 1984; Remus, 1984) have compared graphical and tabular data presentation formats, few have used schematic faces. Mackay and Villarreal (1987) recognize that Chernoff displays capture multivariate data holistically, in a mnemonic way,unique among graphical presentations, making comparisons with other presentation forms potentially difficult. Their study fails to identify any superiority of Chernoff faces over tabular data in financial decisions, but they do not distinguish between Type I and Type II errors. Altman et al. (1977) suggest a relative misclassification cost weighting of 40:1 in favour of Type I errors, relative to Type II errors, suggesting that a processing format is required which minimizes opportunities to misclassify failed companies. While the simplicity of the facial technique is a positive feature in communicating financial information, especially to the less sophisticated of users of accounting information, it can be a barrier preventing its widespread use. It has to be demonstrated that, apart from the novelty of approach, this method can improve on the quality of decisions made using traditional methods. To test the hypothesis that facial profiles might provide an efficient means of representing financial variables, an experiment is devised to test the reactions of respondents to financial information expressed in alternative forms: • accounting statements; • financial ratios derived from these statements; and • “faces” constructed by the application of financial ratios to particular facial features. The experiment is conducted with three different groups of skilled users and a group of naïve[4] users to represent users of all levels of sophistication:
    • AAAJ accounting academics from the Universities of Leeds, Lancaster and 9,2 Birmingham, accounting practitioners from Big 6 companies and MBA finance majors from City University, London. Together they provide a sample size of 121 sophisticated users, comprising 52 accounting academics, 23 practitioners and 46 postgraduate students. The skilled users, though not all practitioners, employ accounting information and financial statement data regularly, and 76 have an extensive knowledge of accounting terminology and format. Comparisons with naïve users, unfamiliar with accounting information, proved unworkable at the pilot stage of the study causing them to be excluded from the sample. These relatively unsophisticated users responded well to the schematic faces but for them the faces were the only accounting medium to convey any meaning. A systematic sample of 20 listed UK manufacturing companies is chosen to provide a majority that are still trading and a minority of failures, together with examples of companies across the whole range of processing difficulty. A random sample of a company database is not employed since it would be unlikely to give many (if any) failures. A 14:6 (i.e. 70 per cent:30 per cent) split between non-failed and failed companies in the sample is adopted because it conforms closely with the percentage split at the time between healthy and distressed companies in the population, based on their computed Z-scores. This division avoids an even distribution of companies while providing enough variety in the sample to illustrate the performance range. Companies with financial year ends between 1974 and 1980 are chosen to reflect the clearly healthy/clearly failed extremes while including several marginal and potentially more difficult cases. At no time are the respondents made aware of the 14:6 division[5]. Accounting statements and financial ratios are prepared and faces constructed for each of the 20 companies over five-year periods and a random numbering system used to separate the statements/ratios/faces information bases. Respondents are familiarized with the use of schematic faces during a 20-minute briefing session immediately prior to the conduct of the experiment. The briefing addresses: • accounting information as a complex multivariate dataset; • alternative graphical means for displaying data; • focus on the schematic face and its computer-based construction; and • advantages and disadvantages for potential applications of such faces in the financial environment Prior to the experiment respondents are issued with sample information sheets to illustrate the manner in which the statements and ratios will be depicted and with an illustration of the assignment of ratios to features in the facial representations. Each respondent is then issued with three separate sets of materials and asked to make failed/healthy decisions for each of the 20 cases, together with an
    • indication of the total time spent in processing the materials. Each set of Communication materials comprises financial information over a five-year period presented in a through cartoon different medium. For failed companies the fifth year is the last prior to failure. graphics On completion of the first decision task, respondents are asked to repeat the exercise successively with 20 sets of company financial ratios and schematic faces respectively. They are informed that the companies are different in each instance[6]. 77 Processing orders are varied so that all six possible orderings of statements- ratios-faces, statements-faces-ratios, etc., are employed. Test materials are ordered randomly for distribution among the three user groups in order to determine any impact that order of processing might have on the accuracy and speed of classification. A common single assignment of financial ratios to facial features is used throughout[7]. On completion of the experiment respondents are informed of the identity of the companies and their financial status. Mackay and Villarreal (1987) express concern over individual differences in cue responsiveness in the use of schematic faces. They note that content validity might be lowered because of the comic appearance of the faces, and that females appear to be more responsive to facial displays than males, both factors having a potential impact on the quality of the resulting decisions. Although desirable, the testing of gender effects is not possible with this sample. Only three of the entire sample of 121 are female, all being MBA students. We might speculate therefore that the subsequent results might even understate the impact of schematic faces. Results Outcomes measuring the “efficiency” and “effectiveness” of the alternative processing media are detailed in Table I for both the complete sample of 121 respondents and each of the separate user groups. An analysis of these results highlights two differences, each statistically significant at the 5 per cent level: (1) The proportion of failed cases misclassified is very much higher than that of the non-failed cases. This is a potentially important feature of the Mean percentage of classification errors Type I Type II Classification time (Healthy when failed) (Failed when healthy) (Minutes) Accounts Ratios Faces Accounts Ratios Faces Accounts Ratios Faces Accountants (n = 23) 29.0 31.2 5.1 15.8 29.5 20.8 12.9 11.6 4.0 Academics (n = 52) 34.0 38.8 15.4 12.8 16.6 13.2 11.2 7.6 3.8 MBA students (n = 46) 31.2 30.4 9.0 16.0 17.9 16.3 11.4 8.0 4.1 Table I. Total 32.0 34.2 11.0 14.6 19.5 15.8 11.6 8.5 4.0 Mean error classification
    • AAAJ results neglected by earlier studies, if, as suspected, the “missed failure” 9,2 is a relatively more important misclassification. (2) The time spent processing the facial profiles is less than half that spent on either accounting statements or financial profiles. Paired-case t-tests are employed to compare the decision-making performance 78 of individual respondents for each of the means of presentation. For each of the 121 respondents the t-statistics generated are shown in Table II together with the corresponding level of statistical significance. The faces produce significantly fewer Type I errors, than either the accounting statements or the financial ratios. The faces produce significantly fewer Type II errors than the ratios, but not the statements. The high rates of misclassification with financial ratios are consistent with the findings of Moriarity (1979) who attribute it to a lack of understanding of what the ratios really represent. Table III shows the corresponding differences for processing times, demonstrating that the faces are processed significantly more quickly than either the accounting statements or financial ratios. The facial profiles therefore Financial ratios Schematic faces Type I Type II Type I Type II Accounting statements Type I 0.9 9.1 (0.358) (0.000) Type II 4.0 1.1 (0.000) (0.277) Financial ratios Type I 10.6 (0.000) Table II. Type II 2.8 t-statistics for error (0.006) differences Note: The levels of statistical significance are in parentheses Processing time Ratios Faces Accounting statements 6.5 18.1 (0.000) (0.000) Financial ratios 13.0 Table III. t-statistics for processing (0.000) time differences Note: The levels of statistical significance are in parentheses
    • generate decisions at least as good, and often better, than using other sources, Communication and much more quickly. through cartoon An analysis of the error classification of Table I demonstrates the extent to graphics which performance differences are attributable to the accounting sophistication of the subjects and the processing order of materials. Tables IV and V reveal the F-statistics, and corresponding levels of significance, resulting from an ANOVA relating error incidence and processing time to the status of subjects and 79 processing time. Table IV shows that order of processing does not significantly influence the incidence of error for any of the alternative media. The level of accounting sophistication does impact on classification errors, with the accounting practitioners making significantly more Type II errors using the financial ratios Accounting Order of sophistication processing Accounting statements Type I 0.7 1.1 (0.506) (0.354) Type II 2.4 1.8 (0.100) (0.123) Financial ratios Type I 2.3 0.9 (0.101) (0.469) Type II 6.5 0.4 (0.002) (0.821) Schematic faces Type I 5.8 1.4 (0.004) (0.214) Type II 5.4 1.2 Table IV. (0.006) (0.314) F-statistics for Type I/II Note: The levels of statistical significance are in parentheses errors Accounting Order of sophistication processing Accounting statements 1.3 0.4 (0.283) (0.798) Financial ratios 7.7 6.2 (0.001) (0.002) Schematic faces 0.7 2.7 Table V. (0.492) (0.023) F-statistics for Note: The levels of statistical significance are in parentheses processing times
    • AAAJ and the accounting academics making significantly more Type I and Type II 9,2 errors with the schematic faces. Observed error patterns help to explain the manner of information processing and the decision-making strategies employed. A simultaneous consideration of profitability, short-term debt and balance sheet strength is sufficient to avoid errors of classification for each of the alternative processing 80 formats[8]. We may speculate on decision-making strategies employed by reference to the error patterns generated by respondents. A naïve processing strategy, applied systematically, of designating companies as “failed” based on a negative profit before tax figure, generates a familiar error pattern comprising three Type I errors and two Type II errors. This pattern of Type I errors arises in 18 per cent of accounting statement misclassifications (and 13 per cent of financial ratio misclassifications); this pattern of Type II errors arises in 27 per cent of accounting statement misclassificaitons (and 19 per cent of financial ratio misclassifications). The frequency of this pattern of errors suggests a myopic profit focus, to the extent that neither balance sheet information nor industry data receive appropriate emphasis.This single-variable fixation apparently extends to the schematic faces, where the most common misclassification pattern (of three Type II errors, but no Type I errors) is consistent with a strategy of designating as failed those companies displaying a down-turned mouth. This misclassification pattern is observed in 26 per cent of error profiles, but the extent of error is apparently less serious because the schematic faces automatically incorporate industry averages where a down-turned mouth is associated not with negative profits, but profitability levels less than the industry average. Poor performers (potential Type I errors) are, therefore, identified and the overprediction of failure (Type II error) becomes the dominant form of error. Overall, 69 per cent of errors on the accounting statements, 57 per cent of errors on ratios and 77 per cent of errors on the faces are consistent with a focus on the profit variable alone. This processing pattern is apparently particularly prevalent among the accounting academics, resulting in a disproportionate number of both Type I and Type II errors. The integration of balance sheet information on the accounting statements and ratios, corresponding to the incorporation of upper-face features on the schematic faces, allows a rapid reduction in the number of misclassifications. Table V shows that the processing time due to the financial ratio analysis is influenced by both levels of accounting sophistication and processing order. The accountants take a significantly longer time to process the financial ratios than either of the other groups, consistent with the unfamiliarity arguments cited above. The processing time for the ratios is significantly shorter when they are considered last of the three datasets. The reduction in elapsed processing time when being processed last is common to all three information media, but is significantly more marked in the case of the financial ratios.
    • Discussion and further research Communication There has been limited study to date of the effectiveness of alternative methods through cartoon of presenting accounting information for financial decision purposes. This graphics paper explores empirically the usefulness of the schematic face as a communication device, in a particular decision context, compared with more conventional presentation formats. The paper addresses the relative usefulness of schematic faces, financial ratios and accounting statements as information 81 formats for decision making, demonstrating that schematic faces provide an alternative means of presentation which might provide a clear and efficient representation, complementing existing methods. In this respect the paper provides substantial support for the findings of Moriarity (1979) but in a manner which produces results far more reliable than those of Moriarity or Stock and Watson (1984). The Moriarity study has the potential for biased outcomes favouring faces attributable to accounting ignorance, associated with the inclusion of too few experimental subjects of any accounting sophistication. Both of the above studies use arbitrary author-driven selections of feature assignments which make no reference to the relevant psychological evidence on the saliency and mobility of facial features. Neither study includes means and standard deviations for the financial ratio information provided, running the risk of producing results attributable to superior information and not superior presentation, given that the schematic faces implicitly incorporate these statistics. This present study overcomes all of these deficiencies associated with earlier research in this area. Evidence is provided that schematic faces are processed more quickly than either of the more traditional methods of information presentation, with no loss of accuracy, by users of varying levels of accounting sophistication. Repeated misclassifications are consistent with overemphasis on the profit figure. Where the facial profiles produce misclassifications not apparent with the other processing media, this is consistent with undue emphasis on the mouth as a facial characteristic. Subject feedback on their response to the use of facial caricatures, relative to more conventional information forms, is most revealing. Very little use is apparently made of mean and standard deviation information when provided, so that the superiority of the face may be at least partly attributable to the way in which it “forces” subjects to employ this additional information. The precise assignment of the financial variables to facial features too, appears to be relatively unimportant once subjects are familiar with the format. This is reassuring since the message conveyed by the face is so clear that the opportunities for manipulation might lead us to call for accounting standards which control the assignment of variables to features. Smith et al. (1993) suggest that the feature assignment is of much less importance to processing in practice than the methodology employed. Evidence from subsequent trials conducted by the authors is consistent with De Sanctis and Jarvenpaa (1989) who suggest the presence of a learning curve in the use of graphical information in the accounting profession. Casual observation suggests that with practice users of schematic faces will develop a holistic
    • AAAJ perspective and reduce the overemphasis accorded the mouth, with the 9,2 potential for further improved decision making. The impact of the use of caricatures on the behaviour of subjects can have both positive and negative effects, with the potential to influence decision making. The novelty value of schematic faces generates interest and makes them fun to work with; however, the trend in the use of graphics and other visuals in 82 the annual report, as reported by Beattie and Jones (1992) has not extended to the use of schematic faces. The message conveyed by the faces may be just too clear for this purpose, effectively preventing their successful obfuscation by firms wishing to disguise poor performance. Interestingly in this context, consulting conducted by the authors to display company schematic faces at the AGM to convey corporate performance, was discontinued once the faces of competitors were clearly superior to those of the client company! On the negative side, the faces may be perceived as trivial and not credible, with the potential for lowering the content validity of the experiment. One potential subject (among the group of accounting academics approached here) refused to take part in the experiment on these grounds. By demonstrating the potential for improved decision making utilizing new technologies even the most reluctant of participants cannot help but be impressed; in this case a group of accountants of a largely conservative demeanour make demonstrably more accurate decisions, much more quickly, through a medium that they had previously not confronted. The results presented here demonstrate the usefulness of schematic faces as a decision tool in the financial environment, with the potential to have a significant impact on the work of bankers, asset managers and financial analysts. By providing a speedy, accurate method of processing information, particularly for extreme cases, the schematic faces may free up management time for the more detailed analysis of complex situations. These might feasibly include the performance of investment managers, being appraised simultaneously on a number of different dimensions of activity, the communication of divisional or departmental performance based on non-financial achievement, or the representation of companies by different aspects of their stock market performance. All of these examples would move away from the failed/non-failed context, the last away from the good/bad distinction, by searching instead for patterns of performance that might yield a balanced portfolio. Future research must also pay more attention to the differences between individual subjects. Mackay and Villarreal (1987) hypothesize that mental state, cultural group, personality and psychological factors may be intervening variables worthy of investigation. Sobol and Klein (1989) echo these concerns, demonstrating empirically that the efficiency and effectiveness of graphical displays is dependent on the cognitive style of the respondents. They suggest that persons with a cognitive style suited to thinking, rather than feeling, have more success with less traditional graphic forms, though their study did not extend to a study of schematic faces. These factors should be taken into account in future studies in this area.
    • Notes Communication 1. The computer code employed to construct Bruckner’s version of the Chernoff face is through cartoon detailed in Wang (1978, pp. 115-20). 2. The four ratios are: graphics Profit before interest and tax  PBIT  • Profitability by  ; Total assets  TA  83 Working capital  WC  • Working capital position by  ; Net capital employed  NCE  Total liabilities  TL  • Financial leverage by   ; and Net worth  NW  Quick assets  QA  • Liquidity by   Current liabilities  CL  These four variables represent the same four dimensions (profitability, working capital, gearing and liquidity) as employed by Taffler (1982) in his Z-score model. The four variables above are preferred to his model variables (PBT/CL, CA/TL, CL/TA, NCI) on the grounds of user familiarity. 3. This visual representation of performance, based on accounting numbers, complements the idea of “corporate personality” developed in a numerical sense by Sorter et al. (1966). 4. The naïve users comprised a group of 30 first-year undergraduate business students who had yet to undertake an accounting course. 5. Previous studies (e.g. Houghton, 1984) show that respondents tend to assume an approximately equal division of failed and healthy companies. Where specific prior probabilities are indicated (e.g. Libby, 1975) respondents may fail to treat each case on its individual merits, preferring to rank cases on a best-to-worst basis and then group on the basis of the given failure base rate. 6. Sample test materials used in the experiment are available from the first author. 7. This is a complex and potentially significant issue. Further empirical work is currently being undertaken to resolve the specification of an optimum assignment of financial variables to facial characteristics. Results to date, from Smith et al. (1993), suggest that it is the adoption of schematic facial profiles which is important to communication, rather than any particular feature assignment methodology. 8. The total sample of 20 cases is classified correctly through the adoption of an appropriate linear discriminant model. A simple decision strategy based on a unit-weighted linear combination of three of the four financial ratios generates one Type II error:  PBIT TL QA   – +  < 0.  TA NW CL  A combination of down-turned mouth, small eyes and perplexed eyebrows similarly correctly identify all of the failed companies from the schematic faces.
    • AAAJ References Altman, E.I. (1968), “Financial ratios, discriminant analysis and the prediction of corporation 9,2 bankruptcy”, Journal of Finance, Vol. 23, pp. 589-609. Altman, E.I., Haldeman, R.G. and Narayanan, P. (1977), “Zeta analysis: a new model to identify bankruptcy risk of corporations”, Journal of Banking and Finance, June, pp. 29-54. Beattie, V. and Jones, M. (1992), “The use and abuse of graphs in annual reports: a theoretical framework and empirical study”, Accounting and Business Research, Vol. 22 No. 88, Autumn, 84 pp. 291-304. Benjamin, M.N. and Pachella, R.G. (1982), “The effects of complexity on interpreting Chernoff faces”, Human Factors, Vol. 24 No. 1, pp. 11-18. Bertin, J. (1983), The Semiology of Graphics, University of Wisconsin Press, Madison, WI. Bruckner, L.A. (1978), “On Chernoff faces”, in Wang, P.C.C. (Ed.), Graphical Representation of Multivariate Data, Academic Press, New York, NY. Canadian Institute of Chartered Accountants (CICA) (1993), Using Ratios and Graphics in Financial Reporting, CICA, Toronto. Chernoff, H. (1971), The Use of Faces to Represent Points in n-dimensional Space Graphically, Technical Report No. 71, Department of Statistics, Stanford University. Chernoff, H. (1978), “Graphic representations as a discipline”, in Wang, P.C.C. (Ed.), Graphical Representation of Multivariate Data, Academic Press, New York, NY. De Sanctis, G. (1984), “Computer graphics as decision aids: directions for research”, Decision Sciences, Vol. 15, pp. 463-87. De Sanctis, G. and Jarvenpaa, S.L. (1989), “Graphical presentation of accounting data for financial forecasting: an experimental investigation”, Accounting, Organizations and Society, Vol. 14 No. 5/6, pp. 509-25. De Soete, G. and De Corte, W. (1985), “On the perceptual salience of features of Chernoff faces for representing multivariate data”, Applied Psychological Measurement, Vol. 9 No. 3, September, pp. 275-80. Diamond, R. and Carey, S. (1986), “Why faces are and are not special: an effect of expertise”, Journal of Experimental Psychology: General, Vol. 115, pp. 107-17. Everitt, B.S. (1978), Graphical Techniques for Multivariate Data, Heinemann, London. Flury, B. and Riedwyl, H. (1981), “Graphical representation of multivariate data by means of asymmetrical faces”, Journal of the American Statistical Association, Vol. 76 No. 376, December, pp. 757-65. Foster, G. (1986), Financial Statement Analysis, Prentice-Hall, Englewood Cliffs, NJ. Garner, W.R. (1978), “Aspects of a stimulus: features, dimensions, and configurations”, in Rosch, E. and Lloyd, B.B. (Eds), Cognition and Categorisation, Lawrence Erlbaum, Hillsdale, NJ. Goldstein, A.G. and Mackenberg, E.G. (1966), “Recognition of human faces from isolated facial features: a developmental study”, Psychonomic Science, Vol. 6, pp. 149-50. Grant, E. (1970), “Face to face”, New Society, 7 May, pp. 769-71. Haig, N.D. (1984), “The effect of feature displacement on face recognition”, Perception, Vol. 13, pp. 505-12. Homa, D., Haver, B. and Schwartz, T. (1976), “Perceptibility of schematic face stimuli: evidence for a perceptual Gestalt”, Memory and Cognition, Vol. 4, pp. 176-85. Houghton, K.A. (1984), “Accounting data and the prediction of business failure: the setting of priors and the age of data”, Journal of Accounting Research, Spring, pp. 361-8. Laughery, K.R., Alexander, J.F. and Lane, A.B. (1971), “Recognition of human faces: effects of target exposure time, target position, pose position and type of photograph”, Journal of Applied Psychology, Vol. 51, pp. 477-83. Libby, R. (1975), “Accounting ratios and the prediction of failure: some behavioural evidence”, Journal of Accounting Research, Spring, pp. 150-61. Libby, R. (1981), Accounting and Human Information Processing, Prentice-Hall, Englewood Cliffs, NJ.
    • Lusk, E.J. (1979), “A test of differential performance peaking for a disembedding task”, Journal of Communication Accounting Research, Vol. 17 No. 1, Spring, pp. 286-94. Mackay, D.B. and Villarreal, A. (1987), “Performance differences in the use of graphic and tabular through cartoon displays of multivariate data”, Decision Sciences, Vol. 18 No. 6, Fall, pp. 535-46. graphics McKelvie, S.J. (1973), “The meaningfulness and meaning of schematic faces”, Perceptions and Psychophysics, Vol. 14 No. 2, pp. 343-8. Moriarity, S. (1979), “Communicating financial information through multidimensional graphics”, Journal of Accounting Research, Vol. 17, April, pp. 205-24. 85 Morton, J. and Johnson, M. (1989), “Four ways for faces to be special”, in Young, A.W. and Ellis, H.D. (Eds), Handbook of Research on Face Processing, Elsevier, North-Holland, pp. 49-56. Reed, S.K. (1972), “Pattern recognition and categorisation”, Cognitive Psychology, Vol. 3, pp. 382-407. Remus, S.K. (1984), “An empirical investigation of the impact of graphical and tabular data presentations on decision making”, Management Science, Vol. 39 No. 5, pp. 533-42. Rhodes, G., Brennan, S. and Carey, S. (1987), “Identification and ratings of caricatures: implica- tions for mental representations of faces”, Cognitive Psychology, Vol. 19 No. 4, pp. 473-97. Ryan, J.A. and Schwartz, C. (1956), “Speed of perception as a function of mode of presentation”, American Journal of Psychology, Vol. 69, pp. 60-69. Schaffer, H.R. (1971), The Growth of Sociability, Penguin, London. Sergent, J. (1984), “An investigation into component and configural processes underlying face recognition”, British Journal of Psychology, Vol. 75, pp. 221-42. Smith, E.E. and Nielsen, G.D. (1970), “Representations and retrieval processes in STM recognition and recall of faces”, Journal of Experimental Psychology, Vol. 85 No. 3, pp. 397-405. Smith, M. and Taffler, R.J. (1984), “Improving the communication function of published accounting statements”, Accounting and Business Research, No. 54, Spring, pp. 139-46. Smith, M., Taffler, R.J. and White, L. (1993), “Cartoon graphics in the communication of accounting information”, paper presented to the British Accounting Association, University of Strathclyde, April. Sobol, M.G. and Klein, G. (1989), “New graphics as computerized displays for human information processing“, IEEE Transactions on Systems, Man and Cybernetics, Vol. 19 No. 4, July/August, pp. 893-8. Sorter, G.H., Becker, S.W., Archibald, T.R. and Beaver, W.H. (1966), “Accounting and financial measures as indicators of corporate personality – some empirical findings”, in Jaedicke, R.J., Ijuri, Y. and Nielsen, O. (Eds), Research in Accounting Measurement, American Accounting Association, Sarasota, FL. Stock, D. and Watson, C.J. (1984), “Human judgement accuracy, multidimensional graphics and human versus models”, Journal of Accounting Research, Vol. 22 No. 1, Spring, pp. 192-206. Taffler, R.J. (1982), “Forecasting company failure in the UK using discriminant analysis and financial ratio data”, Journal of the Royal Statistical Society (Series A), Vol. 145 Part 3, pp. 342-58. Taffler, R.J. and Sudarsanam, P. (1980), “Auditing the board: a new approach to the measurement of company performance”, Managerial Finance, Vol. 5 No. 2, pp. 125-47. Valentine, T. (1986), “Encoding processes in face recognition“, unpublished PhD thesis, University of Nottingham. Wang, P.C.C. (Ed.) (1978), Graphical Representation of Multivariate Data, Academic Press, New York, NY. Wilkinson, L. (1982), “An experimental evaluation of multivariate graphical point representa- tions”, Human Factors in Computer Systems: Proceedings, Gaithersburg, MD, pp. 202-9. Yin, R.K. (1969), “Looking at upside-down faces”, Journal of Experimental Psychology, Vol. 8, pp. 141-5.