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Weekly UpdatesA major factor for any business in the consumer products industry is advertising. Advertising dollars must be used effectively in order to generate sales. Thus, the first industry-specific ratio for consumer products is the change in advertising expense as a percentage of sales (ADSLS1 and ADSLS2). Advertising is used to differentiate products, locations and entire stores. Advertising is often the first link between the customer and the product, and thus plays a substantial role in the success of consumer-related businesses.
Another area of importance in this industry is the ability to manage inventory. The percentage change in inventory turnover (INVTURN1 and INVTURN2) provides a measure of management efficiency. Consumer tastes are constantly changing, making it easy to develop a build-up of inventory that may become obsolete more quickly than in other industries. Adopters of performance or restricted stock plans should exhibit greater inventory turnover than exhibited prior to adoption.
Changes in sales growth (SLSGRW1 and SLSGRW2) are a measure of success with the consumer. According to industry analysts, to remain competitive a company in the consumer products industry needs to have increasing sales growth. The consumer products industry is very competitive, and sales growth serves to expand or maintain market share.
Relying on the discussions from S&P Industry Analysts Reports, the industry-specific measures for the other industries were determined in a manner similar to that employed for the consumer products industry. The ratios employed were those most frequently discussed over the sample period as being indicative of firm performance for each industry group in the reports.
Results
Four types of analysis are employed in evaluating the data. Multivariate analysis of variance, analysis of variance, analysis of means and t-tests were utilized for the various hypotheses. Hypotheses 1, 2 and 3 look at the significance of one dependent variable across time periods and therefore are analyzed using t-tests and analysis of means. Hypothesis 4 examines multiple dependent measures with one independent measure and is analyzed using multivariate analysis of variance, analysis of variance and further by analysis of means.
Hypothesis 1 states that adoption of a performance plan has a positive impact on the profitability measure ROE. As shown in Table 3, hypothesis 1 is rejected. There is no significant difference in either ROE1 or ROE2 after adoption of a performance plan for adopters. Hypothesis 3, which states improvement in the profitability measure ROE is greater for adopters of a performance plan than for non-adopters in the same industry, is also not supported. There is not a significant difference in either ROE1 or ROE2 between adopters and non-adopters.
Results of the t-tests for hypothesis 2, which states adoption of a performance plan results in improvement in industry-specific performance measures, are shown in Table 4. As shown, there is some improvement for each industry group on at least one variable from the pre-adoption period to one of the two post-adoption periods. The industries that seem most affected by adoption are Financial Services, Consumer Products, and Mining & Extractive. The industry group that seems least affected is the Non-Financial Services. Therefore, hypothesis 2 is supported for some industries but not for others indicating that adoption of a performance plan does improve firm performance for some industries.
To see if this improvement in firm performance, or the lack of improvement, is industry specific, multivariate analysis of variance is implemented to analyze hypothesis 4, which states improvement in industry-specific performance measures is greater for adopters of a performance plan than for non-adopters in the same industry.
The industry-specific variables are the dependent variables and the classification of adopter or non-adopter serves as the independent variable. There is a significant difference on each of the industry groups between adopters and non-adopters for the group of variables utilized for each of the two measurement periods. ANOVA is then employed to determine which variables within the groups of industry-specific variables are driving the significant differences. Table 5 presents the results. In seven of the nine industry groups there is at least one variable that is significantly different between adopters and non-adopters. Only in the Financial Services and the Mining & Extractive industry groups are there no differences between adopters and non-adopters on the industry-specific measures. The industry groups that have the most differences between adopters and non-adopters are Consumer Products, Electrical, Food Processing and Textiles. Further study by analysis of means reveals for those industries that have th e greatest number of statistically significant differences on the industry-specific measures between adopters and nonadopters that only in the Consumer Products and the Electrical industries do the adopters have greater mean changes than do non-adopters. In Food Processing and Textiles industry groups however, non-adopters outperform adopters on the industry specific measures. Therefore hypothesis 4 is supported for some industries but not for others. [3]
Summary and Discussion
Summary
The research questions were analyzed across nine industry categories in three different comparisons. For the first comparison it was hypothesized that firm performance would significantly improve after the adoption of a performance plan when compared to the pre-adoption period. The accounting variables used were of two types: a general performance variable that was the same across industries, and a set of industry-specific variables. The pre-adoption period was compared to a short, two-year post-adoption period and to a longer four-year period as well. There are two main results from this analysis. First, the industry-specific variables are more statistically significant indicators of firm performance than is the general performance measure. This implies that the performance variables chosen for each industry capture information that may not be captured solely by using general performance measures such as return on equity. Second, improved firm performance after the adoption of a performance plan is industry -specific since only seven of the nine industries showed significant differences on at least three variables when comparing the pre-adoption period to two post-adoption periods.
The second comparison made in this study was to compare the same measures between adopters and non-adopters in the same industry to determine if results from the initial comparison were industry-wide or compensation composition specific. Results indicate once again that industry specific variables are more informative than the general performance measure. Four of the nine adopting industries showed significant differences on at least three of the industry variables when compared to non-adopters. Closer examination revealed that in only two of these industries did adopters perform better than non-adopters. This suggests that although adoptions of these types of plans may lead to improved firm performance, they do not necessarily improve performance above normal industry performance.
Discussion
Overall, these results have several implications. First, adoption of a performance-based compensation component may improve firm performance, but these adoptions do not seem to ensure that adopting firms will perform as well as the industry as a whole. The question arises as to why this occurs. One possibility is that adopting firms were performing well below the industry prior to adoption. This would imply that adoptions of performance plans are beneficial in some industries in that it helps bring the adopting firm up to industry performance levels. Another scenario is that executives of adopting firms improve firm performance just enough to receive the designated reward but not enough to keep pace with industry standards. Thus, adoptions may result in a certain level of complacency by executives. A third option is that the results are time specific. Lobingier (1998) found that early adopters (1972-1980) had more significant improvement in general performance measures than did late adopters (1981-1991). Thi s may also be true for industry-specific measures but still needs to be tested.
This study suggests that those who design compensation packages should specifically consider implementing industry-specific measures to create goals to be met by executives. This study also implies that firms in specific industries may need to pay very close attention to whether recent adoptions of long-term performance-based plans are likely to be beneficial to all parties involved given that some industries had no pre-post adoption effect. A second implication is that variables such as ROE that are typically used to measure firm performance, and are frequently used in this type of study, are not adequate measures of firm performance. Results from this study imply industry variables provide a greater degree of information than general measures. This suggests future studies in this area may provide more accurate results if less general and more specific measures are utilized.
As in all studies, there are several limitations inherent in this study. One limitation concerns the way the individual companies were categorized into industry groups. Though company placement within an industry based on the criteria of this study seems appropriate, it is possible that a better division of firms exists. In addition, some of the firms in the study are diversified and technically function across several of the industry categories. These firms were analyzed based on their primary SIC code. Rearranging the industry groups, or more carefully manipulating placement of firms within the industries, may change the results of this study.
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