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Erschienen in: Journal of the Academy of Marketing Science 6/2018

30.05.2018 | Original Empirical Research

Selling, general, and administrative expense (SGA)-based metrics in marketing: conceptual and measurement challenges

verfasst von: Annette Ptok, Rupinder P. Jindal, Werner J. Reinartz

Erschienen in: Journal of the Academy of Marketing Science | Ausgabe 6/2018

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Abstract

Many studies use variables from the Compustat database to measure various marketing constructs, yet no clear guidelines detail which metrics correspond with which constructs. Justifications rest mainly on the ready availability of easy-to-use measures that seem related to a particular construct. As a result, various metrics have been utilized to capture the same construct, and the same metric—such as selling, general, and administrative expenses (SGA)—has been applied to capture vastly different constructs. But using SGA inappropriately can lead to biased estimates, questionable support for the hypotheses, and potentially misleading implications for research and practice. To test the validity of SGA for multiple relevant marketing and sales constructs, this study gathers data on benchmark variables from alternative data sources and applies a multitrait-multimethod (MTMM) approach. Results show that, in general, SGA has been applied too liberally in marketing contexts; SGA is an appropriate operationalization only for some constructs. This article provides guidelines for the proper conceptualization and operationalization of marketing constructs.
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Fußnoten
1
Sometimes, use of SGA has been justified by intuitive reasoning. For example, because SGA budgets may be interpreted as a sign of financial resources of a firm, SGA appears to be a good proxy of marketing resources. Such operationalization suffers from lack of proper validation and can be hit or miss. Intuitively, there may be equally good or better proxies available within Compustat. For example, marketing resources which imply items such as cash, customer loyalty, brand equity, and patents could be measured using more direct and conceptually relevant measures such as “goodwill” or “total intangible assets.” One could even employ “working capital” or “cash and short-term investments” or “cash,” which are conceptually aligned to, and better capture, the resources a firm has available to cover its expenses. Of course, to choose the right operationalization one needs to establish content and construct validity, which we propose later.
 
2
We also considered other data sources (e.g., Ebiquity, PIMS, Hoover) of benchmark variables but found them unsuitable. For example, Ebiquity reports data at the country level only, and its consultants advised us against aggregating these country-level data to obtain worldwide data. PIMS provides information at the strategic business unit level for participating companies, so it likewise is unsuitable. Hoover does not include any information related to marketing spending but rather provides qualitative information about big players only.
 
3
We empirically validated the benchmark measures from these alternative sources by collecting data from annual reports of public and private companies. We thank an anonymous reviewer for this suggestion. We note here that these benchmark measures provide purer information on the three focal variables only: advertising expense, promotional expense, and salesforce expense. Whether these measures are also better than SGA at capturing any particular marketing construct depends on both content and construct validity.
 
4
Outliers can have significant influences on correlation coefficients, so extreme outliers should be removed (Schwertman et al. 2004). We used Tukey’s (1977) formula: lower fence: Quartile 1–3*(Quartile 3 – Quartile 1); upper fence: Quartile 3 + 3*(Quartile 3 – Quartile 1). All values outside the fences were removed, which reduced the number of observations to 499. As we explain with our robustness checks, including these extreme outliers still provided similar results.
 
5
There could be a potential sample selection bias as certain firms/industries may be overly represented in Advertising Age than in Compustat. We conducted propensity score matching to check if the smaller sample size used in the empirical analysis is representative of the broader sample drawn from Compustat. The results present no evidence of sample selection bias. The details of the matching procedure are available in Web Appendix 4. We thank an anonymous reviewer for this suggestion.
 
6
Marketing spending, as used in the study for validation of SGA as a measure, has two subconstructs: advertising spending and promotional spending. Arguably, marketing spending on some activities such as advertising may bestow relatively longer-term benefits compared with spending on other activities such as promotions. However, considered in a comparative perspective, the spending construct is relatively short-term when compared with, say, the assets construct. Also, marketing literature that has used SGA—a short-term accounting variable—to measure spending has implicitly considered it short-term.
 
7
We note the difference between marketing and sales functions, which are often organized and executed in different organizational departments and treated differently. Marketing involves activities to start and maintain a customer relationship (van Triest et al. 2009), such as advertising and promotional efforts, which generate customer awareness and establish brand preference. Sales seeks to stimulate actual purchases through sales force activities such as negotiations over price and delivery (Kotler and Rackham 2006).
 
8
In addition to the two common modifications of SGA (SGA – ADV, SGA – R&D), we test another modification (SGA – ADV – R&D) to check if SGA has any significant marketing-related component, beyond ADV and R&D, which may justify its use as a measure of marketing constructs. Thus, scenario 1 includes four MTMM matrices: advertising spending measured using ADV whereas promotional spending measured using SGA, SGA – ADV, SGA – R&D, or SGA – ADV – R&D, respectively. Scenario 2 also uses four matrices, with promotional spending measured as ADV whereas advertising spending measured using each of the four SGA-based metrics.
 
9
For the three MTMM matrices in scenario 1, perceptual assets are measured using ADV in each case, whereas intellectual assets are measured using SGA, SGA – ADV, or SGA – R&D. Scenario 2 also includes three matrices in which intellectual assets are always measured using ADV whereas perceptual assets use the three SGA-based metrics.
 
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Metadaten
Titel
Selling, general, and administrative expense (SGA)-based metrics in marketing: conceptual and measurement challenges
verfasst von
Annette Ptok
Rupinder P. Jindal
Werner J. Reinartz
Publikationsdatum
30.05.2018
Verlag
Springer US
Erschienen in
Journal of the Academy of Marketing Science / Ausgabe 6/2018
Print ISSN: 0092-0703
Elektronische ISSN: 1552-7824
DOI
https://doi.org/10.1007/s11747-018-0589-2

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