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7 - The model by Phillips, Chang, and Buzzell revisited – the effects of unobservable variables
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- By Lutz Hildebrandt, Professor of Marketing and Director of the Institute of Marketing Humboldt University of Berlin; President European Marketing Academy, Dirk Temme, Assistant Professor of Marketing Humboldt University of Berlin
- Edited by Paul W. Farris, University of Virginia, Michael J. Moore, University of Virginia
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- Book:
- The Profit Impact of Marketing Strategy Project
- Published online:
- 22 September 2009
- Print publication:
- 04 November 2004, pp 153-187
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Summary
This chapter reviews some key hypotheses from empirical research on success factors in marketing. These hypotheses on drivers of business profitability, in particular quality and market share, have been a major subject of critique, and these critiques have come primarily from the resource-based view in management research. According to this perspective, general laws of business success based on manageable strategic input factors do not exist. Instead, unobservable, firm-specific variables are regarded as the key drivers of profitability. However, only a few studies have been able to show that strong relations discovered in empirical success factor research disappear if unobservable variables are controlled in econometric models.
In this chapter, we show that some of these results may be methodological artifacts. Based on the hypotheses of Phillips, Chang, and Buzzell (1983) regarding the effects of quality and market share on profitability, we use PIMS data to replicate their study using a modified modeling approach. Whereas Phillips, Chang, and Buzzell use data taken at two points in time to investigate the relationships between some key variables, this chapter uses a six-year cross-section of time series and a panel modeling approach to estimate the parameters. This approach allows us to overcome some major objections to the traditional PIMS approach; key relations between observable success factors and profitability highlighted by the PIMS research can be estimated while simultaneously the effects of different types of unobservable firm-specific factors can be controlled.
3 - PIMS and COMPUSTAT data: different horses for the same course?
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- By D. Eric Boyd, Assistant Professor of Marketing James Madison University, Paul W. Farris, Professor of Business University of Virginia's Darden Graduate School, Lutz Hildebrandt, Professor of Marketing and Director of the Institute of Marketing Humboldt University of Berlin; President European Marketing Academy
- Edited by Paul W. Farris, University of Virginia, Michael J. Moore, University of Virginia
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- Book:
- The Profit Impact of Marketing Strategy Project
- Published online:
- 22 September 2009
- Print publication:
- 04 November 2004, pp 41-72
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- Chapter
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Summary
For researchers investigating questions related to marketing strategy and financial performance, there are few databases that are, in any way, comparable to PIMS. However, COMPUSTAT is one database that researchers have used frequently to address such questions. Other databases that provide limited additional points of comparison are that of the Inland Revenue Service (IRS) and the Federal Trade Commission (FTC) Line of Business data. In this chapter we will provide some comparisons between PIMS and COMPUSTAT data.
From the beginning, researchers were impressed by the “overwhelming superiority of PIMS data to other sources in quantity, number of measured variables, timeliness, [and the] conscientious attempt to minimize potential sources of input error” (Anderson and Paine 1978). Of course, the “timeliness” of the PIMS data is no longer a strong point and, since 1990 or so, publications of empirical findings based on the PIMS data have appeared far less frequently in major marketing and strategy journals – published articles have declined along with the size and currency of the data.
On the other hand, publications based on COMPUSTAT data are appearing with increasing frequency in marketing journals. Further, many of the issues addressed by researchers using COMPUSTAT are similar to those addressed with analyses of the PIMS data. For example, both COMPUSTAT and PIMS data have been used to study: relationships of market share, firm size, and power to profits; determinants of marketing cost ratios and media budgets; and returns from R&D and new products activities, and patents.