Centralization versus Delegation in an Experimental Capital Budgeting Setting

In an experiment, we model two stylized facts about capital budgeting practice, budgetary slack creation and delegation of decision-making authority. In our setting, under centralization, headquarters announces a budget, the division manager gives a cost report, and headquarters decides on the project. Under delegation, headquarters allocates a budget to the manager, and the manager is authorized to make the investment decision. We argue that the ability of headquarters to commit to a budget moderates the effect of delegation, and we find evidence in favor of our argument as there is an interaction effect of delegation and commitment to budgets. The effects of delegation are particularly strong when budgets are non-binding as delegation serves as a substitute for commitment in this case. This leads to smaller expenditures and to a higher headquarters’ payoff under delegation than under centralization. In contrast, when headquarters can commit to the budget, the descriptive data are consistent with our conjectures about the effects of honesty preferences, but the effects are too small to be significant.

Determinants of Advertising Effectiveness: The Development of an International Advertising Elasticity Database and a Meta-Analysis

Increasing demand for marketing accountability requires an efficient allocation of marketing expenditures. Managers who know the elasticity of their marketing instruments can allocate their budgets optimally. Meta-analyses offer a basis for deriving benchmark elasticities for advertising. Although they provide a variety of valuable insights, a major shortcoming of prior meta-analyses is that they report only generalized results as the disaggregated raw data are not made available. This problem is highly relevant because coding of empirical studies, at least to a certain extent, involves subjective judgment. For this reason, meta-studies would be more valuable if researchers and practitioners had access to disaggregated data allowing them to conduct further analyses of individual, e.g., product-level-specific, interests. We are the first to address this gap by providing (1) an advertising elasticity database (AED) and (2) empirical generalizations about advertising elasticities and their determinants. Our findings indicate that the average current-period advertising elasticity is 0.09, which is substantially smaller than the value 0f 0.12 that was recently reported by Sethuraman, Tellis, and Briesch (2011). Furthermore, our meta-analysis reveals a wide range of significant determinants of advertising elasticity. For example, we find that advertising elasticities are higher (i) for hedonic and experience goods than for other goods; (ii) for new than for established goods; (iii) when advertising is measured in gross rating points (GRP) instead of absolute terms; and (iv) when the lagged dependent or lagged advertising variable is omitted.