The financial contribution of customer-oriented marketing capability

Abstract

This article assesses the financial contribution of marketing capability. In contrast with previous research, which conceptualizes marketing capability as the deployment of marketing resources to achieve sales, this study conceives marketing capability as the deployment of marketing resources to achieve the ultimate objectives of customer satisfaction and brand equity (i.e., customer-oriented marketing capability [COMC]). Thus, this research disentangles the dynamic relationships among marketing resources, sales, customer satisfaction, and brand equity through the use of network Data Envelopment Analysis to capture COMC. According to what the value relevance perspective proposes, COMC positively influences the growth of Tobin’s q and improves the growth of analysts’ recommendations. These findings remain robust and consistent with the use of additional measures and methods common to the marketing and financial literatures. Our study provides tools and a framework for analysis for managers to maximize their ability to use marketing strategy to drive performance.

Appendix

Network DEA estimation

We estimate the network output-oriented DEA model with variable returns to scale as follows:

$$ \begin{array}{l}\kern5em \begin{array}{c}\hfill \min .{\beta}_t,\hfill \\ {}\hfill s.t.:\hfill \end{array}\\ {}\begin{array}{cc}\hfill {\displaystyle \sum_{k=1}^K{\lambda}_k\kern.1em \cdot \kern.1em {x}_{jkt}\kern.1em \le \kern.1em {x}_{jt}^0,}\hfill & \hfill j=1,\dots, J,\hfill \\ {}\hfill {\displaystyle \sum_{k=1}^K{\lambda}_k\kern.1em \cdot \kern.1em {y}_{ikt}\kern.1em \ge \kern.1em {y}_{it},}\hfill & \hfill \kern3em i=1,\dots, I,\hfill \end{array}\\ {}\kern6em {\displaystyle \sum_{k=1}^K{\lambda}_k=1}\\ {}\begin{array}{cc}\hfill {\displaystyle \sum_{k=1}^K{\mu}_k\kern.1em \cdot \kern.1em {x}_{jkt}\kern.1em \le \kern.1em {x}_{jt}^0,}\hfill & \hfill \kern3em j=1,\dots, J,\hfill \\ {}\hfill {\displaystyle \sum_{k=1}^K{\mu}_k\kern.1em \cdot \kern.1em {y}_{ikt}\kern.1em \le \kern.1em {y}_{it},}\hfill & \hfill i=1,\dots, I,\hfill \\ {}\hfill {\displaystyle \sum_{k=1}^K{\mu}_k\kern.1em \cdot \kern.1em {y}_{fkt}\kern.1em \ge \kern.1em y{}_{ft}^o/{\beta}_t,}\hfill & \hfill f=1,\dots, F,\hfill \end{array}\\ {}\kern5em \begin{array}{c}\hfill {\displaystyle \sum_{k=1}^K{\mu}_k=1,}\hfill \\ {}\hfill {\lambda}_k\kern.1em \ge \kern.1em 0;\kern0.5em {\mu}_k\kern.1em \ge \kern.1em 0,\hfill \end{array}\end{array} $$

where β _t is the network distance function for the unit under analysis in period t. For the purposes of this study β _t represents the firm’s level of COMC. The term β _t   = 1 indicates that the DMU (decision-making unit) under analysis is efficient, and β_t < 1 indicates that DMU is inefficient (the smaller the β _t , the more inefficient the DMU is in generating intangible resources). The term y _it is the intermediate output vector of the DMU under analysis in period t (in our case, there is only one intermediate output: sales), x ^o _jt is the observed inputs vector of the DMU under analysis in period t (in our case, the marketing resources spent in the current period and the marketing intangibles coming from the previous period), and y _ft is the final output vector of the DMU under analysis in period t (in our case, the marketing intangibles at the end of the period under analysis). Finally, y _ikt and x _jkt refer to outputs and inputs vectors for the k (k = 1, …, K) DMUs forming the total sample, and λ and μ indicate the activity vector.

As Fig. 2 illustrates, this program has two steps, which are solved simultaneously. Step 1 coincides with the restrictions formed with the λ vector, and step 2 includes the remaining restrictions, built with the μ vector as activity vector.

Previous works in the field of network DEA include Färe and Grosskopf (1996, 2000), Sexton and Lewis (2003), Lewis and Sexton (2004), Prieto and Zofío (2007), and Tone and Tsutsui (2009). Our proposal extends the existent proposals in the sense that (1) original inputs are taken into account not only for the optimization of the intermediate but also for the final output and (2) the optimization of steps 1 and 2 is produced simultaneously to maximize the final output, as the isolated optimization of step 1 does not guarantee the achievement of the maximal output in step 2.