International Journal of Economics and Management Engineering (IJEME)
Dec. 2013, Vol. 3 Iss. 6, PP. 268-277
Theoretical Model of Marketing Productivity:
An Application to Retail Service
Companies in Spain
Hanns de la Fuente-Mella*1, Carmen Berné-Manero2, Marta Pedraja-Iglesias3
1
Pontificia Universidad Católica de Valparaíso, Facultad de Ciencias Económicas y Administrativas, Escuela de Comercio,
Avenida Brasil 2830, Valparaíso, Chile
2, 3
University of Zaragoza, Department of Economic and Business Science, Department of Marketing Management and Market
Research, Gran Via, 2, Zaragoza, Spain
*1
[email protected]; [email protected]; [email protected]
Abstract- The absence of a universal measurement of marketing productivity highlights an inability to measure the influence of
marketing assets on company results. It is important, therefore, to develop measurements and quantifiable models of marketing
productivity, particularly in the services case, making it possible to understand and evaluate service company performance.
Following a review of the literature on the topic, this paper sets forth a Theoretical Model of Marketing Productivity (TMMP), to
serve as a base for carrying out measurements of productivity through the identification of possible determinant variables. The
TMMP is validated for the case of the retail services companies operating in Spain. The obtained results establish that marketing
resources, financial performance, and price all exercise a positive effect on marketing productivity, while market-based assets
exercise a negative effect. The implications for service management are that they should control significant factors for the analysis of
the behavior of retail companies’ vis-à-vis their marketing productivity, i.e. the effectiveness of staff as a productive resource, the
appropriate setting of prices, and investment in assets.
Keywords- Marketing Productivity; Retail Business; Econometric Modeling
I. INTRODUCTION
From the perspective of economic theory, high levels of productivity in the production process should have a favorable
impact benefits to the company and on the creation of value for the consumer [1]. This fact leads companies to a difficult
choice: reduce costs, or increase productivity. However, in order to make the right decision, it is necessary for companies to
know and quantify the factors that define their levels of productivity [2].
There are numerous definitions of productivity in the specialized literature, the majority of which establish a relationship
between the use of resources (input) and the obtaining of products (output). The most frequent definitions focus on the primary
sector of the economy. Definitions in the context of the services sector are practically non-existent, since these were considered
economically unproductive activities. Logically, this erroneous conviction was thrown out through the development of
scientific knowledge of the economy, although the influence of these first theories still remains today; tertiary activities are
usually characterized as having little ability to experience intense growth in their productivity [3]. However, when we consider
the impact of the strong prevalence of tertiary activities that modern economies are experiencing, it is appreciated that this
sector, far from halting or weakening general economic growth, has boosted it, from the decade of the 1980s on ([4], [5], [6]).
With the goal of attracting and retaining customers, companies in the services sector need to continuously participate in
marketing activities, which boosts their costs significantly [2] but allows improvements in the net results of the company.
Nevertheless the study of marketing productivity is relatively recent, dating back to the decade of the 1950s. In the review of
the existing literature focused on the analysis of investments in marketing, carried out by [7], one observes that, during the
1950s and 60s, the majority of accounting texts dedicated a whole chapter to distribution costs for companies, including the
currently referred to investments in marketing that, at that time, were considered to be expenses in accounting terms, not
investments. Thus, many of the functions associated with marketing activities were handled by other areas of the company [8].
Marketing productivity is difficult to measure, since marketing produces nothing tangible, carrying out functions around
goods and services [8]. In addition, many of the functions developed by marketing have been absorbed by other divisions
within companies [9]. Thus, the study of marketing productivity has presented problems from its beginning, due to the
intangibility of the effects that this activity produces, leading to the functions of marketing being considered as intrinsically
inefficient, given the nature of their objectives, sphere, and tools. These problems have been accentuated by the fact that there
is more than one way to measure marketing productivity, making it necessary to quantify the measurement, bearing in mind
that, until now, it has been difficult to establish a systematic, quantifiable measurement process of marketing productivity [9].
Moreover and no less important, the problem arises when the marketing productivity is analyzed for service companies.
Anyway, for marketing to be effective, it must be managed, so it is crucial to consider marketing productivity in the services
sector [2].
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International Journal of Economics and Management Engineering (IJEME)
Dec. 2013, Vol. 3 Iss. 6, PP. 268-277
Thus, the main objective of this research is driven to partially solve these problems by proposing a theoretical model to
measure marketing productivity of services, which will present a new measurement of marketing productivity in the services
sector. The model will be applied to a data base of the Spanish retail companies, obtained from a secondary information
source, although it could be applied to other sub-sectors of the services sector. The obtained results allow the validation of the
relationships presented, with a significant positive relationship emerging among the number of employees, total assets, and
growth in the sales and marketing productivity of the Spanish retail businesses.
II. BACKGROUND
A. Marketing Productivity
From the Neoclassic Theory of Economics, productivity represents the conversion, in economic terms of the input to a
process (work, capital) to the desirable output in terms of the output sought (sales, earnings) [10]. This relationship is
expressed and maintained over time in different existing definitions: measurement of efficiency as a combination of the uses of
productive resources, including capital and labor [11]; the ratio between units of obtained results and units of effort or
necessary expenditures in order to obtain the output [12]; the relationship established between production (output) and
consumption (input), both measured in physical units [3].
Marketing productivity is understood to mean the quantifiable added value of the function of marketing relative to
investments [9]. Thus, the achievement of a higher level of productivity by a company will correspond to suitable returns, as
much in terms of benefits as in the value created for customers [1]. Although conceptual and operational definitions of
marketing productivity exist, there is no agreement on a universal definition [9]. For example, [13] defines marketing
productivity as the ratio between the effect produced and the energy spent, which, from the point of view of marketing, would
be the ratio of sales or net benefits (effect produced) to the costs of marketing (energy spent) for a specific segment of
business; [14] understands it as the ratio of output, or production results, to the corresponding inputs (economic resources),
both during a given period of time, and [15] defines it as the relative price of participation in the market over the marketing
costs of the company.
As has been noted, one of the main problems associated with the measurement of marketing productivity is derived from
the intangibility of its variables [2], which makes it difficult to justify the investments destined for productive increases, and
even to maintain adequate levels of productivity. In order to make this justification, it is necessary to be able to quantitatively
measure marketing productivity so that the viability and usefulness of its activities can be justified ([16], [9], [17], [18], [2],
[19], [20], [21]).
B. Determining Factors of Marketing Productivity
It is necessary to establish the factors that determine marketing productivity as a first step in carrying out its measurement.
Price level is one of these factors, since in order to raise the level of marketing productivity, it is necessary to take into account
all activities that have an impact on the acquisition and retention of customers [9]. Thus, marketing productivity could be
increased by controlling such aspects as sales, product development, and advertising and, of course, by defining suitable
pricing levels [22]. Determining price levels allows organizations to increase their level of customer retention in order to be
able to count on adequate budgets that allow them to carry out the marketing activities necessary to reach their proposed
objectives [23]. Therefore, a positive relationship can be seen between price levels and companies’ marketing productivity [9],
taking into account that prices must maintain the levels of customer satisfaction and retention, in addition to fulfilling the
profitability objectives of the organization. The idea consists of defining price levels with the goal of keeping customers
satisfied in the long term [24], and not to attempt to fulfill only one function of the maximization of prices levels. This allows
us to establish the first hypothesis of the investigation:
Hypothesis 1: Price levels defined by the organization will positively affect marketing productivity.
The investments in market-based assets of organizations that affect marketing productivity can be classified by diverse
criteria; for example, the degree of tangibility of their attributes, and their physical or human, intellectual or capital
performance. That is to say, these assets gather all that can be used to attain a competitive advantage in the markets. Given the
complexity of measurement of these concepts, and to facilitate their description and later measurement, [18], define the
concepts as i) market-based assets and ii) marketing support. The first are those that can be delivered directly in the market,
and the second are those that allow the development of activities that contribute indirectly to generating a competitive
advantage. Market-based assets consider four elements: i) the ability to identify what the customer wants, creating suitable
relationships [25]; ii) the reputation and credibility of the organization among its customers, suppliers and distributors; iii) the
ability to innovate in the market [26], and iv) the human resources of the organization that generate staff development and
increase employee loyalty and motivation [27]. Marketing support incorporates two elements, the marketing culture of the
organization, and the ability of management to conduct, coordinate and motivate these activities. Thus, in a company that is
oriented to the market, the resources fit the organization, based on experience and tacit understanding, making it difficult for
competitors to copy or imitate.
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International Journal of Economics and Management Engineering (IJEME)
Dec. 2013, Vol. 3 Iss. 6, PP. 268-277
How to integrate all the above concepts, as well as marketing elements associated with marketing assets and their influence
on the market position of the company's financial position, presented at the chain of marketing productivity of [17], is in the
model proposed by [18]. In this model, marketing support affects investments in market-based assets of the firm. Furthermore,
this affects the financial performance of it, through two channels, i) by creating satisfied and loyal customers (customers
performance), ii) through the creation of an optimal market performance (sales volume, market share) [18].
The results of the study by [18], demonstrate a negative correlation between market orientation and active reputation. This
correlation arises from the fact that well-established companies in the market, with a high reputation and an offer of wellknown brands, can become myopic, failing to be alert to the signs that the media gives them and, in some cases, can become
complacent. The earlier success of these companies leads them to a certain degree of arrogance, thus neglecting the market.
The other negative relationship that exists is produced between the assets of human resources and financial performance that
are indirectly linked through customer performance, which underlines the importance of motivation and training of staff to
prevent company effectiveness from being adversely affected. Thus, those investments that companies undertake in base
market-based assets will have a negative effect on financial performance and on marketing productivity. Thus, we establish:
Hypothesis 2: Investments in market-based assets will negatively affect financial performance.
Hypothesis 3: Investments in market-based assets will negatively affect marketing productivity.
Another determinant of marketing productivity is the marketing resources of the company, which contribute directly to the
value thereof, in particular its profitability. Thus, to identify and measure the performance, one must understand how
marketing resources can contribute to short-term gains, to provide sustained benefit in the long term [17].
Marketing resources are found to be focused on the value of customers to the company, enhancing company value in the
long term, and directly and positively affecting its cost-effectiveness. Generally, marketing resources tend to be grouped in the
value of the brand and the value of the customer [17]. The value of the brand corresponds to the knowledge that the customer
has of the brand, which would produce an intermittent increase in cash flow, as a response to brand awareness by the customer
[28], and the value of the customer is defined as the sum of the net present value of his life cycle [29]. Notable among studies
of the positive effect that brand value has on the market value of the company, are those by [30], who calculate the fraction of
cash flow of the organization that is attributed to the brand value, and by [31], who relate the brand value to the ROI, finding a
positive relationship between the variations presented in the brand value and the value of marketing activities. With regard to
customer value, [29] emphasize that it should be one of the main focal points of companies’ marketing activities, which should
be developed so as to identify customers of greater value, to decrease the costs of acquisition, and to devise marketing projects.
[32] presents a structure that allows the understanding of the way in which customer value affects shareholder value, using the
value of the customer as an intermediary [17]. Thus we establish:
Hypothesis 4: Marketing resources will positively affect financial impact or performance.
Hypothesis 5: Marketing resources will positively affect marketing productivity.
The fourth determinant of marketing productivity corresponds to the investments in marketing made by the company.
When thinking about reducing costs, the main objective tends to refer to marketing activities, since these are the easiest to
justify [33]. It must be taken into account that, if a company opts for an increase in productivity, complications arise from its
measurement, especially in the service sector, due to the intangibility of its product [2]. Therefore, to avoid decreases in
marketing investments, adequate levels of productivity should be maintained. If it were possible to measure this productivity
quantitatively, the viability of marketing activities could be demonstrated, considering these more as an investment than as an
expense. It is necessary to point out that care must be taken, lest the functions of marketing become routine, since they can be
absorbed by other business functions, thus creating the perception that greater expenditures on this activity decrease marketing
productivity, [9]. Therefore:
Hypothesis 6: The investments in marketing will negatively affect financial impact or performance.
Hypothesis 7: The investments in marketing will negatively affect marketing productivity.
A measurable result of the previously analyzed factors, investments in marketing, marketing resources and market-based
assets, is financial impact or performance ([17], [18]). Thus, there will be a positive relationship between the financial
performance of the company and its marketing productivity ([17], [34]). Financial performance can be measured by the ratio
ROI ([35], [34]), since, despite the fact that this indicator only provides results in the short term, it allows us to consider
marketing expenses as an investment, measuring financial returns through marginal profit, which are in turn measured through
percentage increment. This allows the inclusion not only of the increases in income of the organization but also the
expenditures necessary to reach them ([17], [34]). In the literature of marketing productivity, the rationale most employed for
its measurement is marketing expenses measured through the factor of work. On the other hand, the output predominantly
employed as numerator considers the added value of companies in economic terms ([36], [37], [38]). Thus, an increase in the
financial impact or performance of the company will lead to an increase in marketing productivity. That is to say, if the
company obtains greater financial profitability, it is due to the fact that it has invested correctly in marketing activities ([39],
[34]). This relationship gives rise to the next hypothesis of our research:
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International Journal of Economics and Management Engineering (IJEME)
Dec. 2013, Vol. 3 Iss. 6, PP. 268-277
Hypothesis 8: The increase in the financial performance of the company will increase marketing productivity.
The necessity to analyze the efficiency and effectiveness of marketing productivity emerges when the expenditures of
marketing in the total cost of the company increase, which could cause a loss of competitiveness. There are various strategies
to achieve appropriate levels of productivity; for example, through the more efficient use of company resources or the
maximizing over time of the ROI. Another way of achieving suitable levels of productivity consists of including concepts of
efficiency and effectiveness in the company’s marketing productivity, with the goal of developing a productive marketing
structure [9].
The importance to consider the effectiveness and productivity of marketing, especially in services companies, is due
because effectiveness is positively related to marketing productivity [2]. But within the services sector, measurement of the
efficiency of productivity might not be adequate, since a loss of quality could be perceived and, with that, a reduction in
customer satisfaction. In this sector, the most suitable measurement is presumably the observation of productivity of the
service in terms of its profitability [1].
As an example of the measurement of marketing productivity of a hotel chain, [2] measures the technical efficiency in the
budget allocation of marketing and the effectiveness of the consumption of marketing investments on the income obtained,
following [1], as the ratio between the initial inputs (costs) and the final output (income). The authors present as inputs in their
productivity model, total expenditures, the number of rooms, and marketing expenditures. With regard to the definition and
measurement of the outputs, the authors present income per room and income from food and drink. The importance of this
work lies mainly in two aspects, i) it separates the operations related to the allocation of the marketing budget, and the
effectiveness of marketing in a hotel establishment, and ii) it separates marketing expenditures from total company expenses,
considering them as investment. The study represents a considerable effort dedicated to combining technical effectiveness and
the resulting effectiveness in productivity in one single model. Specifically, the authors point out that investments in marketing
generate a decreasing scale of returns on income, and that productivity and technical efficiency are positively related.
Therefore:
Hypothesis 9: Marketing productivity is positively related to technical efficiency.
In this way, the Theoretical Model of Marketing Productivity (TMMP) that is presented is reflected in Figure 1.
Fig. 1 Theoretical Model of Marketing Productivity (TMMP)
In the theoretical model of Marketing Productivity (Figure 1), we can see the importance that should be the price levels for
the products of the company, as this will positively affect marketing productivity. Furthermore, with respect to investments by
firms in market-based assets, they will have a negative effect or impact on financial performance and marketing productivity.
Meanwhile, marketing resources by companies, they will have a positive effect on the financial performance and marketing
productivity. However, the investments in marketing made by the company, will have a negative effect on both financial
performance impact and productivity as the company's marketing. Linking these elements, an increase of impact the company's
financial performance will cause an increase in marketing productivity. Finally, both the marketing productivity and technical
efficiency in business are positively related.
III. METHODOLOGY
A. Method
In order to validate the proposed TMMP in the case of services, an application is made to Spanish retail companies. The
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Dec. 2013, Vol. 3 Iss. 6, PP. 268-277
database used has been obtained from SABI (System of Analysis of Iberian Balance Sheets). At the time the study was carried
out, the SABI database contained a record of the accounts, ratios, activities, stockholders and investee companies of more than
1,000,000 Spanish companies and 250,000 Portuguese companies. Spanish retail companies were selected, using the
classification established by the Codes of National Classification of Economic Activities (CNAE). According to this criterion,
the companies selected belong to “Retail business, except Motor Vehicles, Motorcycles and Mopeds; Repair of Personal
Effects and Household Equipment.” Thus, information is available for 111 companies for the period 1995-2005.
Determining the factors that influence levels of performance as well as quantifying their influence is done through a cross
section of econometric methodologies and panel data. Specifically, through the cross section models, static or permanent
characteristics of the Spanish retail business sector are taken into account and, through the panel models; temporal effects of
this sector are considered [34]. This responds to one of the questions posed by the model of [17], with regard to the
relationship that should exist between the marketing actions undertaken by companies and their effects over the long term,
given that the authors’ proposal to evaluate marketing productivity is based on the separation between the business activities of
the company and its activities directly associated with marketing.
The models presented above are the basis for determining the variables used to define the determinants on marketing
productivity in retail. The methodology used to measure the effects of marketing resources on business results is based on
econometric theory, making this decomposition for the measurement of output through the inputs. Therefore, it will generate
relations between endogenous and exogenous variables, following the steps described by [40]. So, first, identify the variables
of the problem to study, make the relationship or the functional form between the set of variables specified by incorporating in
it the random term, and finally proceed to validate the TMMP model through the use of the corresponding database.
B. Analysis and Results
The exploratory analysis of the 111 companies for the period 2005 shows significant differences in the number of
employees1. This indicates the need to eliminate possible noise in the sample, undertaking a segmentation process (K-means
cluster) with attention to the number of employees. The results show that the only company that stands alone is Zara Spain, a
company that has the largest number of employees in the sample. Therefore, this company is eliminated, leaving 110
companies to be analyzed.
The TMMP will be validated for the specific case of the Spanish retail business sector, measuring marketing productivity
through the productivity of the work factor ([36], [37], [38]). Thus, marketing productivity will be affected by the price level of
company products measured through the growth of sales numbers ([41], [42]); by market-based assets, measured through the
number of employees ([41], [43]), and by the marketing resources of the companies measured through their total assets [44].
Finally, marketing productivity is related to financial impact and performance, as measured through the marginal benefits of
the companies2 ([44], [17], [34], [43]) (see Figure 2). With these measurements, the two econometric models mentioned will be
developed: i) a cross-section model for the last year of the sample that contains all of the information of the sample (2005);
and, ii) a model of panel data for the whole period 1995-2005. It is important to mention that, since the data base contains
insufficient information about all the variables of the proposed general TMMP, specifically for those associated with
investments in marketing and with technical efficiency, it will not be possible to validate these relationships in this research.
Fig. 2 Adaptation of the TMMP to the Spanish retail business sector
1
Mean = 448.35, Standard deviation = 946.94.
The incorporation in the models of the variable marginal benefit as measurement of the financial impact or performance is necessary, given
the significance of counting on a proxy variable of productivity ([44], [34], [43]).
2
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International Journal of Economics and Management Engineering (IJEME)
Dec. 2013, Vol. 3 Iss. 6, PP. 268-277
C. Productivity Analysis Cross-Section Model (2005)
The economic model that serves as the basis is that of Cobb-Douglas [10]. As has been established in TMMP, the
dependent variable used is marketing productivity, and the independent variables are the number of employees, the marginal
benefit (percentage increase), and total assets, as a measurement of capital.
To validate the model, various tests are employed: White’s test to contrast heteroskedasticity, the Durbin-Watson test to
contrast auto-correlation, and the Jarque-Bera test to verify the normality of the residuals. The obtained results for these tests
validate the hypotheses of the proposed model of regressions3.
Thus, the productivity model based on the Cobb-Douglas function is specified in the following way:
Productivity (V.A. / Staff Expenditure) = e (-2.49)*Number of Employees-0.14 * Marginal_ Benefit0.075*Total_Assets0.22 (1)
All the variables incorporated in the model are highly explanatory of the productivity of the retail companies of the sample
in the period 20054. While the number of employees negatively affects the productivity of the retail companies, the total of
assets and marginal benefits has a positive effect. These results allow the validation of the hypotheses proposed for the TMMP
in the Spanish retail business sector (see Figure 2), with the exception of the hypothesis that relates the variable price to
marketing productivity, since it has not been able to be tested in this model.
The major influence on marketing productivity is exerted by marketing resources, measured by total assets, which produces
an increase of 0.22% in productivity if the total assets are increased by 1%. The effect of market-based assets, measured
through the number of employees, has a negative effect on marketing productivity presented in the TMMP, since an increase of
1% in the number of employees produces a decrease of 0.14% in marketing productivity.
An increase of 1% in the marginal benefit produces an increase of 0.075% in marketing productivity, thus validating the
use of marginal benefit as a proxy variable of marketing productivity, since the resulting sign is more significant than the value
of the relationship.
D. Analysis of Productivity Panel Model (1995-2005)
The temporary effect of the analysis of marketing productivity is incorporated through the panel model. For this, a
regression analysis is developed with panel data for the period 1995-2005.
To carry out this analysis, it is necessary to present and estimate two models. In the first model, the same variables are
established as those used in the previous cross-section analysis. That is, marketing productivity will be considered as a
dependent variable, measured as added value to the cost of the factors over the expenditures of staff, and the number of
employees, the marginal benefit (percentage increase), and total assets as a measure of capital are considered as independent
variables. The economic function on which the model is based is the same as in the prior case, i.e. the Cobb-Douglas model of
production.
In the second model, the independent variable “growth of sales figures” is incorporated, reflecting marketing activities in
the company results through the incorporation of price. The impact of price on marketing productivity is a proxy variable [41].
To estimate the value of the parameters, the panel models are worked initially with constant coefficients, with fixed effects
of cross sections, with fixed effects of time, and the general model of random effects. To check the applicability of the model
of random effects, the Hausman test is used [45], the effects of which confirm that individual effects are not uncorrelated.
Therefore, the random effects model is not suitable.
Thus, a model of fixed effects is established, this model being preferable to that of cross-sections due to the fact that the
number of companies is considerably greater than the periods of study [40]. Regarding the panel model of fixed effects of
cross-sections and the panel model with common coefficients, the latter is discounted by the low value of the obtained adjusted
R2 coefficient5, in addition to presenting problems of auto-correlation6.
In the validation stage, the Durbin-Watson test for auto-correlation and the Jarque-Bera test for normality of residuals are
both applied. The results allow the validation of the hypotheses presented by the regression model7.
The first model of productivity, based on the Cobb-Douglas function is specified in the following way:
Productivity (V.A. / Staff Expenditure)it = e(-0.68)*Number of Employeesit-0.17 *Marginal_Benefitit 0.10*
3
R2adjusted = 45%; F-statistic = 25.62, P-value = 0.0000, α = 0.01; White: F-statistic = 3.76, P-value = 0.0022; Durbin-Watson = 1.968, dL
= 1.421, dU = 1.670, α = 0.01.
4
p-valor = 0.0000, α = 0.01.
5 2
R adjusted = 48%.
6
Durbin-Watson = 0.62, dL = 1.421, dU = 1.670, α = 0.01.
7 2
R adjusted = 77%; F-statistic = 31.42, P-value = 0.0000, α = 0.01; Durbin-Watson = 1.52, dL = 1.421, dU = 1.670, α = 0.01.
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Total_Assetsit0.12*e10.66*e2-0.007*e30.54*e50.07*e6-0.20*e7-0.001*e80.04*e90.02*e10-0.27*e11-0.02*e12-0.04*e13-0.2*e140.24
0.19
*e150.000*e160.31*e17*e210.34*e220.18*e23-0.02*e24-0.01*e25-0.06*e26-0.23*e27-0.22*e28-0.05*e290.12*e30
-0.13
*e310.006*e320.36*e33-0.01*e34.0.02*e35-0.31*e36-0.33*e37-0.08*e38-0.07*e39-0.18*e400.13*e410.01*e42
-0.13
*e430.11*e440.01*e450.2*e46-0.08*e47-0.09*e480.33*e49-0.01*e500.1*e520.28*e530.23*e54-0.22*e55-0.01*e560.03*e57
-0.15
*e580.06*e590.00*e60-0.02*e610.15*e62-0.09*e63-0.07*e64-0.06*e650.10*e660.11*e670.29*e68-0.20*e69-0.40*e70-0.31*e71
-0.06
*e720.07*e730.06*e740.10*e750.18*e760.05*e77-0.14*e78-0.28*e79-0.25*e80-0.08*e81-0.08*e82-0.00*e83-0.37*e84-0.26*e85
-0.06
*e860.018*e87-0.11*e88-0.08*e89-0.08*e90-0.06*e91-0.01*e920.11*e93-0.22*e94
-0.00
*e950.06*e960.07*e970.03*e980.1*e990.04*e1000.12*e1010.12*e1020.03*e103
-0.02
*e1040.02*e1050.04*e1060.13*e1070.07*e1080.19*e109-0.25*e110-0.29
(2)
8
The elevated significance of each of the variables incorporated in this model indicates that all adequately explain the
productivity of the companies included in the sample for the period 1995-2005. In addition, the fixed effects for each of the
companies in the sample show, in general terms, the lack of significant differences among them. Only one stands out for its
difference in the value that its fixed effect would have on the constant of the model9.
The obtained results report on the negative effect that the number of employees has on the productivity of retail companies,
and on the positive effect of total assets. A similar relationship emerges between the marginal benefit and productivity.
Therefore, all the hypotheses presented in the TMMP for the Spanish retail business sector are confirmed (see Figure 2), with
the exception of the hypothesis that relates the price variable to marketing productivity, since in this model the price variable is
not included. The greatest influence on marketing productivity is market-based assets, measured through the number of
employees, since an increase of 1% in the number of employees produces a decrease of 0.17% in productivity. The positive
effect of marketing resources, measured through total assets, on marketing productivity has the positive effect presented in the
TMMP, observing that an increase of 1% in total retail company assets produces an increase in marketing productivity of
0.12%. On the other hand, an increase of 1% of the marginal benefit means an increase of 0.10% in marketing productivity,
which validates the use of the marginal benefit as a proxy variable of productivity, and also confirms the existing relationship
between marketing productivity and the financial performance variable.
The second model incorporates the price variable measured through the growth in sales figures [41]. Thus, it is possible to
measure the temporal effect of price, and contrast the relationship proposed in the TMMP between price and marketing
productivity (see Figure 2). The dependent variable is the productivity of the work factor (value added to the cost of the factors
over staff expenditures), the independent variables being the number of employees, the marginal benefit (percentage increase)
as a proxy of the financial impact or performance, the total assets as a measurement of capital, and the growth of sales figures
as a proxy variable of price.
Panel models with constant coefficients, fixed effects of cross-sections, fixed effects of time, and the general model of
random effects will be those to develop. To validate whether the random effects model is suitable, the Hausman test is used,
the result of which indicates that the hypothesis stating that individual effects are uncorrelated ought to be rejected; thus, the
random effects model is not suitable. The decision is taken to use the model of fixed effects of cross-sections, rather than the
model of fixed effects of the company, since the number of companies is ostensibly greater than the periods of study [40].
Regarding whether to employ the panel model of fixed effects of cross-sections, or the panel model with common coefficients,
the latter is discounted given the low value of the obtained adjusted R2 coefficient10, in addition to presenting problems of autocorrelation.
In the validation stage, the Durbin-Watson tests are used to test auto-correlation and the Jarque-Bera test to verify
normality of the residuals. All the tests applied validate the hypotheses of the proposed econometric model11, 12.
Thus, the second model of productivity on the basis of the Cobb-Douglas function, with an independent variable sales
figure growth, is specified in the following way:
Productivity (V.A. / Staff Expenditure)it = e(-0.72)*Number of Employeesit-0.13 *Marginal_Benefitit 0.11*Total_Assets it0.11*
Sales_Growthit0.022 *e1.67*e20.004*e30.56*e50.012*e6-0.17*e7-0.03*e80.14*e9-0.02*e10-0.24*e11-0.06*e12-0.08*e14-0.21*e150.006*e160.26*e170.14
*e180.027*e190.23*e200.42*e210.09*e220.15*e23-0.03*e24-0.03*e25-0.19*e26-0.23*e27-0.18*e28-0.07*e290.08*e30-0.12*e31-0.03*e320.51*e330.00
*e340.044*e35-0.33*e36-0.31*e37-0.07*e380.03*e39-0.19*e400.13*e41-0.02*e42-0.11*e430.16*e440.02*e450.26*e46-0.06*e470.14
*e480.36*e490.02*e500.04*e520.28*e530.16*e54-0.21*e550.02*e560.001*e57-0.22*e580.05*e59-0.004*e60-0.02*e610.09*e62-0.08*e63-0.08*e648
P-value = 0.0000, α = 0.01.
This would be the case of the companies Los Guerrilleros S.A., Walkia S.A., Microsistems S.A. and Boston Scientific Iberica.
10 2
R adjusted = 49%
11 2
R adjusted = 80%; F-statistic = 31.74, P-value = 0.0000, α = 0.01
12
Durbin-Watson = 1.30, dL = 1.421, dU = 1.670, α = 0.01
9
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0.08
*e650.05*e660.11*e670.37*e68-0.2*e69-0.38*e700.32*e71-0.08*e720.09*e730.08*e740.026*e750.11*e76-0.01*e77-0.13*e78-0.24*e79-0.24*e80*e81-0.08*e820.026*e83-0.35*e84-0.21*e85-0.04*e860.008*e87-0.13*e88-0.08*e89-0.06*e90-0.09*e91-0.03*e920.05*e93-0.22*e94-0.015*e950.01*e960.001
*e970.01*e980.08*e990.01*e1000.09*e1010.04*e1020.02*e103-0.03*e1040.03*e1050.005*e1060.15*e1070.03*e1080.21*e109-0.28*e110-0.26
(3)
0.09
Together with the good significance obtained for each of the variables included in this model13, it should be noted that all
adequately explain the productivity of the 110 companies for the Spanish retail business sector, during the period 1995-2005.
In addition, when analyzing the fixed effects for each of the companies in the sample, it is possible to observe, in general
terms, the lack of significant differences among them. It is only worth pointing out some for the difference regarding their
fixed value might have on the constant of the model14:
The results show that the variable number of employees negatively affects the productivity of retail companies in the period
analyzed, and that total assets positively affect productivity. A similar relationship exists between the marginal benefit and
productivity. Thus, all the hypotheses presented in the TMMP for the Spanish retail business sector are confirmed (see Figure
2). The major influence on marketing productivity is newly exerted by market-based assets, measured by the number of
employees, since an increase of 1% in the number of employees produces a decrease of 0.13% in productivity. The effect of
marketing resources, measured by total assets, has the positive effect presented in the TMMP on marketing productivity, since
an increase of 1% of total company assets leads to an increase in productivity of 0.11%. With regard to the hypothesis that
establishes the relationship between the variables marketing productivity and financial performance measured by marginal
benefit, the obtained result shows that an increase of 1% in the marginal benefit causes a variation of 0.11% in marketing
productivity. This validates the use of marginal benefit as a proxy variable of productivity. Finally, the hypothesis that
establishes a positive relationship between price, measured through growth in sales and marketing productivity, is confirmed
with a high level of significance. Thus, with an increase of 1% in growth of sales, the marketing productivity of retail business
companies increases by 0.022%.
IV. CONCLUSIONS
Since a non-productive use of company resources has direct negative effects on benefits and profitability, the research
developed is especially significant. The main objective of the investigation, constituting its principal value, is the proposal and
validation of models of measurement of the impact of marketing resources on the financial position of service companies, thus
allowing an explanation of the relationships that exist among the distinct components of the marketing productivity of
organizations, mainly those of services.
The more specific conclusions and implications for retail services management derived from the empirical investigation are
the following:
(i) The variable market-based assets affects negatively the productivity of retail companies in the period analyzed. The
remaining variables, marketing resources and financial performance, are positively related. The former suggests immediate
action from retail management directed toward controlling the effectiveness of staff as a productive resource measured by the
number of employees.
(ii) The inclusion in the model of growth in sales as an approximation of price allows us to identify the significant effect it
has on the marketing productivity of the Spanish retail companies. It signals the relative importance of decisions regarding the
setting of prices for retail management, who must work on making wise decisions, in both the strategic and tactical fields,
regarding price levels adequate for achieving customer satisfaction and retention, while still meeting the profitability objectives
of the organization.
(iii) Both the market-based assets and the marketing resources of the company used in the decomposition of retail output
are determinants of the retail marketing productivity of the Spanish retail companies. This suggests that companies should
consider both the number of employees and total assets as significant factors in the study and analysis of their marketing
productivity. However, given the value of the coefficient, and the negative effect of the variable market-based assets on the
productivity of retail companies, it is essential to consider in a first stage of analysis the effect of this factor, and as a second
stage, maintain and as much as possible increase the positive effect of the variable marketing resources on the productivity of
retail companies.
Among the limitations of the research are the size of the sample, the geographic sphere analyzed (Spain) and dealing with
an analysis that does not consider the specific situation of each individual company within the sample. In addition, due to a
lack of the necessary information in the database, it was not possible to contrast the influence of investments in marketing by
the companies, nor to measure the technical efficiency of companies in the sector. Another limitation was the lack of
qualitative information available in the database studied, which could have permitted the incorporation of these measurements
in the theoretical models presented. These limitations highlight the necessity for all research to rely on databases as complete
13
14
P-value = 0.0000, α = 0.01
This is the case of Leica, Microsistemas S.A., Enraf Nonius Iberica S.S., W.M. Bloss S.A.
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as possible; they open the way to broadening the study to other databases, and in other geographic, sectorial and business
contexts, in which the inclusion of all relevant financial and accounting indicators may be feasible.
ACKNOWLEDGMENT
This research is supported by ECO- 2010-20880, i+D+I Spanish Research Plan, IMPROVE research group, and Proyecto
DI Iniciación PUCV 2013 (037.464/2013).
REFERENCES
[1] Grönroos C. and Ojasalo K., “Service Productivity: Towards a Conceptualization of the Transformation of Inputs into Economic Results
in Services”, Journal of Business Research, vol.57, pp.414–423, 2004.
[2] Keh H.; Chu S. and Xu J., “Efficiency, Effectiveness and Productivity of Marketing in Services”, European Journal of Operational
Research, vol.170, pp.265-276, 2006.
[3] Diéguez J. and González V., “Precisiones en torno a los Conceptos de Productividad, Eficiencia, Eficacia, Rendimiento y
Economicidad”, Costos y Gestión, vol.3, pp.275-291, 1994.
[4] Griliches Z., “Output Measurement in the Service Sector”, University of Chicago, Press for NEVER, 1992.
[5] Musolesi A. and Huiban J.P., “Innovation and Productivity in Knowledge Intensive Business Services”, Journal of Productivity
Analysis, vol.34, pp.63-81, 2010.
[6] Ostrom A.; Bitner M.; Brown S.; Burkhard K.; Goul M.; Smith-Daniels V.; Demirkan H. and Rabinovich E., “Moving Forward and
Making a Difference: Research Priorities for the Science of Service”, Journal of Service Research, vol.13, pp.4-36, 2010.
[7] Schiff M. and Schiff J.B., “Marketing Cost Analysis for Performance Measurement and Decision Support”, Montalve N.J., Institute of
Management Accountants, 1994.
[8] Buzzel R., “Marketing Productivity”, Doctoral dissertation, Ohio State University, U.S, 1957.
[9] Sheth J. and Sisodia R., “Marketing Productivity, Issues and analysis”, Journal of Business Research, vol.55, pp.349-362, 2002.
[10] Solow R., “A Contribution to the Theory of Economic Growth”, Quarterly Journal of Economics, vol.70, pp.65-94, 1956.
[11] Fabricant S., “A Primer on Productivity”, New York: Random House Ed, 1969.
[12] Bucklin L.P. and Takeuchi H., “Productivity in Retailing: Retail Structure and Public Policy”, Journal of Retailing, vol.53, pp.35-46,
1977.
[13] Sevin C., “Marketing Productivity Analysis”, St. Louis, MO: McGraw Hill, 1965.
[14] Beckman T.N.; Davidson W.R. and Talarzyx W.W., Marketing, 9th ed. New York: The Ronald Press, 1973.
[15] Hawkins D.; Best R. and Lillis C., “The Nature and Measurement of Marketing Productivity in Consumer Durable Industries: A Firm
Level Analysis”, Journal of Academy Marketing Science, vol.4, pp.1-8, 1987.
[16] Meyer C. 1994, “How the Right Measures Help Teams Excel” Harvard Business Review, vol.72, pp.95-103, 1994.
[17] Rust R.; Ambler T.; Carpenter G.; Kumar V. and Srivastava R., Measuring Marketing Productivity: Current Knowledge and Future
Directions, Journal of Marketing, vol.68, pp.76–89, 2004.
[18] Hooleya G., Greenleya G., Cadogana J., and Fahy J., “The Performance Impact of Marketing Resources”, Journal of Business Research,
vol.58, pp.18– 27, 2005.
[19] Betancourt R.; Cortiñas M.; Elorz M. and Mugica J.M., “The Demand for and the Supply of Distribution Services: A Basis for the
Analysis of Customer Satisfaction in Retailing”, Quantitative Marketing and Economics, vol.5, pp.293-312, 2007.
[20] Fenn P.; Vencappa D.; Diacon S.; Klumpes P.and O’Brien C., “Market Structure and Effiiciency of European Insurance Companies: A
Stochastic Frontier Analysis”, Journal of Banking and Finance, vol.32, pp.86-100, 2007.
[21] Marketing Science Institute, Prioridades de Investigación 2006-2010. Disponible en http://www.msi.org, 2006, 2008.
[22] Moe W. and Fader P. “The Role of Price Tiers in Advance Purchasing of Event Tickets”, Journal of Service Research, vol.12, pp.73-86,
2009.
[23] Dawes J., “The Effect of Service Price Increases on Customer Retention The Moderating Role of Customer Tenure and Relationship
Breadth”, Journal of Service Research, vol.11, pp.232-245, 2009.
[24] Shapiro C. and Varian H., “Information Rules: A Strategic Guide to the Network Economy”, Harvard Business School Press, 1999.
[25] Day G.S., “The Capabilities of Market-Driven Organizations”, Journal of Marketing, vol.58, pp.37–52, 1994.
[26] Han J.K.; Kim N. and Srivastava R.K., “Market Orientation and Organizational Performance: Is Innovation the Missing Link?”, Journal
of Marketing, vol.62, pp.30–45, 1998.
[27] Youndt M.A; Snell S.A.; Dean J.W. and Lepak D.P., “Human Resource Management. Manufacturing Strategy and Firm Performance”,
Journal Academy Management, vol.39, pp.836– 66, 1996.
[28] Keller K., “Strategic Brand Management”, Upper Saddle River, NJ: Prentice Hall, 1998.
[29] Blattberg R. and Deighton J., “Managing Marketing by the Customer Equity Test”, Harvard Business Review, vol.75, pp.136–44, 1996.
[30] Simon C. and Sullivan M., “The Measurement and Determination of Brand Equity”, Marketing Science, vol.12, pp.28–52, 1993.
[31] Aaker D. and Jacobson R., “The Financial Information Content of Perceived Quality”, Journal of Marketing Research, vol.31, pp.191201, 1994.
[32] Berger P.; Eechambadi N.; George M.; Lehmann D.; Rizley R., and Venkatesan R., “From Customer Lifetime Value to Shareholder
Value - Theory, Empirical Evidence, and Issues for Future Research”, Journal of Service Research, vol.9, pp.156-167, 2006.
- 276 -
International Journal of Economics and Management Engineering (IJEME)
Dec. 2013, Vol. 3 Iss. 6, PP. 268-277
[33] Weber J.A., “Managing the Marketing Budget in a Cost-Constrained Environment”, Industrial Marketing Management, vol.31, pp.705–
717, 2002.
[34] Bou J.C. and Satorra A., “The Persistente of Abnormal Returns at Industry and Firm Levels: Evidence from Spain. Department of
Economics and Business”, Universitat Pompeu Fabra, Barcelona, España, 2006.
[35] Jorge J. and Laborda L., “Factores Condicionantes de la Relación entre la Cuota Relativa de Mercado y el ROI. Evidencia Empírica en
el Sector Manufacturero Español”, Documento de trabajo, Universidad de Alcalá, Departamento Ciencias Empresariales, España, 2002.
[36] Yuengert A., “The Measurement of Efficiency in Life Insurance: Estimates of a Mixed Normal-gamma Error Models”, Journal of
Banking and Finance, vol.17, pp.483-496, 1993.
[37] Cummins J.; Tennyson S.and Weiss M. “Efficiency, Scale economies, and Consolidation in the U.S. Life Insurance Industry”, Journal
of Banking and Finance, vol.23, pp.325-357, 1999.
[38] Cummins J. and Weiss M., “Consolidation in the European Insurance Industry: Do Mergers and Acquisitions Create Value for
Shareholders?”, Working paper, University of Pennsylvania, Wharton Financial Institutions Center. Pennsylvania, U.S., 2004.
[39] Jacobson R. and Aaker D., “Is Market Share All that it’s Cracked up to Be?”, Journal of Marketing, vol.49, pp.11-22, 1985.
[40] Greene W., Econometric Analysis, 5th ed. Upper Saddle River, New Jersey: Pentice Hall, 2002.
[41] Ratchford B. and Stoops G., “A model and Measurement Approach for Studying Retail Productivity”, Journal of Retailing, vol.64,
pp.241-263, 1988.
[42] Parsons L.J., “Productivity Versus Relative Efficiency in Marketing: Past and Future”, School of Management, Georgia institute of
Technology, Atlanta, EEUU, 1995.
[43] Reynolds J., “¿Es Eficiente el Sector Europeo de la Distribución Comercial Minorista?”, Tendencias de la Distribución Comercial en el
Ambito Internacional, vol.828, pp.25-34, 2006.
[44] Lusch R. and Ingene C., “The Predictive Validity of Alternative Measures of Input and Output in Retail Production Functions”, In
Beckwith N. et al. (eds.). AMA 1984 Educator`s Conference Proceedings, American Marketing Associaton, Chicago, 1979.
[45] Hausman J. A., “Specification Test in Econometrics”, Econometrica, vol.46, pp.1251-1271, 1978.
Dr. Hanns de la Fuente-Mella, is Professor of Quantitative Methods, School of Economics and Management,
Pontifical Catholic University of Valparaiso, Chile.
Dra. Carmen Berné-Manero, is Professor of Department of Marketing Management and Market Research,
Department of Economic and Business Science, University of Zaragoza, Spain.
Dra. Marta Pedraja-Iglesias, is Professor of Department of Marketing Management and Market Research,
Department of Economic and Business Science, University of Zaragoza, Spain.
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