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Purpose - Evolution and stationarity are key time series empirical concepts which need theoretical assessment by extant research. This study presents a model to explain brand sales dynamics in emerging markets using two dimensions: sales behavior in time (stationary or evolution) and final position (negative, neutral or positive). Design/methodology/approach - A three-step methodological approach was performed. First, individual brand sales series were classified (stationarity or evolution) after unit root tests. These series were then regressed against a time variable. These two steps enabled a qualitative classification of six proposed positions, ranging from the worst to the best scenario for marketing managers. A final multinomial model identified the marketing effect to these positions. Findings - Descriptive statistics reveal an insignificant prevalence of stationary sales series and a small number of positive brand sales series (ascending or promising). The multinomial model shows that price is negatively associated to positive brand sales positions, the important effect of service strategies and how product decisions can lead to an avoidance of negative positions. Research limitations/implications - The model is limited to short time series of a unique transactional dataset from a multinational energy company based in Brazil. Practical implications - The research provides a rational empirical framework to managers involved with decisions regarding brand sales dynamics in emerging markets. Originality/value - The approach advance into the development of models to uncover conditions for market evolution and stationarity in a context marked by the shortage of data.
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How marketing balances the battle
between premium and regular
products? Brand sales dynamics in
an emerging market
Marcos In
acio Severo de Almeida
Universidade Federal de Goi
as, Goi^
ania, Brazil and
Universidade de Bras
ılia, Bras
ılia, Brazil
Rafael Barreiros Porto
Universidade de Bras
ılia, Bras
ılia, Brazil, and
Ricardo Limongi França Coelho
Universidade Federal de Goi
as, Goi^
ania, Brazil
Abstract
Purpose Evolution and stationarity are key time series empirical concepts which need theoretical assessment
by extant research. This study presents a model to explain brand sales dynamics in emerging markets using two
dimensions: sales behavior in time (stationary or evolution) and final position (negative, neutral or positive).
Design/methodology/approach A three-step methodological approach was performed. First, individual
brand sales series were classified (stationarity or evolution) after unit root tests. These series were then
regressed against a time variable. These two steps enabled a qualitative classification of six proposed positions,
ranging from the worst to the best scenario for marketing managers. A final multinomial model identified the
marketing effect to these positions.
Findings Descriptive statistics reveal an insignificant prevalence of stationary sales series and a small
number of positive brand sales series (ascending or promising). The multinomial model shows that price is
negatively associated to positive brand sales positions, the important effect of service strategies and how
product decisions can lead to an avoidance of negative positions.
Research limitations/implications The model is limited to short time series of a unique transactional
dataset from a multinational energy company based in Brazil.
Practical implications The research provides a rational empirical framework to managers involved with
decisions regarding brand sales dynamics in emerging markets.
Originality/value The approach advance into the development of models to uncover conditions for market
evolution and stationarity in a context marked by the shortage of data.
Keywords Emerging markets, Marketing models, Time-series analysis, Brand sales, Market evolution,
Marketing dynamics
Paper type Research paper
Introduction
A marketing manager involved with brand management in emerging markets should
understand the behavior of brand sales in time and evaluate sales positions because decisions
in this reality are usually supported by what time-series analysis classify as small time
windows (Pauwels and Hanssens, 2007). The reasons underlying these demands are
threefold. First, managers usually interpret performance based on sales trend signs and
changes (Pauwels and Hanssens, 2007). Second, managerial decisions in emerging markets
Brand sales
dynamics in
emerging
markets
The authors acknowledge Harald van Heerde and Peter Danaher for their valuable insights in previous
versions of this paper.
Funding This work was supported by CAPES Foundation, Ministry of Education of Brazil, grant
number BEX 9567/14-3.
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1746-8809.htm
Received 22 June 2019
Revised 21 October 2019
8 January 2020
Accepted 22 February 2020
International Journal of Emerging
Markets
© Emerald Publishing Limited
1746-8809
DOI 10.1108/IJOEM-06-2019-0457
seem to be different from traditional markets. Business models, for example, need to adapt
(Pels and Sheth, 2017) and for companies based in an emerging market, this is no different
(Sharma et al., 2018), mainly due to the existence of political, business, social and internal
contingencies for companies (Paul, 2020). The last reason refers to particular event factors
that vary from country to country, which may base managerial decisions in this very
particular context (Lim et al., 2017).
Marketing decisions that analyze brand sales dynamics use time series processes related
to constant or ever-changing means of the performance variable, classified as stationary or
evolution (Hanssens et al., 2002). However, as Pauwels and Hanssens (2007) point out,
market-response research dedicated to explaining sales still have not reached a point to
provide managers with established frameworks about brand sales behavior in time. This
opportunity is even more latent in emerging markets, where there is shortage of data about
marketing dynamics (Narasimhan et al., 2015) and where the business environment
developed to an own-specific history (Austin et al., 2017).
Another striking feature of decision-making is that managers usually rely on subjective
judgments and rules of thumb decisions (Leeflang et al., 2015). Context in emerging markets
seems to resemble and even intensify this characteristic, as they present particular forms of
unstructured and unorganized retail competition (Kumar et al., 2015;Sharma et al., 2018).
Distribution strategies vary across brands, product forms and retail competition (Sharma
et al., 2019). While in mature market strategies such as distribution and prices reach a steady
state, establishing performance in almost equilibrium (Pauwels and Hanssens, 2007) and
environment conditions in emerging markets are more diverse (Roberts et al., 2015;Rottig,
2016) and present significant differences in socioeconomic and regulatory systems (Burgees
and Steenkamp, 2006;Rottig, 2016). This may influence on how brand sales behave over time.
The purpose of this article is to address these issues by developing a straightforward
methodology to explain brand sales dynamics. Our approach classifies dynamicsusing two
dimensions: brand sales behavior in time (stationary or evolution) and brand sales position
(negative, neutral or positive). These dimensions determine six possible positions, which range
from the worst (descending) to the best (ascending or promising) scenario for managers. Hence,
this methodology incorporates univariate time-series analysis and a multinomial regression
model that identify the influence of marketing mix strategies on the proposed qualitative
positions. In this sense, we present two significant contributions. First, the setting where it is
applied, after all, the extant econometric and time series models restrict to fast-moving consumer
goods (FMCG) (Hanssens and Pauwels, 2016), while our study is about gasoline retailing.
Second, it is applied to a decision-making time-series methodology-based framework to
marketing managers in emerging markets, who must decide howto allocate marketing inputs in
a context distinguished by unique features (Rottig, 2016).
The setting for our empirical approach is the gasoline retailing in Brazil, a specific marketing
situation marked by a robust regulatory system where companies market premium (branded)
and regular (unbranded) gasoline and ethanol for passenger cars and where the government
only authorizes diesel for heavier vehicles. This situation is in line with what Nielsen et al. (2018,
pp. 1,679) characterize as emerging markets context in focusthat drive researchers to
recognize the distinct contextual settings of specific countries labeled as emerging markets.
Petrol stations are the distribution channels of gasoline and ethanol in Brazil, sometimes
assuming the position of business partners, signalized by the energy company brand in the
petrol stations. Since most petrol stations are independent and under different contracts,
multinational energy companies must rely on their partners and invest in additional
marketing strategies to increase performance, such as branding, service (in the form of
convenience stores), promotion and loyalty cards to induce frequency purchase patterns.
The proposed model contributes to the general understanding of brand competition and
dynamics in a specific sector (gasoline retailing) in emerging markets, marked by the existence of
IJOEM
branded and unbranded products. As pointed out by Sheth (2011), one defining characteristic of
emerging markets is the existence of unbranded competition and we show how the influence of
marketing strategies in this context, marked by branded and unbranded products alongside
with branded and unbranded intermediaries (the petrol stations). Sheths (2011)
conceptualization about emerging markets underscores a nascent research area as the fusion
of existing perspectives with alternative perspectives generated by the context(p. 179). Our
approach focuses on this gap, recognizes distinct characteristics of emerging markets and builds
on initiatives on empirical studies based on FMCG data (Ataman et al.,2008,2010;Hwang and
Thomadsen, 2016), to unveil how marketing mix variables behave in this context.
We organize this article as follows. First, we present a brief overview of brand sales studies,
followed by our descriptive model. Next, we discuss branded and unbranded competition in
emerging markets. The methodology portrays the development of the two dimensions of our
model before the construction of the final model. Results, discussion and managerial implications
for emerging markets and managers are provided before the final remarks and study limitations.
A proposal of brand sales dynamics in emerging markets inspired by the
Brazilian gasoline retailing context
Ever since the publication of the empirical generalizations about sales evolution and
stationarity (Dekimpe and Hanssens, 1995), researchers have been detailing marketing mix
effects to identify which elements are most critical in providing success for brand sales
(Ataman et al., 2010). Dynamic models concentrate on the long-term of marketing strategy,
usually resorting to price and advertising (Ataman et al., 2010) and the use of these as
spending measures resulted in the well-known identification of sales response given to
spending over time (Dekimpe and Hanssens, 1999) but opened avenues for investigations
dedicated to analyze the impact of other variables in different contexts.
The same empirical generalization that provided the basis for what studies recognize as
necessary conditions for evolution or stationarity also stressed the need for further research
(Dekimpe and Hanssens, 1995). Pauwels and Hanssens (2007) addressed this question when
divided the sales series of frozen dinner brands in rolling time windows and found unstable
brand performance during short periods in mature markets. Recent studies with emerging
marketsdata advanced into the explanation of the impact of marketing mix variables on the
sales performance of FMCG (Bahadir et al., 2015). However, two gaps persist: how to provide
managers a straightforward methodology to classify and analyze brand sales dynamics in
emerging markets? How is the influence of marketing mix strategies in contexts marked by
unbranded competition, such as emerging markets?
Recent research suggests that brand sales tend to evolve in what authors classify as
spurts movements, which means short alternating periods of evolution with more extended
stability (Hanssens et al., 2016). Wang and Zhang (2008) detailed the two possible outcomes
for sales growth, according to intrinsic characteristics. They defined markets as intrinsically
stationary, if marketing spending induces sales evolution, and intrinsically evolving if sales
growth is evolving without any intrinsic marketing link. Despite the progress in the
understanding of brand sales movements in time series, certain particularities from emerging
markets must be addressed by empirical research.
Our methodology establishes itself on these advancements to develop a descriptive model
to identify brand sales behavior and position in an emerging market. First, it offers an
additional point of view to brand sales dynamics, as brand sales are defined in the reality of
gasoline retailing as the ratio between the sales of the premium (branded) and the regular
product (unbranded), as this is a characteristic of emerging markets (Sheth, 2011).
These sales could be in a mean-reverting position (stationarity) or a situation without any
fixed mean along with increasing variance, defined as evolution (Dekimpe and Hanssens,
Brand sales
dynamics in
emerging
markets
1995). However, this is not the only possible outcome for performance, which can also be
unstable (Pauwels and Hanssens, 2007). Comparatively, sales can also be, in the present, in a
negative, neutral or positive position, due to its trend significance (or insignificance).
Therefore, our descriptive model grounds on two dimensions (sales behavior in time and sales
final position) to define six possible positions to brand sales in a short period window.
Figure 1 details the framework developed.
It is necessary to analyze two dimensions concurrently to classify a given sales time series
inside the proposed positions. On top of Figure 1, the behavior in time plays a role in
separating brand sales positions which are stationary (ascending) from evolving (promising).
These two are the most favorable scenario for managers in emerging markets, as sales
finishin positive positions, when compared to its initial movement. In the middle, there are
scenarios where the time trend is not significant, but one is stationary (steady) and the other
evolving (unsteady). At the bottom, there are the two worst scenarios: sales can be descending
in a free-falling stationary situation, difficult to revert, or faltering in hesitation, due to an
evolutionary scenario opened to future changes (Dekimpe and Hanssens, 1995). This
classification can be useful for marketing strategy in emerging markets, mainly if it includes
the possible effect of marketing to favorable positions.
Decreasing sales Neutral sales Increasing sales
Promising Ascending
Unsteady Steady
Faltering Descending
Brand sales in time
0102030
01020
30
Brand sales in time
Brand sales in time
0102030
Brand sales in time
010 20 30
Brand sales in time
01020
30
Brand sales in time
0102030
Time trend is
positive and
significant
Time trend is
not significant
Time trend is
negative and
significant
Evolving Stationary
Dimension 01: Sales behaviour in time
Dimension 02: Sales final Position
Figure 1.
Time series of brand
sales based on actual
data from an emerging
market
IJOEM
Branded and unbranded competition in the gasoline retailing context in an
emerging market
The inspiration for our framework is the gasoline retailing context in Brazil. In this
environment, multinational integrated energy companies compete against a
government-controlled oil corporation. Each competitor distributes products such as
premium and regular gasoline, ethanol and diesel to petrol stations under contract, and
two particular characteristics in Brazil deserve attention. First, Brazilian law ensures,
since the 1990s, that passenger cars to use gasoline or ethanol only, relegating diesel for
commercial trucks, pickup trucks and sports utility vehicles (SUV) (Di
ario Oficial da
Uni~
ao, 1989). Second, a resolution published by a Brazilian agency ensured competitors
to add exclusive additive components to gasoline (Ag^
encia Nacional do Petr
oleo, G
as
Natural e Biocombust
ıveis, 2014) and market different brands, the premium version of
the product.
A defining characteristic of emerging markets is the existence of unbranded competition,
given the poor infrastructure, which poses difficulties to big brands and companies in doing
business (Dawar and Chattopadhyay, 2002;Sheth, 2011). Accordingly, doing business in
emerging markets require an understanding of four specific dimensions, defined as the PBSI
relationship model by Paul (2020): political relationships that constraint companies activities,
business relationships that influence norms and marketing mix strategies (such as pricing),
social relationships, that influence consumersexpectations, and internal relationships, which
refer to firm interdepartmental interactions.
Gasoline retailing in Brazil presents an unusual condition that reflects Pauls (2020)
framework, where Petrobras, a Brazilian energy company, is the only one to refine petrol,
despite the deregulating process that occurred in 1997 (Mesquita, 2010). As a result, the
regular gasoline is the same (produced by Petrobras) wherever the consumer refuels his/her
car. Conversely, the premium gasoline is a product that receives different mixtures of
additives and Petrobras and other major multinational companies offer their brand to the
market, exclusively distributing in their partners (the petrol stations). Consequently, firms
operating in this sector must manage regular and premium brands using different focus, in a
process that resembles the theoretical proposal of Parment (2008) for distribution strategies
for volume and premium brands in competitive consumer markets: while an efficiency focus
must be designed for volume (regular) brands, a differentiation focus is applied to
premium ones.
The entire marketing mix management deserves special consideration in emerging
markets. Local channel partners of international brands respond differently to
manufacturersmarketing mix strategies (Venkatesan et al., 2015). This difference is
explained by structural differences from channels regarding the level of relationship and
integration with the multinational manufacturer. Sharma et al. (2019) argue that
manufacturersretail distribution decisions are more complicated in emerging markets
because of the underdeveloped infrastructure, price sensitivities, penetration issues and
others. International brands play a central role in developing countries due to a brand origin
effect (Lee, 2019), which means that brands from these countries (the United States, for
example) benefit from a phenomenon classified as mass prestige value by (Kumar and Paul
(2018),Paul (2019). These particularities may suggest specific characteristics in gasoline
retailing competition in Brazil, such as:
(1) Corrivalry between premium and regular products in this sector, not observed in
developed markets, where competition is already consolidated (Roberts et al., 2015).
This characteristic underscore a need to analyze product substitutability in an
emerging market, as raising the price of the premium gasoline may lead to an increase
in sales of the regular version (Shocker et al., 2004);
Brand sales
dynamics in
emerging
markets
(2) Therefore, the competitive structure between petrol stations occurs not only at the
local of a specific station with additional services (Haucap et al., 2017) but also at the
product-level, where products exhibit direct (premium and regular gasoline against
ethanol) and indirect (premium and regular gasoline against diesel) competition. This
condition does not occur in developed markets, such as Europe (Haucap et al., 2017)or
the United States (Iyer and Seethuraman, 2008);
Method
Dataset and research design
The dataset refers to exclusive distribution partners of a major multinational energy
company and this guarantees that in the period of analysis these partners only bought and
sold to their consumers the gas-related products from the focal firm. Data comprises of sales
and marketing data of 195 petrol stations for 27 months, 2011 to 2013. Not all stations had the
complete set of 27 periods, as some specific observations had started or terminated their
distribution contracts after January 2011 or before March 2013. Hence, as one stage of the
empirical model involves time-series analysis, the final set included only observations with 24
or more periods, the cutoff defined as the limit for inclusion in the sample. The final models
concentrated on 174 petrol stations with at least 24 sequential periods. These observations
are from an entire sales region from the focal company, a sample of 3.7% of the 4,700 petrol
stations under contract into the companys chain of distribution. The petrol stations are
located in 41 cities, distributed in five different states of Brazil, which ensures a minimum
variability criterion (Leeflang et al., 2015) of samples of the petrol stations inside the company
distribution chain.
Despite resorting solely to data from one individual company, the convenience sample
reflects general marketing strategies from multinationals in the energy sector in an emerging
market such as Brazil. Multinationals from this sector usually employ traditional cost
leadership strategies (Baack and Boggs, 2008), where distribution is delegated to stations that
are owned and operated by a refiner, partially or entirely vertically integrated (Hosken et al.,
2008). It is usual to rely on unique datasets when analyzing the dynamics of gasoline retailing
(Byrne and Roos, 2019) and relatively common to observe empirical initiatives that resort to
single-company datasets (multinationals) that cover an entire geographic region. One
concrete example applied in Brazil was the study by Venkatesan et al. (2015) about full and
self-service channels using a unique dataset from a multinational beverage industry.
The populationis composed of the entire set of petrol stations under contract with the
multinational energy company. This company is divided into different sales regions and each
region embraces petrol stations from different Brazilian cities and states, with specific
marketing characteristics. Particularly, the dataset under analysis includes petrol stations
located at state capitals and major cities. Data information also covers petrol stations located
in small, peripheral cities or located at highways and interstates. Although this sample was
not originated from random sampling, it secures some variability, resembling a stratified
sample (Doane and Seward, 2011) of petrol stations. It is relatively common for marketing
time series empirical studies to resort to a portion of observations to generate insights.
Dekimpe and Hanssens (2000) review on time-series models in marketing shows this practice
and unveils that the existing knowledge is solely based on developed economies/
markets data.
Modeling decision for brand sales
The focal variable of our study is brand sales, measured in the form of a ratio between
premium and regular gasoline in a given month. All product sales information in the dataset
IJOEM
is at the B2B level (from the company to petrol stations) and in cubic footage, a volume
measure that reflects quantity, the most desirable form to explain sales, given the
appropriated form that quantity provides to marketing planning (Hanssens and Parsons,
1993). An additional procedure was necessary, as 48 petrol stations (635 observations of the
entire dataset) had not (at least in one month of their time series) bought from the focal
company the premium product, swapping to regular only in some periods. We sorted out this
limitation by adding one small volume number (1) to the premium and regular gasoline sales
series, a solution commonly employed in marketing response modeling studies (Rao et al.,
1988;Hanssens and Parsons, 1993). Subsequently, the variable underwent a natural
logarithm transformation, followed by a difference of logarithms operation, the standard
mathematical procedure for treating logarithms in a ratio format. Equation (1) summarizes
the brand sales variable [1].
Brand salesit ¼Lnsales of the premium gasoline þ1
sales of the regular gasoline þ1(1)
The primary managerial assumption behind Equation (1) is to provide knowledge about how
sales of the premium product grow in comparison to the regular product. The underlying
rationale is that premium gasoline is vertically differentiated from the regular one, due to
attributes differentials which may influence individual preference. According to Barron et al.
(2000, p. 550), if offered at identical prices, all consumers would choose to consume products
of higher quality over those of inferior quality.The primary methodological assumption of
the development of Equation (1) is the presentation of a variable which refers to what
Hanssens et al. (2002) signalizes as marketing response, a performance measure in which
managers base their decisions.
A methodological approach to build brand sales behavior and position
We operationalized the methodology in three steps. At first, we classified the observations
according to the sales behavior in timedimension depicted in Figure 1. To identify if brand
sales were stationary or non-stationary, we conducted formal unit root tests and Zivot and
Andrews (1992) test for structural breaks. It is necessary because the buying pattern of some
gas stations for premium and regular gasoline was very irregular in some months, creating
data dependent breakpoints and, therefore, structural changes in sales series. The use of
multiple tests was also necessary to cover the common problem that time series not being
very informative about the presence of a unit root (Kwiatkowski et al., 1992).
According to Kwiatkowski et al. (1992, p. 159), it is a well-established empirical fact that
standard unit root tests fail to reject the null hypothesis of a unit root, and we resorted to a
series of tests to ensure a precise classification. A process is either stationary, around a fixed
component (such as a mean, a set of seasonal means or a deterministic trend), or in evolution,
when it does not revert to a deterministic component. However, it is crucial to outline that the
routine developed was inspired by extant time-series literature directions for classification of
a marketing performance variable (for a concise summary, see Leeflang et al., 2017).
These procedures enabled the second step of the empirical model, designed to classify
brand sales variable according to the second dimension of the framework in Figure 1 (sales
final position). For this case, we used brand sales as a dependent variable and the natural
logarithm of the time trend as the only independent variable in a simple double-log
regression. The inspiration for this step was Pauwels and Hanssens (2007, p. 297)
least-squares estimation of a time trend. This component, expressed in Equation (2) as δ,
yields a t-statistic, which reveals the sign and significance of the time trend.
Brand sales
dynamics in
emerging
markets
Lnsales of the premium gasoline þ1
sales of the regular gasoline þ1it
¼
α
it þδLnðtimeÞþ
ε
it (2)
The assumption behind Equation (2) is what Pauwels and Hanssens (2007) characterize as
marketing performance diagnostics. The operationalization of 174 individual regressions
enabled the comparison between brand sales (Equation 1) final against an initial position. The
idea is to allow managers to identify if, compared to the initial period of the time series, sales
were neutral (not significant), positive and significant or negative and significant. So, it was
possible after the storage of the t-statistic of the natural logarithm of the time trend and the
resulting identification of performance declining, stability or growth (Pauswels and
Hanssens, 2007) for each petrol station brand sales variable. In the end, we had two
precise classifications for each brand sales series of the 174 observations in the dataset: one
referring to dimension one (sales behavior in time) and the other alluding to dimension two
(sales final position). Figure 2 details the full methodological flow to classifying brand sales
behavior and position for each time series of the dataset.
Detailing the independent variables
After the definition of six sales positions (Figure 1), derived from the construction of the
brand sales variable, we defined the independent variables. They refer to a group of five
marketing actions, which occur in the gasoline retailing context, accompanied by two control
variables. Specifically, the marketing variables allude to the following managerial marketing
decisions:
(1) Product sales, in the form of substitutable (ethanol) and non-substitutable product
(diesel);
(2) Service, describing the existence of an increasing level of service in the petrol station
(convenience store);
(3) Price;
(4) Loyalty, indicating the number of loyalty cards issued to the consumers of a given
petrol station;
(5) Sales force manifested by the presence of an employee of the petrol station
responsible for motivating others to sell more loyalty card proposals to petrol stations
consumers.
174 petrol stations
with at least 24 months
of sales information
(n = 4682)
Brand sales variable
174 individual brand sales
time series
01
Identification of evolution
or stationarity for each
brand sales variable
employing a series of tests
02
174 individual simple
regressions of the brand sales
variable against time
Sales positions
- Ascending
- Promising
-Unsteady
-Steady
- Faltering
- Descending
Qualitative unordered
dependent variable of
the multinomial model
Figure 2.
Amethodological
approach to developing
brand sales behavior
and position
framework
IJOEM
These variables refer to marketing actions that occur in a simple marketing system where a
manager expects that these actions generate sales revenue (Hanssens et al., 2002). Control
variables refer to an internal characteristic, which can positively affect brand sales, the size of
the petrol station and an external, state taxation that usually incurs on energy product
commercialization. Themido et al. (1998) highlight the importance of size to attend
commercial interests from petrol station owners. The aliquot varies from state to state in
Brazil. Extant research in the fuel industry shows that state sales-tax increase can increase
prices (Yilmazkuday, 2017) and potentially negatively influence the dependent variable
(Coglianese et al., 2017). Figure 3 illustrates the empirical model developed, including
independent and dependent variables.
The development of two multinomial logit models
The third and final methodological stage encompassed running two multinomial logit
models (one for branded petrol stations and another for unbranded petrol stations),
where we grouped the dependent variable into six mutually exclusive and unordered
groups, the brand sales positions depicted in Figure 1.Werunthesamemodel
considering two realities: petrol stations that signal the refiner brand and others
classified as open dealerscontracts (Shepard, 1993), where a refiner exclusively
distributes their products to petrol stations but does not charge a franchise fee. The
resulting implication is that these stations do not use the refiner brand and do not
benefit from the reputation produced by branding issues (Kleit, 2005) at the gas
station level.
One assumption of the multinomial logit model is to have g1 logit functions, where
gis the number of groups. Assuming the final model with the six groups of the
proposed framework, where stationary and fixedsteadysales(whentimetrendisnot
significant) is the reference category (Y50), one intercept and eight independent
variables, as shown in Figure 3,Equation (3) identifies the basic structure of the logit
function for one of the six possible brand sales positions. We chose to represent in
Equation (3) the promising position as an example using Hosmer et al. (2013) notational
system, assuming pcovariates and a constant term, all represented by a vector x,of
length pþ1.
Sales positions
- Ascending
- Promising
- Unsteady
-Steady
- Faltering
- Descending
Dependent variable
Independent variables
Marketing actions
-Product
Non-substitutable (Diesel sales)
Substitutable (Ethanol sales)
- Service
-Price
-Loyalty
- Salesforce
-Size
- State taxation
Control variables
Figure 3.
Empirical model
Brand sales
dynamics in
emerging
markets
g1ðxPromisingÞ¼lnPr ðY¼1jxÞ
Pr ðY¼0jxÞ
¼β1;0þβ1;1x1þβ1;2x2þβ1;3x3þβ1;4x4þβ1;5x5þβ1;6x6þβ1;7x7þβ1;8x8
¼x0β1
(3)
The econometric assumption behind Equation (3) is to yield explanation about the factors
responsible for increasing the probability for a brand sales petrol station variable to assume a
promising position (Evolving with time trend positive and significant, according to Figure 1)
in comparison with the reference group of the multinomial model (steady position). At least
six of these factors are marketing decisions present in the gasoline retailing context, as shown
in Figure 3, while two of them refer to the control variables. As there are four more equations
(not shown here) similar to Equation (3), one for each brand sales position, marketing
managers are promptly able to identify the potential influence of marketing to these different
positions. Accordingly, the set of eight independent variables for the final models refers to
marketing variables and two control variables. Table 1 provides dependent and independent
variablesdescriptions, along with their means and standard deviations before necessary
transformations.
Results
Descriptive results
Table 2 identifies the frequency of observations classified as stationary or evolution. Data
reveal an insignificant prevalence of stationary sales series (54.6%), with the proportion of
evolving being statistically the same (45.4%). Albeit being only descriptive, these results
address at least two remarks given by Dekimpe and Hanssens (1995) time series assessment
of performance variables: (1) the need for investigation into the circumstances responsible for
generating evolution or stationarity and (2) the apparent singularity of the context, as
distinctions in evolution and stationarity may be caused by differences in legal and
competitive structure(Dekimpe and Hanssens, 1995, p. G118).
In Table 3, we describe the six brand sales positions. Only 28.7% of the sales series are in a
favorable position (ascending or promising groups in Figure 1), revealing that brand sales
behavior usually is in a neutral or in an unfavorable position compared to the initial period.
Another interesting question regards the noteworthy values of the t-statistic for ascending
and promising positions. They exhibit values close to four standard deviations away from the
mean (3.74 and 3.98). These descriptive results uncover the need to assess the specific
marketing conditions responsible for driving sales to positive positions and preventing from
neutral or negative ones. In this case, the one-sample chi-square test rejected the null
hypothesis for equal probabilities, with a
χ
2
51.47 and a p-value 50.00.
Multinomial model results and coefficients interpretations
Table 4 reports the estimated parameters from the multinomial models, and the first
noteworthy result regarding to different impacts promoted by some marketing mix variables
(product, service and price) considering branded and unbranded petrol stations. Price, for
example, is negatively associated with favorable brand sales positions (ascending and
promising) only in unbranded petrol stations, presumably revealing that the presence of the
refinery brand mitigates the effect of price on positive positions.
A second result reveals the importance of service, as the existence of convenience stores
increases the logarithm of the odds for brand sales to stay ascending or promising, indicating
IJOEM
an essential effect of this strategy for both types of petrol stations (branded and unbranded).
However, this effect is more prominent in unbranded petrol stations. For example, as shown
in column six of Table 4, the presence of service in unbranded petrol stations multiplies the
Variable Mean SD Transformation procedure before running the models
Brand sales (Ratio) 5.90 46.64 Natural logarithm of the ratio between branded and
unbranded gasoline sales
Diesel sales (non-
substitutable product)
Ethanol sales
(substitutable product)
168.30 433.64 Geometric mean of sales of a non-substitutable product
(diesel) inside the time period
a
39.68 44.67 Geometric mean of sales of a substitutable product (ethanol)
inside the time period
Service 0.17 0.38 Dummy variable indicating the existence of a convenience
store in the petrol station
Price (branded gasoline)
Price (unbranded
gasoline)
2.96
2.83
0.11
0.12
To build the price variable we used the following steps: (1)
calculated a geometric mean for actual price of premium
gasoline and regular gasoline; (2) calculated a natural
logarithm for both price informations; (3) took the difference
in logs operation, reproducing the same structure for brand
sales variable (Equation 1)
Loyalty 6.31 4.94 Natural logarithm of the number of months that a petrol
station participated in a loyalty card schema designed to
enhance brand sales
b
Sales force 0.50 0.50 Dummy variable indicating if a petrol station had a special
sales force effect for the loyalty card schema
c
Size 102.09 42.24 Natural logarithm of size, measured as the total capacity (in
cubic footage) of the fuel tanks inside the petrol station
d
State taxation 0.21 0.19 Specific and variable per state tribute (circulation of goods
and services tribute), which incur on energy products.
Aliquot is different for diesel, ethanol and gasoline and we
calculated a geometric mean per state to capture the dynamic
effect of this variable
Note(s): Variables are in raw values for descriptive statistics. The independent quantitative variables were
standardized (observations were divided by the mean of that variable) before entering in the final multinomial
models.
a
Geometric means were used for product sales series to normalize the system (Datta et al., 2017) and to
capture dynamic fluctuations of the values across the time periods.
b
The construction of this variable also
involved adding a unity, because some petrol stations presented zero values. To count and consider a month
valid, we resorted to the following criteria: petrol stations should have issued at least five proposals inside a
month and stayed inside the loyalty card schema for at least three consecutive months.
c
This marketing action
was performed only in the second year of the dataset. An employee of the gas station was responsible for
motivating other employees to sell loyalty cards to petrol station consumers. This employee could receive
monthly prizes from the energy company if the team achieved some predetermined sales goals.
d
Retrieved
information from an official Brazilian agency responsible for regulating the energy sector in the country. For
missing data on six observations, we resorted to the average size of all petrol stations inside the city where the
information for size was missing
Brand sales behavior in time Frequency Percent
Stationary 95 54.6
Evolving 79 45.4
Note(s): Hypothesis of equality of proportions cannot be rejected at p< 0.05, as evolution or stationarity occur
with equal probabilities inside the sample, with a
χ
2
(1.47), p-value 50.22
Table 1.
Descriptive analysis of
model variables
Table 2.
Identification of
stationarity and
evolution of brand
sales series
Brand sales
dynamics in
emerging
markets
odds of brand sales to remain in an ascending position by 15.62 and by 12.38 to stay in a
promising position. This effect for branded petrol stations is lower (column 10 of Table 4) and
observed only for a promising position (3.63).
Product variables are significant only on some occasions and may explain how to avoid
negative positions in order to stay neutral (as steady is the reference category of the
multinomial model). It is important to note the negative coefficient for a non-substitutable
product on descending and faltering positions in Model 1. Signals and significances of these
variables reveal that increases in diesel sales, the non-substitutable product, can sustain
brand sales on a neutral and stationary position (given the negative signal for descending and
faltering). Control variablesresults indicate a relationship between size and positive brand
sales positions in branded petrol stations (Model 2), i.e. increases in petrol station size can
avoid brand sales to go from ascending and promising to steady, denoting a more significant
area for branded petrol stations, which probably presents more service options (and then,
more sales).
Discussion and managerial implications
Marketing mix decisions resulted from the empirical models
Price was found relevant in emerging countries, but econometric models should
accommodate specific aspects of competition, such as the presence of unbranded products,
as shown by the study of Bahadir et al. (2015) with aggregated brand sales data. Unbranded
competition is a unique challenge to multinationalswho seek to establish their presence in
emerging markets. Fragmented markets accompany this characteristic with a diverse
presence of owner-managed small enterprises (Sinha and Sheth, 2018). These aspects are also
present in gasoline retailing and should be taken into consideration by businesses in this
market.
The multinomial model presented in Table 4 provides directions on how marketing mix
strategies work in Brazil. International marketing managers not familiarized with this reality
will benefit from the differential influence of marketing actions considering branded and
unbranded petrol stations. These managers are the ones responsible for persuading
independent petrol station owners to strengthen their level of relationship with the energy
company. The retail petrol market in Brazil is exceptionally fragmented, exhibiting more than
35 thousand registered stations (Silva et al., 2014).
The applicability of our model rests on the situation and data authenticity. The marketing
models quantity is expanding in the last few years and dedicated to different market
conditions, but there are still a large number of unresolved issues(Fok, 2003, p. 2) resulted
from data availability (Hanssens et al., 2002). Emerging markets are one of these, and they
Brand sales groups Frequency Percent Mean of t-statistic
a
SD of t-statistic
Steady 48 27.6 0.06 1.01
Unsteady 35 20.1 0.24 1.10
Promising 30 17.2 3.98 1.59
Descending 27 15.5 4.75 2.34
Ascending 20 11.5 3.74 1.51
Faltering 14 8 3.76 1.18
Note(s):
a
We resorted to Tukey honest significance tests (HSD) to assess difference in means. With equal of
variances not assumed, we have not found difference in means between ascending and promising, descending
and faltering and steady and unsteady. For all other combinations, difference in means was statistically
different
Table 3.
Descriptive statistics
for brand sales groups
and their respective t-
statistics
IJOEM
Group
a
Variable
Model 1: Unbranded petrol stations (N560) Model 2: Branded petrol stations (N5114)
Beta SE Sig. Exp(B)
b
Beta SE Sig. Exp(B)
b
Ascending Intercept 19.51 28.35 0.49 42.21 20.16 0.03**
Diesel sales 19.30 20.52 0.34 4.14E-009 33.12 19.46 0.08* 2.43Eþ14
Ethanol sales 2.12 6.48 0.74 0.11 0.60 6.60 0.92 0.54
Service 2.74 1.50 0.06* 15.62 1.24 0.76 0.10 3.47
Price 0.02 0.01 0.08* 0.97 0.01 0.02 0.45 0.98
Loyalty 0.41 1.07 0.69 1.52 0.12 0.48 0.80 0.88
Sales force 2.28 1.51 0.13 9.79 0.73 1.04 0.48 0.47
Size 10.98 10.89 0.31 1.69E-005 8.24 4.35 0.05* 3809.15
State taxation 10.03 7.19 0.16 22,714.97 -0.13 3.98 0.97 0.87
Promising Intercept 28.32 38.84 0.46 29.10 17.68 0.10
Diesel sales 34.13 19.88 0.08* 1.01E-013 0.08 12.89 0.99 0.91
Ethanol sales 45.18 37.18 0.22 4.21Eþ19 12.27 15.60 0.26 31634206.64
Service 2.51 1.32 0.05* 12.38 1.28 0.68 0.05* 3.63
Price 0.03 0.01 0.03** 0.96 0.00 0.01 0.67 1.00
Loyalty 0.02 0.82 0.97 1.02 0.21 0.48 0.66 1.23
Sales force 1.15 1.53 0.45 3.17 0.98 0.81 0.23 2.66
Size 2.71 8.44 0.74 15.14 10.03 4.60 0.02** 22,729.78
State taxation 13.19 6.12 0.03** 536260.94 0.10 3.72 0.97 1.10
Descending Intercept 8.54 39.76 0.83 24.76 18.50 0.18
Diesel sales 50.61 18.97 0.00** 1.00E-013 3.83 17.14 0.82 46.27
Ethanol sales 52.05 34.09 0.12 4.05Eþ22 17.90 15.69 0.25 59452561.67
Service 0.71 1.51 0.63 0.49 0.61 0.90 0.49 0.54
Price 0.03 0.01 0.01** 0.96 0.01 0.01 0.46 1.01
Loyalty 1.44 0.90 0.10 4.24 0.52 0.44 0.23 0.59
Sales force 0.81 1.79 0.65 0.44 0.85 1.27 0.50 0.42
Size 11.72 9.44 0.21 8.06E-00 5.11 4.67 0.27 167.24
State taxation 0.03 6.82 0.99 1.03 2.28 3.89 0.55 0.10
(continued )
Table 4.
Parameter estimates
for the
multinomial model
Brand sales
dynamics in
emerging
markets
Group
a
Variable
Model 1: Unbranded petrol stations (N560) Model 2: Branded petrol stations (N5114)
Beta SE Sig. Exp(B)
b
Beta SE Sig. Exp(B)
b
Faltering Intercept 9.87 33.82 0.77 38.30 26.19 0.14
Diesel sales 55.92 19.24 0.00** 1.00E-013 1.75 17.56 0.92 0.17
Ethanol sales 20.20 30.72 0.51 594413265.4 23.59 23.63 0.31 17660331564
Service 0.62 1.52 0.68 1.87 1.13 1.03 0.27 3.12
Price 0.01 0.01 0.35 0.98 -0.00 0.03 0.97 0.99
Loyalty 0.02 0.96 0.98 0.97 -0.47 0.67 0.48 0.62
Sales force 0.82 1.82 0.65 2.28 0.95 1.26 0.44 2.60
Size 15.69 10.00 0.11 6571234.10 8.76 6.37 0.16 6,381.88
State taxation 9.41 6.08 0.12 12,244.50 5.50 5.61 0.32 245.19
Unsteady Intercept 34.09 25.54 0.18 1.11 11.44 0.92
Diesel sales 28.55 19.31 0.13 4.97E-013 4.76 8.80 0.58 0.00
Ethanol sales 0.11 10.43 0.99 0.89 2.11 6.98 0.76 8.30
Service 0.00 1.22 0.99 1.00 0.06 0.62 0.92 1.06
Price 0.02 0.01 0.08* 0.97 0.01 0.01 0.46 1.01
Loyalty 1.21 0.83 0.14 3.35 0.09 0.41 0.82 1.09
Sales force 0.49 1.41 0.72 1.63 0.95 0.76 0.21 2.60
Size 11.50 9.02 0.20 1.00E-005 5.85 4.33 0.17 348.20
State taxation 4.81 5.75 0.40 123.24 2.64 3.45 0.44 0.07
R
2
Nagelkerke 64.8% 41.4%
2 Log Likelihood (Null model ) 148.10**(207.34) 325.27**(382.90)
Note(s):*0.10 is significant;**0.05 is highly significant. Model was also estimated by not considering branded and unbranded petrol stations (full dataset,
N5174). For this specific case, we used a dummy independent variable for brand signalization. Results of this model yielded roughly the same signs and significances for
the independent variables. The brandvariable was also significant and we opted to present these two models due to completeness.
a
Reference category for Group is
neutral and stationary (steady sales).
b
According to Hosmer Jr. et al. (2013), taking the exponential from betas ensures an interpretation in terms of odds ratio for dummy independent variables
Table 4.
IJOEM
became central to multinational companiesstrategies, which are forced to adapt (Enderwick,
2009). Marketing decisions derived from our model shed light on a specific market that
undergone extensive transformations since the 1990s, such as the creation of a regulatory
agency, presence of independent fuel retailers and competition with a state-controlled
company (Silva et al., 2014).
Service importance and actionable implications for managers considering brand sales
behavior and position
Eckert (2013) conducted an economic-based review of empirical studies of gasoline retailing,
and what draws attention is the reduced amount of research on what the author classifies as
non-price variables. Service-related research stress the positive effects of service on firm
performance (Aas and Pedersen, 2011) and our investigation expands this general
understanding by presenting the effect of service on positive brand sales positions in an
industry where there is a tangible integration between products and services (Azimont and
Araujo, 2010), in the form of convenience stores and other ancillary services (Iyer and
Seetharaman, 2008).
Our framework proposes brand sales positions based on two dimensions which resort to
the definition of evolution or stationarity of brand sales, an important marketing response
variable (Hanssens et al., 2002) located at the operational level in the marketing performance
outcome chain (Katsikeas et al., 2016). Evolution and stationarity are established and
cornerstone concepts for time-series analysis in marketing. The empirical generalizations
published by Dekimpe and Hanssens (1995) detailed the conditions under which marketing
performance variables (e.g. sales and market share) evolve or remain stationary, but opened
an avenue to assess how marketing could affect the behavior over time of these measures.
Despite the widely accepted statement that assures that sales evolution is related to the
marketing actions(Dekimpe and Hanssens, 2005, p. 858), there is space for empirical
initiatives which seek to supply managers with information about marketing influence on
sales movements or spurts in short periods (Hanssens et al., 2016).
The empirical model shows this relative influence considering two different contractual
forms (Shepard, 1993) of the gasoline retailing context analyzed: one for branded and one for
unbranded petrol stations. These situations not only define how different are gas stations and
their competitive environments (Haucap et al., 2017;Iyer and Seetharaman, 2008) but endue
the importance of specific marketing strategies that provide actionable managerial
implications considering brand sales behavior and position in emerging markets. Table 5
summarizes actionable implications for managers.
Implications for emerging markets
Emerging markets (such as Brazil) are a productive environment for business research. First,
they are marked by a strong institutional presence that affects organizationsprocesses and
decision-making (Hoskisson et al., 2000) in the form of institutional changes and transitions
that affect multinational corporationsoperations (Rottig, 2016). Consequently, theories
promulgated to developed markets are not appropriate for emerging markets (Hoskisson
et al., 2000), as their socioeconomic, demographic, cultural and regulatory systems are mostly
different from the Western reality (Wright et al., 2005).
Our empirical research is an attempt to address these issues because it is based on specific
emerging market reality, the gasoline retailing in Brazil. Hoskisson et al. (2000, p. 257) argue that
emerging economies are not a homogeneous or identifiable and recognizable group,which
means that the framework about brand sales behavior and position proposed and estimated
comply with a need detailed by Sheth (2011): the ability to contend with unique characteristics.
This consideration is relevant given the dominance of studies on emerging markets involving
Brand sales
dynamics in
emerging
markets
Marketing mix
decision
Summary of findings
Brand manager and petrol
station owner applicationsUnbranded petrol stations Branded petrol stations
Loyalty No consistent effect found
on brand sales for petrol
stations
No consistent effect found
on brand sales for petrol
stations
While the loyalty schema
seems important to
multinationals, in the sense
that promote integration with
their distributors (petrol
stations), petrol station
owners should invest on
other marketing mix decision
variables to move brand sales
to positive positions
(ascending or promising)
Non-
substitutable
product (diesel)
Decreases the probability
of brand sales to stay in a
positive position
(promising), in relation to a
steadyposition, the
reference category
Increases the probability
of brand sales to stay in a
positive position
(ascending), in relation to a
steadyposition, the
reference category
Non-substitutable product
sales contribute to brand
sales positive positions only
on branded petrol stations.
Company support in this case
seem to leverage the most
important product (branded
gasoline) sales, while appears
to jeopardize in cases where
there is no company support
(unbranded petrol stations)
Price Negative influence on
brand sales to be
associated to positive
positions. Raises in price
decrease the probability of
brand sales to stay in
ascending or promising
positions
No consistent effect found
on brand sales for petrol
stations
Company support seem
relevant for petrol stations
when price is the focus of
analysis. This mean that
when vertical differentiation
is not present (unbranded
petrol stations) price effect is
more salient to reduce the
probability of brand sales to
stay in positive positions
Sales force No consistent effect found
on brand sales for petrol
stations
No consistent effect found
on brand sales for petrol
stations
Sales force strategy is
associated with the loyalty
schema promoted by the
multinational energy
company. As no consistent
effect was found for brand
sales, petrol station owners
should invest on other
marketing mix variables to
move brand sales to positive
positions (ascending or
promising)
(continued )
Table 5.
Actionable
implications for
managers considering
brand sales behavior
and position
IJOEM
China and India (Nielsen et al.,2018). The negative, neutral and positive positions found in our
research should be tested in other emerging realities, as it is very little in common amongst
countries classified as emerging markets or economies (Roberts et al., 2015).
Limitations and directions for future research
The present research provides two essential outputs. First, an evidence of marketing role to
brand sales positions in emerging markets; and, second, a rational and straightforward
framework to managers involved with decisions regarding brand sales based on short time-
series analysis. One important limitation must be outlined and regard to the use of a unique
transactional dataset from a multinational company. Despite the similarity of the strategies
used by energy companies and the number of observations of the initial dataset
(representative of five different states of Brazil), they do not cover the singularities of
petrol stations under contract with other companies.
Another question refers to the lack of generalization from our findings. The dataset covers
one individual emerging market (Brazil), where the international multinational marketing
strategy in the energy sector developed to a quite specific form. Despite these limitations, our
Marketing mix
decision
Summary of findings
Brand manager and petrol
station owner applicationsUnbranded petrol stations Branded petrol stations
Service Increases the probability of
brand sales to stay in
positive positions
(ascending or promising),
in relation to a steady
position, the reference
category
Increases the probability
of brand sales to stay in a
positive position
(promising), in relation to a
steadyposition, the
reference category
Service influence is more
consistent on unbranded
petrol stations. Petrol station
owners should primarily
invest on additional services
if they plan not to integrate
into the multinational energy
company chain. The effect of
this strategy is almost two
times higher in unbranded
petrol stations than branded
petrol stations (for promising
brand sales position)
Substitutable
product
(Ethanol)
No consistent effect found
on brand sales for petrol
stations
No consistent effect found
on brand sales for petrol
stations
Ethanol sales do not put
brand sales at risk. This is
important to outline in a
context that ethanol could
negatively influence the sales
of the premium product
(branded gasoline), assuming
the position of a substitutable
product. For this specific case
multinationals should
continue to invest in
programs to boost the sales of
the branded gasoline,
dedicated to intermediaries,
while petrol station owners
should be careful with the
possible negative influence of
ethanol sales on the
unbranded gasoline sales Table 5.
Brand sales
dynamics in
emerging
markets
approach underscores the way that firms encountered to solve a problem different from
developed markets contexts, as stated by Newburry et al. (2016). Consequently, as the interest
in marketing strategy and influence in emerging markets (Newburry et al., 2016) grow, there
is a need to stress how emerging markets internal characteristics (Arbix, 2010) organize
around different distribution structures.
An enduring concern in marketing research is finding what is different about emerging
markets and what does this mean for marketing theory and practice (Roberts et al., 2015).
Burgees and Steenkamp (2006) and Sheth (2011) identified institutional subsystems and
dimensions where emerging markets operate and provided theoretical foundations for
research-oriented marketing initiatives capable of assessing distinct characteristics of these
markets. Future research could explore marketing instruments effects and synergies in
different response measures, not only brand sales. Furthermore, there are plentiful
opportunities for assessing and comparing the influence of these instruments in emerging
and developed markets.
Note
1. We have also modeled the brand sales variable in the form of percentages of the total volume of
gasoline (in Equation 1 the denominator would be the total volume of gasoline of a given petrol
station). The results of the final model yielded roughly the same signs and directions for the
independent variables in the final multinomial model. We stuck to the first option because managers
in this industry usually base their decisions using the ratio between premium and regular gasoline.
Hence, our approach tends to use what Neelamegham and Chintagunta (2004) classify as a brand
managers point of view.
References
Aas, T.H. and Pedersen, P.E. (2011), The impact of service innovation on firm-level financial
performance,Service Industries Journal, Vol. 31 No. 13, pp. 2071-2090.
Ag^
encia Nacional do Petr
oleo, G
as Natural e Biocombust
ıveis (2014), “‘ANP Resolution,n.1,
available at: http://nxt.anp.gov.br/NXT/gateway.dll/leg/resolucoes_anp/2014/janeiro/
ranp%201%20-%202014.xml?f5templates$fn5document-frameset.htm$q5%5Band%
3A%5Bfield,num_norma%3A1%5D%20%5Bfield,data_dou%3A%5Band%3A%5B%3E
%3D%3A1.1.2014%5D%20%5B%3C%3D%3A31.12.2014%5D%5D%5D%5D%
20$x5server$3.0#LPHit1/ (accessed 11 December 2017).
Arbix, G. (2010), Structural change and the emergence of the Brazilian MNEs,Interantional Journal
of Emerging Markets, Vol. 5 Nos 3/4, pp. 266-288.
Ataman, M.B., Mela, C.F. and Heerde, H.J.V. (2008), Building brands,Marketing Science, Vol. 27
No. 6, pp. 1036-1054.
Ataman, M.B. and Heerde, H.J.V. (2010), The long-term effect of marketing strategy on brand sales,
Journal of Marketing Research, Vol. 47 No. 5, pp. 866-882.
Austin, G., D
avila, C. and Jones, G.G. (2017), Emerging markets and the future of business history,
working paper, Harvard University, Harvard Business School.
Azimont, F. and Araujo, L. (2010), The making of a petrol station and the on-the-move consumer:
classification devices and the shaping of markets,Industrial Marketing Management, Vol. 39
No. 6, pp. 1010-1018.
Baack, D.W. and Boggs, D.J. (2008), The difficulties in using a cost leadership strategy in emerging
markets,International Journal of Emerging Markets, Vol. 3 No. 2, pp. 125-139.
Bahadir, S.C. Bharadwaj, S.G. and Srivastava, R.K. (2015), Marketing mix and brand sales in global
markets: examining the contigent role of country-market characteristics,Journal of
International Business Studies, Vol. 46 No. 8, pp. 596-619.
IJOEM
Barron, J.M., Taylor, B.A. and Umbeck, J.R. (2000), A theory of quality-related differences in retail
margins: why there is a premiumon premium gasoline,Economic Inquiry, Vol. 38 No. 4,
pp. 550-569.
Burgees, S.M. and Steenkamp, J.E.M. (2006), Marketing renaissance: how research in emerging
markets advances marketing science and practice,International Journal of Research in
Marketing, Vol. 23 No. 4, pp. 337-356.
Byrne, D.P. and Ross, N.d. (2019), Learning to coordinate: a study in retail gasoline,The American
Economic Review, Vol. 109 No. 2, pp. 591-619.
Coglianese, J., Davis, L.W., Kilian, L. and Stock, J.H. (2017), Anticipation, tax avoidance, and the price
elasticity of gasoline demand,Journal of Applied Econometrics, Vol. 32 No. 1, pp. 1-15.
Datta, H., Ailawadi, K.L. and Heerde, H.J.V. (2017), How well does consumer-based brand equity align
with sales-based brand equity and marketing-mix response?,Journal of Marketing, Vol. 81
No. 3, pp. 1-20.
Dawar, N. and Chattopadhyay, A. (2002), Rethinking marketing programs for emerging markets,
Long Range Planning, Vol. 35 No. 5, pp. 457-474.
Dekimpe, M.G. and Hanssens, D.M. (1995), Empirical generalizations about market evolution and
stationarity,Marketing Science, Vol. 13 No. 3_supplement, pp. G109-G121.
Dekimpe, M.G. and Hanssens, D.M. (1999), Sustained spending and persistent response: a new look
at long-term marketing profitability,Journal of Marketing Research, Vol. 36 No. 4,
pp. 397-412.
Dekimpe, M.G. and Hanssens, D.M. (2000), Time-series models in marketing: past, present and
future,International Journal of Research in Marketing, Vol. 17 Nos 2-3, pp. 183-193.
Dekimpe, M.G. and Hanssens, D.M. (2005), Persistence models and marketing strategy,Tijdschrift
voor Economie en Management, Vol. 50 No. 5, pp. 855-883.
Di
ario Oficial da Uni~
ao (1989), Brazilian government, Section 1, pp. 7-8.
Doane, D.P. and Seward, L.E. (2011), Applied Statistics in Business & Economics, McGraw-Hill Irwin,
New York, NY.
Eckert, A. (2013), Empirical studies of gasoline retailing: a guide to the literature,Journal of
Economic Surveys, Vol. 27 No. 1, pp. 140-166.
Enderwick, P. (2009), Large emerging markets (LEMs) and international strategy,International
Marketing Review, Vol. 26 No. 1, pp. 7-16.
Fok, D. (2003), Advanced Econometric Marketing Models, Erasmus Research Institute of Management,
Rotterdam.
Hanssens, D.M. and Parsons, L.J. (1993), Econometric and time-series market response models,in
Eliashberg, J. and Lilien, G.L. (Eds), Handbooks in Operations Research and Management
Science Marketing, North-Holland, London, pp. 409-464.
Hanssens, D.M. and Pauwels, K.H. (2016), Demonstrating the value of marketing,Journal of
Marketing, Vol. 80 No. 6, pp. 173-190.
Hanssens, D.M. Parsons, L.J. and Schultz, R.L. (2002), Market Response Models,KluwerAcademic
Publishers, New York, NY.
Hanssens, D.M. Parsons, L.J. and Schultz, R.L. (2016), Performance growth and opportunistic
marketing spending,International Journal of Research in Marketing, Vol. 33 No. 4, pp. 711-724.
Haucap, J., Heimeshoff, U. and Siekmann, M. (2017), Fuel prices and station heterogeneity on retail
gasoline markets,Energy Journal, Vol. 38 No. 6, pp. 81-103.
Hosken, D.S., McMillan, R.S. and Taylor, C.T. (2008), Retail gasoline pricing: what do we know?,
International Journal of Industrial Organization, Vol. 26 No. 6, pp. 1425-1436.
Hoskisson, R.E., Eden, L., Lau, C.M. and Wright, M. (2000), Strategy in emerging economies,
Academy of Management Journal, Vol. 43 No. 3, pp. 249-267.
Brand sales
dynamics in
emerging
markets
Hosmer, D.W. Jr, Lemeshow, S. and Sturdivant, R.X. (2013), Applied Logistic Regression, John Wiley &
Sons, New Jersey, NJ.
Hwang, M. and Thomadsen, R. (2016), How point-of-sale marketing mix impacts national-brand
purchase shares,Management Science, Vol. 62 No. 2, pp. 571-590.
Iyer, G. and Seetharaman, P.B. (2008), Too close to be similar: product and price competition in retail
gasoline markets,Quantitative Marketing and Economics, Vol. 6 No. 3, pp. 205-234.
Katsikeas, C.S., Morgan, N.A., Leonidou, L.C. and Hult, G.T. (2016), Assessing performance outcomes
in marketing,Journal of Marketing, Vol. 80 No. 2, pp. 1-20.
Kleit, A.N. (2005), The economics of gasoline retailing: petroleum distribution and retailing issues in
the U.S,Energy Studies Review, Vol. 13 No. 2, pp. 1-28.
Kumar, A. and Paul, J. (2018), Mass prestige value and competition between American versus Asian
laptop brands in an emerging market theory and evidence,International Business Review,
Vol. 27 No. 5, pp. 969-981.
Kumar, V., Sunder, S. and Sharma, A. (2015), Leveraging distribution to maximize firm performance
in emerging markets,Journal of Retailing, Vol. 91 No. 4, pp. 72-80.
Kwiatkowski, D., Phillips, P.C., Schmidt, P. and Shin, Y. (1992), Testing the full hypothesis of
stationarity against the alternative of a unit root,Journal of Econometrics, Vol. 54 No. 1,
pp. 159-178.
Lee, S. (2019), When does the developing country brand name alleviate the brand origin effect?
Interplay of brand name and brand origin,International Journal of Emerging Markets, Vol. 15
No. 2, pp. 387-402.
Leeflang, P.S.H., Wieringa, J.E., Bijmolt, T.H. and Pauwels, K.H. (2015), Modeling Markets Analyzing
Marketing Phenomena and Improving Decision Making, Springer, New York, NY.
Leeflang, P.S.H., Wieringa, J.E., Bijmolt, T.H. and Pauwels, K.H. (2017), Advanced Methods for
Modeling Markets, Springer, New York, NY.
Lim, C., Hemmert, M. and Kim, S. (2017), MNE subsidiary evolution from sales to innovation: looking
inside the black box,International Business Review, Vol. 26 No. 1, pp. 145-155.
Mesquita, M.A.B. (2010), Valores do aditivo: o comportamento do consumidor de gasolina aditivada na
perspectiva da Teoria das Cadeias Meios-Fins,Gest~
ao & Produç~
ao, Vol. 17 No. 3, pp. 603-616.
Narasimhan, L., Srinivasan, K. and Sudhir, K. (2015), Editorial: marketing science in emerging
markets,Marrketing Science, Vol. 34 No. 4, pp. 473-479.
Neelamegham, R. and Chintagunta, P.K. (2004), Modeling and forecasting sales of technology
products,Quantitative Marketing and Economics, Vol. 2 No. 3, pp. 195-232.
Newburry, W., McIntyre, J.R. and Xavier, W. (2016), Guest editorial: exploring the interconnections
between institutions, innovation, geography, and internationalization in emerging markets,
International Journal of Emerging Markets, Vol. 11 No. 2.
Nielsen, U.B., Hannibal, M. and Larsen, N.N. (2018), Reviewing emerging markets: context, concepts
and future research,International Journal of Emerging Markets, Vol. 13, No. 6, pp. 1679-1698.
Parment (2008), Distribution strategies for volume and premium brands in highly competitive
consumer markets,Journal of Retailing and Consumer Services, Vol. 15 No. 4, pp. 250-265.
Paul, J. (2019), Masstige model and measure for brand management,European Management Journal,
Vol. 37 No. 3, pp. 299-312.
Paul, J. (2020), Marketing in emerging markets: a review, theoretical synthesis and extension,
International Journal of Emerging Markets, (in press).
Pauwels, K. and Hanssens, D.M. (2007), Performance regimes and marketing policy shifts,
Marketing Science, Vol. 26 No. 3, pp. 293-311.
Pels, J. and Sheth, J.N. (2017), Business models to serve low-income consumers in emerging markets,
Marketing Theory, Vol. 17 No. 3, pp. 373-391.
IJOEM
Rao, V.R., Wind, J. and DeSarbo, W.S. (1988). A customized market response model: development,
estimation, and empirical testing,Journal of the Academy of Marketing Science, Vol. 16 No. 1,
pp. 128-140.
Roberts, J., Kayande, U. and Srivastava, R.K. (2015), Whats different about emerging markets, and
what does it mean for theory and practice?,Customer Needs and Solutions, Vol. 2 No. 4,
pp. 245-250.
Rottig, D. (2016), Institutions and emerging markets: effects and implications for multinational
corporations,International Journal of Emerging Markets, Vol. 11 No. 1, pp. 2-17.
Sharma, D., Verma, V. and Sharma, S. (2018), Examining need for uniqueness in emerging markets,
Marketing Intelligence and Planning, Vol. 36 No. 1, pp. 17-31.
Sharma, A., Kumar, V. and Cosguner, K. (2019), Modeling emerging-market firmscompetitive retail
distribution strategies,Journal of Marketing Research, Vol. 56 No. 3, pp. 439-458.
Shepard, A. (1993), Contractual form, retail price, and asset characteristic in gasoline retailing,The
RAND Journal of Economics, Vol. 24 No. 1, pp. 58-77.
Sheth, J.N. (2011), Impact of emerging markets on marketing: rethinking existing perspectives and
practices,Journal of Marketing, Vol. 75 No. 4, pp. 166-182.
Shocker, A.D., Bayus, B.L. and Kim, N. (2004), Product complements and substitutes in the real
world: the relevance of other products,Journal of Marketing, Vol. 68 No. 1, pp. 28-40.
Silva, A.S., da Vasconcelos, C.R., Vasconcelos, S.P. and Mattos, R.S.de. (2014), Symmetric
transmission of prices in the retail gasoline market in Brazil,Energy Economics, Vol. 42,
pp. 11-21.
Sinha, M. and Sheth, J. (2018), Growing the pie in emerging markets: marketing strategies for
increasing the ratio of non-users to users,Journal of Business Research,Vol.86,
pp. 217-224.
Themido, I.H., Quintino, A. and Leit~
ao, J. (1998), Modelling the retail sales of gasoline in a Portuguese
metropolitan area,International Transactions in Operational Research, Vol. 5 No. 2, pp. 89-102.
Venkatesan, R., Farris, P., Guissoni, L.A. and Neves, M.F. (2015), Consumer brand marketing through
full-and self-service channels in an emerging economy,Journal of Retailing, Vol. 91 No. 4,
pp. 644-659.
Wang, F. and Zhang, X. (2008), Reasons for market evolution and budgeting implications,Journal of
Marketing, Vol. 72 No. 5, pp. 15-30.
Wright, M., Filatotchev, I., Hoskisson, R.E. and Peng, M.W. (2005), Strategy research in emerging
economies: challenging the contentional wisdom,Journal of Management Studies, Vol. 42
No. 1, pp. 1-33.
Yilmazkuday, H. (2017), Asymmetric incidence of sales taxes: a short-run investigation of gasoline
prices,Journal of Economics and Business, Vol. 91, pp. 16-23.
Zivot, E. and Andrews, D.W.K. (1992), Further evidence on the great cash, the oil-price shock, and
the unit-root hypothesis,Journal of Business and Economic Statistics,Vol.10No.3,
pp. 25-44.
Further reading
Burgees, S.M. and Steenkamp, J.E.M. (2013), Editorial: introduction to the special issue on
marketinginemergingmarkets,International Journal of Research in Marketing,Vol.30
No. 1, pp. 1-3.
Enders, W. (1995), Applied Econometric Time Series, John Wiley & Sons, New York, NY.
Meyer, K.E. and Peng, M.W. (2015), Theoretical foundations of emerging economy business
research,Journal of International Business Studies, Vol. 47 No. 1, pp. 3-22.
Brand sales
dynamics in
emerging
markets
Ng, S. and Perron, P. (1995), Unit root tests in ARMA models with data-dependent methods for the
selection of the truncation lag,Journal of the American Statistical Association, Vol. 90 No. 429,
pp. 268-281.
Santos, G.F. (2013), Fuel demand in Brazil in a dynamic panel data approach,Energy Economics,
Vol. 36, pp. 229-240.
Corresponding author
Marcos In
acio Severo de Almeida can be contacted at: misevero@ufg.br
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