Integrated management of marketing risk and efficiency/Integruotas marketingo rizikos ir efektyvumo valdymas.
Rutkauskas, Aleksandras Vytautas ; Ginevicius, Adomas
1. Introduction
Every marketing professional or expert knows from his/her
experience that marketing is an ocean of various risks and swimming in
this ocean is extremely dangerous without having a universal theoretical
approach to risk management as well as proper risk identification,
quantitative evaluation and economic assessment technique (Suhobokov
2007; Vlasenko, Kozlov 2009). If it is true that achieved the lowest
risk management level helps to save useful resources by 10-15 percent in
any kind of activities, then, these figures for marketing, searching for
ways of promoting goods and services, should be at least doubled.
However, there is the reverse of the medal, implying that the
research into marketing risk management requires high competence and
vast expenses (Ginevicius, R.; Ginevicius, A. 2008). There are many
problems in marketing, which in other areas of activity are addressed
with standard methods, and in marketing they require new theoretical
approaches (Pennings 2004; Tikkanen et al. 2007; Martinez-Lopez,
Casillas 2009; Morgan et al. 2009; Sharma et al. 2009; Watkins, Hill
2009; Corsaro, Snehota 2010). Management of marketing risk and
efficiency can also be treated as a multicriteria problem and the
methodology of solving such problems in various related areas of
economic research is properly presented in Ginevicius, Podvezko (2008)
and Ginevicius, Zubrecovas (2009).
In fact, the problems of marketing risk management, which are as
old as marketing itself, are not thoroughly analyzed and described in
the literature. Even in the most recent databases in the Internet many
scientific papers are presented only for limited use.
2. Marketing risk--where did it come from?
Marketing risk identification is probably derived from the
identification of market risks in general and designing of their
management schemes. The papers of Mark R. Greene (1969) and Donald R.
Tull (1967), where marketing risks are separated from the problems
associated with common market risks and their study, deserve special
attention in this respect. Marketing risk researches are practically not
separable from the researches on international business risk. Therefore,
the framework for integrated risk management in international
business' suggested by Kent D. Miller (1992), is considered by many
to be a move towards crystallizing market risks out of common entirety
of risks, including transnational business risks. In Table 1 (which is
based on the work of Zhang et al. 2008a), the crystallized types of
marketing risks and a set of factors influencing them are presented for
a retail trade company together with layers for evaluation of risk
index.
However this work, like many other studies of marketing risks, is
restricted to ranking various types of risk (possible harm made)
according to certain points (Zhang et al. 2008b; Zhou et al. 2006; Wang
2009; Wen-Fei 2004). Though for risk management decision-making usually
a universal quantitative evaluation is needed, allowing in parallel to
determine the possible harm made to recipients.
The paper of Greene (1969) 'How to rationalize your marketing
risk' considers a hypothesis that 'managers who estimate
possible losses and honestly evaluate the risk involved can vastly
improve their marketing decisions'.
The paper also provides a logic flow chart for marketing risk
decisions, where 5 steps present marketing risks collectively, outlining
major problems of marketing risk analysis aimed at collecting the
information required for making risk management decisions (see Fig. 1).
In fact, a profound risk concept is described in this five- step
analysis, and decision-making logic based on combining risk and
confidence is suggested.
Step 2 defining the extent of maximum loss and its probability
which may be used for integral evaluation of possibility and its
confidence deserves special attention. However we think that step 3,
presenting risk (possible loss) as a negative consequence of riskiness
of a particular process (object) and the interaction of loss
possibilities and abilities of a recipient (subject) is also very
important. It may be stated that most of recent publications lack such
profound risk concepts.
[FIGURE 1 OMITTED]
It should also be noted that the authors do not just play with such
impressive terms as macroeconomic and microeconomic risk, currency
exchange rate risk, etc., which being powerful in expressing risk
probability are still closely related to the particular criteria
describing marketing, as far as their possible effects are concerned.
3. A scheme of identification of marketing risk criteria,
quantitative risk evaluation and economic assessment
3.1. Marketing risks identification and management peculiarities
Today, any kind of activities is exposed to various types of risks
closely associated with the process of globalization (Macerinskas et al.
2003). This primarily applies to marketing which, making usable a part
of international business, on the one hand, and being the investment
activities, on the other, is exposed to a great variety of risks
(Saboniene 2009). Taking it not seriously, we may say that it is much
easier to name risks that do not concern marketing than list all of
them.
Some of the risks in international business are strategic risk,
operational risk, political risk, country risk, technological risk,
environmental risk, economic risk, financial risk, terrorism risk.
Types of investment risks are as follows: inflation risk, interest
rate risk, business risk, financial risk, tax risk, event risk,
liquidity risk and etc.
One can see that the above risks directly concern marketing,
however, separate analysis of marketing risks, even the most important
ones, along with that seeking to develop management models, is hardly
possible in practice and not acceptable from the theoretical point of
view.
It may be stated that the methods of comprehensive marketing risk
analysis, allowing the dangers of risks to be associated with expenses
required to avoid losses, have not been developed yet. Development of
such methodology or marketing risk management scheme is the primary
objective and means of marketing cost efficiency increasing.
It is expected that marketing risk pools could become a tool of
marketing risk analysis and help generate information required for
decision making. On the one hand, they could evaluate risks for major
marketing activities, while, on the other hand, they could stimulate
centres of marketing risk costs to achieve the goal described above (see
Fig. 2).
What items could become risk pools or structures fixing natural
results of risk effects and allowing the demand for risk expenses to be
quantitatively evaluated? It seems that it would be difficult to suggest
an alternative to ideology generally dominating in business, according
to which centres integrating results of all the activities could serve
as the centres of expenses. On the one hand, the effect of all risks to
which a particular activity is subject to is accumulated in these total
items. On the other hand, the dynamics of these items reveals the need
for risk management and possibilities of the latter.
Structural elements of marketing, denoted as 4P, 7P or other P
number, which can be used as the centres of risk costs, offer
exceptional possibilities to this activity. At the same time, they are
the centres of direct marketing expenses and investments (see Fig. 2).
However, assessing the effect or effectiveness of marketing
(Valanciene, Gimzauskiene 2009) and each structural element in
particular, the problems associated with the ambiguity and even lack of
the account data arise. Nevertheless, theoretical and practical works
emphasize the importance and urgency of these problems. In marketing,
whose aims and goals are directed towards the near or distant future,
possibilities are usually considered to be stochastic values or
processes. This provides the necessity that, assessing a possibility,
its size, confidence and risk, considered to be the riskiness of a set
of possibilities and ability of a recipient of risk consequences to deal
with risk, should be defined.
[FIGURE 2 OMITTED]
Thus, three-dimensional presentation of a set of possibilities
requires adequate methods of determining their significance to various
recipients (Rutkauskas 2006; Rutkauskas et al. 2008a, b; Rutkauskas,
Ginevicius 2010), which are commonly based on the use of a
three-dimensional utility function u:
U = u (e, g, r), (1)
where e is the guarantee of effectiveness (effect) indicator, g is
possibility's guarantee and r is possibility's risk.
3.2. Marketing risks portfolio management
Further, general marketing risk as a portfolio of risk
components' structure management possibilities will be analysed.
The value at risk determination of various risk types is thoroughly
analysed in literature. However, the answer to the reverse problem of
risk-influencing parameters change in order to alter the value at risk
in the desired direction is not obvious and cannot be described
quantitatively. Along with that, management of the general risk as an
entirely accumulating all the types of risk requires certain resources
distribution among separate risk types in order to get the highest
effect. In other words, resources used for risk management should allow
to maximally reduce the expected losses concerning risk.
Of course, for solution of this type of problem an analysis of the
particular activity processes is necessary. First of all, an analysis of
risks contents and factors influencing value at risk should be
performed. Further, factors of influencing the means of value at risk
management, as well as their interdependencies, behaving in the same
direction, should be inventoried. There is no doubt that for every type
of risk there exists a complex of means allowing the reduction of value
at risk to the desired extent.
Also, while selecting complexes of means for value at risk
reduction, a problem of the so-called efficient complexes arises
pointing out that efficient complex should allow reducing expected
losses with minimal costs or obtain the highest reduction with available
resources.
It is obvious that the reasoning of efficient complex of means or,
moreover, their efficiency evaluation can be rarely presented using
typical and universally spread schemes. Often such evaluations can be
performed only with the help of expert systems. Moreover, many processes
and dependencies have clear stochastic nature, therefore expert systems
must also be adequately oriented.
Thus, the main objective becomes clear--to distribute financial
resources intended for value at risk reduction among value at risk
reducing means in order to obtain the maximum reduction of value at risk
(the resultant of all the risks), i.e. possible loss, described by the
magnitude of loss possibility, reliability of possibility and risk, and
also to measure it in scale adequate for such
evaluation--three-dimensional utility function's values'
scale.
With regard to what is said earlier, optimal risk portfolio
management problem should be formulated as follows:
To find such a distribution of resources intended for value at risk
reduction among separate risk types
[w.sub.i] : [w.sub.i] [greater than or equal to] 0; [n.summation
over (1)] [w.sub.i] = 1 (2)
which, considering the obtained probability distributions
[R.sub.1] ([a.sub.1], [s.sub.1]), [R.sub.2] ([a.sub.2], [s.sub.2])
... [R.sub.n] ([a.sub.n], [s.sub.n]) (3)
of loss reduction means efficiency possibilities of a unit
investment into separate risk types [r.sub.i] would generate a utility
function
U = u (e; p; r) = exp{e/r} x p {[xi] [greater than or equal to] e}
(4)
maximizing the probability distribution of general loss reduction
possibilities.
Thus here [R.sub.i]([a.sub.i], [s.sub.i]) are the unit-value
effects of possible loss reduction of random variables with presented
parameters [a.sub.i] and [s.sub.i], and [w.sub.i] is a part of expenses
intended for risk management which is devoted for implementation of
[i.sup.-th] risk management mean. As it was already mentioned,
estimations of means' effects [R.sub.i]([a.sub.i], [s.sub.i]) in
the research were obtained with the help of experts.
Technical analysis of situation can be interpreted as a solution to
stochastic optimization task.
As the object of the research is the problem of marketing
efficiency management considering risk, for methodical explanation of
further research it is worthwhile to recall that in the context of 4P
there are four different types of risk - [r.sub.1], [r.sub.2],
[r.sub.3], [r.sub.4], i.e. one for every component of marketing
structure [P.sub.1], [P.sub.2], [P.sub.3], [P.sub.4].
Visually decision-making is presented using the following scheme
(Fig. 3).
With the help of statistical data and experts valuations it was
determined that investment of 1 Lt into means of avoiding losses under
separate risk types (here--under separate components of structure)
should guarantee effects, described by Normal probability distributions
of effect possibilities, namely:
N([a.sub.1] = 1.35; [s.sub.1] = 0.13), N([a.sub.2] = 1.51;
[s.sub.2] = 0.25), N([a.sub.3] = 1.83; [s.sub.3] = 0.48), N([a.sub.4] =
1.12; [s.sub.4] = 0.39),
here [a.sub.i] are the mean values of respective probability
distributions of effect possibilities, and [s.sub.i] are the standard
deviations.
Further, according to the logic of Fig. 3, the solution to (1)-(3)
problem is presented.
Fig. 4 section a presents Markowitz portfolio, which depicts all
the possibilities (discrete case) of distributing a unit of investment
among various risk loss reduction means. Fig. 4 section b presents an
efficiency line of portfolios, with only maximum mean values of
portfolios under the predetermined risk level.
If one analysed only mean values, then the schemes of a and b
sections would provide the comprehensive information on the possibility
of rational distribution of funds. However, in practice it is necessary
to know all the possibilities and evaluate their reliability. Fig. 4
section c presents an efficiency zone as an analogue of the efficiency
line, or, simply, a spatial view of portfolio possibilities when
possibilities are characterized by their extent, reliability and risk.
If one took more detailed values of quintiles of probability
distributions,--percentiles, milipercentiles, etc.,--the result would be
a continuous set. The geometrical view of such a possibility is
presented in Fig. 4 section c, where the set of quintiles is represented
by percentiles.
[FIGURE 3 OMITTED]
[FIGURE 4 OMITTED]
As every portfolio possibility out of possibilities' set is
characterized by three already mentioned parameters--the extent of
avoidable loss, the guarantee of avoidance and risk, related to a
possibility under analysis, the selection of the best possibility is
getting complex in the sense that all the mentioned parameters have
different dimensions: unit of money--for loss, probability--for
avoidance guarantee and probability distribution of possibilities--for
loss because of risk. Therefore for selection of the best possibility an
adequate utility function is needed. In case of success, utility
function can become the functional, i.e. the rule, which would provide a
financial estimation of the loss avoided for every possibility evaluated
by three parameters. However, in general this is a problem requiring
separate analysis, and utility function becomes a means of
possibilities' grading or expert valuation. Fig. 4 section d
presents a utility function which evaluates utility of every possibility
according to the following formula:
U = exp {e/r} x p, (5)
here e - the extent of possibility, measured in monetary
expression; p - reliability of possibility; r - risk related to the
analysed possibility.
Thus, U is an indicator without dimension.
In Fig. 4 section e the tangency point of efficiency zone (section
c) and utility function (section d) is presented. This point allows us
to determine the values of all the coordinates of the three-dimensional
surface--extent of possibility, reliability of possibility and risk
class of the analysed possibility--and also a structure of the
respective portfolio.
In the presented case the structure is [w.sub.1] = 0.52; [w.sub.2]
= 0.22; [w.sub.3] = 0.06; [w.sub.4] = 0.2. The value of possibility e =
1.43; the guarantee of possibility p = 0.55; and r = 1.12. Results of
decisions of integrated marketing efficiency and risk management
possibilities will be presented in the same sequence.
4. Efficiency against or with risk
Today motivation of almost every activity is disclosed with the
help of certain "diptic", when one line of story is intended
for organizers of activity (owners) interests' satisfaction, and
the second represents a risk of possibilities having encouragement
powers and nurturing caution, threatening with possible losses. Thus
cherishment of utility for the owner of activity is possible with a
provision of "tempering risk", as well as with a provision of
"risking in the name of maximizing the value under creation".
Nevertheless, many researches on risk management more apparently
disclose the former line--to manage risk in order to decrease the
threats for efficiency extent.
Further in this paper, while projecting the scheme of marketing
efficiency increasing, the provision of risking in the name of
maximizing the value being created will be followed. This scheme is
based on the system of estimations in line with all the expert valuation
rules and involving all the components of 4P marketing structure. Every
component of 4P structure was also analysed as a whole of four
components. This system is presented in Table 2. Here every estimation
shows the possibilities of a unit investment to develop a marginal
effect in the respective marketing segment. These possibilities are
described by possibilities' probability distribution D ([a.sub.i],
[s.sub.i]), where [a.sub.i] is a mean value of possibilities and
[s.sub.i] is the standard deviation of possibilities' set.
Now the task is different than the task of paragraph 3.2,--how to
use available resources in order to obtain value at risk reduction
maximum. Now we attempt to maximize the effect of costs described by the
three indicators: effect's possibility, reliability of this
possibility and risk related to this possibility.
There is no doubt that both problems arise from the same
objective--how to use in the best manner the resources intended for
marketing efficiency increase, but it is necessary to notice that there
is no courage to say that the result of the solution would be the same,
i.e. that in both cases the same investment portfolio would be selected.
However, attempting to compare the solutions of both problems would
encourage experts to take universally the evaluation of probability
distributions of possibilities.
Thus, having the unit values presented by the experts in Table 2
and the pairs of parameters of probability distributions of investment
possibilities for every component of marketing structure, the problem
can be formulated as follows:
To find such a distribution of resources intended for marketing
efficiency increase among separate components of marketing structure
[w.sub.i] : [w.sub.i] [greater than or equal to] 0; [n.summation
over (1)] [w.sub.i] = 1. (6)
Which, considering the obtained probability distributions
[D.sub.1] ([a.sub.1], [s.sub.1]), [D.sub.2] ([a.sub.2], [s.sub.2])
... [D.sub.n] ([a.sub.n], [s.sub.n]) (7)
of unit investment possibilities to create marginal effect in every
component of marketing structure, would generate a utility function
U = u (e; p; r) = exp{e/r} x p (8)
maximizing the probability distribution of general effect of
possibilities.
Here e is the value of investment effect possibility, p - the
guarantee of this possibility and r - the class of risk where the
possibility belongs.
The influence of possibilities' probability distribution form
and statistical interdependence on optimal solution
Before analysing particular situations it is worth noticing that
experts, presenting their own estimations, i.e. mean values and standard
deviations of possibilities as a measure of possibilities'
variability usually do not present their opinion about the form of the
distribution (Normal, Pareto, etc.). Thus searching for a particular
solution the forms of the decisions under analysis will be selected,
retaining the values of parameters set by the experts.
Similarly, experts have not presented the indicators of
interdependencies of the analysed possibilities, however, they have
stated that such dependencies should certainly exist. Nevertheless, in
order to evaluate the indicators of possible statistical dependencies
with the help of experts one should possess certain software for
processing the expert opinions.
In order to measure the influence of the forms and interdependences
of probability distributions on possible decisions the following
situations will be examined: First, when probability distributions are
Normal and not correlated; Second, when probability distributions are
Lognormal and not correlated; Third, when random variables describing
the possibilities are correlated and this correlation is expressed by a
correlation matrix; Fourth, when there are additional constraints
[w.sub.i].
It is worth noticing that in marketing research 4P receives a
status of certain invariance, in the sense that even if marketing object
varies substantially, the costs of 4P marketing structure retain the
proportions in the set limits.
In the expert valuations such an appearance is perceived as a
phenomenon of the structure hierarchy.
Experts in their valuations also have pointed out a certain
structural hierarchy, a priori orienting towards a certain structure of
costs between P1, P2, P3 and P4.
While performing estimations it was attempted to evaluate what
changes of 4P costs structure would be favourable for optimization of
the general decision.
A situation when probability distributions are Normal, not
correlated random variables, available resources are distributed in
equal parts among P1, P2, P3 and P4, and optimization is performed in
distributing resources among subcomponents. The results of the decision
are presented in Fig. 5. Here the logic of Fig. 4 is retained.
In 5a section all possible cases of funds distribution are
presented, i.e. all possible portfolios as a set of pairs of random
variables' "standard deviations--mean values", and in
section 5b only the possibilities' set of efficient portfolios is
presented. Here the portfolios having the highest mean value under the
certain risk level are presented. Section 5c presents the efficiency
zone, where analogically to the efficiency line of "standard
deviation--mean value" all the efficiency lines "standard
deviation--quintile" are presented. Section 5d shows the
geometrical view of adequate utility function, and section 5e--the
mutual position of efficiency zone and utility function, when utility
function is approaching the surface of possibilities (efficiency zone),
and the first tangency point is indicating the solution (section 5f).
Thus the resources distribution structure (portfolio) among
subcomponents P1, P2, P3 and P4 is determined, which is oriented towards
a possibility allowing to obtain the maximum of the selected utility
function. Further in the text the graphical representations of, in our
opinion, expected situations are presented.
Fig. 6 presents the visualization of solution analogical to Fig. 5
only with an assumption of correlation dependency between probability
distributions, which is described by the conditional correlation matrix
(9), where presented correlation coefficients describe average
correlation dependencies between sub-elements [P.sub.i].
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (9)
Figs. 7 and 8 present the visualization of solution analogical to
Fig. 5 with initial distribution among P1, P2, P3 and P4 is made
according to the proportion 4:3:2:1 and according to the structure
provided by experts, respectively.
[FIGURE 5 OMITTED]
[FIGURE 6 OMITTED]
[FIGURE 7 OMITTED]
[FIGURE 8 OMITTED]
5. Conclusions and suggestions
1. Marketing efficiency management is inseparable from the
marketing risk management.
2. Marketing risk peculiarities require adequate such risk
management concepts and measure systems.
3. Calculating dependencies in marketing research, analytical
research methods often directly confront with variety interference in
information provision and other forms. Experts' generated
dependencies between the desired effect and incurred costs totally
fulfilled the characteristics of expert estimations.
4. In order to achieve the optimal distribution of possible
resources according to the detailed components of marketing structure,
optimized calculations have been accomplished with reference to the
expertly given estimations, formed as stochastic variables.
5. Optimization evaluations showed that the marketing cost
structure greatly depends on the allocation forms of expectations
probability costs to become effect, as well as on the statistical
interdependence rate of the effect turns in a separate component of
marketing structure.
doi: 10.3846/16111699.2011.555357
References
Corsaro, D.; Snehota, I. 2010. Searching for relationship value in
business markets: are we missing something?, Industrial Marketing
Management 39: 986-995. doi:10.1016/j.indmarman.2010.06.018
Ginevicius, R.; Ginevicius, A. 2008. Sustainability decisions in
marketing complex cost optimization process, in The 12th World
Multi-Conference on Systemics, Cybernetics and Informatics. Orlando,
Florida, USA June 29th--July 2nd, 35-40.
Ginevicius, R.; Podvezko, V. 2008. Daugiakriterinio vertinimo budu
suderinamumas [The problem of compatibility of various multiple criteria
evaluation methods], Verslas: teorija ir praktika [Business: Theory and
Practice] 9(1): 73-80.
Ginevicius, R.; Zubrecovas, V. 2009. Selection of the optimal real
estate investment project basing on multiple criteria evaluation using
stochastic dimensions, Journal of Business Economics and Management
10(3): 261-270. doi:10.3846/1611-1699.2009.10.261-270
Greene, M. R. 1969. How to rationalize your marketing risks,
Harvard Business Review 47(3): 114-123.
Macerinskas, O.; Matekoniene, J.; Pupinyte, A. 2003. Impact of
Globalization on National Economies: the Main Concepts, Engineering
Economics 3(34): 68-75.
Martinez-Lopez, F. J.; Casillas, J. 2009. Marketing Intelligent
Systems for consumer behaviour modelling by a descriptive induction
approach based on Genetic Fuzzy Systems, Industrial Marketing Management
38: 714-731. doi:10.1016/j.indmarman.2008.02.003
Miller, K. D. 1992. A Framework for Integrated Risk Management in
International Business, Journal of International Business Studies 23:
311-331. doi:10.1057/palgrave.jibs.8490270
Mogran, N. A.; Slotegraaf, R. J.; Vorhies, D. W. 2009. Linking
marketing capabilities with profit growth, International Journal of
Research in Marketing 26: 284-293. doi:10.1016/j.ijresmar.2009.06.005
Pennings, J. M. E. 2004. A marketing-finance approach towards
industrial channel contract relationships: a model and application,
Journal of Business Research 57: 601-609.
doi:10.1016/S0148-2963(02)00394-6
Rutkauskas, A. V. 2006. Adequate investment portfolio anatomy and
decisions, applying imitative technologies, Economics: Research Papers
75: 52-76.
Rutkauskas, A. V.; Ginevicius, A. 2010. The Need for and
possibilities of Marketing Risk Management, in The 6th International
Scientific Conference "Business and Management 2010", 13-14
May, 2010, Vilnius, Lithuania, 154-161. ISSN 2029-4441.
Rutkauskas, A. V.; Miecinskiene, A.; Stasytyte, V. 2008a.
Investment Decisions Modelling along Sustainable Development Concept on
Financial Markets, Technological and Economic Development of Economy
14(3): 417-427. doi:10.3846/1392-8619.2008.14.417-427
Rutkauskas, A. V.; Stasytyte, A.; Ginevicius, A. 2008b.
Three-dimensional measurement of market behaviour, in The 5th
International Scientific Conference "Business and Management
2008". Vilnius, Lithuania, 16-17 May. Vilnius, 297-302.
Saboniene, A. 2009. Lithuanian Export Competitiveness: Comparison
with other Baltic States, Inzinerine Ekonomika--Engineering Economics
(2): 49-57.
Sharma, A.; Iyer, G. R.; Raajpoot, N. A. 2009. A framework for
offshoring marketing processes in business-to-business marketing
relationships, Industrial Marketing Management 38: 419-425.
doi:10.1016/j.indmarman.2009.03.007
Suhobokov, A. 2007. Application of Monte Carlo simulation methods
in risk management, Journal of Business Economics and Management 8(3):
165-168.
Tikkanen, H.; Kujala, J.; Artto, K. 2007. The marketing strategy of
a project-based firm: The Four Portfolio Framework, Industrial Marketing
Management 36: 194-205. doi:10.1016/j.indmarman.2006.03.006
Tull, D. S. 1967. The Relationship of Actual and Predicted Sales
and profits in New Product Introductions, Journal of Business 40(3):
233-250. doi:10.1086/294974
Valanciene, V.; Gimzauskiene, E. 2009. Dimensions of performance
measurement system in chantes research, Inzinerine
Ekonomika--Engineering Economics (4): 41-48.
Vlasenko, O.; Kozlov, S. 2009. Choosing the risk curve type,
Technological and Economic Development of Economy 15(2): 341-351.
doi:10.3846/1392-8619.2009.15.341-351
Wang, Q. 2009. Multi-agent Assessment on Marketing Risk Based on
Evidence Theory, in Electronic Commerce and Business Intelligence. ECBI
2009. International Conference, 6-7 June 2009, 370-373.
Watkins, A.; Hill, R. P. 2009. A simulation of business-to-business
decision making in a relationship marketing context, Industrial
Marketing Management 38: 994-1005. doi:10.1016/j.indmarman.2008.06.002
Wen-Fei, L. U. 2004. Managing Marketing Risks. Dept. of Applied
Economics and Management, Cornell University [online], [cited 12
December 2009]. Available from Internet:
<http://hortmgt.aem.cornell.edu/pdf/smart_marketing/uva4-04.pdf>.
Zhang, D.; Xiao, S.; Lin, M. 2008a. Research on transnational
marketing risk and evaluation index system of Chinese retail
enterprises, in Service Operations and Logistics, and Informatics,
IEEE/SOLI 2008. IEEE International Conference on Volume 2, 12-15 Oct.
2008, 2592-2597.
Zhang, L.; Chen, Y.; Chen, F. 2008b. Marketing Risk Evaluation of
Supply Chain, in Innovative Computing Information and Control. ICICIC
'08. 3rd International Conference on 18-20 June 2008, 142-142.
doi:10.1109/ICICIC.2008.346
Zhou, Mei-Hua; Wang, Fu-Dong; Zhang, Hong-Hong; Meng. 2006. Method
of Enterprise Marketing Risk Early-warning and The Index System
Construction, in Management of Innovation and Technology, IEEE
International Conference on Volume 2, 21-23 June 2006, 718-722.
Aleksandras Vytautas Rutkauskas (1), Adomas Ginevicius (2)
Vilnius Gediminas Technical University, Sauletekio al. 11, LT-10223
Vilnius, Lithuania
E-mails: (1)
[email protected] (corresponding author); (2)
[email protected]
Received 20 May 2010; accepted 15 December 2010
Aleksandras Vytautas RUTKAUSKAS. Doctor Habil, Professor, the Head
of the Faculty of Business Management, Vilnius Gediminas Technical
University. Research interests: capital and exchange markets,
sustainable investment strategies development, regional development.
Adomas GINEVICIUS. PhD student at the Department of Finance
Engineering, Vilnius Gediminas Technical University. Research interests:
marketing structure optimization, marketing risk, expert estimations.
Table 1. Environmental risk types and factors influencing their
occurrence in retail enterprise transnational marketing (Zhang et al.
2008a)
Risk types Risks factors Project layer
Macro Polity Polity certainties The certainty of
environmental risks in host countries; policy Attitude
risks strike; economic to foreign
crisis; force of investors The
religion and certainty of
nationalism; economy The
threatening local certainty of
retailers; currency/
inharmonious exchange rate
relationship with Social
communities and environment and
residents in host ideological
countries system
Economic Strict market
risks entering policy;
retail control;
change of exchange
rate; deterioration
of international
balance of payments
in host countries;
inflation; foreign
exchange control;
economic policy
change
Cultural Cultural difference
risks between the host
country and home
country; nationalism
tendency in the host
country; retailers
being unfamiliar
with culture in the
host country, etc.
Environmental Market Business recession Degree of retail
industry risks in local retail industry boom
risks industry; incorrect Degree of retail
commodities sale; industry
wrong market competition
forecast; lack of
price
competitiveness,
etc.
Competition Intense competition
risks between local retail
enterprises; the
entering of
transnational retail
groups; intense
competition on
domestic market and
so on
Supply Credit situation of
chain suppliers or
risks partners; relative
by big conflict with
local suppliers;
lack of information
communication of
supply chain;
lacking localization
purchasing and so on
Internal Expanding Capital chain break The rate of sales
risks of risks caused by expanding; profit Market
enterprise risks of development share Price
private brand; sensitivity
excessive Internal risks of
investment; enterprise Price
insufficient competitiveness
revolving fund; Evaluation of
interest increase promotion effect
The proportion of
Credit Poor quality of sold sales cost Degree
standing goods; poor image of of customer
risks origin; satisfaction
environmental Degree of
pollution resulting customer loyalty
from commodity
production
Internal Inaccurate
management management culture
risks comprehending; high
frequency changes of
managers or brain
drain; inefficient
communication and
cooperation between
employees, etc.
Promotion Unreasonable retail
risks marketing mix;
frequent promotion;
potential risks
caused by promotion,
etc.
Table 2. Marketing 4P structure of expert valuations
according to the effect of unit costs
PRODUCT
[a.sub.1] = 1.4; [s.sub.1] = 0.042
[a.sub.2] = 1.3; [s.sub.2] = 0.0165
[a.sub.3] = 1.275; [s.sub.3] = 0.038
[a.sub.4] = 1.375; [s.sub.4] = 0.043
PRICE
[a.sub.5] = 1.2; [s.sub.5] = 0.03
[a.sub.6] = 1.32; [s.sub.6] = 0.03
[a.sub.7] = 1.325; [s.sub.7] = 0.045
[a.sub.8] = 1.275; [s.sub.8] = 0.039
PROMOTION
[a.sub.9] = 1.25; [s.sub.9] = 0.018
[a.sub.10] = 1.22; [s.sub.10] = 0.025
[a.sub.11] = 1.225; [s.sub.11] = 0.04
[a.sub.12] = 1.375; [s.sub.12] = 0.045
PLACE
[a.sub.13] = 1.41; [s.sub.13] = 0.043
[a.sub.14] = 1.17; [s.sub.14] = 0.015
[a.sub.15] = 1.15; [s.sub.15] = 0.035
[a.sub.16] = 1.125; [s.sub.16] = 0.038
