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Value Proposition and Firm Performance:
Segmentation of Polish Online Companies.
Tymoteusz Doligalski, Piotr Zaborek, Sylwia Sysko-Romańczuk
Paper will be published in International Journal of Business Performance Management in
late 2014/early 2015
Abstract:
The purpose of the paper was to identify approaches to value proposition in online companies
and their consequences to firm performance. The paper presents a deconstructivist view on
online value proposition. Based on this approach a survey of 150 Polish online firms was
conducted. The research allowed to distinguish five segments of companies: suppliers of
unique offerings, specialized newcomers, comprehensive incumbents, productivity enhancers
and run-of-the-mill retailers. The findings contradict some results reported in other related
studies, e.g. despite different characteristics identified segments do not show statistically
significant differences in sales profit margin.
Keywords:
Value proposition, Internet, e-commerce, e-marketing, firm performance, segmentation,
Poland, CEE
Introduction
The focal point of this paper is the value proposition of Internet companies. In particular, what
are its building blocks and how it is related to business performance metrics. We define value
proposition as an outcome of a strategic process reflecting company’s believes on what its
customers value the most and how it should be delivered to provide competitive advantage
(Rintamaki et al., 2007). In other words, a value proposition determines functional and
emotional benefits that customers can derive from a company’s offering (Payne and Frow,
2014).
Despite its popularity in academic and managerial literature there is little empirical research
on the value proposition of firms operating on the Internet. Addressing this knowledge gap the
paper aims to identify approaches to value proposition in online companies and how they
differentiate firms’ performance. Following literature review and own observations, we
identify relevant aspects of value proposals of Internet companies and subsequently verify our
choice with a statistical segmentation and profiling procedure performed on original survey
data from Polish online businesses
The paper is structured as follows. First, we present pertinent theoretical perspectives that
informed the research design in this study. Then we outline the research approach including
sampling procedure and statistical analysis methods. Survey findings are discussed next with
discussion to follow. The final section includes an overview of limitations of the study and
suggestions for further research.
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Literature review
The major theoretical framework that informed the research design was the deconstructivist
view of strategy popularized by Kim and Mauborgne (2005). Specifically, it was instrumental
in selecting a set of value proposition components that were pertinent for B2C Internet
companies. Kim and Mauborgne used it as a key element of their strategy canvas analytic tool
for identifying a blue ocean strategy through mapping out industries and individual
companies. The deconstructive approach visually depicts major factors that an industry
competes on and utility or value levels received by customers across all factors from various
offerings available in the market. In the Internet context, the method served to examine the
value proposition of Amazon.com (Lindič and da Silva, 2011), which building blocks were
identified as performance, ease of use, reliability, flexibility and affectivity. According to this
perspective, all Amazon’s offerings bring total customer value by lowered or heightened
involvement in each of those factors. In another study, Clarke (2001) argued that
implementing m-commerce results in a value proposition enhanced by new factors of
ubiquity, localization, personalization and convenience. A primary source of inspiration for
the authors of the present study was the understanding of value proposition put forward by the
work of Amit and Zott (2001). They suggested value creation in e-business is achieved
through novelty, lock-in, complementarities and efficiency. In their view, developing each of
the four dimensions should benefit companies with increased market and financial
performance. However their later research (Amit and Zott, 2007a, 2007b) as well as that by
other authors (Zaborek et al., 2013) provided mixed evidence on achieved performance gains.
Segmentation of companies based on the type of employed value proposition has a long
history in management literature. Probably the best known contribution in this field is
Michael Porter’s view on strategy (1998). According to Porter, a company can either be a
differentiator or a price leader. Attempts to combine both approaches lead to lower financial
performance. Hax (2010) distinguished between three strategies in networked economy: best
product (concentration on product superiority), total customer solutions (concentration on
customer need fulfilment and relationships) and system lock-in (alliance with companies
offering complementary products). As stated in their research, best product and total customer
solutions were the most often adopted strategies, though the most effective in terms of
financial performance was lock-in.
Theoretical framework of the study
As already mentioned, deconstructive approach to value proposition aims to capture the
totality of constituent benefits for a customer as well as costs that they have to incur. In
traditional economy, companies are often faced with the problem of relating customer value
proposition to the level of prices. Usually, companies that offer lower value also charge lower
fees than the companies with more valuable offerings. In the case of the Internet following
such patterns is not always justified. On the Internet, the strategy of providing extraordinary
value at a high price is quite rare (Kim et al., 2004). In traditional economy, it is typically
reserved for products of high quality or of a recognisable brand. Internet-based brands,
however, seem to be of a more egalitarian character. Moreover, many online companies
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provide their customers with free products. As the price in a product category remains
constant (here: equals zero), it cannot serve as a basis for distinguishing different quality-price
strategies.
As a consequence of the above limitations, we use a more sophisticated, multidimensional
approach to value proposition modelling, which seems to better reflect the specificity of the
Internet. Accordingly, the Internet-based customer value proposition can be conceptualized as
consisting of five dimensions including customer efficiency, free benefits, complete
customer solutions, uniqueness and value co-creation (Doligalski, 2015). These
dimensions can be used for analyzing value propositions of companies with online operations.
It has to be noted, that in keeping with the deconstructive approach these dimensions do not
represent mutually exclusive strategies but rather a company can create its own unique value
proposition by combining different levels of all dimensions. As such, a company does not
have to excel in all five dimensions, but an increase in any of these should lead to a higher
attractiveness of the resulting offer.
Table 1: Dimensions of the Internet-based customer value proposition
Customer
efficiency
Providing solutions that decrease costs and allow customers to perform various tasks
faster or with better results. In consequence customers take advantage of time and
money savings and increased productivity. Usually customers may solve their
problems without these solutions but at considerably higher transactional costs.
Free
benefits
Offering free benefits may take the form of direct cross-subsidies (paying for some
products and receiving another product or service for free - e.g. free delivery); three
party market (servicing two complementary groups of customers, one of which is
subsidised, a.k.a. multisided markets – e.g. readers and advertisers); freemium model
(offering basic solutions for free and charging only for premium solutions);
nonmonetary market (providing goods for free without being motivated by possible
financial benefits - e.g. free software) or piracy (e.g. websites allowing file exchange)
(Anderson, 2009).
Complete
customer
solutions
Providing a wide range of products of particular types resulting from the long tail
strategy (both popular and niche products) or economies of scope (products of assorted
yet related categories).
Uniqueness
Offering solutions which cannot be easily found on a given market. From the
company’s perspective, this technique may be very efficient, since it allows charging
high prices for its unique products or services. The downside is the difficulty in
developing unique solutions and sustaining their long-term scarcity.
Value co-
creation
Active role of customers in the individualisation process of value proposition (mass
customisation) or in value co-creation oriented on other users (creating solutions which
will satisfy the needs of other customers - e.g. product reviews, open-source
movement).
Based on: T. Doligalski (2015), Internet-based Customer Value Management, Springer,
Heilderberg (forthcoming).
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The above strategies can be employed simultaneously in any number of possible
combinations. In this context, the case of an online auction seems particularly informative.
This service provides customers with enhanced efficiency, enabling its users to buy and sell
everyday items. It is surely possible without online auctions but typically at higher
transactional costs. Online auctions often provide its buyers with free values, as it is the seller
who pays commission. The scope of items offered in the auctions, as well as additional
services such as online payments fall in the category of complex solutions. From the buyer’s
perspective the company may offer unique value if the range of sellers is wider or includes
unique and desirable target groups as compared to alternative marketplaces. Additional utility
is given by scoring systems reflecting reliability of buyers based on their past behavior. This
credibility based on a scoring system is, however, not transferable to other websites, making
for a unique utility of a given auctioning solution. The value proposition of online auctions is
only possible through co-creation of buyers, sellers and businesses that manage electronic
trading platforms. Co-creation is here achieved through communication, transactions and
other interactions of involved parties. In contrast, some online ventures can adopt the five
value proposition dimensions only to a limited degree. As an example, a typical online store
selling specialized products may benefit from a unique offer of a narrow range (low
comprehensiveness). The store may not provide services for free except free shipment of large
orders. It increases customer's efficiency by reducing time and effort in travelling to a
traditional store. It may also employ value co-creation in the form of product reviews or posts
from a customer community that emerged around the store.
The above components of value proposition served as the conceptual basis of the study and
were operationalized in the questionnaire as Likert scale items.
Research method
Data on characteristics of business models employed by Polish Internet companies were
collected through CATI survey in August 2012. The respondents were managers of Polish
firms using Internet as a distribution channel for retailing and services. The sample
purposefully excluded several types of companies due to their exceptionally complex and
distinctive value creation mechanisms that were difficult to operationalize with questionnaire
items. The omissions included major Internet portals, advertising and web design agencies,
media brokers, telecommunications companies, banks, insurers and operators of large popular
news and lifestyle portals. The net sample of 150 units was obtained by random selection
from a database of major Internet companies compiled by the authors of the present study by
merging several available rankings and listings of various types of Internet businesses
operating in Poland. To the authors’ best knowledge the resultant sampling frame
encompassed almost every medium and large Internet business in Poland and thus could be
considered an accurate representation of the general population of the survey. The studied
companies comprised 57% of retailers and 43% of service providers; 63% of them had sales
of tangible products as the main revenue stream, 17.3% generated most incomes from sales of
virtual products, while 16% relied above all on proceeds from advertising. Around 25% of
businesses generated more than half of their sales outside of the Internet.
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The first step in statistical analysis involved exploratory factor analysis to decrease the
number of variables. Through the application of the principal components method the original
pool of 27 Likert scale items was reduced to 9 more general variables. Subsequently, 5 of
those components, that represented the most common value drivers on the Internet, were used
as inputs in a cluster analysis. The objective of the cluster analysis was to identify
homogenous segments of Polish Internet companies in terms of their means of creating value
proposals for customers. The used clustering procedure was the two-step method available in
SPSS 22, with the log-likelihood distance measure and AIC criterion for selecting the optimal
number of groupings. Considering that the segmentation variables were ratio scaled, had
approximately normal distribution and were independent (as components from the orthogonal
factor analysis always are), the assumptions for the two two-step procedure were fully met
and hence the method was expected to yield reliable and interpretable results (Bacher et al.,
2004).
The resultant segments were subsequently profiled using ANOVA tests. Profiling involved
studying differences between segments across substantive variables not employed in the
clustering algorithm, which gave more meaning to the groupings and suggested the degree of
nomological validity of the classification.
Research findings
The principal component analysis identified 9 latent components with eigenvalues greater
than 1 that represented 72% of the total variance contained in the 27 items included in the
survey questionnaire (for the full rotated component matrix refer to appendix). The amount of
extracted variance as well as the KMO measure of sampling adequacy (0.743) both suggested
that the obtained composite variables are an adequate representation of the raw data. Based on
the factor loadings of individual variables the retained components were labeled as follows:
- Co-creation of value through cooperation with business partners and customers
- Providing customers with productivity benefits (e.g. time savings)
- Providing free content or services to customers (e.g. offering articles free of charge
and sourcing revenues from advertisements)
- Employing measures to enhance customer loyalty
- Offering unique products (i.e. products that serve common needs but in a unique
manner)
- Building exit barriers for customers (i.e. inducing switching costs)
- Proposing personalization options
- Offering a comprehensive range of products
- Offering niche products (i.e. products that serve the needs of a small fraction of the
market)
Considering that segmentation is thought to be the most effective when performed on the
basis of a limited number of variables, that are uncorrelated and were selected to accurately
reflect the most important aspects of the investigated phenomenon (Mooi and Sarstedt, 2011,
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p. 242) the authors of the present article chose as the inputs for the segmentation algorithm the
5 characteristics listed below:
Customer efficiency
Free benefits
Complete customer solutions
Uniqueness
Value co-creation
The segmentation routine yielded a solution with 5 distinct groupings. The interpretation of
differences and similarities among clusters was guided by cluster centroids, which are defined
as vectors of means of segmentation variables computed separately for each segment. The
centroids are shown in the next table.
Table 2: Centroids of segmentation variables and p-values for ANOVA tests
Segmentation variables
Segments
p-values for
ANOVA
tests
1
2
3
4
5
Total
sample
Customer efficiency
0.36
-0.56
0.2
1.68
-0.59
0.00
<0.001
Value co-creation
-0.37
0.03
-1.9
0.37
0.3
0.00
<0.001
Complete customer solutions
-0.74
-1.51
0.53
0.21
0.33
0.00
<0.001
Uniqueness
1.55
-0.42
-0.82
0.01
0
0.00
<0.001
Free benefits
-0.51
-0.09
-0.61
0.23
0.15
0.00
0.002
Number of firms in a
segment
13
19
15
28
75
150
X
To allow for easier interpretation the shade coding was used, indicating differences in means
across segments, with darker shades of gray representing greater segment averages for
respective variables. The table also contains p-values for ANOVA tests, which are all below
the 0.05 threshold, showing statistically significant differences between groupings.
Considering that all variables were components obtained through factor analysis, the
measurement units of the means are standard deviations of respective variables. Hence, with
similar approximately symmetrical distributions the means are comparable across all five
characteristics.
Additional information useful in more detailed profiling of the segments is given below. The
table comprises those characteristics which were left out from the algorithm that yielded the
segmentation solution. The rationale for including these variables in the analysis is to
ascertain validity and reliability of the cluster classification. A reliable and valid cluster set
should consist of segments that show significant variance with regard to other (external)
attributes of substantive importance to the studied phenomenon. As before, the statistical
significance of differences was investigated with ANOVA tests.
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Table 3: Centroids of external variables and p-values for ANOVA tests
External variables
Segments
Total
sample
p-values
for
ANOVA
tests
1
2
3
4
5
Strengthening customer loyalty
0.05
-0.08
0.07
0.17
-0.06
0.00
0.843
Building exit barriers for
customers
-0.13
0.00
-0.45
-0.05
0.13
0.00
0.194
Personalized offer
-0.28
-0.06
0.00
0.12
0.02
0.00
0.742
Offering niche products
0.76
-0.04
0.01
0.01
-0.13
0.00
0.017
Employment
33.85
26.32
25.36
38.21
36.90
34.39
0.773
Time in years from founding the
company to starting operation on
the Internet
2.77
1.58
3.13
0.82
2.53
2.17
0.047
Years on the Internet
6.69
4.00
7.53
7.36
6.63
6.53
0.014
Percentage of firm’s revenues
from Internet operations
74.04
63.82
55.83
78.57
71.50
70.50
0.028
Percentage of loyal customers
50.96
38.82
54.17
47.32
41.83
44.50
0.041
Sales profit margin
13.65
13.82
13.67
15.80
16.63
15.57
0.152
Last year, we have increased our
revenues faster than competitors
4.33
4.19
5.08
4.42
4.33
4.40
0.286
Last year, we have acquired a
larger number of new customers
than direct competitors
3.75
4.12
5.08
4.35
4.42
4.37
0.052
We have a larger proportion of
satisfied customers than
competitors
4.18
4.61
5.08
4.60
4.54
0.36
0.210
Number of firms in a cluster
13
19
15
28
75
150
X
As can be seen from both tables, the clusters are distinguished by relatively high or low means
which suggest strong or weak reliance on given determinants of value proposition or atypical
levels of other external variables. In table 2, the shades-of-grey formatting was applied to only
a few rows, which was due to only six external variables having shown systematic differences
across clusters, as was reported by the significant outcomes of ANOVA tests.
Important insights about segments of firms are also provided by the next two tables which
illustrate similarities and differences between clusters regarding types of main Internet
activities and major sources of income. The basic outputs of chi-square tests given underneath
each table indicate that segments varied systematically on dominant Internet operations and
types of revenues.
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Table 4: Profiles of Internet operations of surveyed companies by segment membership
Segments
Total
1
2
3
4
5
Which of the
following best
describes the
profile of your
company’s
Internet
operations?
Internet storefront
30,8%
89,5%
46,7%
7,1%
73,3%
56,7%
Providing services to
business and public sector
30,8%
10,5%
0,0%
10,7%
13,3%
12,7%
Providing services to
consumers
7,7%
0,0%
26,7%
7,1%
8,0%
8,7%
News site/portal
30,8%
0,0%
20,0%
75,0%
5,3%
21,3%
Social network portal
0,0%
0,0%
6,7%
0,0%
0,0%
0,7%
Total
100%
100%
100%
100%
100%
100%
Chi-square test of independence: Pearson Chi-square=94.442; df=16; p<0.001
Table 5: Main sources of revenues from Internet operations of surveyed companies by
segment membership
Segments
Total
1
2
3
4
5
What was the main
source of revenues of
your company from
its Internet
operations?
sales of tangible
products
38,5%
100%
33,3%
14,3%
82,7%
63,3%
sales of virtual/digital
products or services
30,8%
0,0%
20,0%
39,3%
10,7%
17,3%
advertising proceeds
30,8%
0,0%
46,7%
35,7%
4,0%
16,0%
intermediation
commission
0,0%
0,0%
0,0%
10,7%
2,7%
3,3%
Total
100%
100%
100%
100%
100%
100%
Chi-square test of independence: Pearson Chi-square=70,336; df=12; p<0.001
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Summarizing the data contained in the 4 preceding tables it is possible to propose the
following characteristics of the clusters:
Segment 1: Suppliers of unique offerings (8.7% of the sample): Providers of unique
products with a narrow range of complementary items and options. They rarely employ
freemium strategy and thus their Internet operations are typically not supported by revenues
from advertising. There is very limited reliance on involvement from business partners and
customers to enhance the value proposition. The unique products offered by the segment
members tend to be addressed to market niches. The segment is also distinguished by the
above average percentage of revenues from the Internet (74,04%) and more than half of
returning customers (50,96%). Many of the firms belonging in this category started out using
traditional distribution channels on average in 2003 and moved onto the Internet only after
about 2.77 years. According to the managers’ declarations this segment was the least dynamic
in terms of attracting new customers, which was shown by a relatively low mean score (3.75)
on a 7-point Likert-type scale item intended to measure this characteristic. This segment is the
most balanced concerning dominant types of business activities and sources of revenue: it has
almost equal proportions (about 30% each) of Internet storefronts, providers of services for
business and public sector and news portals. Main streams of revenues originate from sales of
tangible products (38.5%), sales of virtual products and services (30.8%) and advertising
(30.8%).
Segment 2: Specialized newcomers (12.7%): The lowest score on comprehensiveness
suggests that this grouping is focused on providing specialized offer (i.e. not comprehensive)
of rather conventional products, which is hinted at by a small mean of product uniqueness.
The customer benefits provided by the segment members do not entail productivity gains.
Here are the youngest companies, with the average founding year in 2006, which expanded
online in 1.48 years after their set-up date. Interestingly, this is the group of Internet
companies that have the lowest percentage of regular customers, which was estimated by the
managers at 38%, on average. The segment 2 companies are mostly Internet storefronts
(89.5%) which source their incomes from sales of tangible products (100%).
Segment 3: Comprehensive incumbents (10%): A typical member is a company that relies
on a wide offer of popular items (the lowest rating on offer uniqueness). The operations are
not supported through co-creation by partners and customers and availability of useful free
content is the lowest among all segments. Another distinguishing feature is that they are the
oldest companies (established on average in 2001) with the longest period from the funding to
the moment of starting doing business through Internet channels (3.13 years). The latter seems
to at least partially explain the smallest percentage of sales (55.83%) derived from the
electronic marketplace. Curiously, the firms in this category recorded on average the highest
proportion of loyal customers (54.17%) and the fastest pace of acquiring new patrons, as
compared to their direct competitors. The dominant type of company is Internet storefront
(46.7%) with providers of consumer services coming in second (26.7%). Chief sources of
income are sales of advertising (46.7%) and tangible products (33.3%).
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Segment 4: Productivity enhancers (18.7%): The firms in this cluster display the strongest
concentration on solutions that can enhance effectiveness and efficiency of their customers.
The companies do not shy away from offering free content and involving partners and
customers in co-creation. The products on sale are not unique and the offer seems to be rather
wide in scope. They were on average set up in 2004 and have the shortest time lag between
the year of funding and the year of getting onto the Internet (0.82 year) and, fittingly, the part
of the revenues from the Internet is here the greatest (78.57%). This type of operation is rather
typical of new portals, which make up 75% of the segment. The most incomes are provided
by sales of virtual products and services (39.3%; possibly through subscription) and
advertising (35.7%).
Segment 5: Run-of-the-mill retailers (50%): This is the largest cluster that does not reveal
many markings of distinctive strategies. Its typical member seems to benefit from some extent
of co-creation, has an offer that encompasses multiple options and the incidence of offering
free valuable content is higher in only one other cluster. They were on average founded in
2003 and went online after three years. The cluster has the second lowest percent of loyal
customers (41.83%) and the second fastest acquisition of new customers if the direct
competitors were to serve as a benchmark (average score 4.42 points on the Likert scale). The
companies in the segment are also the least likely to service market niches (mean score -0.13).
Segment 5 is similar to segment 2 in that it has the largest percentage of virtual stores (73.3%)
and derives its revenues mostly from sales of tangible goods (82.7%).
Discussion
The aim of the paper was to identify approaches to value proposition in online companies and
their consequences to firm performance. We managed to identify five segments of companies,
which profiles were presented in the previous section of the paper.
Possibly most striking was the observation that the identified segments do not show
statistically significant differences in financial performance, which was represented here by
sales profit margin. This is an important conclusion which contradicts some of the previous
findings. According to Min and Wolfinger’s research on online stores (2005) „specialists sell
less, but set higher profit margins than do generalists”. In our study, companies which bear
the greatest similarities to the concept of specialist belong to the segment labelled “specialized
newcomers”. The generalists, on the other hand, are best represented by the cluster of
comprehensive incumbents. Both segments rank similarly in terms of profit margin.
Another perspective is offered by the Delta Model (Hax, 2010). Specialized newcomers and
comprehensive incumbents bear similarities to the two types of companies distinguished in
the Delta Model: providers of the best product and total customer solutions, respectively. The
first type of companies concentrates on product superiority while the other focuses on
customer relationships. According to Hax, businesses who adopt total customer solutions
enjoy higher financial benefits than firms choosing the best product approach. The financial
metrics used by Hax as the outcome measures were market value added and market-to-book
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value. Again, our research shows that the identified segments have similar levels of profit
margin. The discrepancy in findings might be due to dissimilar outcome measures considered
in our research or due to different sample structure of companies from the networked
economy (including not only pure internet companies).
The findings concerning firm performance can also be set against some popular managerial
concepts like the one presented in the book „Differentiate or Die: Survival in Our Era of
Killer Competition” (Trout, 2001). The book emphasizes the need of differentiating from
competitors in the marketplace. Again, the results of our research do not support this
recommendation. Suppliers of unique offerings have in fact the lowest profit margin
(13.65%), while probably the least differentiated run-of-the-mill retailers enjoy the highest
(16.63%). However, the difference is not statistically significant (p=0.152) and so may be
unique to the collected sample and not transferable to the general population.
There is also an important divergence in customer loyalty between the specialized newcomers
and comprehensive incumbents. The first segment has the lowest percentage of loyal
customers (38.82%), the latter – the highest (54.17%); the difference amounts to 15.43
percentage points. According to Gupta and Lehman’s formula (2003), raising the customer
retention rate from 38.82% to 54.17% will increase the customer lifetime value nearly 4 times
(to be exact, in their study accumulated profits discounted at a 10% rate increased by a factor
of 3.95 as a result of improved customer retention; customer acquisition cost was not
considered). What is more, the business advantages may have been more profound, if online
customers of comprehensive incumbents were also buying at offline branches of the
companies, however this topic was not addressed in our survey. The highest loyalty enjoyed
by comprehensive incumbents may suggest that online customer loyalty is enhanced by a
wide range of complementary products or services, longer offline presence and a recognized
brand resulting from the longest operation period. These are the dimensions in which
comprehensive incumbents achieved the highest scores among all segments. As a
qualification to the above conclusions it should be added that the study used a rather
simplistic measure of loyalty which operationalized the concept in terms of percentage of
returning customers. Thus, the metric encapsulated only one behavioral aspect of loyalty, not
tapping into its attitudinal and cognitive dimensions. In consequence, some of the customers
reported as loyal may truly have repeated their purchases only due to force of habit or as a
result of convenience; they did not need to have any emotional or cognitive attachment to
their supplier of products or services.
Another interesting dissimilarity between these two types of companies concerns year of
funding and year of starting operations on the Internet. Specialized newcomers were on
average established in 2006 and started online operations in 2008. The comprehensive
incumbents have had longer histories. On average, they have been in business since 2001 and
run online operations since 2004. The profit margin of both company segments are similar;
however, as already mentioned, comprehensive incumbents have attained a higher percentage
of returning customers and higher level of traditional sales. Similar pattern can be found in
other segments: the two groups of companies with the second and third highest percent of
loyal customers (i.e. suppliers of unique offerings and productivity enhancers, respectively)
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had time on the Internet longer than the two clusters with the smallest loyalty ratio. This may
suggest that the dynamics of the Polish online market reward long term presence on the
Internet with more loyal customers.
The last segment, run-of-the-mill retailers, can be perceived as largely terra incognita. The
companies here lack distinguishing identity. Their mean scores are middling in comparison to
other segments across almost all dimensions. The only notable exception is that they rely the
least on enhancing customer efficiency. According to many management and marketing
concepts companies offering undifferentiated value proposition should suffer from low
financial and otherwise performance. Porter (1998) claims that companies which are not
differentiators or low cost providers will “get stuck in the middle”. There are some exceptions
from this rule, but one could expect to find higher profit margins in segments with more
focused strategies, which apparently was not the case here. In more recent publications, Porter
admits that the two strategies (i.e. diversification and cost leadership) may be used jointly, e.g.
when a company, as a sole market competitor, has access to an important technology.
Examples of such companies are Amazon.com and Dell. Porter’s amendment to his earlier
theory, although interesting and intellectually stimulating, cannot explain why
undifferentiated run-of-the-mill firms were not characterized by comparatively low sales
margins. This group of retailers amounts to 50% of researched companies. Due to their large
number and a lack of distinguishing identity, they should not be treated as adopters of unique
breakthrough technologies or disruptive managerial solutions. Hence, the absence of
convincing explanation of the run-of-the-mills’ value creation mechanisms warrants further
research on the segment.
Limitations and directions for further research
The present study has several important limitations. Firstly, among online companies, the
sample included Internet stores, e-service providers and news websites. Hence, the sample
was characterized by profound heterogeneities in terms of employed business models. On the
other hand, previous research on value proposition quoted earlier in the article was also
conducted on similarly diversified groups of companies. Considering a similar approach to
sampling by other researchers and the general objective of the study, which sought to uncover
rather universal patterns that are independent of industry idiosyncrasies, that feature of the
sample probably did not compromise the presented results.
Second limitation stems from the fact that the sample comes from a single country. Polish
online market comprises of sectors typically displaying characteristics of perfect competition
(e.g. several hundred online bookstores) or oligopoly (e.g. portals). The consumer's
motivation to use Internet is time saving, convenience, and lowest price seeking (Żbikowski
2012). Hence, the market is highly competitive. A few global players tried to enter the Polish
market: some of them resigned (e.g. Yahoo!, AOL), while others got satisfied with a low
market share (e.g. eBay). In consequence, not all of the identified patterns may repeat in other
national environments with different market structures and dynamics.
Somewhat problematic was that a single large segment accounted for 50% of the sample. One
explanation could be that those companies were indeed undifferentiated, average market
players. But it could also be argued that the questionnaire employed in the survey lacked
13
scales to serve as more complete and subtle metrics that could better distinguish between
value proposals. For example, the study did not include measures related to capital structure,
human resources and many aspects of innovation policies and operations. Also, it would
definitely be useful to include other financial metrics, such as ROA and ROE. However this
problem could not have been easily remedied, as it was impossible to include more items on
the questionnaire so that the average interview time did not exceed the 20 minutes
recommended for the CATI method. In addition, given the form of contact, questioning
respondents about other financial metrics was likely not to be successful (the authors’ own
experience strongly implies that in Poland the highest item response rates are to be achieved
for sales profitability ratio; other performance metrics are typically much more problematic in
this respect).
As was already mentioned, the limitations of the study correspond with the constraints in
many other research projects in this area. The existence of a large segment of companies
without any clear identity may suggest the need for a closer investigation. That may be
conducted with one of the qualitative methods (e.g. case study research) to better control for
the unique conditions of each company. Also, a more refined questionnaire design could be
considered, where the variables that apparently did not differentiated meaningfully between
companies are replaced by new metrics, capturing other aspects of value propositions. In
addition, it would be interesting to see results from a similar study focused on a single type of
companies, for instance encompassing only online stores.
Appendix
Table: Rotated component matrix from principal components analysis of value drivers in Polish
Internet companies
(for the sake of clarity, only factor loadings greater than 0.4 were displayed)
Component
1
2
3
4
5
6
7
8
9
Only a few firms offer solutions similar to
ours
,696
In the markets we operate in we are
recognized as pioneers
,780
For the most part, customers choose our
solutions because of their innovativeness
,530
Resigning from our offer and changing to
our competitors’ brings about high switching
costs to our customers, such as extra time,
effort or financial expenses
,892
It happens that customers are not fully
satisfied with our offer but they stay with us
due to switching costs
,874
We provide our customers with personalized
solutions
,854
14
Most of our customers use our personalized
solutions
,707
We consider it important to maintain for as
long as possible even those customers who
are less profitable
,813
Regular customers are rewarded through
loyalty programs and other measures
,663
We have implemented specific mechanisms
for retaining customers
,414
,681
Our key partners have strong impact on
uniqueness of our offer for customers
,792
An important criterion for selecting our key
business partners is enhancing our capacity
for retaining customers
,625
Our key partners have strong influence on
how comprehensive our offer is
,616
Our customers are choosing our offer for the
attractiveness of the available
complementary products
,685
Customers try to use our solutions together
to benefit from synergy effects
,668
Our solutions allow customers to take
advantage of savings in time and effort
,663
Because of our solutions customers can solve
their problems more easily
,722
Internet grants customers a more efficient
access and use of our products than
traditional channels
,742
Our partners make significant contribution to
savings in time and effort afforded customers
by our offer
,580
Our offer is for free: we earn our money
from advertising revenues
,909
Our Internet site has only informational
features without transactional capabilities
,891
Our dominant strategy is “freemium”: the
basic offer is for free but customer have to
pay for premium features
,707
We offer a wide selection of items in our
chosen product category
,827
We offer popular products
,750
We offer niche products
,653
15
We offer products from several related
categories
,516
The final value for the customer is strongly
affected by actions, contributions and/or
transactions by other customers
,680
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser
Normalization.
Labels corresponding to component numbers in the table:
1. Co-creation of value through cooperation with business partners and customers
2. Providing customers with productivity benefits (e.g. time savings)
3. Providing free content or services to customers (e.g. offering articles free of charge and
sourcing revenues from advertisements)
4. Employing measures to enhance customer loyalty
5. Offering unique products (i.e. products that serve common needs but in a unique manner)
6. Building exit barriers for customers (i.e. inducing switching costs)
7. Proposing personalization options
8. Offering a comprehensive range of products
9. Offering niche products (i.e. products that serve the needs of a small fraction of the market)
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