DISCUSSION PAPER SERIESMay 2014

No.2014-06

Loyalty Program Point Exchange Networks and their
Impact on Marketing Performance

Sotaro Katsumata
Takahisa Wakabayashi

Loyalty Program Point Exchange Networks and their Impact
on Marketing Performance

Sotaro Katsumata(Nagasaki University)
Takahisa Wakabayashi(Takasaki City University of Economics)

Abstract:
We focus on points exchange alliances and their networks as a new loyalty program (LP)design. We examine the nature of such networks and their impact on marketing
performance. We find that they are asymmetrical, scale-free networks involving a few
hub LPs that form many exchange alliances. Further, on analyzing the relationshipbetween the number of alliances in a network and marketing performance such as
membership rate, we find that points exchange programs positively affect marketing
performance.
Keywords: Loyalty Program; Loyalty Program Exchange Alliance; Inter-firm Network
Research Highlights:

We study points exchange alliances and their networks as a new LP design.

We examine the nature of points exchange networks by applying the indicators
relevant to complex networks.



We find a positive relationship between the number of alliances and marketing
performance.

11. Introduction
In recent years, many firms have introduced Loyalty Programs (LPs) toestablish relationships with their customers. In 2002, the market value of LPs in the
United States is roughly 6 billion dollars, with 1.8 billion member identifications issued(Dorotic, Bijmolt, and Verhoef, 2012). This equates to an average of seven LPs perperson. According to Berman (2006), 95% of people in Canada participate in at least one
LP. In the United Kingdom, 92% of people participate in at least one LP, and 78%
percent participate in more than two. In Japan, the largest three mobile phone service
firms that dominate the country’s market have all introduced LPs. According to Japan’s
Ministry of Internal Affairs and Communications (MAC), 95% of people own their own
mobile phone (MIAC, 2012). Therefore, the majority of people in Japan are participating
in a LP, whether they are aware of it or not.
As shown above, firms worldwide are introducing LPs all. However, somestudies challenge the contribution of such programs to market performance. Zhang andBreugelmans (2012) argued that since most firms now utilize LPs, they are no longer
effective in contributing to competitive advantage. This argument suggests that LPs
cannot retain customer loyalty, which is one of their ultimate goals. Consequently, many
firms are attempting to redesign LPs to enhance their effectiveness, and LP points
exchange alliances are one such example. In this LP design, Firm A and Firm B share apoints exchange alliance. When a customer accrues points with firm A, s/he become
eligible to receive a service provided by firm B. Many firms in Japan form points
exchange alliances, with some points convertible to electronic money. These exchange
alliances form a large complex network. The Nomura Research Institute (2013) reportsthat the gross amount of LP points issued in Japan 2013 is estimated at over one trillion
yen (approximately ten billion US dollars).
To date there is little empirical evidence to support the contribution of points
exchange to improved market performance. Taken at face value, exchanging points
between firms implies loss of business. We have to explore the relationship between
points exchange alliances and marketing performance.

2. LPs: Their purpose, issues, and designs
2.1.

Purposes of LPs
Nunes and Drèze (2006) proposed that LPs serve five purposes. The first is toprevent customer turnover, or “churn”, as firms look for ways of increasing customer
loyalty (Kumar and Reinartz, 2005) and retention rates (Verhoef, 2003). The second
purpose is to win the greatest share-of-customer spending. A number of studies have2

explored this aspect of LPs (Wirtz, Mattila, and Lwin, 2007; Meyer-Waarden, 2007). The
third purpose is to encourage customers to make additional purchases (Taylor and
Neslin, 2005). The fourth purpose is to provide firms with potentially valuable
information about customer purchasing behavior and preferences. This information
database can assist firms to track and forecast customer equity (Blattberg and Deighton,1996) and design customer relationship management strategies. The fifth purpose is for
LPs to have a positive effect on business profit margins.
A numberofstudieshave examinedtherelationshipbetweenLP

implementation and performance. Some studies suggest that LPs increase customer
switching costs and reduce price competitiveness (Klemperer, 1987; Kim, Shi, andSrinivasan, 2001). Conversely, Lal and Bell (2003) suggest that LPs, especially frequent
shopper programs, increase profitability, particularly with lower spending customers.
This study proposes that LPs contribute to firms’ profitability by increasing their lower
spending, rather than higher spending, customers. Other studies support the effect of
LPs in short term customer retention (Lewis, 2004; Taylor and Neslin, 2005).
2.2.

Recent Issues with LPs
Although a number of studies support the positive impact of LPs on market

share, others challenge their effectiveness. For example, Sharp and Sharp (1997) argue
that the positive effect of LPs on repeat purchases is not supported by research. Other
studies

also

do

not

supporttheireffectiveness(Magi,

2003;

DeWulf,Odekerken-Schroder, and Iacobucci, 2001). One of the issues these studies identify isthat the saturation of LPs in the market has resulted in loss of competitive advantage(Zhang and Breugelmans, 2012).
2.3.

New LP Designs and Exchange Alliances
A number of recent studies claim that the design of the LP is critical inachieving firms’ marketing goals (Leenheer et al., 2007; Zhang and Breugelmans, 2012;
Kumar and Shar, 2004; Kivetz and Simonson, 2002; Roehm, Pullins, and Roehm, 2002;
Van Osselaer, Alba, and Manchanda, 2003; Yi and Jeon, 2003).
There have recently been some progressive approaches to LP design. One
example is “customized pricing,” which differentiates pricing by individual customer(Acquisti and Varian, 2005). Using this approach, firms can implement heterogeneous
customer strategies to avoid customer churn and increase loyalty. Another example is“exclusive promotion,” where sales promotions are tailored to individual customers(Barone and Roy, 2010). Another more complex LP design, the “Item-Based Loyalty
3Program (IBLP)” aims to differentiate product items (Zhang and Breugelmans, 2012).
While these new designs differentiate customers or products to implement
marketing strategies, others employ a different approach. LP partnering and points
exchange aims to increase the value of LPs by forming alliances with other LPs. For
example, O’Brien and Jones (1995) discuss an LP introduced by several partneringfirms with successful results. Dorotic et al. (2012) propose that LP partnering andpoints exchange have significant business marketing potential.
This study focuses on LP exchange, which differs from traditional LP designs
in that it exchanges points provided by different firms across a variety of industries. For
example, an airline forms an exchange alliance with a retail store. Customers who haveaccrued mileage points with that particular airline can utilize the points to receive
services provided by the partner retail store, including discounts. If there are manyexchange alliances and form a network as mentioned by O’Brien and Jones (1995),
customers can obtain desired point through several exchanges.
While authors such as O’Brien and Jones (1995) and Dorotic et al. (2012)
discuss points exchange alliances as a new LP design, an empirical study is needed to
examine the relationship between points exchange alliances and marketing
performance. This study aims to provide such empirical results.

3. The Points Exchange Network
3.1.

Study Objectives and Definitions
This study examines the properties and performance of the LP points exchange

network. We focus on LPs collected by Poitan, a Japanese website, as an objective
dataset. Poitan, Inc. is a firm that provides an Internet-based automatic multiple LP
points management system. Member customers participate in multiple LPs in order tomanage their points on Poitan. Visitors to the Poitan website can also browse exchange
alliance information.
Poitan manages most of the major points programs issued in Japan. However,some points are not classified as LP points, for example electronic money. This is despite
consumers purchasing electronic money through financial transactions. The purpose ofsuch electronic money services appears to be different to that proposed in Nunes andDrèze (2006). Customers can also exchange LP points for these electronic money
services, which further supports the idea that such services do not share the propertiesof an LP. This study focuses on the entire LP network, so we do not omit these electronicmoney services in our analysis. We will discriminate them from LPs, during this study if
4required and identify when this is the case. The following section will show that almost
all objective points programs satisfy LP conditions.
Terms and notations are defined as follows. The exchange alliance (𝑖, 𝑗)
describes the relationship between points programs that allows customers to exchange
LP 𝑖’s points to LP 𝑗’s points. In this alliance, LP 𝑖 is termed the “initial” LP and 𝑗 as
the “terminal” LP. We describe the number of LP 𝑖 alliances as 𝑘𝑖𝑂𝑢𝑡 and “out-degree.”LP 𝑗 alliances are described as 𝑘𝑖𝐼𝑛 and “in-degree.” LPs are sometimes also called“firm” or “node” depending on the context in which they are described. Some LPs relate
to more than one firm, as some firms introduce more than two LPs or issue more than
two points. Conversely, some LPs are jointly introduced by several firms. Whendiscrimination is needed between “firm” and “LP,” we use whichever term is mostappropriate. Again, In the context of network analysis, we use “node” instead of “LP,”and “path” instead of “alliance.” However, these terms denote the same objects.
3.2.

Data Collection
In this study, we apply two datasets to examine the LP alliance network

structure. The first dataset is the network data of alliances, where each element
indicates binomial state, whether the alliance is formed or not. The second is the points
accumulation data. Since Poitan automatically manages its members’ LP points, we canobtain each member’s points accumulation status.
In this study, we collect both datasets twice. In the alliance network dataset thedata is collected on March 14th, 2011 and June 6th, 2013. The 2011 points accumulation
data is obtained from the two observed datasets collected on May 20th, 2011 andOctober 1st, 2011. The 2013 data is obtained from the two datasets collected on May
17th, 2013 and September 24th, 2013. As customers regularly redeem points and theaccumulation data will be changing, we gather data at two spaced time intervals in eachselected year and obtain the average volume. Some customers may temporarily have nopoints immediately after redemption, and we use the average variable in analysis toaddress this occurrence.
3.3.

Objective Industries and Firms
Table 1 gives a summary of information collected in the datasets. Since theaccumulation of objective points is automatically updated, the accumulation volume is
not a self-report or manual entry. Within the objective LPs, 117 exist in both 2011 and2013 suggesting that many LPs were providing services prior to 2011. Two exist only in2011 and 20 exist only in 2013, suggesting that a substantial number of LPs began
5

providing services within that two year period.
Table 1: Summary of Dataset Statistics
2011

2013

Growth rate (%)

119

137

115.1%# of LPs which have any alliances (N)

100

128

128.0%# of isolated nodes

19

9

47.4%

Sum of alliances

433

582

134.4%

Density

0.03080.0312101.3%# of members

48324

52021

107.7%

Average # of LP participation

3.36

3.67

109.3%

Objective number of LPs (N)Network dataset

Accumulation dataset

Table 2 shows the summary of objective LPs (firms) classified according toindustry. Poitan classifies firms into 16 industries based on similarity of service. Firms
within the same industry are essentially in competition with each other. For example,
the airline industry’s main purpose of the firms is to retain customers and prevent
switching. Credit card firms aim to encourage customers to use their card more
frequently. Within objective firms, the points industry has a different purpose. Firms
within the industry generate revenue as an intermediate agent. The business model ofthese firms is based on attracting as many members as possible to generate a high levelof advertising revenue. If large numbers of members decide to exchange their points forother points or electronic money services, they can exchange these at a bulk volume
discount. The attractiveness of the firm (points program) corresponds to the number of
exchange alliances, particularly the number of terminal nodes.
The purpose of firms in the electronic money industry is different fromtraditional LPs, and are not classed as “narrowly-defined” LPs.

6

Table 2: Industries and the number of LPs (firms)
No.

Industry

2011

2013

1

Airline9112

Credit Card

18

22

3

Points3135

4Home Electronics Retailer

4

4

5

Internet Commerce

9

10

6

Book, CD, DVD

2

2

7

Telephone, Internet Provider

6

7

8

Railway4

6

9

Car Dealer, Gas Station Chain

3

4

10

Supermarket, Department Store, CVS

7

7

11

Bank and Securities

4

4

12

Electronic Money

6

5

13

Hotel

7

7

14

Travel1

415Inter-firm

2

2

16

Other

6

7

Total

119

137

In the following section, we examine the properties of the two datasets in moredetail, by analyzing the datasets from two aspects. The first analysis examines theproperties of the LP exchange network mainly from the network dataset. The results
are outlined in the fourth section. The second analysis uses quantitative analysis to
examine the relationship between positions of LPs within the network and theirmarketing performance. The results are outlined in the fifth section.

4. Structure of the LP Exchange Network
4.1.

Overview of the Analysis
In this section, we summarize the indicators of the exchange network in order

to better understand the structure of the LP exchange market. As there are twodatasets (2011 and 2013), we compare the two and observe any changes.
Iacobucci (1996) examines the issues related to marketing networks. In thisstudy, both inter-firm and inter-personal networks are discussed. With inter-personal
networks, some studies examine the consumer interaction (Manski, 2000; Yang andAllenby, 2003). Inter-firm network issues are mainly discussed in the field of7

organizational studies (Gulati, 1998; Gulati and Gargiulo, 1999; Kenis and Oerlemans,
2008). LP studies seldom consider network effects; therefore in this study we initiallyexplore the network properties of the objective datasets.
4.2.

Network Properties
First, we calculate the in-degree and out-degree. To summarize the network,

Albert and Barabási (2002) propose several indicators, some of which are applied in thisstudy. In analysis, as we omit the isolated node (LP) the number of objective LPs is 100
in 2010 and 128 in 2013.
Figure 1 shows the distribution of the in(out)-degree for each year. The upper
three figures are drawn from the 2011 dataset. The left figure is in-degree, the center
figure is out-degree, and the right figure is the scatterplot of in and out-degree. The
bottom three figures are the same configuration from the 2013 dataset.
In Figure 1, we can see that a small number of LPs have multiple alliances andthe majority of LPs have only a few. This suggests that the networks have scale-freeproperties (Watts and Strogatz, 1998). We further examine the network indicators thatare proposed by Barabasi and Albert (2002). Table 2 shows the calculated indicators.
From the values proposed in Table 3, we find that the LP exchange networks
are scale-free. From the log-log transformed plot of Figure 1 (see Appendix) we findlinear decreasing relationships. This supports the suggestion that the networks are
scale-free.
Additionally, from the right hand side figures in Figure1, the obtainedcorrelation coefficients are 0.018 in 2011 and −0.001 in 2013. This suggests that the
LP exchange networks are asymmetrical.

8

Figure 1: Distributions of In-degree and Out-degree
Outdegree (2011)

Scatter plot (2011)

5

10

15

20

25

30

35

0

5

10

15

20

25

30

0

5

10

15

20

25

IndegreeOutdegree

IndegreeIndegree (2013)

Outdegree (2013)

Scatter plot (2013)

30

35

0

10

20

30

40

15

Outdegree

0

0

0

5

20

20

10

40

Frequency

40

Frequency

60

60

20

80

25

80

100

0

0

0

0

10

5

20

20

10

15

Outdegree

40
30Frequency

40

Frequency

50

20

60

60

25

Indegree (2011)

0Indegree

5

10

15

20

25

30

0

Outdegree

10

20

30

40

IndegreeTable 3: Network Indicators
2011

2013

Number of Nodes

100

128

Average number of Paths

4.33

3.44

Degree Distribution Parameters (Out-degree)

−2.00

−1.99

Degree Distribution Parameters (In-degree)

−1.78

−1.49

Average Path Length

3.21

3.42

Note: Average path length becomes infinity when the two nodes are
disconnected. This table averages only connected relations.
4.3.

Concentration Rate
In this section, we compare the concentration rates between 2011 and 2013

datasets. We make our calculations from the accumulation dataset, then summarize thetotal number of accounts (members) for all LPs and obtain the Herfindahl-Hirschman
and CRn indices (CR3, CR5, CR10, CR20). Table 3 shows the results. We find that theconcentration rates are slightly decreasing. As we show in Table 1, the density of the LP
9exchange network is increasing. It is possible that customers will gravitate to aparticular LP. In fact, as Dorotic et al. (2012) observes, some LPs are introduced by ajoint venture partnership established by several firms. Moreover, some observers
predict an increase in concentration (Weekly Toyo Keiaizi, 2013). Interestingly, thisresult suggests that the concentration rate is actually decreasing. This supports thetheory that although larger LPs are actively recruiting members, the network expands
when new LPs are introduced to the market..
Table 4: Competition Indicators

4.4.

2011

2013

Herfindahl-Hirschman Index

302.65289.48

Concentration Rate

CR3

20.55

20.20

CR5

29.46

28.80

CR10

46.07

44.98

CR20

65.46

64.16

Summary
There are two main findings. First, the LP exchange network has been shownto have scale-free properties. In the wider business environment there are many
scale-free networks (Barabási and Albert 2002) and the LP exchange network can be
included as one of these networks. We can apply findings that are proposed in social
networks or complex network studies to LP exchange network studies. The second
finding is that the LP exchange network is growing., Despite a number of recent
integrations and reorganizations of LPs, the LP exchange network has increased during
2011 to 2013. Further, we find that the average number of consumer participation in
LPs is increasing and concentration rates are decreasing. This suggests that the
introduction of new LPs is more active than existing LP integration.

5. Exchange Alliances and Marketing Performance
5.1.

Overview and Hypotheses
In this section, we examine the relationship between the network position of

LPs and their marketing performance to evaluate the LP exchange strategy as a new LP
design. We propose three hypotheses and test them using empirical analysis.
The first hypothesis is related to customer acquisition. In an exchange alliance,
there are two LPs: initial and terminal. When a customer who has accrued points with10

the initial LP and wants to receive services provided by the terminal LP, s/he exchanges
points between the initial and terminal LP. This suggests that if an LP has many
existing LPs, it has the potential to acquire new customers from these alliances.
Therefore, we propose the following hypothesis:

H1: An LP that has more initial LPs achieves higher marketing performance.
The second and third hypotheses are conversely related. The exchange alliancein the role of terminal node is potentially exposed to both benefits and disadvantages.
Although exchangeability carries a risk of customer attrition, it increases theattractiveness of the LP. Customers can receive a variety of services from LPs that havemore terminal LPs. Therefore, we propose the following two hypotheses:

H2a: An LP that has more terminal LPs achieves higher marketing performance.
H2b: An LP that has more terminal LPs settles for lower marketing performance.
In this study, H2a is based on increased program attractiveness and H2b isbased on customer attrition. To examine these relationships further, ideally we need to
conduct a customer satisfaction survey. However, as we cannot obtain such data for the
purposes of this study, we examine the statistical relationship between network
positions and performance indicators.
5.2.

Performance Indicators and Model
As a number of previous studies have shown, LP performance ismultidimensional. Consequently, we define three performance indicators and apply
independent variables using quantitative analysis.
The first performance indicator is the “membership rate.” This is an importantintermediate indicator in assessing the profitability of LPs. Customer acquisition is thefirst step of LP management and fulfills other purposes such as increasing switchingcosts (Verhoef, 2003; Klemperer, 1987; Carlsson and Löfgren, 2006), raisingconsumption levels (Leenher et al., 2007), and acquiring customer data (Kumar and
Shah, 2004). Membership rate is an important indicator, not only for firms that haveone main service such as airlines and department stores, but also points and electronicmoney firms. These firms need to achieve economies of scale in order to reap the
benefits of LPs and ensure their business remains profitable. In this study, themembership rate is obtained from the accumulation dataset. For both datasets collected11

in 2011 and 2013, the membership rate of LP 𝑗 is calculated from the number ofmembers of LP 𝑗 divided into the total Poitan members (objective customers).
The second performance indicator is the total amount of LPs issued, which isthe sum of accumulated volume issued for each customer at a time. An LP that has ahigher total issued amount is accepted and utilized by many customers. However, theaverage accumulated volume may vary depending on the industry. For example, airline
points (mileage) are not redeemed by customers as often as CVS points. We introduce
the industry dummy variable to control these analysis variations. In addition, as thevalue of one point differs among LPs, we convert each point into Japanese yen using theconversion rate of each LP provided by Poitan.
The third performance indicator is the average accumulated points volume ofeach customer. Membership rate and total issued amount may be correlated, so we
introduce the performance indicator variable from another aspect. We calculate theaverage accumulated volume of LP 𝑗 from the total amount issued, divided into thenumber of members of the LP 𝑗. As with the total issued amount, we control theindustry variance by introducing dummy variables. This variable is also converted toJapanese yen for consistency of value across all LPs.
We apply these three performance indicators as dependent variables andincorporate in-degree, out-degree, and industry dummy variables as independent
variables. The variable range of the membership rate is (0,1), so we convert thevariable using normal inverse transformation. As a result, the variable range becomes(−∞, ∞). For the total issued amount and the average accumulated volume, we take thenatural logarithm. We also examine whether the coefficients change between 2011 and2013. We estimate two models. The first model is a “static model” that assumes theparameters have not changed within the two periods. The second model is a “dynamic
model” that assumes the parameters have changed.
5.3.

Results
Table 4 shows the estimation results. For each cell, the numbers outside thebrackets are estimated values, with standard deviations bracketed. Looking at the tablefrom left to right, the columns show the parameters of membership rate, total issued
amount, and accumulated volume of individual customers.
First, we determine the overall usability of the models. For all three
performance indicators, the “static models” that assume common parameters in both2011 and 2013 achieve higher Adj. R2 and lower AIC compared with the “dynamic
models.” As R2 increases as the number of parameters increase, we cannot use this12

indicator for model comparison. Conversely, Adj. R2 and AIC do not have suchproperties, making these indicators suitable for comparison. The results of thiscomparison suggest that the “static models” yield more positive results than the“dynamic model” for all three performance indicators. It would appear that the overalltendencies have not changed during the two periods. In addition, the correlation
coefficients of the “dynamic models” parameters between 2011 and 2013 are very highfor all three indicators (membership rate = 0.973; total issued amount = 0972;
accumulated volume = 0.992). We also find from this result that the tendencies have notchanged.
From the in-degree and out-degree parameters, we find that there are positive
significant relationships between membership rate and total issued amount. However,
we cannot find positive relationships between in(out)-degree and accumulated volume.
In the previous section, we proposed three hypotheses, and we now examine these.
H1 proposed that there are positive relationships between in-degree andperformance. The results support H1 with regard to membership rate and total issued
amount. LPs that have many terminal LPs can attract more members and issue morepoints. However, we cannot find any positive relationships with the averageaccumulated volume of customers. As this indicator varies depending on the service, theresult is reasonable. It is unlikely that the increasing number of terminal LPs positively
affects the accumulated volume.
We find positive relationships between out-degree, membership rate, and total
issued amount. We proposed two contradicting hypotheses for out-degree. H2a predicted
positive relationships, while H2b predicted negative relationships. The results support
H2a. Although we cannot find positive relationships between out-degree and theaverage accumulated volume of customers, these results do not necessarily support H2b.
Therefore, we find that the number of out-degree positively affect membership rate andtotal issued amount. Further, there are no noticeable negative impacts on performanceindicators. This suggests that customer attrition risk is far lower than expected.
In the “dynamic model,” when comparing parameters for 2011 and 2013, theestimated values tend to be higher. Although not a significant result, the impact of LP
exchange relationships may have increased between these periods.
From the results of this empirical analysis, we can conclude that the number of
in-degree and out-degree positively affects marketing performance. In particular, wefind a significantly positive relationship to membership rate, which is one of the mostimportant performance indicators. This suggests that utilizing LP exchange alliances
can result in positive outcomes for partner firms.
13

Table 5: Estimation Results
Membership rate
StaticTotal issued amount

Dynamic
2011

Static2013(Intercept)−𝟑. 𝟎𝟐𝟏 (𝟎. 𝟏𝟒𝟐)

−𝟐. 𝟗𝟖𝟗 (𝟎. 𝟐𝟏𝟑)

In-degree

𝟎. 𝟎𝟒𝟕 (𝟎. 𝟎𝟎𝟓)

𝟎. 𝟎𝟓𝟓 (𝟎. 𝟎𝟎𝟗)

Accumulated volume

Dynamic
2011

Static2013

𝟏𝟏. 𝟖𝟕𝟔 (𝟎. 𝟓𝟓𝟑)

𝟏𝟏. 𝟖𝟒 (𝟎. 𝟖𝟑𝟗)

𝟎. 𝟎𝟒𝟒 (𝟎. 𝟎𝟎𝟕)

𝟎. 𝟏𝟑𝟓 (𝟎. 𝟎𝟐𝟏)

𝟎. 𝟏𝟔𝟑 (𝟎. 𝟎𝟑𝟕)

𝟎. 𝟏𝟐𝟎 (𝟎. 𝟎𝟐𝟖)

Dynamic
2011

2013

𝟔. 𝟗𝟓𝟔 (𝟎. 𝟐𝟗𝟓)

6.838 (0.445)

0.022 (0.011)

0.032 (0.02)

0.016 (0.015)

Out-degree

𝟎. 𝟎𝟒𝟖 (𝟎. 𝟎𝟎𝟖)

𝟎. 𝟎𝟓𝟏 (𝟎. 𝟎𝟏𝟑)

𝟎. 𝟎𝟒𝟔 (𝟎. 𝟎𝟏𝟏)

𝟎. 𝟏𝟒𝟖 (𝟎. 𝟎𝟑𝟐)

𝟎. 𝟏𝟓𝟗 (𝟎. 𝟎𝟓𝟑)

𝟎. 𝟏𝟑𝟕 (𝟎. 𝟎𝟒𝟐)

0.018 (0.017)

0.020 (0.028)

0.013 (0.023)

Airline−0.166 (0.189)−0.172 (0.288)−0.182 (0.264)

𝟐. 𝟒𝟗𝟓 (𝟎. 𝟕𝟑𝟗)

𝟐. 𝟑𝟖𝟓 (𝟏. 𝟏𝟑𝟎)

𝟐. 𝟓𝟎𝟗 (𝟏. 𝟎𝟑𝟕)

𝟑. 𝟎𝟓𝟓 (𝟎. 𝟑𝟗𝟑)

𝟐. 𝟗𝟕𝟎 (𝟎. 𝟔𝟎𝟎)

𝟑. 𝟏𝟎𝟎 (𝟎. 𝟓𝟓𝟏)

Card

𝟎. 𝟑𝟓𝟎 (𝟎. 𝟏𝟔𝟖)

0.417 (0.257)

0.296 (0.232)

1.191 (0.654)

1.367 (1.010)

1.060 (0.912)

0.215 (0.349)

0.156 (0.537)

0.275 (0.485)

Points𝟎. 𝟒𝟖𝟔 (𝟎. 𝟏𝟓𝟒)

0.449 (0.234)

𝟎. 𝟓𝟏𝟔 (𝟎. 𝟐𝟏𝟔)

𝟐. 𝟏𝟑𝟔 (𝟎. 𝟔𝟎𝟑)

𝟏. 𝟗𝟔 (𝟎. 𝟗𝟏𝟗)

𝟐. 𝟐𝟗𝟏 (𝟎. 𝟖𝟒𝟕)

𝟎. 𝟖𝟏𝟔 (𝟎. 𝟑𝟐𝟏)

0.736 (0.488)

𝟎. 𝟖𝟗𝟔 (𝟎. 𝟒𝟓𝟎)

Home Electronics Retailer

𝟎. 𝟖𝟖𝟑 (𝟎. 𝟐𝟐𝟖)

𝟎. 𝟖𝟏𝟏 (𝟎. 𝟑𝟑𝟕)

𝟎. 𝟗𝟑𝟏 (𝟎. 𝟑𝟐𝟔)

𝟒. 𝟏𝟒𝟕 (𝟎. 𝟖𝟗𝟎)

𝟒. 𝟎𝟒𝟔 (𝟏. 𝟑𝟐𝟑)

𝟒. 𝟏𝟖𝟎 (𝟏. 𝟐𝟖𝟏)

𝟏. 𝟔𝟔𝟖 (𝟎. 𝟒𝟕𝟒)

𝟏. 𝟕𝟑𝟓 (𝟎. 𝟕𝟎𝟑)

𝟏. 𝟓𝟗𝟎 (𝟎. 𝟔𝟖𝟎)

Internet Commerce

0.252 (0.185)

0.201 (0.279)

0.274 (0.260)

0.049 (0.723)−0.092 (1.096)

0.089 (1.022)−0.53 (0.385)−0.537 (0.582)−0.548 (0.543)

Book, CD, DVD−0.277 (0.289)−0.509 (0.422)−0.065 (0.416)−1.351 (1.127)−2.089 (1.659)−0.683 (1.634)−0.456 (0.600)−0.515 (0.881)−0.415 (0.868)

Telephone, Internet Provider

𝟎. 𝟓𝟒𝟏 (𝟎. 𝟐𝟎𝟎)0.557 (0.302)

0.513 (0.279)

𝟐. 𝟒𝟏𝟒 (𝟎. 𝟕𝟕𝟗)

𝟐. 𝟓𝟔𝟓 (𝟏. 𝟏𝟖𝟖)

𝟐. 𝟐𝟑𝟔 (𝟏. 𝟎𝟗𝟓)

𝟎. 𝟖𝟒𝟖 (𝟎. 𝟒𝟏𝟓)

0.933 (0.631)

0.763 (0.582)

Railway

𝟎. 𝟒𝟔𝟔 (𝟎. 𝟐𝟏𝟐)

0.493 (0.335)

0.447 (0.289)

0.733 (0.830)

0.658 (1.315)

0.752 (1.134)−0.647 (0.442)−0.813 (0.699)−0.566 (0.602)

Car Dealer, Gas Station Chain

0.362 (0.236)

0.340 (0.366)

0.382 (0.325)

0.731 (0.923)

0.551 (1.437)

0.851 (1.275)−0.407 (0.492)−0.556 (0.763)−0.317 (0.677)

Supermarket, Department Store, CVS

𝟎. 𝟓𝟔𝟐 (𝟎. 𝟏𝟗𝟒)0.548 (0.288)

𝟎. 𝟓𝟕𝟔 (𝟎. 𝟐𝟕𝟕)

1.121 (0.758)

1.113 (1.130)

1.148 (1.087)−0.565 (0.404)−0.528 (0.600)−0.580 (0.578)

Bank and Securities

0.098 (0.227)

0.061 (0.334)

0.134 (0.325)

0.026 (0.886)−0.106 (1.313)

0.180 (1.277)−0.298 (0.472)−0.312 (0.698)−0.262 (0.678)

Electronic Money−0.216 (0.213)−0.265 (0.307)−0.201 (0.313)

0.373 (0.831)

0.219 (1.207)

0.444 (1.230)

𝟎. 𝟖𝟗𝟐 (𝟎. 𝟒𝟒𝟐)

0.868 (0.641)

0.919 (0.654)

Hotel−0.334 (0.196)−0.393 (0.289)−0.278 (0.282)

𝟏. 𝟖𝟑𝟏 (𝟎. 𝟕𝟔𝟕)

1.547 (1.137)

2.141 (1.107)

𝟐. 𝟖𝟐𝟓 (𝟎. 𝟒𝟎𝟖)

𝟐. 𝟕𝟓𝟖 (𝟎. 𝟔𝟎𝟒)

𝟐. 𝟗𝟐𝟑 (𝟎. 𝟓𝟖𝟖)

Travel

−𝟎. 𝟓𝟑𝟕 (𝟎. 𝟐𝟔𝟓)−0.582 (0.559)−0.488 (0.324)−1.424 (1.036)−2.001 (2.197)−1.241 (1.274)

0.357 (0.552)

0.056 (1.167)

0.376 (0.677)

Inter-firm

𝟏. 𝟓𝟑𝟔 (𝟎. 𝟐𝟗𝟏)

𝟏. 𝟒𝟓𝟖 (𝟎. 𝟒𝟐𝟔)

𝟏. 𝟓𝟖𝟗 (𝟎. 𝟒𝟏𝟖)

𝟑. 𝟕𝟗𝟗 (𝟏. 𝟏𝟑𝟒)

𝟑. 𝟓𝟓𝟑 (𝟏. 𝟔𝟕𝟑)

𝟑. 𝟗𝟕𝟒 (𝟏. 𝟔𝟒𝟑)

0.170 (0.604)

0.083 (0.889)

0.249 (0.873)

2013 dummy−0.065 (0.292)

0.069 (1.146)

0.231 (0.609)

F

16.91

7.965

11.8

5.471

16.97

7.899

R^2

0.547

0.559

0.457

0.465

0.548

0.557

Adj R^2

0.515

0.489

0.419

0.38

0.516

0.486

AIC

564.88599.31425.39

1447.79765.62796.48

Note: Numbers in bold indicate significance at 5%; standard deviations are shown in brackets.

146. Discussion and Conclusion
6.1.

Discussion
The results of this statistical analysis suggest that the number of alliances

positively affect firms’ marketing performance. However, the formulation of an alliancerequires the agreement and cooperation of both the initial and terminal firms.
Therefore, it is possible that LPs achieving higher membership rates and points issued
will form alliances more easily than LPs that have fewer members, as a node thatconnects to multiple paths can more easily connect with others. Barabási and Albert(1999) proposed that networks generated by “rich get richer” mechanisms are more
likely to become a scale-free network. Such mechanisms may affect the LP exchange
network. The hypothesis is that an LP that has multiple alliances can attract manycustomers and positively affect the formation of partner alliances. This study only
examines the effect of the number of alliances on performance. However, we need to
employ more dynamic and recursive models to examine the process more closely.
This study suggests a positive relationship between out-degree andperformance, with the negative effect being significantly lower than previously
proposed.
6.2.

Conclusion
In this study, we focus on the LP exchange alliance as a new LP design. We

examine the properties of the LP points exchange network and the relationship between
alliances and marketing performance. We find that the LP points exchange network
possesses scale-free properties, and that a small number of hub LPs that form multiple
alliances. We also find that the network is asymmetrical. The statistical analysis
examines the impact of the number of alliances on marketing performance and findsthat both for in-degree and out-degree, the number of alliances positively affectmarketing performance, particularly membership rate and the total points issued. Thissuggests that the LP exchange alliance is one of the most effective designs in obtaining
LP benefits.
We identify two issues for future exploration. First, we recommend further
investigation into the impact of exchange alliances on market performance. This study
suggests a positive relationship between the number of alliances and marketing
performance; however, more research is needed to explore this connection in more detail.
For example, we need to examine whether the number of alliances (or other factors)
affects the attractiveness of the LP. As such information cannot be gained using
behavioral data, we recommend conducting a customer survey. Further, we need to15

observe the volume of exchange within an LP alliance in order to measure its
performance more accurately.
The second task is to examine the difference between exchange alliances andthe partnering of LPs. As mentioned in this paper, some LPs are introduced through a
combined joint venture of large firms. This is an example of partnering LPs. Customers
can use and redeem points from both firms within the LP partnership. Partnering
involves a closer working relationship than exchange alliances as the partnering firms
share the same alliances. We recommend that further research be undertaken toobserve whether LP partnering will more often be substituted for exchange alliances in
the future.

Appendix
Figure A: Log-log transformed plot of Figure 1 histograms
Indegree (2011)

Outdegree (2011)

+

0.10

+
+

0.05

p(k)

0.10

0.20

+

0.05

p(k)

0.20

0.50

0.50

+

+

0.02

R2=0.95

0.01

0.01

0.02

+

+ +
5

10

R2=0.89

+

20

5

10

k

k

Outdegree (2013)

Indegree (2013)

+

+

0.05

0.05

p(k)

p(k)

0.10

+

0.10

0.20

0.50

+

0.20

0.50

+ +
20

+

+

+
+

0.02

0.02

+

0.01

0.01

+
R2=0.93+ +5

10

R2=0.89

+
5

20

10

20

k

k

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19