DP2020-18

Unplanned Purchaseof New Products
Ryo KATO
Takahiro HOSHINO

May 26, 2020

Unplanned Purchase of New Products
Ryo Kato
Research Institute for Economics and Business Administration, Kobe University, 2-1
Rokkodai-cho, Nada-ku, Kobe, Japan
and
Takahiro Hoshino
Department of Economics, Keio University, 2-15-45 Mita, Minato-ku, Tokyo, Japan /RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku,
Tokyo, Japan

Abstract
Manufacturers are eager to recoup the investment on developing new products bypromoting the unplanned first (trial) purchase on the consumer’s first sight and byencouraging the succeeding repurchases because they had put in large amount of
resources for developing new product. However, studies are yet to investigate therelationship between unplanned purchases and the succeeding repurchase for new
product because data on this, including consumers’ extensive purchase history and aquestionnaire on unplanned buying, are simultaneously unavailable. This study
employed a novel “instant research” method to obtain both the purchase history and aquestionnaire on impulse buying. Regulatory focus theory, which has recently received
considerable attention in psychology and marketing, is helpful in predicting the result.
Promotion-focus positively affects unplanned buying of new products as well as
customer satisfaction, and leads to an increase in subsequent repeat purchases. The
results are consistent with our expectations, indicating that if the first purchase of a new
product is unplanned, the succeeding repeat purchases tend to increase compared to aplanned trial purchase. We also found that this propensity is more prominent forcustomers who have higher loyalty to the same or similar category product.

Keywords: Unplanned purchase, new product, regulatory focus theory

1 Introduction
Developing new products and product innovations can be large and longlasting sources of margins for innovators (Geroski et al. 1993). They can also help intaking advantage of the competition (Wind and Mahajan 1997). However, developing
new products is a risky investment (even though much of the research and development
costs are defrayed) as there exists a high degree of uncertainty about whether theseproducts will be accepted in markets. If manufacturers invest in a large number ofresources when developing a new product, they are eager to recoup the investment on it
by actively causing shoppers’ trial purchases and the succeeding repeat purchases.
Following the importance of new products to firms, most marketing and management
literature has focused on product innovations with adoption and diffusion in markets(Rogers 1995).
Another field of study that has gathered substantial attention is unplanned (orimpulse) purchasing. When shoppers browse new products in their shopping path anddecide to buy it because of, say, exposure to the commercial stimuli (displays or
promotions), they commit to an unplanned purchase. If this purchase is at first sight andif the customer recognizes the new product, the purchase must be unplanned buying.
Thus, unplanned purchases and the (first) trial purchase of a new brand are strongly
associated.
Simultaneously, manufacturers invest large amounts on promotions for new
products before launching them to let potential users recognize and be interested inthem beforehand. Advertising is a significantly important marketing strategy to
influence customers into trying a new product (Krishnan and Jain 2006). Besides
promotions, customers become aware of new products also through in-store displays,
websites, or word‐of‐mouth communication, which can trigger customers’ plannedpurchases. Thus, the trial purchase of new products can be caused in both planned andunplanned manners.
Despite the importance of understanding unplanned purchases of new products
for scholars and professionals, academic literature on unplanned purchases has been
limited to existing products (Stern 1962; Rook 1987; Inman et al. 2009; Bell et al. 2011;
Hui et al. 2013). These prior studies ask shoppers after they shop at a supermarket orgrocery store if each brand purchased was planned or unplanned. They investigated thefactors affecting the probability of making unplanned purchases such as consumer
characteristics, product category, or customer activities in the store (Wood 1998; Inman
et al. 2009; Bell et al. 2011; Hui et al. 2013). However, the newness and uncertainty of
new products lead to a different kind of decision-making process compared to that ofexisting products, and most research treats innovative products as a different subject ofstudy. Therefore, the results on unplanned purchases of innovative products can bedifferent from those of existing products.
The absence of surveys on unplanned purchases of a new product that employ
real purchase data may be attributed to data limitations; researchers cannot find the true
first purchase and following repeat purchases for shoppers unless they grasp customers’
entire purchase history across all channels, such as supermarkets, grocery stores, drug
stores, vending machines, and so on. Surveying simply one retail store is inadequate forunderstanding the first purchase of a new product, as shoppers may have alreadysampled that product at other stores or via other channels. Furthermore, their purchasehistory data must be combined with questionnaires about purchase impulsiveness.
In contrast to prior studies, we use a scanner panel dataset linked with a shortquestionnaire on unplanned purchases, which enables us to understand the entire
purchase history of all survey respondents. Therefore, we can determine if the “real”
first trial purchase of a new product is planned or unplanned. Moreover, the datacontains respondents’ succeeding repeat purchases of the product as well as past
purchase history for existing products.
We investigate the relationship between the first unplanned trial purchase andthe following repeat purchases of a new product. Wood (1998), Hoch and Loewentein(1991), and Saleh (2012) indicated the consistent result that unplanned buying results inpost-purchase regret. However, as a conflicting hypothesis, regulatory focus theory(Higgins 1997, 1998) implies that customers who engaged in unplanned buying are
more likely to gain satisfaction. Given the importance of succeeding repeat purchases
for long-lasting profit to the innovator and marketing strategies to increase customers’
unplanned purchases using in-store stimuli, it is important to investigate the effect of theunplanned trial purchase on the repeat purchases of new products.
In addition, we consider the effects of loyalty (or knowledge) built by an existingproduct. New product knowledge is associated with unplanned purchase behavior(Harmancioglu et al. 2009) and existing products that are similar to new products set
similar expectations (Olshavsky and Spreng 1996). We take into account the effect of
new product knowledge that consists of a similar existing brand on the unplanned
buying behavior of new products using real purchase history. As some brand loyalty or
knowledge proxies can be calculated using purchase history data, the implications
obtained from the analysis are substantial for managers who are willing to succeed in
new product launches.
Based on these motivations and our dataset, we investigate:
1. If the unplanned trial purchase of new products affects the quantity of succeeding
repeat purchases
2. How the brand loyalty and knowledge of other related existing products affect the
new product trial purchase and succeeding repeat purchase.

2. Background and hypothesis
Although it is very important to induce the shopper’s intention to new brandunplanned purchases from the perspective of shopper marketing strategies, few studies
exist on the unplanned purchase of a new brand (Harmancioglu et al. 2009). Many
researchers have taken an interest in the unplanned purchase of an existing brand, but
the buying process of new products is different from that of an existing brand (e.g.,
Hirshman 1980). As noted, we study the effect of trial unplanned purchase of a new
product on succeeding repeat purchase. As our prediction mainly depends on regulatory
focus theory, first, we introduce the theory itself.
2.1 Regulatory focus theory
Regulatory focus theory accounts for the way people employ self-regulation
during goal-pursuing (Haws et al. 2010). The theory distinguishes two regulatoryorientations, promotion-focus and prevention-focus. The former is more sensitive toopportunities for advancements and more motivated to achievements, while the latter ismore sensitive to occasions to threats and more motivated in avoiding hazards to reachsafety and security. It is recently being studied in the marketing context, especially inthe consumer behavior (Chernev 2004; Yeo and Park 2006; Herzenstein et al. 2007;
Arnold and Reynolds 2009; Jung and Yoon 2015; Das 2016) because of its explanatory
power for various phenomena as shown in many relevant studies.
2.2 Promotion /prevention-focus, new product, and unplanned purchase
It is implied that regulatory focus also influences the decision making on new
products. Such products are riskier than existing products as shoppers do not know their
attributes well and they have many types of risk (perceived risk, functional risk, social
risk, economic risk, and physical risk; Ram and Sheth 1989). However, they also attainpotential benefits for consumers by fulfilling unmet needs or satisfying needs better(Herzenstein et al. 2007). Therefore, when consumers evaluate a new product, they
more or less incorporate these risks and benefits, and their motivational principle to
make decisions on new product purchases can be explained by the consumer’s
regulatory focus.
Crowe and Higgins (1997) showed that promotion-focus is eager to find hits
and exhibits risky behavioral bias, whereas prevention-focus is vigilant against mistakes
and exhibits conservative behavioral bias. Moreover, individuals with prevention-focus
are more likely to exhibit a reluctance to exchange currently possessed objects than
individuals with promotion-focus (Liberman et al. 1999). Also, promotion-focused
subjects are more flexible to change and pay more attention to revolutionary goods thanprevention-focused subjects (Herzenstein et al. 2007). In fact, Herzenstein et al. (2007)
showed that promotion-focused individuals have higher purchase intentions of new
brands. Therefore, promotion-focused decision-makers tend to readily purchase new
products, and prevention-focused decision-makers are reluctant to try them.
Unplanned purchases and new product buying are strongly related as thepurchase of a new product is always unplanned for a customer when it is at first sight.
Impulsiveness is the characteristic of human beings (Ozen and Engizek, 2014), and it
differs from person to person (Dittmar and Drury 2000). Regulatory focus theory
implies impulsive buying and new product adoption are closely related. Promotion-

focused individuals tend to join impulse oriented purchase environment, whereas
prevention-focused individuals prefer functional-based activities (Das 2006).
Prevention-focused individuals plan more and carefully consider the product before
buying them (Semin et al. 2005) whereas promotion-focused subjects have more
impulsive personalities (Friedman and Forster 2002). Thus, promotion-focused
individuals tend to engage in unplanned purchases, whereas prevention-focused
individuals tend to avoid impulse buying.
2.3 Unplanned / planned trial purchase and repeat purchases
Unplanned purchase causes a change in emotion or mood (Gardner and Rook
1988, Ozer and Gultekin 2015), and the post-purchase emotion or the degree ofcustomer satisfaction is one of the most important factors that determine repeat purchaseintention (Anderson and Sullivan 1993; Selnes 1993; Hartline and Jones 1996). Hence,
we make predictions based on the emotional aspect of unplanned purchases.
Shoppers make impulse purchases to keep or improve mood (Rook 1987), andtheir behavior is reflected by the changes in moods and self-feelings (Faber andChristensen 1996). It is also indicated that consumers buy impulsively not only for the
economic benefit but also for entertainment or fantasy (Hausman 2000). The trait of
impulsiveness is strong for certain customers because planned purchases cannot provide
the same excitement (Lee and Yi 2008). As a result, some consumers feel better after
engaging in unplanned buying (Rook 1987). In fact, Gardner and Rook (1988) showed
impulse-buying shoppers feel satisfied after shopping.
At the same time, several existing studies showed that unplanned buying isrelated to the feeling of regret and dissonance after purchasing (Hoch and Loewentein
1991; Wood 1998; George and Yaoyuneyong 2010). Consumers regret impulsive
purchases made without adequate forethought (Hoch and Loewenstein 1991). George
and Yaoyuneyong (2010) stated that unplanned purchase is strongly related to lessinformed purchases. Without sufficient information, this may result in the wrong
product choice, which causes cognitive dissonance (Imam 2013). Lim et al. (2017) also
showed that impulsive buying causes post-purchase regret.
Thus, there exist two different results which are obtained from post-purchasefeelings of unplanned buying. The degree of satisfaction or regret that stems from
unplanned buying is strongly associated with the quantity of repurchasing as repeatpurchase intention can be a consequence of customer satisfaction and it may be thepredictor of subsequent purchasing (Das 2014). Lim et al. (2017) showed that postpurchase emotions are a good predictor of repurchase intention.
Based on these findings, we construct a hypothesis. A promotion-focused
individual focuses on more favorable aspects and tends to obtain more positive andhappier emotions (Lanaj et al. 2012; Andrews et al. 2014). Moreover, Lin et al. (2018)
showed that promotion-focus positively affects customer satisfaction. As it evokes asensitivity to the presence and absence of positive outcomes, they tend to feel the
positive aspects of new product unplanned purchases, such as novelty, entertainment,
and fantasy. Moreover, promotion-focused individuals are more likely to repurchaseproducts after enjoying positive emotions (Luoro et al. 2005). Therefore, promotionfocused individuals incur after-purchase satisfaction, leading to higher repurchaseintentions. In contrast, as prevention-focus individuals are more sensitive to unfavorable
aspects, they accept the negative side of impulsive purchase like regret or dissonance,
resulting in a decrease in repurchase intention.
As noted, the unplanned purchasing of new products would be more prominent
to promotion-focused individuals than prevention-focused individuals. New productunplanned buying is expected to increase the number of repeat purchases.
H1: Unplanned purchasing of new products positively affects repeat purchases.
2.4 Unplanned new product purchases and category-level loyalty
Brand loyalty is a key driver for repurchasing a product (Dick and Basu 1994;
Jacoby and Chestnut 1978). Although our study focuses on new product and as brandloyalty cannot be measured before purchase, the loyalty is also formed at the productcategory level (Olsen 2002, 2007) because consumers usually classify products into a
category and make a purchase decision based on a hierarchical process (Johnson 1989).
Moreover, consumer purchase planning often occurs at the category level, rather than at
the brand level or stock-keeping unit (SKU) level (Block and Morwitz 1999).

Lin et al. (2018) showed that loyalty is positively associated with consumer
satisfaction and that promotion-focus orientation is positively related to satisfaction as
they are more sensitive to the positive aspect of their jobs (Lanaj et al. 2012; Andrews etal. 2014). Therefore, higher loyalty to the category level is associated with a promotionfocus, and they tend to repurchase the product after engaging in new product unplannedpurchases. Thus, we posit the following hypothesis:
H2: Those who have higher category loyalty tend to repurchase the new
product more after they engage in new product unplanned purchases.

3 Data and the model
3.1 Data
A unique point of our study is the use of scanner panel data combined with ashort questionnaire on unplanned purchases. We can combine these two datasets basedon the customer's unique ID code.
The scanner-panel data we used are obtained from MHS (MACROMILLHousehold Spending Survey) database. MHS is a consumer panel providing informationon their every purchase. MHS includes about 20,000 active monitors and is designed torepresent the demography. Consumer panelists scan the receipt or the products using asmartphone; then, the product name, the price, and the quantity are recorded based on
the image recognition system. Therefore, all the purchases including supermarkets,convenience stores, drug stores, online shopping, vending machines, and so on, arerecorded, and this system enables us to identify the first (trial) purchase and thesubsequent repeat purchases.
We focus our analysis on 8 beverage brands with one new product that has
been placed on the market. The new product is a carbonated beverage and also
categorized as “food for specified health uses” (FOSHU). FOSHU refers to foods
containing ingredients with better health functions and officially approved to claim
positive physiological effects on the human body, as defined by the Ministry of Health,Labor, and Welfare in Japan (MHLW). The other 7 beverage products consist of 3
carbonated beverages with FOSHU, 2 carbonated beverages without FOSHU, and 3
green tea beverages with FOSHU. As FOSHU beverages are most prevalent in the green
tea category in Japan, 3 green tea brands with FOSHU are included in the analysis. The
7 brands are best-selling products within each category. If consumers of MHS panelists
purchased some of the targeted 8 beverages at any channels, they were soon asked tocomplete a questionnaire on whether the purchase was planned or unplanned as part ofthe instant research (IR) method through their smartphones.
3.2 Model
We model the number of repeat purchases of a new brand considering the effectof trial planned or unplanned purchase using the following zero-inflated Poisson
formulation.

 i + (1 −  i ) exp(−i )Pr( REPi = j ) = 
i exp(−i )(1 −  i ) REP !
i( j = 0)( j  0),where

i = 0 + 1UP + 2 LOY _ Ci + 3 LOY _ SimCi + 4 LOY _ Bi
+UPi  (5 LOY _ Ci + 6 LOY _ SimCi + 7 LOY _ Bi )+8 AGEi + 9 MALEi + 10 FAM _ SIZEi + 11INCi + 12 MAIN i+ λ1 AREA _ FEi + λ 2 JOB _ FEi + λ 3CHANNEL _ FEit

and
  
log  i  =  0 +  1UPi +  2 LOY _ Ci +  3 LOY _ SimCi +  4 LOY _ Bi .1
 1− i 

The definition of variables is provided in Table 1. As some customers no longer
buy new products after the trial and the outcome can be zero, we employed zero-inflated
specifications. Although we also tried zero-inflated negative binomial regression, the1

For the problem of identifiability, we do not include the cross-term effect and customer demographics.

zero-inflated Poisson regression model had better predictive power. In the regression
model predicting repurchase quantity to be zero, we assume logistic regression where
variables on unplanned purchase and loyalties are included as predictors.
Table 1 Definition of variables

Variable nameDefinition

REP i

The number of repeat purchases of new brand by shopper i.

UP i

Equals to 1 if the purchase is unplanned, and 0 otherwise

LOY_C i
LOY_SimC i
LOY_B iCalcualted loyalty to the category to which the new productbelongs (carbonated beverages with FOSHU) of shopper i.
Calcualted loyalty to the similar category to which the new
product belongs (green tea beverages with FOSHU) of shopperi.
Calcualted loyalty to the products of new brand manufacturer of
shopper i.

AGE i

Age of the shopper i (year).

MALE iEquals to 1 if the shopper i is male, and 0 otherwise.

FAM_SIZE i

Family size of the shopper i belongs to.

INC i

Natural logarithm of household income the shopper i belongs to.

MAIN iEquals to 1 if the shopper i is the main shopper of the family,
and 0 otherwise

AREA_Dummy i

Residence area dummies of the shopper i (10 areas).

JOB_Dummy i

Job type dummies of the shopper i (13 job types).

In addition to a variable indicating unplanned purchase of new product UP, we
included variables on consumer demography, such as age, gender, family size, income,dummy indicating if they are the main shopper in their family, residence area fixed effect,and job fixed effect.
Customer brand level category LOY _ Ci is measured as follows:

LOY _ Ci =number of purchases of 3 carbonated beverages with FOSHU by inumber of purchases of all tea and carbonated beverages category by iThis proxy is calculated based on Bucklin and Lattin (1991). Oliver (1997) and
Olsen (2007) also defined loyalty as repeated consumption over time by a unit of a given
product category. Therefore, our loyalty metric based on Bucklin and Lattin (1991) isconsistent with the other studies.
This study also considered two other types of loyalty calculated from consumers'purchase history, the loyalty to the similar category (green tea beverages with FOSHU):
LOY _ SimCi , and the loyalty to the products of new brand manufacturer: LOY _ Bi .

They are defined as follows:

LOY _ SimCi =LOY _ Ci =number of purchases of 3 green tea beverages with FOSHU by inumber of purchases of all tea and carbonated beverages category by inumber of purchases of 11 new brand manufacturer products by inumber of purchases of all tea and carbonated beverages category by i

Note that the denominators of three loyalty variables are calculated for all the tea
category products and carbonated beverages category products, which are not restricted
to the above 8 brands we have used for the analysis.

4 Results
4.1 Descriptive statistics
Table 2 shows the descriptive statistics of variables. The mean of UP variable
is 0.78, indicating 78 % of the first buying of new products is unplanned purchases.
Note that the rest 22 % is planned purchases as the manufacturer had given a large
amount of publicity on the new brand before launching.
Table 2 Descriptive statistics of variables
Variable
UP
LOY_C
LOY_SimC
LOY_B
AGE
MALE
FAM_SIZE
INC
MAIN

N
219
219
219
219
219
219
219
219
219

Mean
0.780
0.052
0.030
0.161
42.815
0.483
2.498
6.234
0.741

(S.D.)(0.415)(0.152)(0.084)(0.192)(8.868)(0.501)(1.178)(0.562)(0.439)

p5
0
0
0
0
30
0
1
4.605
0

p95
1
0.333
0.192
0.520
57
1
5
7.003
1

4.2 Main results
Table 3 shows the results from the zero-inflated Poisson regression model,
where MODEL 1 considered the case cross term of UP and loyalty variables are notincluded. The results from MODEL 1 indicate that if the first purchase of a new productis unplanned, succeeding repeat buying tends to increase. The expected quantity of
repurchase of a new product for a group with unplanned purchase is exp(0.526) = 1.69
times the expected quantity of repurchase for a group with planned buying intention
while holding all other variables in the model constant. The result supports hypothesis
1.
If we consider the cross term of UP and loyalty variable (MODEL 2), we findinteresting results. The effect of UP becomes moderate compared with MODEL 1(0.526 vs 0.313), the cross term plays a more influential role in the regression model.
The estimated coefficients of the cross term between LOY _ C and UP imply thatwhen the shopper has higher loyalty to the category, the unplanned first purchase of anew product considerably increases the succeeding repeat purchases. Hence, hypothesis
2 is also supported. The result LOY _ SimC  UP indicates that a similar category also
forms the category loyalty and becomes a trigger to increase repurchases after new
product unplanned buying.
However, the customer who has higher loyalty to the new productmanufacturer tends to increase the quantity of repeat buying if the first trial is plannedpurchase. This might be because those with a higher loyalty to new productmanufacturers have already known about the new product launch before market release.
Such highly loyal customers engage in new product planned purchase and they continue
to repurchase after trial.
The rest of the results obtained from other variables are as follows. If thecustomer has higher loyalty to the same or similar category of new products, therepurchase quantity would increase. The quantity of repeat purchase of new products
would increase by 1-exp(0.493) = 64% and 1-exp(0.148) = 52% when the shopper ismale and the customer is the main shopper of the family, respectively. The number of
repeat purchases of a new product would increase by 1-exp(0.142)=15% when family
size increases by 1.

The logistic regression predicting repurchase quantity to be zero shows similar
results to the Poisson regression model predicting repurchase quantity. The probabilityof a customer engaging in the repurchase of a new product is higher for the unplanned
trial purchase case. It is also observed the shoppers, who have a higher loyalty to similar
products from the new brand and the product of new brand manufacturers, tend to
repeatedly purchase new products.
Table 3 Regression results
MODEL 1
Independent Variable
Estimate(S.E.)
t-Value
Poisson regression predicting repurchase quantity
Intercept-0.107(1.443)-0.074UP
0.526(0.193)2.726 ***
LOY_C
0.123
(0.033)3.726 ***
LOY_SimC
0.242(0.092)2.642 **
LOY_B
-0.146(0.411)-0.355
LOY_C×UP
LOY_SimC×UP
LOY_B×UP
AGE-0.015(0.008)-1.762 *MALE
0.396(0.249)
1.589
FAM_SIZE-0.073(0.079)-0.926INC-0.007(0.152)-0.045MAIN
0.420(0.215)
1.951 *
Included
AREA_FE
Included
JOB_FE
Logistic regression predicting repurchase quantity to be zeroIntercept-0.862(0.538)-1.602UP-0.763(0.444)-1.720 *LOY_C-0.353(1.041)-0.339LOY_SimC-0.322(0.184)-1.753 *LOY_B-0.224(0.125)-1.802 *953.281
BIC
219
N

Estimate

MODEL 2
(S.E.)
t-Value

0.745
0.313
0.391
0.654
-1.0510.578
0.359-0.855-0.0130.493
0.142-0.0170.418(1.504)(0.167)(0.143)(0.527)(0.716)(0.147)(0.230)(0.393)(0.009)(0.248)(0.082)(0.158)(0.221)
Included
Included

0.495
1.873
2.725
1.241
-1.4683.923
1.564-2.176-1.4771.987
1.728-0.1061.892-0.811-0.691-0.463-0.341-0.207(0.484)(0.390)(1.072)(0.193)(0.124)929.340219-1.675-1.774 *-0.432-1.766 *-1.675

*
***

***
**
**
*
*

5 Concluding remarks
This study investigated the effect of an unplanned purchase of a new producton its succeeding repeat purchase quantity. Following regulatory focus theory, the result
using IR showed that if the trial purchase is unplanned, the probability and quantity of
new product repurchase tend to increase when compared to planned buying. We also

found that the impact of unplanned trial purchases is prominent when customers have ahigher loyalty to the new product category. Ours is the first study to examine theunplanned buying of new products using real purchase history data.
5.1 Implications
This study has several managerial implications. First, the hypothesis revealed
that the unplanned purchase of the trial increases repeat purchase. Therefore, shopper
marketing strategies like in-store displays, which raises awareness of new products andprovides in-store stimuli to customers are extremely helpful to encourage them topurchase again. Contemporary retail stores or shopping malls build recommendersystems, and many companies employ mobile ads and coupons, which can be linked tocustomer location information. This suggests that such in-store promotions causing
unplanned new product buying are also useful in recouping the investments made in
developing a new brand.
Second, the results also imply that benefits obtained from inducing unplannedpurchases of new products would be larger for shoppers who have a higher loyalty to
the same or similar category. The loyalty indices used in this study can easily becalculated from customers’ purchase history data for retailers who adopt loyalty
programs such as frequent shopper programs. As retailers incur the cost of stocking andmanaging new product introduction, they can recover this cost by stimulating customers
with a higher loyalty on the same or similar category, facilitating unplanned purchases
and succeeding repeat purchases. In contrast, for shoppers who have more loyalty to thenew brand manufacturer, retailers and manufacturers should encourage plannedpurchases: manufacturers should let their loyal customers recognize the new productbefore launching it, let them experience a desire to purchase it, and let them purchase it
in a planned manner using advertisements or online communications.
Third, the results of the hypothesis built upon regulatory focus theory provide
guidelines for managers. As promotion-focused customers react more to positiveoutcome messages, conducting such in-store promotions would persuade customers andinduce unplanned buying. Aaker and Lee (2001) conducted experiments where theycreated a persuasive message for promoting a grape juice product. For promotion-

focused subjects, the message highlighted positive outcomes such as “energy creating”
or “rich in vitamin C and iron” (while “disease-fighting” or “rich in antioxidants” forprevention-focused subjects). They found that such appealing depending on selfregulatory orientation are more persuasive. Hence, the in-store advertising or message
appealing benefit obtained from new products would be useful to trigger unplannedpurchases for promotion-focused shoppers, leading to long-lasting repeat purchases.
5.2 Limitations
We note a potential limitation of our study. Our focus was on a specificbeverage category and one new product, and thus, we did not consider the heterogeneity
caused by product category differences. As we handled newly introduced products, weconsidered it mandatory to prepare the research design with a questionnaire about theunplanned purchase before the launch of the new product. However, products where
there was a significant time gap between the new release announcement and brought inthe market are extremely limited. As such, our study focused on new products that had asufficient announcements and promotions before they were introduced in the market.
Although our hypotheses and prediction might not depend heavily on the category
difference, any future research must consider a wider variety of products.

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