The effect of equivalent monetaryiIncentives on customer channel switching decisions : the impact of framing and other factors

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The Effect of Equivalent Monetary

Incentives on Customer Channel

Switching Decisions: The Impact of

Framing and Other Factors

Supervisor : Dr. Umut Konus

Student: Vlad Goanta-Barbulescu

Student number:

10599363

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1. Introduction

Customers are increasingly using multiple channels, as they have become familiar

with using various interface technologies. In the last years we have witnessed the rise of the

web-based shopping platform, while more recently we observed the birth of the mobile-based

channel. Given these new developments, retailers hope to maximize their profits by pursuing

the optimal multichannel retailing strategy. However little is known about how firms should

manage the customer’s use of the different channels. It is often the case that the customer’s preferred purchasing channel does not coincide with what the retailer sees as the optimal

shopping channel. To overcome this undesired situation, the firm can offer monetary

incentives to itscustomers to “smoothen” their migration to the channel preferred by the firm.

We explore which are the most common monetary incentives used in literature and develop

hypotheses with respect to the effectiveness of the incentives in convincing customers to

migrate to the desired channel.

The aim of the current study is to identify if companies can convince customers to

switch shopping channels and what is the optimal framing of the monetary incentives offered

to customers, in the context of right-channeling them. In an experiment, we present

participants with four different framings of the monetary incentives: absolute-price-reduction,

relative-price-reduction, gift and price-reduction-for-future-purchases, and then measure the

participants’ attitudes towards the retailer and the intention to switch channels, in the context of purchasing a laptop (setting 1) and a touristic package (setting 2). We argue that the

framing of the incentive offered to customers to switch shopping channels influences the

attitude towards the retailer and the customers’ purchase intention. More specifically, we hypothesize that an absolute-price-reduction will lead to better outcomes in comparison to a

relative-price-reduction. Furthermore, we suggest that a gift offered as incentive to customers

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price-reductions, in terms of customer response. Finally, a

price-reduction-for-future-purchases would have the worst outcomes of all the four framings used in our research design.

The relevance and timeliness of our study stem from the important increase in the

number of channels that a company can use to sell its products and services. However, the

profitability often varies considerably across the different channels. After identifying the most

profitable distribution channel, firms face the daunting process of convincing their customers

to migrate to the channel preferred by the firm. Our research will contribute to the

multichannel marketing literature by investigating if it is possible to persuade customers to

change their shopping channel, and by identifying whether there are differences in the

attitudes and intentions of customers, as a result of different framing of the incentives used by

firms in order to optimize their customers’ channel migration. The findings of our research support marketing managers by providing them with a set of guidelines on how to persuade

customers to buy from the desired channel (what is the optimal framing), in what context this

is possible (boundary conditions), and what are the expected success rates, with implications

on the resource and communication management of firms.

In the following sections of the paper we firstly present several examples of academic

papers focusing on channel migration and comparisons between different channels in terms of

customer-related consequences. Secondly, we look at incentives and review the academic

literature which investigates the use of different framings for monetary incentives on which

our study is focused. Thirdly, we identify gaps in the literature, we formulate our research

question and then develop the hypotheses. In the subsequent section we outline the

methodology used in our study. Finally, we wrap up by giving a preview of the potential

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2. Literature review

2.1. Channel choice. Channel migration

Multichannel retailing is “the set of activities involved in selling merchandise or

services to consumers through more than one channel” (Levy & Weitz, 2009). Over the past decade, multichannel retailing environments have grown in variety, scope, and sophistication

(Dholakia et al., 2010). Therefore there is an increasing need to optimize the multichannel

environment, through multichannel customer management, which can be defined as “the design, deployment, and evaluation of channels to enhance customer value through effective

customer acquisition, retention, and development” (Neslin et al., 2006).

Customers frequently use different channels at different stages of their

decision-making and purchase processes. To give an example, it is not unusual that customers look for

deals on the internet, urgent purchases in stores, place customized and complex orders by

telephones, and offer gifts through catalogs. Previous research (Balasubramanian et al., 2005;

Dholakia et al., 2010) shows that customers use channels to satisfy the following goals:

economic goals (obtaining a good deal), self-affirmation goals (demonstrating expertise in

channel selection and use), symbolic meaning goals (being thoughtful and thorough during

the shopping process), socialization and experiential goals (being part of social world and a

stimulating environment), and routine or script maintenance goals (achieving regularity and

familiarity in the shopping process). Channel choice can also be driven by customers' price

expectations, the category of product being purchased, perceptions of switching costs,

efficiency concerns, risk aversion, and demographic characteristics (Dholakia et al., 2010;

Inman, Shankar, & Ferraro, 2004). In their study of Dutch consumers, Konuş, Verhoef, and

Neslin (2008) identify three separate multichannel segments. The first segment is labeled as

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channels covered in the study (i.e. stores, the internet, and catalogs), high innovativeness and

see shopping as a pleasant experience. The members of the second segment, the

“store-focused consumers”, are inclined towards brick and mortar stores and have high levels of

brand and channel loyalty. Finally, the third segment, the “uninvolved shoppers” includes

shoppers that have little interest in any of the channels and their shopping involvement is low.

Channel choice is not static since it changes over time, as customers migrate from one

channel to another. Next, we present several examples of academic papers which focus on

channel migration and comparisons between different channels in terms of customer-related

consequences. Ansari, Mela, and Neslin (2008) analyze the migration of customers across

various channels and find that a significant segment migrated from traditional channels to the

Internet and subsequently purchased less when compared to other segments. They explained

this finding by suggesting that migration to the internet lowered customers' switching costs,

and the attendant lack of personal contact reduced customers' loyalty to the retailer. In their

2004 study, Gupta, Su, and Walter reveal that 52% of multichannel shopper migrated from

offline to online channels across different product categories. Their channel migration

behavior was predicted by channel risk perceptions, price search intentions, evaluation effort,

and waiting time, but unrelated to customer demographics.

Channel migration can also be driven by the vendor firm. Sullivan and Thomas's

(2004) study of customer channel migration across stores, catalog, and Internet shopping,

show that analyses can be conducted to enhance the targeting and management of customers

in a multichannel context with the goal of forecasting shifting channel choices over time.

Building on their study, we aim to identify how companies can convince customers to switch

shopping channels by offering them monetary incentives, in the following section of our

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we review the literature which compares the effectiveness of the different framings for

monetary incentives.

2.2. Incentives in customer management

An incentive is something that motivates an individual to perform an action. Oliver

(1984) makes a distinction between positive and negative incentives. In inducing collective

action as a group, positive incentives are defined as “if everyone cooperates, everyone should be rewarded”, whereas negative incentives are the opposite “if everyone cooperates, no-one gets punished, but if everyone defects, everyone will be punished”. In the context of dilemma games, Oliver (1980) shows that punishments, not rewards, are predicted to be effective for

enforcing cooperation, however, many players experienced harmful effects of punishment by

increasing the risk of retaliatory spirals. Moreover Oliver (1984), reviewing the literature,

arrives to the same conclusion: punishments are more effective than rewards for producing

cooperation by a group of subjects. However, as inducements for individual compliance, there

is no difference between rewards and punishments.

Andreoni, Harbaugh, and Vesterlund (2003) suggest that less cooperation is expected

in societies where positive behavior is rewarded than in those where negative behavior is

punished, however, for maximum efficiency, reward and punishment should be used

simultaneously as incentives. The same authors, support their statement by giving real live

examples: “some universities now use a combination of raises and differential teaching loads to encourage good performance. Similarly, procurement and production contracts and

government regulations in areas ranging from meat inspection to sulphur dioxide pollution

often include both bonuses for good performance and various sorts of clawbacks for bad

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According to Dollman (1996) incentives can be monetary rewards – usually money,

solidary rewards (e.g. socializing, camaraderie), status rewards (e.g. prestige, recognition) and

purposive rewards (e.g. a sense of group mission). In the context of choice experiments,

Beattie and Loomes (1997) posit that monetary incentives are very powerful so that, instead

of using the simpler approach of dealing with each decision in turn, subjects undertake the

demanding task of processing large sets of problems simultaneously.

Customer management literature has focused mainly on monetary incentives. We refer

to monetary incentives when there is an agent (i.e. the customer) that expect some form of

material reward – especially money – in exchange for acting in a particular way (i.e. migrate

from one channel to another). In our study we use equivalent monetary incentives with

different framings: absolute-price-reduction, relative-price-reduction, gift and

price-reduction-for-future purchases.

2.3. Monetary Incentives: The Effect of Framing

The fact that cognitive judgments are influenced by the way in which decision

problems are framed is well established (Kahneman & Tversky, 1984). As Sinha and Smith

(2000) point out, customers often exhibit economically non-rational behaviors as a result of

contextual cues, including semantic cues, they derive from price offers. In other words, the

way in which price offers are framed influences the customer’s response to them.

Framing is "the way the story is written or produced, including the orienting headlines,

the specific words choices and the rhetorical devices employed” (Druckman, 2001). Framings

often have an important role in shaping the decision-making process. The equivalency

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employment vs. 5% unemployment or, 97% fat-free vs. 3% fat) cause individuals to change

their preferences.

Framing may affect customers' estimates of the received value and, hence, choice also

in the context of assessing a monetary incentive (i.e. price promotion) (DelVecchio, Krishnan,

& Smith, 2007). The framing of price promotion may affect whether customers calculate the

revised price. It appears that some customers, but not all, calculate the revised price stemming

from a price reduction. Framing is likely to influence the chance of a discount being

transformed into a revised price rather than being perceived in general terms or ignored

altogether. When customers are exposed to a discount, the likelihood that they will compute

the new price is expected to be a function of the ease of calculating that price. Calculating the

new price resulting from absolute-price-reduction requires a customer to read the regular

price, to read the discount, and then to subtract the discount from the regular price.

Subtraction is a relatively easy task that results in a high level of accuracy compared to

calculating percentages. Given the computational ease, customers are likely to calculate the

price associated with an absolute-price-reduction and should be accurate in their calculations.

In contrast to an absolute-price-reduction, a relative-price-reduction requires an additional

processing step; the percentage must be multiplied by the base price to find the value of the

discount. Beyond requiring an additional step, the multiplication process required is relatively

difficult, which makes relative-price-reductions harder to calculate than

absolute-price-reductions. Such difficulty should make customers less likely to compute the revised price

and, even if they perform the calculation, they may be uncertain of the resulting price because

of the higher difficulty. Hence, when discounts are framed in terms of

relative-price-reduction, both failing to calculate the revised price and lower confidence in the accuracy of

the calculation should result in less weight being placed on the perceived price resulting from

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Interestingly, when consumers are asked to provide their general sense (without

engaging in any calculations) of whether a promotion is large or small, they tend to perceive

relative-price-reductions as larger than equivalent absolute-price-reductions (Krishna et al.

2002). Thus, when customers do not calculate the value of a promotion,

relative-price-reductions should lead to greater choice (DelVecchio et al., 2007). However, when customers

are motivated to calculate its value, the difficulty of estimating the value of a

relative-price-reductions should result in uncertainty regarding the resulting price. Therefore, customers

should be more influenced by an absolute-price-reduction more than a relative-price-reduction

when they are motivated to calculate the discounted value.

Das (1992) found that the “mere phrasing” of a deal influences the deal’s evaluation and purchase intention. Generally, the “2 for $x” and “Buy 1, get 1 at half price” framings

produced higher deal evaluation and higher purchase intentions. These effects were

moderated by price. At a high price, the “save $n on purchase of 2” was equally effective as the two volume discounts, suggesting that customers’ reaction to semantic cues is also

dependent on the total financial implication of the deal.

In a later study, Sinha and Smith (2000) show that consumer perceptions of

transaction value varies across economically equivalent price promotions. In a laboratory

experiment involving US college students, Sinha and Smith (2000) concluded that, overall, a

relative-price-reduction (“50 per cent off”) was more attractive than a volume promotion

(“buy one get one free”), which in turn was more attractive than a mixed promotion (“buy 2, get 50 per cent off”; equivalent to “two for the price of one”).

In their study, Chen et al. (1998) hypothesized that, for high-priced items, customers

will see a price reduction framed as absolute-price-reduction as more significant than a

relative-price-reduction, and that the opposite would be true for low-priced products. Their

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higher for higher priced products, whereas, for a given absolute price discount, the relative

percentage reduction is higher for lower-priced products. Chen et al. (1998) tested this

proposition in a study that used a $1595 computer, a $7.95 box of floppy disks and a 10%

discount at each price level. The study confirmed the authors’ hypothesis. However, while the

framing of the price discounts influenced the respondents’ evaluation of these discounts it did not have a significant influence on purchase intentions. Similar results were found by Gendall

et al. (2006) who show that for high priced items, such as stereos and computers, framing a

discount as absolute-price-reduction was significantly more effective than expressing it as

relative-price-reduction.

2.4. Literature gap

Customers are increasingly shopping across multiple channels of the same retailer.

While many retailers recognize that these multichannel shoppers are the most profitable, little

is understood about how firms should optimize the customers’ use of the different channels. The presumption of a customer segmentation multichannel strategy, and even of a

cost-reduction strategy, is that certain customers should use certain channels (Neslin et al., 2006).

In the ideal situation, the firm simply provides a list of potential channels and the customer

can self-select itself into the appropriate channel. The problem is that customers may not

naturally use the channel that the retailer sees as optimal. Hence, the question that arises is

should customers be encouraged to use certain channels? Moreover, how can the firm

accomplish this? The main danger is that customers may be turned off by being forced into

using channels contrary to their preferences. In order to avoid this, the firm can provide

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By reviewing the existing literature in the field of channel migration, optimization of

the multichannel strategies and use of different incentives, we fail to identify any study that

focuses on what a company should do in order to move its customers to the channel preferred

by the company, which type of incentive should it use and which is the recommended framing

for that specific incentive. Furthermore, no previous study focuses on the optimization of

marketing communication and marketing resources by using the best incentives in firm driven

channel migration setting. In order to overcome this gap we aim we investigate how

customers respond to equivalent monetary incentives with different framings, in the context of

convincing customers to shop from the internet site instead of the classical brick-and-mortar

store.

The reason why we focus on web-based shopping as the preferred channel is that it has

been observed that the web-oriented “migration” segment has the highest sales volume

(Ansari, Mela, & Neslin, 2008). The setting that we use in our experimental design is

relevant, since numerous firms try to shift the channel use behavior of their customers by

using incentives. The results of our research have the potential to support marketing managers

by providing them with the optimal framing that they should use for the monetary incentives

in the context of persuading customers to switch shopping channels.

2.5. Hypotheses development

Framings often have an important role in shaping the decision-making process.

Framing of decision problems influence cognitive judgments (Kahneman & Tversky, 1984).

Often, the framing of different, but equivalent, words or phrases causes individuals to change

their preferences. The way the incentives based on reduced price are framed induce

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way in which price offers are framed influences the customer’s response to them. Following

this rationale we formulate the following hypotheses:

H1a: The framing of the incentive offered to customers to switch shopping channels

influences the attitude towards the retailer.

H1b: The framing of the incentive offered to customers to switch shopping channels

influences switching rate to the desired channel (web-store).

Because customers, being faced with a price reduction, calculate the revised price of

the product or service, the ease with which they do this calculation influences the estimation

of the revised price (DelVecchio et al., 2007). Calculating the revised price resulting from

absolute-price-reduction requires a customer to read the regular price, read the discount, and

then subtract the discount from the regular price. These multiple and sometimes complex

operations, increase the uncertainty felt by the customers, because they fear that their

calculation is not accurate. In contrast, given the computational ease, customers much more

easily calculate the revised price associated with an absolute-price-reduction and should be

accurate in their calculations. Hence, absolute-price-reductions should be more effective in

comparison to relative-price-reductions.

H2a: In the context of equivalent monetary incentives, an absolute-price-reduction (vs.

a relative-price-reduction) will lead to a better attitude towards the retailer.

H2b: In the context of equivalent monetary incentives, an absolute-price-reduction (vs.

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Campbell and Diamond (1990) argue that monetary promotions were more noticeable

to consumers than nonmonetary promotions. They provide an example in which they show

that a $5 discount offered by Kodak for the purchase of a new camera is more noticeable than

an offer of two free rolls of film that can even have a higher value than $5. A possible

explanation of this is that gifts offered as incentives are perceived as suboptimal by the

customer. With money they can purchase whatever they wish, but by receiving a gift of the

same value, they are bound to not having a choice. Building on this reasoning we hypothesize

that:

H3a: In the context of equivalent monetary incentives, a gift offered as incentive to

customers to switch shopping channels (vs. absolute-price-reduction and

relative-price-reduction incentives) negatively influences the attitude towards the retailer.

H3b: In the context of equivalent monetary incentives, a gift offered as incentive to

customers to switch shopping channels (vs. absolute-price-reduction and

relative-price-reduction incentives) negatively influences the switching rate to the desired channel.

Munger and Grewal (2001) state that “retailers and marketers need to be aware of the extent to which perceptions of the time and effort are involved in redeeming different types of

discounts”. Indeed, customers value their time. The time and effort involved with redeeming the price-reduction-for-future-purchases is likely to have a negative effect of customer

perceptions and reduce their purchase intentions. Research done by Folkes and Wheat (1995)

suggests that because of the temporal distance involved in getting the discounts associated

with the future purchases customers are likely to evoke price perceptions similar to regular

prices than discounts. Furthermore, consumers tend to see future outcomes less favorable then

immediate outcomes. Thus, price-reduction-for-future-purchases are likely to be viewed as

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H4a: In the context of equivalent monetary incentives, a

price-reduction-for-future-purchases (vs. other incentives) negatively influences the attitude towards the retailer.

H4b: In the context of equivalent monetary incentives, a

price-reduction-for-future-purchases (vs. other incentives) negatively influences the switching rate to the desired

channel.

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3. Methodology

3.1. Experimental design. Manipulation.

In order to test our hypotheses we performed a single factor between-subjects

experimental design in which we manipulate the monetary incentives. We chose the

experiment as our research strategy because have four different, but value-equivalent,

framings and we intend test them in a controlled environment. We want to make sure that the

variance in the dependent variables is a consequence of the experimental manipulation, and,

thus, we can draw a clear causality between the manipulated variable and the dependent

variables.

We conducted this experiment in two different settings, as dictated by the different

scenarios used. The only difference between the two settings is that in setting 1, participants

are faced with the context of buying a laptop, whereas in setting 2, participants are purchasing

a touristic package. This allows us to make infer whether products differ from services in how

customers react to the different framings of the monetary incentives. In both settings, the four

different types of monetary incentives represent the experimental conditions:

absolute-price-reduction vs. relative-price-absolute-price-reduction vs. gift vs. price-absolute-price-reduction-for-future-purchases. These

monetary incentives are equivalent in value. The context of the manipulation is the purchase

of a laptop at the store price of 500 euro in setting 1, and a touristic package with a store price

of 900 euro in setting 2. Different incentives (each is the basis of one the 4 different

experimental conditions) are offered to participants in order to persuade them into shopping

for the produc/service using the online platform. Participants in the absolute-price-reduction

condition are offered a 25 euro discount (45 euro in setting 2), participants in the

relative-price-reduction are offered a 5% discount on the store price, participants in the gift condition

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price-reduction-for-future-purchases condition are offered a discount of 25 euros at the next

order from the retailer (45 euro in setting 2).

3.2. Participants

Because of the limited resources allocated to this project and also because its main

purpose is didactic, the participants in these scenario-based experiment are students who were

asked to fill in an online questionnaire. The participants were randomly assigned in the 4

experimental conditions. The participants are Facebook users who are acquaintances of the

researcher, mostly students. In setting 1, 85 participants (38% male; Mage = 25.9, SD = 4.7;

Romanian 87%, Dutch 8%, other nationalities 5%) filled in the questionnaire. In setting 2, 94

participants (50% male; Mage = 23.7, SD = 2.9; Romanian 46%, Dutch 24%, other

nationalities 30%) filled in the questionnaire. The nature of the online questionnaire did not

allow missing data.

3.3. Procedure

Both settings of the experiment were based on online questionnaires. Upon following

the link to the experimental web site, participants were welcomed and told that the data

collected through the questionnaire will be treated anonymously and for statistical purposes

only. Subsequently, the participants were presented with a scenario. The begin by asking

participants to imagine that they are in a shopping setting in a brick-and-mortar store and that

they have found a laptop (a touristic package in setting 2) that they want to purchase. The

participants are further instructed that when they approach the selling personnel in the store,

they are told that the retailer has an offer for them, if they agree to purchase the laptop

(touristic package in setting 2) from the online shop of the same retailer. The nature of the

offer represents the manipulation. Each participant will only receive one of the four offers,

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participant has received the offer, they are asked to respond to the items measuring the

customers’ switching rate and the attitude towards the retailer. After this, the participants fill in the control variables. Finally, participants are thanked for their participation.

3.4. Dependent variables

We measured the effect of the monetary incentives (the manipulated independent

variable) on the customers’ switching rate to the desired channel and on the attitude towards the retailer (dependent variables). The switching rate was measured using two separate items:

Would you agree to purchase the laptop from the (name of retailer) online shop, after

receiving the offer? “Yes” or “No”

How likely is that you buy the laptop from the online shop? 1 Not likely at all – 7 Very much

likely

The attitude towards the retailer (referred from now on as “Attitude”) was measured using a

scale with four items adapted from Fiore, Kim, & Lee (2005):

How did this affect your attitude towards (name of retailer)? 1 very negative – 7 very positive

(name of retailer) offers good services. 1 I Strongly disagree – 7 I Strongly agree

How favorable is your overall evaluation of (name of retailer)? 1 Very bad – 7 Very good

I would recommend (name of retailer) to my friends. 1 Not likely – 7 Very likely

3.5. Control variables

The control variables measured in our experiment are impulsiveness, time pressure,

online shopping behavior and the socio-demographical variables gender, age, nationality and

years of formal education. For detailed measures, please consult the questionnaires (Appendix

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4. Results

4.1. Manipulation Check

The manipulation check was tested using the question “What kind of incentive was

offered to you in order to convince you to buy from the online shop?” with multiple choice

response. The choices represent the four different framings used for the equivalent monetary

incentives. In the first setting, only 46% of the participants mentioned in the manipulation

check variable the correct framing that they received at the beginning of the questionnaire. In

the second setting, 71% of the participants have passed the manipulation check. The subjects

that failed the manipulation check were removed from further analyses, thus the remaining

sample sizes are 39 for the first setting and 67 for the second setting; somewhat low numbers

taking into account the 4 experimental conditions. In this context, the study is slightly

underpowered, which may lead to type 2 errors.

4.2. Hypotheses testing for experimental setting 1

Table 1. Descriptive statistics and correlation matrix of the dependent variables, for experimental setting 1 Variable Descriptive statistics Correlation with 1 Correlation with 2 Correlation with 3

1. Attitude towards retailer M = 4.96, SD = 1.1 - 0.4 0.25

2. Purchase intention (Likert) M = 5.23, SD = 1.6 0.4 - 0.62

3. Purchase intention (dichotomous) Yes 87%, No 13% 0.25 0.62 -

Before we proceed to hypothesis testing, we computed the (unweighted) scale mean

for Attitude, by averaging the scores of the 4 items that constitute the scale. The reliability of

the scale is Cronbach’s Alpha = 0.864, hence we can consider the Attitude scale as reliable.

In the above table we can see the correlation coefficients between the dependent

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significant at the 1% significance level, the 0.4 correlation coefficient between attitude

towards retailer and purchase intentions measured with the Likert scale is significant at the

5% significance level, while the 0.25 correlation coefficient between attitude towards retailer

and purchase intentions measured with the dichotomous scale is not significant.

Table 2. Absolute and relative frequencies of the four experimental conditions and means of the dependent variables, per condition, for experimental setting 1

Absolute Percent Mean Attitude Purchase Intentions (Likert) Purchase Intentions - Yes (dichotomous) 1 - absolute-price-reduction 11 28.2 4.8636 5.00 82% 2 - relative-price-reduction 13 33.3 4.8462 5.31 92% 3 - gift 8 20.5 4.9688 5.50 88% 4 - price-reduction-for-future-purchases 7 17.9 5.2857 5.14 86%

In testing the hypotheses, several statistical analyses were performed. Some of our

hypotheses involve differences in means of variables such as attitude towards the retailer and

purchase intention measured with the Likert scale. These hypotheses were tested with the

t-test when only two conditions were compared and with ANOVA when more than two groups

were compared. Other hypotheses refer to the effect of the framing of incentives on purchase

intentions measured dichotomously. Such hypotheses were tested with the Chi-Squared test

and with logistic regression (which, in contrast to the Chi-Square test, allows control

variables).

4.2.1. Testing hypothesis 1a

An ANOVA (F(3,35) = 0.26, p = ns) was performed on Attitude, therefore the

hypothesis that framing of the incentive offered to customers to switch shopping channels

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An ANOVA (F(3,35) = 0.15, p = ns) was performed on purchase intention (Likert),

therefore the hypothesis that framing of the incentive offered to customers to switch shopping

channels influences purchase intentions is not supported. These results are consistent with

testing this hypothesis using the depend variable measured dichotomously, as shown by the

Chi-Square test (Chi-Square(3) = 0.6, p = ns). Next, we test the hypothesis, by performing a

logistic regression on the dependent variable purchase intention, measured dichotomously.

The independent variable is framing of monetary incentives while gender, frequency of online

purchases (number of online purchases in the last 30 days), impulsiveness and time pressure

are the control variables. Impulsiveness was measured with four items (description in

appendix 1) with a Cronbach’s Alpha of 0.82. Time pressure was measured with three items

(description in appendix 1) with a Cronbach’s Alpha of 0.89.

Table 3. Logistic regression for setting 1. Overall model fit.

Model

Model Fitting Criteria Likelihood Ratio Tests

-2 Log Likelihood Chi-Square df Sig.

Intercept Only 29.871

Final 27.686 2.185 7 .949

We use the likelihood ration test to evaluate model fit. The likelihood value can be

compared between equations (intercept only model, and final model) to assess the difference

in predictive fit from one equation to another, with statistical tests for the significance of these

differences. We follow the approach presented in Hair et al. (2010). The first step is to

calculate a null model, which acts as the baseline for making comparisons of improvement in

model fit. The null model is one without any independent variables. The logic behind this

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independent variables can he compared. The second step is to estimate the proposed model,

containing the independent variables included in the logistic regression model. Hopefully,

model fit will improve from the null model and result in a lower -2 Log Likelihood value. The

final step is to assess the statistical significance of the -2 Log Likelihood value between the

two models (null model versus proposed model). If the statistical tests support significant

differences, then we can state that the set of independent variables in the proposed model is

significant in improving model estimation fit. In the model that we tested, the model fit of the

proposed model is not significantly better than the empty model, as shown by the

Chi-Square(7) = 2.18, p = ns. This means that the predictor variables fail to explain a significant

amount of variation in the dependent variable.

Table 4. Logistic regression for setting 1. Significance of variables included in the model

Effect

-2 Log Likelihood of Reduced Model

Chi-Square df Sig. Intercept 27.686 .000 0 . Frequency of online purchases 27.687 .001 1 .970 Gender 27.743 .057 1 .811 Impulsiveness 28.149 .463 1 .496 TimePress 28.307 .621 1 .431 Condition 28.518 .832 3 .842

As we can see in the table above, the likelihood ratio tests show that all variables

included have a significant influence on the dependent variable.

4.2.3. Testing hypothesis 2a and 2b

The difference in means between the participants in the absolute-price-reduction

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Attitude (t(22) = 0.04, p = ns), nor purchase intention (Likert) (t(22) = -0.4, p = ns). We

conclude that hypotheses 2a and 2b are not supported. Hypothesis 2b was also tested using the

dependent variable in which purchase intentions were measured dichotomously, but both the

Chi-Square test and the logistic regression failed to reject the null hypothesis.

4.2.4. Testing hypotheses 3a and 3b

In order to test hypotheses 3a and 3b, we had to build the contrast between the gift condition

and the absolute-price-reduction and relative-price-reduction conditions. We did this by

recoding the variable condition so that the gift condition is coded with 1 while both the

absolute-price-reduction and relative-price-reduction conditions are coded with -0.5. After

doing this we tested the results with ANOVA. We performed two ANOVAs, on Attitude

(F(1,30) = 0.06, p = ns) and on purchase intention (Likert) (F(1,30) = 0.23, p = ns). We

conclude that neither hypothesis 3a nor hypothesis 3b is supported. Hypothesis 3b was also

tested using the dependent variable in which purchase intentions were measured

dichotomously, but both the Chi-Square test and the logistic regression failed to reject the null

hypothesis.

In order to test hypotheses 4a and 4b, we had to build the contrast between the

price-reduction-for-future-purchases condition and the absolute-price-reduction,

relative-reduction and gift conditions. We did this by recoding the variable condition so that the

price-reduction-for-future-purchases condition is coded with 1 while the absolute-price-reduction,

relative-price-reduction and gift conditions are coded with -0.33. After doing this we tested

the results with ANOVA. We performed two ANOVAs, on Attitude (F(1,37) = 0.75, p = ns)

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hypothesis 4a nor hypothesis 4b is supported. Hypothesis 4b was also tested using the

Chi-Square test and the logistic regression failed to reject the null hypothesis.

4.3. Hypotheses testing for experimental setting 2

Table 5. Absolute and relative frequencies of the four experimental conditions and means of the dependent variables, per condition, for experimental setting 2

Absolute Percent Mean Attitude Purchase Intentions (Likert) Purchase Intentions - Yes (dichotomous) 1 - absolute-price-reduction 14 20.9 5.41 6.07 86% 2 - relative-price-reduction 21 31.3 4.77 5.10 81% 3 - gift 18 26.9 4.28 4.94 72% 4 - price-reduction-for-future-purchases 14 20.9 3.43 3.29 43%

Before we proceed to hypothesis testing, we computed the (unweighted) scale mean

for Attitude, by averaging the scores of the 4 items that constitute the scale. The reliability of

the scale is Cronbach’s Alpha = 0.949, hence we can consider the Attitude scale as reliable.

Table 6. Descriptive statistics and correlation matrix of the dependent variables, for experimental setting 2 Variable Descriptive statistics Correlation with 1 Correlation with 2 Correlation with 3

1. Attitude towards retailer M = 4.5, SD = 1.5 - 0.84 0.77

2. Purchase intention (Likert) M = 4.9, SD = 2.1 0.84 - 0.83

3. Purchase intention (dichotomous) Yes 72%, No 28% 0.77 0.83 -

As we can see in the table above, the dependent variables are highly correlated, all

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An ANOVA (F(3,63) = 5.25, p < 0.01) was performed on Attitude, confirming that the

framing of the incentive offered to customers to switch shopping channels influences the

attitude towards the retailer.

4.3.2. Testing hypothesis 1b

An ANOVA (F(3,63) = 5.24, p < 0.01) was performed on purchase intention (Likert),

confirming that the framing of the incentive offered to customers to switch shopping channels

influences the purchase intention. These results are supported by testing this hypothesis using

the depend variable measured dichotomously, as shown by the Chi-Square test (Chi-Square(3)

= 7.97, p < 0.05). Next, we test the hypothesis, by performing a logistic regression on the

dependent variable purchase intention, measured dichotomously. Similar to testing this

hypothesis in the first experimental setting, the independent variable is framing of monetary

incentives while gender, frequency of online purchases, impulsiveness and time pressure are

the control variables. In this case Impulsiveness was measured with four items (description in

appendix 1) with a Cronbach’s Alpha of 0.89. Time pressure was measured with three items

(description in appendix 1) with a Cronbach’s Alpha of 0.93.

Table 7. Logistic regression for setting 2. Overall model fit.

Model

-2 Log Likelihood Chi-Square df Sig.

Intercept Only 79.905

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Following the same procedure as for the first experimental setting, we use the

likelihood ration test to evaluate model fit. In the model that we tested, the model fit of the

proposed model is significantly better than the empty model, as shown by the Chi-Square(7) =

28.3, p < 0.001. This means that the predictor variables explain a significant amount of

variation in the dependent variable.

Table 8. Logistic regression for setting 2. Significance of variables included in the model

Effect

-2 Log Likelihood of Reduced Model

Chi-Square df Sig. Intercept 51.586 .000 0 . Frequency of online purchases 53.522 1.936 1 .164 Gender 52.550 .964 1 .326 Impulsiveness 56.416 4.830 1 .028 TimePress 56.232 4.646 1 .031 Condition 59.529 7.943 3 .047

As we can see in the table above, the likelihood ratio tests show that impulsiveness,

time pressure and framing have a significant influence on channel switching, while gender

and frequency of online purchases do not significantly impact purchase intentions. Analyzing

the parameter estimates (table in appendix), we observe that impulsiveness positively

influences channel switching, while time pressure has a negative influence on channel

switching. We conclude that hypothesis 1b is supported.

4.3.3. Testing hypothesis 2a and 2b

The difference in means between the participants in the absolute-price-reduction

condition and the participants in the relative-price-reduction is not significant for neither

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conclude that hypotheses 2a and 2b are not supported. Hypothesis 2b was also tested using the

In order to test hypotheses 3a and 3b, we had to build the contrast between the gift

condition and the absolute-price-reduction and relative-price-reduction conditions, in the

same way as we did in the first experimental setting, more specifically we recoded the

variable condition so that the gift condition is coded with 1 while both the

absolute-price-reduction and relative-price-absolute-price-reduction conditions are coded with -0.5. After doing this we

tested the results with ANOVA. We performed two ANOVAs, on Attitude (F(1,51) = 4.4, p <

0.05) and on purchase intention (Likert) (F(1,51) = 1.1, p > 0.1). We conclude that hypothesis

3a is supported, while hypothesis 3b is not supported. Hypothesis 3b was also tested using the

In order to test hypotheses 4a and 4b, we had to build the contrast between the

price-reduction-for-future-purchases condition and the absolute-price-reduction,

relative-price-reduction and gift conditions, in the same way as we did in the first experimental setting,

more specifically we recoded the variable condition so that the

price-reduction-for-future-purchases condition is coded with 1 while the absolute-price-reduction,

relative-price-reduction and gift conditions are coded with -0.33. After doing this we tested the results with

ANOVA. We performed two ANOVAs, on Attitude (F(1,65) = 9.96, p < 0.01) and on

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and 4b are supported. The support for hypotheses 4b is also shown by testing this hypothesis

using the depend variable measured dichotomously, as shown by the Chi-Square test

(Chi-Square(1) = 7.22, p < 0.01). Furthermore, performing a logistic regression on the dichotomous

dependent variable, we obtain a Chi-Square(1) = 6.65, p < 0.01, consistent with hypothesis

4b.

Table 9. Summary of results

Hypothesis Experimental setting 1 Experimental setting 2

Hypothesis 1a Not supported Supported

Hypothesis 1b Not supported Supported

Hypothesis 2a Not supported Not supported

Hypothesis 2b Not supported Not supported

Hypothesis 3b Not supported Not supported

Hypothesis 4b Not supported Supported

We have also tested our hypotheses without excluding the participants who failed the

manipulation check. In both experimental settings, none of the hypothesis was supported.

5. Discussion

The aim of the current study was to identify how companies can convince customers

to switch shopping channels by offering them monetary incentives. Our study investigated

how customers respond to four equivalent monetary incentives with different framings, in the

context of right-channeling them. In an experiment, we presented participants with four

different framings of the monetary incentives: absolute-price-reduction,

relative-price-reduction, gift and price-reduction-for-future-purchases, and then measure the participants’

attitudes towards the retailer and the intention to switch channels, in the context of purchasing

a laptop (setting 1) and a touristic package (setting 2). Our results confirm that the framing of

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the retailer and switching rate to the desired channel, but this holds only in the case of the

experimental setting where a touristic package was the focal product, whereas when the focal

product was a laptop, the effects of framing were not significant. This is an indication that, at

least in the case of services being the focal product, the mere framing of the incentive to

switch shopping channel can influence the cognitive judgments and decisions made by

consumers.

For both settings (laptop and touristic package), our results failed to show the

hypothesized difference between an absolute-price-reduction and a relative-price-reduction in

terms of attitude towards the retailer and switching rate to the desired channel. We fail to

confirm the findings of previous studies (DelVecchio et al., 2007; Krishna et al. 2002). Under

the assumption that customers, being faced with a price reduction, calculate the revised price

of the product or service, calculating the revised price resulting from an

absolute-price-reduction is easier than calculating the revised price resulting from a relative-price-absolute-price-reduction.

A possible explanation is that, given the fact that both focal products in our experiment were

relatively expensive, the participants naturally engaged into calculating the revised price in

both the absolute-price-reduction and the relative-price-reduction conditions. The “round”

prices that the focal products had in our experiment made it easier for participants to calculate

the revised price; hence the difference between the two conditions was reduced.

When we used a service as the focal product, a gift offered as incentive to customers to

switch shopping channels would lower the attitude towards the retailer in comparison with an

absolute-price-reduction and a relative-price-reduction, but would not significantly reduce the

switching rate to the desired channel. A possible explanation of this effect is that gifts offered

as incentives are perceived as suboptimal by the customer. With money they can purchase

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choice, since the gift, although it has the same monetary value, has already been chosen by the

retailer, or the options to choose from are limited.

Furthermore, our results confirmed the hypothesized negative influence of the

price-reduction-for-future-purchases framing. This framing negatively influences the attitude

towards the retailer and the switching rate to the desired channel, in comparison with the other

framings used in our study, but again this effect holds only for services (i.e. the touristic

package experimental setting) and not for products (i.e. the laptop experimental setting). This

effect indicates that the time and effort involved with redeeming the

price-reduction-for-future-purchases is likely to have a negative effect of customer perceptions and reduce their

purchase intentions. As Folkes and Wheat (1995) suggest the temporal distance involved in

getting the discounts associated with the future purchases stimulate the customers to see the

present monetary value of such an incentive as lower, hence

price-reduction-for-future-purchases are likely to be viewed as less attractive than an immediately received incentives.

6. Managerial contributions

Our study contributes to the multichannel marketing literature by showing that it is

possible to persuade customers to change their shopping channel and by identifying what

companies can do in order to facilitate the customers’ migration to the channel preferred by

the company. Besides the scientific contributions, our research also has multiple managerial

contributions, as it provides a set of guidelines on how to persuade customers to buy from the

desired channel, in what context this is possible, and what are the expected success rates.

Firstly, the results of our research have implications for the resource management of the firm.

We show that a price reduction of 5% produces a 87% change in shopping channel in the case

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communication managers. We identify four different framings of equivalent monetary

incentives, and we compare their effects on attitude towards the retailer and switching rate to

the desired shopping channel. The absolute-price-reduction framing seems to have the best

results. Thus, our research sheds light on the process of optimizing framing of the monetary

incentives offered to customers for switching to the shopping channel preferred by the firm.

The challenge for managers of multichannel retailers will be firstly to identify which shopping

channel is the most profitable for them. Once that is sorted out, our research provides these

managers with an indication of an expected success rate of convincing their customers of

switching to the preferred shopping channel and also a recommendation regarding what

framing they should use for the monetary incentives that they offer for right-channeling their

customers. Finally, our research offers determines the boundary conditions of the effect of

framing. Differences in the effect of framing the monetary incentives were found only in the

case when a service was used as the focal product. This suggests that in the case of goods, the

framing of the monetary incentives offered to convince the customer to switch shopping

channels is less important.

7. Limitations and Further Research

Several limitations influence the accuracy and validity of our study. Firstly, the

non-probabilistic nature of our study could have led to self-selection bias. The subjects of our

experiment were found on Facebook and no incentive was offered to them to participate or to

give reliable answers. A probabilistic sample and a more limited self-selection would have

been better. Secondly, quite many of the participants failed the manipulation check (54% of

the participants in the first experimental setting and 29% in the second setting). This is an

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manipulations were based, or they did not understand the texts that they had to read. The fact

that the experiment was in English could have caused problems, because for the vast majority

of the participants English is not their native language. Thirdly, the sample sizes which

entered the statistical analyses (39 for the first setting and 67 for the second setting) are very

low given the fact that there were 4 experimental conditions. In this context, the study is

slightly underpowered, which may lead to type 2 errors.

Future research should address the limitations of our study. Furthermore, it would be

worthwhile to investigate the channel switching rate of other type of focal products, for

example products with a lower price. In addition, we only explored the context of online

shopping, but for many retailers, it could be the case that another shopping channel is deemed

as optimal. Finally, a cross-cultural study would shed light on the cultural aspects of the

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APPENDIX 1 Questionnaire Setting 1

(Introduction)

My name is Vlad Goanta, I am a student at University of Amsterdam. The data collected through the present questionnaire will be treated anonymously and for statistical purposes only. I personally appreciate your participation in this study, as it is part of my master thesis.

(Manipulation)

Imagine the following situation:

You need a new laptop. You visit the electronics store Galaxy Market, located in your neighborhood, and find a laptop that is suitable for your needs, at the price of 500 euro. Then, you approach the cash desk with the intention to purchase the new laptop. At the cash desk you are told that if you buy the same product from the online shop of Galaxy Market, you will receive 25 euro discount (a 5% discount/a gift which has a value of 25 euro/a 25 euro voucher that can be used for future purchases from Galaxy Market).

(Attitude)

Please answer the following questions:

How did this affect your attitude towards Galaxy Market? 1 Very negative – 7 Very positive Galaxy Market offers good services. 1 I Strongly disagree – 7 I Strongly agree

How favorable is your overall evaluation of Galaxy Market? 1 Very bad – 7 Very good I would recommend Galaxy Market to my friends. 1 Not likely – 7 Very likely (Purchase intention)

Would you agree to purchase the laptop from the Galaxy Market online shop, after receiving the offer? “Yes” or “No”

How likely is that you buy the laptop from the physical store? 1 Not likely at all – 7 Very much likely How likely is that you buy the laptop from the online shop? 1 Not likely at all – 7 Very much likely

(Impulsiveness)

I am often impulsive in my buying behavior. 1 I Strongly disagree – 7 I Strongly agree I sometimes feel that something inside pushed me to go shopping. 1 I Strongly disagree – 7 I Strongly agree

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There are times when I have a strong urge to buy. 1 I Strongly disagree – 7 I Strongly agree I am one of those people who often respond to discounts. 1 I Strongly disagree – 7 I Strongly agree (Time pressure)

Usually I am busy. 1 I Strongly disagree – 7 I Strongly agree I have too many things to do and too little time. 1 I Strongly disagree – 7 I Strongly agree Most of the time I have to hurry. 1 I Strongly disagree – 7 I Strongly agree (Online behavior)

For how many hours do you use the internet per week? Did you ever shop online? Yes/No

How many times in the last 30 days did you shop online? (Manipulation check)

What kind of incentive was offered to you in order to convince you to buy from the online shop? a 25 euro discount/a 5% discount/a gift which has a value of 25 euro/a 25 euro voucher that can be used for future purchases from Galaxy Market

(Demographic variables)

What is your nationality? (String) What is your gender? M/F What is your age?

How many years of formal education do you have (starting and including elementary school)?

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Questionnaire setting 2

(Introduction)

My name is Vlad Goanta, I am a student at University of Amsterdam. The data collected through the present questionnaire will be treated anonymously and for statistical purposes only. I personally appreciate your participation in this study, as it is part of my master thesis.

(Manipulation)

Imagine the following situation:

You want to go on a vacation in Kenya and you are looking for several tour offers. You visit the travel agency Worldwide Experience, located in your neighborhood, and you find a tour that you like, at the price of 900 euro. Then, you approach the cash desk with the intention to purchase the touristic package. At the cash desk you are told that if you buy the same touristic package product from the online shop of Worldwide Experience, you will receive 45 euro discount (a 5% discount/a gift which has a value of 45 euro/a 45 euro voucher that can be used for future purchases from Worldwide Experience).

(Attitude)

Please answer the following questions:

How did this affect your attitude towards Galaxy Market? 1 Very negative – 7 Very positive Worldwide Experience offers good services. 1 I Strongly disagree – 7 I Strongly agree

How favorable is your overall evaluation of Worldwide Experience? 1 Very bad – 7 Very good I would recommend Worldwide Experience to my friends. 1 Not likely – 7 Very likely (Purchase intention)

Would you agree to purchase the laptop from the Worldwide Experience online shop, after receiving the offer? “Yes” or “No”

How likely is that you buy the laptop from the physical store? 1 Not likely at all – 7 Very much likely How likely is that you buy the laptop from the online shop? 1 Not likely at all – 7 Very much likely

(Impulsiveness)

I am often impulsive in my buying behavior. 1 I Strongly disagree – 7 I Strongly agree I sometimes feel that something inside pushed me to go shopping. 1 I Strongly disagree – 7 I Strongly agree

The effect of equivalent monetaryiIncentives on customer channel switching decisions : the impact of framing and other factors