ArticlePDF Available

How Framing Effect Impact on Decision Making on Internet Shopping

Open Journal of Business and Management, 2015, 3, 96-108
Published Online January 2015 in SciRes.
How to cite this paper: Li, X.Y. and Ling, W.Q. (2015) How Framing Effect Impact on Decision Making on Internet Shopping.
Open Journal of Business and Management, 3, 96-108.
How Framing Effect Impact on Decision
Making on Internet Shopping
Xiaoying Li, Wenquan Ling
Management Institute, Jinan University, Guangzhou, China
Received 27 December 2014; accepted 17 January 2015; published 20 January 2015
Copyright © 2015 by authors and Scientific Research Publishing Inc.
This work is licensed under the Creative Commons Attribution International License (CC BY).
http://creativecommons. org/licenses/by/4. 0/
Framing effect is an understanding and assessment that individual implements different beha-
viors when facing to the multiple choice that expressing the same meaning. Previously, scholars
went through numerous empirical studies regarding impact of the method as well as environment
in making decision of online purchasing [1]-[4]. Also, scholars have proved that there are complex
influences of psychological factors when consumers make decisions of shopping. Therefore, the
article aims to analyze the impact of faming effect in e-commerce when consumers make decisions
so as to summarize the shortage in academic and practical aspects.
Framing Effect, Online Shopping, Making Decision, E-Commerce
1. Introduction
Nowadays, online shopping has become the trend of consuming model. According to the report announced by
Chinese Online Shopping Marketing Analysis in 2012, the number of transaction in online consuming market
reached approximately one thousand two hundred billion yuan (RMB) in 2012, increasing by 66.5 percent than
in 2011. In 2012, the rate of transaction in online retail took up 6.1 percent among social retails. In this case, the
data from online shopping in China is drawn attention to scholar for research about consumer behavior in mak-
ing decision, which drives the development of E-commerce.
Consumers constantly confront the situation that when you intend to buy a shampoo in one online store where
provides the free-delivering service if paying 45 yuan RMB, another store, on the contrary, charges 55 yuan
RMB for the same product including price of product (45 yuan RMB) and cost of delivering (10 yuan RMB). In
this case, which store will you choose? Whats more, when we see the slogan on the website from an online
store where signs that those who find out the fake product could get the compensation with ten times of the retail
X. Y. Li, W. Q. Ling
price; similarly, another store insists that no fake for sale because the store is qualified by the brand, how do we
make a decision?
The situations we described above are belonging to the different recognition, value perception and decision-
making under the same information with the different expression. The significant difference between online
consuming and physical consuming is that most of consumers purchase products on the internet before searching
information of products that provided by the website in the uncertain or potentially risky environment, while
shopping in the physical store runs the opposite. Hence, how we can make decisions rationally is a hotspot in
academic of framing effect.
As for the novelty of the study is on one thing that we suppose there is a relationship between online shopping
and diversity of framing effect because most of the previous researches have proved that in different occasion
traditional consuming decision will be influenced by the framing effect. However, the research did not involve
more detail in whether there are different impacts on shopping behavior on the internet or decision-making in e-
commercial environment as a result of different conditions and decisional environment in online shopping,
which doesnt have the same factors as traditional consumption. Similarly, the previous researches have proved
experimentally that different environments and conditions would have different impacts on purchase decisions
so that it forces us to believe that framing effect integrated by e-commercial situation would bring the another
effect on decisions of consumption and behaviors. On the other things, in terms of the category of precise con-
suming behavior and decision behavior, there is no relevant research made on these before. As for the conse-
quence of purchase, it is related to various aspects (i.e., whether to purchase, the quantity of product, the types of
2. Definition, Origin and Category of Framing Effect
2.1. Definition and Origin of Framing Effect
Before the theory of framing effect, scholars presented the Expected Utility theory demonstrating that in uncer-
tain and potentially risky situation, individuals estimate the potential consequence before making decisions. The
theory dominated the crucial place in risky decision until 1944; Von Neumann and Morgenstern [5] carried out a
new utility model according to this theory and supposed that human preferred to make decisions to match the
highest expectation.
However, not all the people could exhibit rational behavior in our daily life because of the limited knowledge.
Individuals are not competent to make perfect in decision with rational principle during the period that people
lack of knowledge and unforeseen risks. That is the reason why decision-making could not stand in line with
Expected Utility Theory, which stimulates psychologists and economists to research the related theory about de-
Framing effect is origin from the problem of Asian disease that Kahneman and Tversky studied [6]. They got
ready for preventing from extremely terrible disease which was coming and anticipated 600 people would lose
lives in this disaster. Therefore, they showed us two expressions of plans about the solution.
A classic example of framing effect is Tversky and Kahneman’s [6] Asian disease problem. In their study, the
projects were required to select between a confirmed consequence that led to a certain survival of one third of
600 hypothetical patients (200 people) and a risky probabilistic consequence, a one-third probability that all 600
people would survive and a two-thirds probability that no one would survive.
In contrast, the projects described as loss were required to select between a certain consequence that led to a
certain death of two thirds of 600 hypothetical patients (400 people) and risky probabilistic result, a one-third
possibility that no one would die and a two-thirds possibility that 600 people would lose their lives.
As a result, most of their subjects (72%) favored the certain thing when the choice results were framed in
terms of lives saved whereas most of the subjects (78%)in another group favored the gamble (the possibility)
when the same choice outcomes were framed in terms of lives lost. We could find out that these problems ex-
pressed the same meaning. The unique distinctness was the key word in beneficial environment was described a
survival, another group was the death.
Accordingly, people code the possible choice outcomes as gains and losses, and tend to be risk averse when
choosing among prospects seen as gains but risk seeking when choice selections were framed positively, people
are fancy to recognize them as gain and become more risk averse. On the contrary, when the same selections are
framed negatively, people are more likely to foresee them as lost and become more risk finding. There is no
X. Y. Li, W. Q. Ling
doubt that Tversky’s theory makes a contribution to framing effect analysis and our essay.
2.2. Category of Framing Effect
With development of theory of framing effect, scholars expand the research of various phenomena, and the tech-
nological innovation develops numerous new situations, which arouse awareness of psychologists and econo-
mists. For these reasons, framing effect should have a clear category.
2.2.1. Bidirectional and Unidirectional Framing Effects
X.T. Wang [7] found out dynamic and mechanism of framing effect in terms of risky choice of death, public
property and individual benefit. As a result, there are two types framing effects defined with theory and empiri-
cal recognition, namely bidirectional framing effect and unidirectional framing effect.
Bidirectional framing effect, a traditional framing effect, drives to change risk preference when positive
framing choices are transferred to negative framing choices. Faming Hou designed the experiment to demon-
strate that in terms of personal property, individual tended to risk averse while the one who stayed in the nega-
tive framing effect focus on risk seeking.
However, there is no preference change under unidirectional framing effect. In fact, when the hypothetical pa-
tients were described as subjectsown family members, the subjects, although clearly being risk seeking, be-
come extremely more risk seeking if the choice consequence was framed negatively.
2.2.2. Risky Framing Effect, Attribute Framing Effect & Action Framing Effect
Risky framing effect, the most typical explanation, is used for highest times in framing research. F.M. Hou [8]
preceded the experiment that how post-graduate students and employed people made risky decision in terms of
life, money and personal things. At last, he found out that there were extremely different preferences of making
decision under the framing effect.
Attribute framing effect will infulence individuals encoding and assessment of the object or characteristics.
F.M. Xu [9] summarized the traits of attribute framing effect including: 1) no risk; 2) distinctive; 3) no prefe-
rence change by comparing with other types of framing effects. As a common phenomenon in economics, Xu
concluded the previous explanation of attribute framing effect from other researchers, addressed the perspective
and explored widely regarding the origin, factors of impact, application, the relationship with other decisions. In
addition, this framing effect is applied in consuming domain constantly so that the contribution of research could
bring huge impact on consuming decision.
C.J. Liang and X.R. Li [10] deemed that action framing effect was a behavioral purpose that individuals
wanted to achieve one certain goal and a behavioral consequence related to one certain goal. The positive and
negative action framing effect is the relationship between the individual and goal achievement. An experiment,
designed by C.J. Liang and X.R. Li, illustrated that positive action framing effect intensified the impulsive be-
havior of consumer who is directional. Since the appearance of action framing effect, researchers struggled to
study mechanism and factor of that to understand the origin and performance so that company could set up an
effective strategy to persuade consumer to take action and prevent negative effect.
2.3. Theoretical Interpretation of Framing Effect
2.3.1. Prospect Theory
Prospect theory, was put forward by Kahneman and Tversky in 1979, is the first theoretical explanation of
framing effect, which reveals the people who would not complement completely rational action always prefer to
deviate rationality. The theory of the individuals in decision-making can be divided into two stages, editing and
evaluation. Kahneman and Tversky argued that, in the editing stage, individual decision makers mainly through
coding, combination, decomposition, reducing or other related processes methods, integrated information; mean-
while, in the evaluation stage, according to the value function and weight function the individual decision mak-
ers selected different options of utility value [6].
In prospect theory, the reference point is an important criterion of evaluation when making decision [6].
Choosing what kind of reference point is the vital role in changing and manipulating peoples decisions. Whats
more, the value function is to coordinate the reference point as the center, presenting an s-shaped curve. Loss
area presents the convex curve and decreases rapidly resulting in that people are more sensitive, while yield area
X. Y. Li, W. Q. Ling
appears concave curve. That explains the reason why under framing effect, when making decision, individuals fac-
ing with a positive, active benefit, tend to avoid risk as opposite as the negative, more people tend to seek risk.
Comparing to the pain from lost, the joy of the equivalent gain is more strong, namely loss aversion [11].
With the development of research, scholars have found that there are drawbacks in prospect theory that cannot
fully explain the framing effect. First, the reference point and the value function is difficult to measured and es-
timated; second, the differences of the individual decision-making cannot be reflected in the prospect theory. In
order to solve these problems, scholars turned to consider cognitive theory where the fuzzy-trace theory hit the
2.3.2. Fuzzy Theory
Fuzzy theory was created by Reyna (1991), used to interpret the traditional framing effect [12]. The principle of
theory is to prefer to operate to reason informational instinctive gist rather than a logical way. Also, theory
proved that the advanced judgment and decision-making was based on a simple, selective gist of mental repre-
sentation (fuzzy memory trace) rather than a more specific, quantitative digital representation (literal memory
trace). Trace, a single meaning extracted from the information (semantic representation), is related to the per-
sonal knowledge, understanding, culture and development level [13]-[16]. Because in the field of personal de-
velopment and the need for more professional knowledge, their decisions would tend to be based on the meaning
of information, rather than the details of verbatim [17].
Nowadays, scholars attempt to apply actually to practice with the theory. They deem that people are parallel
and independent to extract essence trace and literal trace information, prefer to trace information and make
judgments based on the information [18]. The expression of fuzzy effect of tax revenue can promote the devel-
opment of the market. Individual preferences exist fuzzy processing but there is no linear relationship between
the objective digital results and individual subjective perception of its. In that case, bonus will become the extra
income for individual in our mind; in addition, the overall wealth will be rising as a cognitive. On the contrary,
no one focus on the digital information which impacts our preference in decision-making by the description of
the fuzzy model [19]. J.W. Duan [20], by means of empirical experiment in terms of market competition and
starting business defined as two risky choices, proved that the influence by fuzzy theory in market competition
situation outweighed the counterpart of entrepreneurship, and the former has a significant level, the latter run the
opposite which to verify that under circumstance of the entrepreneurial risk, most people in the judgment of
characterization of digital information roughly estimate fuzzy information, and the fuzzy representation has sig-
nificant impact on the final choice.
Indeed, the fuzzy theory in terms of memory, reasoning, intuition and other related cognitive factors inter-
preted the decision-making behavior, but because gist of trace is complicated to be measured and a strong fuzzy
cognitive causes different interference, therefore scholars put forward another theory called “equate-to-diffe-
rentiate theory.
2.3.3. Equate-to-Differentiate Theory
The theory, presented by Chinese scholars [21]-[24] was based upon limited rational point of view [25]. As the
content of the theory, living in risky and uncertain circumstances, human being does not tend to match the
maximum expectation in decision-making, but choose upon identifying one form whether there are advantages
and disadvantages. The significantly difference compared with Expected Utility theory is equate-to-differen-
tiate theory denies decision preference that is to achieve maximum expectation. The specific process is divided
into two parts, equate and differentiate. Decision maker will equate the less different result on one dimension
and define the extremely different dimensions as top priority of options. This is the perfect example that making
decisions by means of the principle of weaken advantage.
Equate-to-differentiate model illustrated and explained the classic framing effect, since the framework of the
subjective option can be described in two dimensions containing best possible outcome and worst possible out-
come. Human beings with lower cognitive ability consider that the results are subjective or equal, leaving an-
other single dimension be regarded the difference of options. The process of “equatecan let decision makers
have lower cognitive ability. In the “differentiatestage, if the decisive dimension is one of the best possible re-
sults, individuals tend to pursue the best goal during this period, or if the definitive dimension is the worst poss-
ible result, then the decision make us avoid the worst possible target.
X. Y. Li, W. Q. Ling
3. Decision of Consumer on Online Shopping
3.1. The Concept
Every day each person has to make numerous decisions with all aspects of daily life. Broadly speaking, deci-
sion-making is to select the best one from two or more choices and the choice must be feasible as well. Actually,
buying decision is a part of the consumer behavior [26]. Some scholars believe that decision-making of con-
sumer is the purchasing intention, reflecting the psychological mechanism of consumption decisions. Therefore,
this article upon the background of network consumption decisions, compared the traditional consumption mode,
researches new consumption patterns of consumers decision-making behavior on internet environment.
Some researches focused on consumers decision-making process on network. Simon thought that the process
of shopping decision-making was divided into the following steps: cognition, design and choice [25]. Addition-
ally, buying decision, defined by AMA (the association of American market) and based on switching, contains
perception, emotion, cognition, behavior and the interaction between environmental factors. Haubl and Trifts
believe that potential consumers seem to utilize two steps to reach the decision [27]. The first is to identify the
possibility of choice to obtain the demand by means of scanning a large number of products. The second is to
precede evaluation of the choice above in deep way and compare according to specific reason and consumer de-
cision-making. S. Chen supposes that consumers purchasing decision refers to the process of purchase [28]. In
other words, the consumer achieves the certain requirements with information searching, seeking solutions,
choosing and determining the optimal process, post-purchase evaluation and a series of activities. C.S. Wu and
F.F. Cheng believed that consumers purchase decision is mainly made up of three parts, including purchase in-
tentions, purchase attitude and willingness to pay [29].
To sum up, the emphasis of the different scholars of understanding consumers’ network decision-making is
multifarious, but the essence of decision-making described by scholars has a consistency, namely meeting the
needs of consumers. Hence, in this paper, the concept of a purchase decision is defined that consumers meet
their own needs somehow, through the network media to make decisions mainly including the purchase inten-
tions, purchase attitude and willingness to pay.
3.2. Purchasing Decision Model
3.2.1. Nicosia Model
Nicosia Model was put forward in 1966, as the core content of the book called Process of Consumerspurchase.
There are four areas simulated by Nicosia. Phase 1 is a procedure from information releasing to consumer atti-
tudes. In that procedure, the enterprise through the dissemination channels pass relevant brand’s contents to
consumers, this information will be treated with consumers to form a specific attitude towards a product. Phase
2 is called evaluation. After the consumers receive information from dissemination, they will form a certain at-
titude of brand in their mind. After that, consumers search the relevant information of the product with this cer-
tain attitude and estimate the content of the information for its corporate communications and product to trigger
purchase motivation. Phase 3 is called purchasing behavior that the driving of the consumersmotivation in
consumer decision and the specific purchase behavior. Phase 4 is called feedback that shape the experience as
the memory in the brain, which guide future consumption, after consumption of experience or product and that
feedback to enterprise from the consumption experience of consumers. As shown in Figure 1.
Nicosia Model concisely induces the consumer decision-making process, but the process does not illustrate
the impact on external factors and ignores the situation that the interaction and communication between con-
sumers and enterprises are multitudinous.
3.2.2. Howard-Sheth Model
This model was put forward by Howard and Sheth together in 1977, enriched the consumer decision-making
process and classified into four categories: input variety, cognitive structure, the structure of the study, output
results. The input variety is stimulus or input including three factors: product essential factor (i.e., quality, price,
features, service, and utility); the symbolic factor (i.e., medium as the transmission of information constitute);
social environmental factor which is the external environment for product information transferring stimuli (i.e.,
family, related groups and the social hierarchy). Cognitive and learning structures describe that after stimulated
X. Y. Li, W. Q. Ling
Figure 1. Nicosia model.
by external factors, consumer trigger a series of changes in psychological activity and the psychological process
of the purchase decision. Cognitive structure includes the public search, fuzzy knowledge and attitude of stimuli
and preferences of perception, While learning structure between consumer decision-making and external stimuli
contains the criteria of selection, the brand understanding, attitude, trust, motivation, satisfaction, intention, re-
flecting the process of processing and forming a attitude toward products in our brain as a vital procedure of
making purchasing decision after accepting external stimuli. Finally, the output results, reflecting that the con-
sumer finally trigger a product purchasing behavior, contains three meanings. First, the attention of consumers
about a product and the understanding of the brand; second, emotional response related to the learning structure;
third, interacting behavioral responses whether decided to implement a specific behavior. Exactly as shown in
Figure 2.
The description of Howard and Sheth model are logical and practical, especially suitable for consumers of
various products brand choice and purchase [30]. But the consumer decision-making process is embedded in a
particular social system or deciding correlative system, therefore, the study overlooked the consumer decision-
making in the correlative system between each other [31].
3.2.3. EBM Model
EKB model is put forward by Engel, Blackwell and Kollat as a systematic and comprehensive model of deci-
sion-making in 1968 [32]. The core content of model is to distill systematically each variety and the relationship
among them from the consumer decision-making process. In order to make the EKB model more effective de-
scription of consumer behavior, so Englel, Blackwell and Miniard revised the content of EKB model and turned
out a new model that was called EBM model, which became a kind of explanatory model and was divided into
four parts: 1) the central control system, namely process of the consumer psychological activity; 2) part of in-
formational process; 3) the decision-making process; 4) environment. They believed that external stimulating
factor would stimulate the brain cortex to filter pro- cessing information. Stimulating factors mainly include com-
modities, the mass media and enterprises promotion, etc., through the central control systemvisual, hearing,
feeling and memory, and standard of consumption would be integrated, so as to know the goods and make a com-
prehensive assessment and selection, eventually become the decision result. At the same time, environmental
factors which are also defined as economic, political, cultural and social factors can affect the decision-making
process as well. Consumers after decision-making began to implement specific and concrete consumption beha-
vior and evaluate the product after purchase use and consumption experience to feedback to the central control
system, which became the influence factors of consumer spending decisions in the future. As shown in Figure 3.
3.2.4. Kotler Stimulus-Response Model (Kotler Model)
Kotler stimulus-response model was presented by Kotler (1995) with the perspective of psychology and systematic
analysis on the consumers decision-making behavior as well as combining the general pattern of human behavior,
also called “S-O-R” model [33]. S for stimulate, O for organisms and R for response. Part S is a stimulus that arouse
X. Y. Li, W. Q. Ling
Figure 2. Howard-Sheth model.
Figure 3. EKB mode.
X. Y. Li, W. Q. Ling
the attention of consumers, including external stimulus (i.e., product, price, channel and promotion) and envi-
ronmental stimuli (i.e., economy, politics, culture, science). Part O is called Black Box, representing the con-
sumer’s psychology, mainly including the characteristics of consumers (i.e., social, individual, psychological and
cultural) and the consumer decision-making process (i.e., problem recognition, information searching, standards,
assessment and purchase behavior). Meanwhile this part, which plays an essential role in the consumer behavior,
brings an important impact on consumer behavior. Whereas Part R is the concrete consumers decision-making
behavior (i.e., commodity choice, the choice of suppliers, brand choice and purchase quantity). As shown in
Figure 4.
The shortcoming of the model is that it neglects brain processes after accepting information and over empha-
sizes on black box, namely the characteristics of consumers and the consumer psychological activity influence
on consumer decision-making.
3.2.5. Model of Consumer Decision-Making on Network
Decisions between consumption on network and consumption in traditional way have many similarities. Differ-
ent scholars analyzed network consumption and generalized the corresponding model according to the tradition-
al consuming decisions. Q. Ke [34], who searched from Kotler model and combined the feature of network
consumer behavior (individuation, rationalization, initiative), built up pattern of the network consumerspur-
chasing behavior. As shown in Figure 5.
S.S. Li [35] combined Nicosia and Howard-Sheth model with the characteristics of online consumer to put
forward the integration of the traditional shopping model, online information searching and offline consumption
and online consumption of the comprehensive model. The model is added the way of purchasing into the scope
of the consumer decision-making to which is needed to consider and makes up the shortage of traditional and
online shopping decision model when considering decisions. Whats more, the model is based on limited type of
consumers and dilated consumers. Limited type of consumers are characterized by medium intervention, limited
information searching, simple rules, limited backup solution, while dilated consumer runs opposite side [36]. As
shown in Figure 6.
With the development of e-commerce and information technology, consumer spending patterns will become
diverse, and factors which influence behaviors of shopping will also increase, because the consumer decision-
making model also needs to be integrated into numerous comprehensive factors, but the main process is inse-
parable from the general decision model. To be honest, the environment of e-commerce is complex, consumer
demand and behavior must be analyzed constantly so that we can fully understand the consumers decision-
making behavior.
4. Related Research about Relationship between Framing Effect and Consumer
Currently, application of framing effect obtained certain achievements in terms of investment, management, pro-
fessional, tourism, insurance, etc. All of them have the empirical research. In recent years, the studies of the
framing effect that was applied in consumer decision-making behaviors are also gradually got attention [37] [38].
But the framing effect in the field of consumption decisions did less, especially in the network of consumer de-
Levin [39], C.L. Liang [10] and others combined with the framing effect and utilized beef products and digital
camera as two subjects to research empirically intension and impulse buying decisions of college students. The
results showed that under the risky framing effect, subjects tended to select the products with the description of
the loss. On the other hand, under attributed framing effect, most of subjects favor the positive description. In the
action framing effect, Levin and others showed that the framework didn’t impact on the decision and there was no
action framing effect. However, the result gone through by the experiment of C.L. Liang was just opposite.
Figure 4. Kotler model.
X. Y. Li, W. Q. Ling
Figure 5. Network shopping decision model.
Figure 6. Integrated network shopping decision model.
In addition, the frame will affect consumer perception of price. Bingbing Mou and their partners designed the
questionnaire and experiment to prove how the cognitive closure need and promotion strategies presented versus
discount impact 240 college students in fuzzy consuming decision [40]. On one hand, the author thinks that with
the high cognitive closure in circumstances of fuzzy purchasing, consumer decide what they want to bur without
too much hesitation but being more vulnerable to promotional informational framework. On the other hand,
the influence of low cognitive closure runs an opposite trend. There is nothing different reaction between dis-
counts and gifts.
S.L. Li and her partners [41] made an empirical analysis of the relationship between price framework in the
X. Y. Li, W. Q. Ling
bundling and consumer preferences from the perspective of product attributes. They found that when a pleasure
and a firm supplies were bundled, price discount embodied in hedonic products had less expected guilt and high
purchase intention than those of the real products which save money. Hence, expected guilty is the intermediary
role between price framing effect and consumerspurchase intentions.
Schindler and his teammates [42] focused on how the process of sales promotion information effect influ-
ences customers when buying a car. The results revealed that consumers were not willing to remove the stuff
from car that did not operate as a function, comparing to add functionality. The reason was that the function re-
moved from the car was regarded as a lost, while adding a new function was viewed as a benefit.
The scene experimental method of Y.R. Cheng [43] was adopted. Under conjunction with the framing theory
and cognitive theory, Cheng introduced how attribute framing effect, action framing effect, cognitive needs and
the integration among three factors influence on college studentsconsumption decisions. The results illustrated
that attribute framing effect and cognitive needs had a significant influence on college students purchase inten-
tions, attitude and the way of payment. Initially, intention, attitude of target products and payment of college
students who are low cognitive need are significantly higher than counterpart of university students with high
cognitive need. In addition, when students, with the low level of cognitive needs and who was influenced by the
framework of positive characteristics, have the higher level of purchase intention than university students that
impacted by the negative description for target product. On the contrary, there is no obvious effect on purchas-
ing intentions, attitudes and way of payment under both description of framing effect. Finally, the action
framework would bring no impact on college studentsconsumption intention, attitude and payment.
5. Shortages and Prospects
5.1. The Shortage of Current Research
On one hand, compared with characteristic of traditional consumption, the network consumer behavior has new
trait, namely the consumers cannot let consumer directly contact with the goods what they want, which is re-
garded as a highly complex shopping environment. On the other hand, the platform of internet offers a high in-
formation exchange environment. In other words, consumers who shop online interact with machines constantly
where the web pages are exhibited by the complicated content. Not only do they have to make a based decision
of online shopping, but also make a decision of the purpose, brand, channel, payment, time and quantity.
The development of the Internet, reaching the peak of information transfer, provides people with a bigger
platform to communicate with each other, interact with each other, but no matter how enormous the consump-
tion environment change, study in traditional purchase or online purchase is related to the characteristics of to-
days consumers. To be honest, most of consumers are more personalized; initiative, considerate and psycho-
logical mature in terms of cutting off the cost and time than any time before. In that case, the process of network
consumption contains a lot of text information as the major stimulation factors; however the influences of
graphic form of information description on diversity of consumer decisions are the same. Whats more, a few
previous researches are related to the influence of framing effect in the field of consumption. At the same time,
scholars just focused on college studentsgroup or a particular category as subjects rather than a wide range.
Hence, this kind of research could not represent the general consumers, broader target product or more compre-
hensive consumption environment. We need more empirical study to adapt to the development of Internet and e-
commerce and emerging products in the e-commerce platform.
5.2. The Future Research Prospects
In the classification of the framing effect, risky framework, attributed framing v and action framing effect in the
field of consumption lead to the different results. Risk framework can influence consumer about the related risky
decision (e.g., the channels of consuming, way of payment). And in attributed framing effect, positive characte-
ristics framework is the key attributes of goal and the event which symbolize the information that make decision
makers are comfortable and satisfactory as opposed to negative framework. This frame is a kind of manifesta-
tion in terms of psychology of consumer, which affect the consumer’s intention, purchase attitude, willingness
to pay and other psychological mood; But in action framing effect, positive action framework is that if consum-
ers who choose this option will have a particular interest or benefit, otherwise, they will suffer from economic,
healthy, experiential or others loss, which is defined as negative action framework.
Above all, the authors have several important key points for the future research. First, in e-commerce scenario,
X. Y. Li, W. Q. Ling
is there an effect of framing effect on intangible product consumption decisions? As we know, most of intangi-
ble products are the kinds of consumption experience, such as travel services, the process of information gather-
ing and processing is the most important part of tourist destination products and services, and now increasing
number of consumers choose online consumption on tourism products and offline experiences, which forms a
new consumption environment. Thus, the study of the effect of framework in the field can offer a good scientific
basis to tour operators, managers who are competent to come up with product information effectively and con-
tribute to the development of tourism.
Second, under e-commerce business environment, how new promotions in terms of price framework influ-
ence brand preferences. In current period, many e-commerce platforms (e.g., Taobao, Jingdong, Suning) deliver
a new form of information of promotion that captures attention of consumers, for example, some electronic sup-
pliers offer 0 yuanproduct (RMB), or free cost for products in a limited time. This kind of strong and positive
action framework attract consumers to lock certain products with the obvious advantages compared with other
brand promotion. Will the consumers put their whole mind into plundering this brand with attractive discount?
Third, living in the e-business environment, what is the effect of framework on quantity of consumption?
With the convenience of logistics system, online shopping impels consumers to buy the same brand with mul-
tiple units. However, for whether there is influence on quantity of consumption, we need more empirical re-
searches in the future, because there are different ways of promotions reveal that buy more get more benefit and
they do not need to consider how heavy the product. This topic has strategic significance for the retailers and
suppliers who could come up with the attractive expression of promotion to capture the market share.
Fourth, how do the combination of cognitive need and framing effect impact consumption on the internet?
H.H. Liang’s [44] experiment revealed that cognitive need has a significant impact on individual decisions. Ad-
ditionally, results of individuals’ investing decisions are influenced by decision goal and different individual
factors. But the relationship between cognitive needs and framing effect and whether the combination of them
will impact consumption on the internet are questioned by some scholars. We need more cases and scenes to
Fifth, research has focused on decisions about the framing effect of risk framework, but little study regarding
attribute framing effect and the action framing effect, especially whether there is a relationship between attribute
framework and action framework, so more researches are needed.
[1] Colin, F.C., George, L. and Matthew, R. (2010) New Progress in Behavioral Economics. China Renmin University Ex-
press, Beijing.
[2] Del, I.H., Roger, J.B. and Kenneth, A.C. (2004) Consumer Behavior (Translator, Fu Guoqun). China Machine Press, Bei-
[3] Jagdish, N.S. and Banwari, M. (2004) Perspective of Consumer Behavior Management (Translator, Luo Li). China Ma-
chine Press, Beijing.
[4] Leon, G.S. and Leslie, L.K. (2007) Consumer Behavior (Translator, Fu Guoqun). China Machine Press, Beijing.
[5] Von Neumann, J. and Morgenstern, O. (1994) Theory of Games and Economic Behavior. Princeton University Press,
[6] Kahneman, D. and Tversky, A. (1979) Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47, 263-
[7] Wang, X.T. (1996) Framing Effects: Dynamics and Task Domains. Organizational Behavior and Human Decision
Processes, 68, 145-157.
[8] Hou, F.M. (2013) The Factor of Framing Effect in Risky Decision. Capital Normal University, Beijing.
[9] Xu, F.M., Wen, G.C., Yu, H.H., Huang, B.Z. and Wang, L. (2011) The Psychological Mechanism and Influential Fac-
tor of Attribute Framing Effect. Advances in Psychological Science, 19, 1822-1833.
[10] Liang, C.L. and Li, X.R. (2012) The Effect of Framing Message on Impulse Buying Behavior. Journal of Shandong
University of Finance, 1, 72-81.
[11] Liu, M. and Liu, X.W. (2008) To Review on Research of Loss Aversion under Prospect Theory. Value Engineering, 10,
[12] Reyna, V.F. (1991) Class Inclusion, the Conjunction Fallacy, and Other Cognitive Illusions. Developmental Review, 11,
X. Y. Li, W. Q. Ling
[13] Reyna, V.R. and Brainerd, C.J. (1993) Domains of Fuzzy-Trace Theory. In: Howe, M.L. and Pasnak, R., Eds., Emerg-
ing Themes in Cognitive Development, Springer-Verlag, New York.
[14] Reyna, V.F. (2004) How People Make Decisions That Involve Risk: A Dual-Processes Approach. Current Directions
in Psychological Science, 13, 60-66.
[15] Reyna, V.F. and Brainerd, C.J. (1998) Fuzzy-Trace Theory and False Memory: New Frontiers. Journal of Experimen-
tal Child Psychology, 71, 194-209.
[16] Reyna, V.F. and Farley, F. (2006) Risk and Rationality in Adolescent Decision Making: Implications for Theory, Prac-
tice, and Public Policy. Psychological Science in the Public Interest, 7, 1-44.
[17] Reyna, V.F. and Lloyd, F.J. (2006) Physician Decision Making and Cardiac Risk: Effects of Knowledge, Risk Percep-
tion, Risk Tolerance, and Fuzzy Processing. Journal of Experimental Psychology: Applied, 12, 179-195.
[18] Reyna, V.F. and Mills, B.A. (2007) Converging Evidence Supports Fuzzy-Trace Theorys Nested Sets Hypothesis (But
Not the Frequency Hypothesis). Behavioral and Brain Sciences, 30, 278-280.
[19] Epley, N., Mak, D. and Idson, L.C. (2006) Bonus of Rebate: The Impact of Income Framing on Spending and Saving.
Journal of Behavioral Decision Making, 19, 213-227.
[20] Duan, J.Y., Wang, Z.M. and Tian, X.M. (2013) The Study of Cognitive Theory for Framing Effect in Entrepreneurial
Risky Decision-Making. Psychological Exploration, 33, 123-128.
[21] Li, S. (1994) Is There a Problem with Preference Reversals. Psychological Reports, 74, 675-679.
[22] Li, S. (2001) Extended Research on Dominance Violations in Similarity Judgments: The Equate-to-Differentiate Inter-
pretation. Korean Journal of Thinking and Problem Solving, 11, 13-38.
[23] Li, S. (2003) Violations of Conjoint Independence in Binary Choices: The Equate-to-Differentiate Interpretation. Euro-
pean Journal of Operational Research, 148, 65-79.
[24] Li, S. (2004) Equate-to-Differentiate Approach: An Application in Binary Choice under Uncertainty. Central European
Journal of Operations Research, 12, 269-294.
[25] Simon, H.A. (1976) Administrative Behavior—A Study of Decision-Making Processes in Administrative Organization.
3rd Edition, Macmillan Publishing Co. Inc., New York.
[26] Dong, L. (2004) Perspective of Consumer Decision to Perceive Customer Service of B to C (E-Commerce). Journal of
Intelligence, 8, 27-28.
[27] Haubl, G. and Trifts, V. (2000) Consumer Decision Making in Online Shopping Environments: The Effects of Interac-
tive Decision Aids. Marketing Science, 19, 4-21.
[28] Chen, S. (2005) Marketing Psychology. Jinan University Press, Guangzhou.
[29] Wu, C.S. and Cheng, F.F. (2011) The Joint Effect of Framing and Anchoring on Internet Buyers’ Decision-Making. Elec-
tronic Commerce Research and Applications, 10, 358-368.
[30] Xu, P. (2001) Consuming Psychology Study. Shanghai University of Finance & Economic Express Co. Ltd., Shanghai.
[31] Zhou, Y.L. (2010) The Study of Network Consumer Decision and Application. Shandong University, Jinan.
[32] Engel, J.F., Kollat, D.T. and Blackwell, R.D. (1968) Consumer Behavior. Holt, Rinehart &Winston, New York.
[33] Kotler, P. (1995) Marketing Management Analysis, Planning Implementation and Control. Prentice-Hall, Englewood
[34] Ke, Q. (2004) The Research on E-Consumer Buying Behavior Model. Central China Normal University, Wuhan.
[35] Li, S.S., Chen, Y.W. and Li, J.Y. (2006) Shopping Decision-Making Model: An analysis of Consumer Online. Advances
in Psychological Science, 14, 294-299.
[36] Hawkins, D.J., Best, R.J. and Coney, K.A. (2003) Consumer Behavior: Building Marketing Strategy. 8th Edition, Ma-
chine Industry Press, Beijing, 475-479.
[37] Yang, C.Y. (2007) The Difficult Choice: Research on Choice of Consumer Based on Psychology. Modern Economic
Research, 3, 71-74.
[38] Zhou, F.W. and He, H. (2010) The Choice of Framing Effect—The Influence of Circumstance for Choice of Consumer.
Productivity Research, 9, 99-100.
[39] Levin, L.P., Schneider, S.L. and Gaeth, G.J. (1998) All Frames Are Not Created Equal: A Typology and Critical Anal-
ysis of Framing Effects. Organizational Behavior and Human Decision Processes, 76, 149-188.
X. Y. Li, W. Q. Ling
[40] Mou, B.B. and Si, J.W. (2012) The Influence of Promotion Strategy and Need for Cognitive Closure on Ambiguity
Consumption Decision Making. Psychological Research, 3, 61-69.
[41] Li, S.L. and Jing, F.J. (2014) The Effects of Utilitarian and Hedonic Attribute on Price Frame in Bundling. Commercial
Research, 442, 105-112.
[42] Schindler, R.M., Maureen, M. and Nada, N.B. (2005) Shipping Charges Skepticism: Implications for Direct Marketers
Pricing Formats. Journal of Interactive Marketing, 19, 41-53.
[43] Cheng, Y.R. (2013) The Effect of Need for Cognition and Framing Effect on College StudentsPurchase Decisions:
An Empirical Research. Southwest University, Chongqing.
[44] Liang, H.H. (2009) The Impact of Decision Goal and Cognition Needs on Sunk Cost Effect. Henan University,
... Çerçeveleme Etkisi, son yıllarda araştırmacılar tarafından birçok alana uygulanmıştır (Duhachek, Agarwal ve Han, 2012;Kühberger, 1998;Krishnamurthy vd., 2001;McClure, 2009;Li ve Ling, 2015). Özellikle haber, politika bilimi, psikoloji, halkla ilişkiler ve reklamcılık alanlarında da sıkça karşımıza çıkan çerçeveler, bireylerin beyin defosu olarak değerlendirilip rasyonel bir düşünme ortamı oluşmasına engel olan etkilerden biridir. ...
... Framing Effect has been applied to many areas by researchers in recent years (Duhachek, Agarwal, and Han, 2012;Kühberger, 1998;Krishnamurthy et al., 2001;McClure, 2009;Li and Ling, 2015). Frames, which we frequently encounter especially in the fields of news, political science, psychology, public relations and advertising, are considered as brain defects and are one of the effects that prevent the formation of a rational thinking environment. ...
Full-text available
Geleneksel İktisat ana varsayımının tersine bireylerin her koşulda rasyonel olmadığı, bilişsel önyargılar nedeniyle tercihlerin değişkenlik gösterdiği birçok çalışma ile kanıtlanmıştır. Bu kanıtlardan biri olan Çerçeveleme Etkisi, olayların veya durumların sunuluş şeklinin değiştirilmesiyle bireylerin kararlarında meydana gelen değişimi gösteren etkidir. Davranışsal yaklaşımda medyadan pazarlamaya kadar birçok alanda kullanılan bu etki, bireylerin sahip olduğu deneyimler, duygusal değişimler, yanlılık durumları gibi faktörlerden dolayı her zaman kendini göstermeyebilmektedir. Bu çalışma yapay zeka uygulama senaryolarıyla oluşturulan Çerçevelemelerin etkisinin toplumsal anlamda hassasiyet teşkil eden “işsizlik” olgusuyla sınanması üzerine kurulmuştur. Çevrimiçi anket yöntemiyle elde edilen veriler 250’si kadın, 230’u erkek olmak üzere 480 kişiden toplanmış ve Bağımsız Örneklem t Testi ile analiz edilmiştir. Analiz sonuçları göstermektedir ki, pozitif ve negatif risk çerçevelemeleri ile istatistiki olarak anlamlı sonuç veren Çerçeveleme Etkisi, aynı sorulara işsizlik olgusu yerleştirildiğinde ortadan kaybolmaktadır. Bununla birlikte kontrol ve deney gruplarında, çerçeveler arasındaki farkın “işsizlik” değişkeninin eklenmesinden kaynaklanıp kaynaklanmadığı konusunda daha net bir bakış açısı sunabilmek adına, ifadeler pozitif ve negatif çerçeveler şeklinde yeniden analize tabi tutulmuştur. Buradan elde edilen nihai sonuç ise farklılaşmayı engelleyen asıl unsurun “işsizlik” değişkeni ile ilgili olduğudur.
... Framing effect is one of the cognitive biases that can affect the investors in terms of their behavior and in making investment decisions so that it creates anomalies in the capital market (Pompian, 2006;Li & Ling, 2015). This framing effect usually happens to the investors who were given the same information but packaged in different ways so that the information will be responded differently by the investors. ...
Full-text available
This research aims to test the reaction of the investors when the investors receive a dividend information presented with positive or negative frames at the same market conditions, either bullish or bearish market conditions. In addition, it also test the reactions of investors when they are in the bullish or bearish market conditions, the investors receive a dividend information with the same frame, both positive and negative frames. The investors reaction is indicated by the magnitude of the stock price prediction on the next day with absolute value. Experimental method is used to test the hypotheses in this research by using independent sample t-test. The results showed that there is an information framing effect to the investors when receiving the information with a positive frame in the bullish market conditions and gain domain effects occur to the investors in the bullish market conditions when the investors receive the information with a positive frame. These results are expected to provide a new insight on the different reactions of investors when receiving the same information.
... In empirical studies, Bateman et al. (1997) tested the impact of reference dependent preference on the exchange of private goods by experimental methods, and Herne (1998) estimated the property of loss aversion. Peters (2012), Zeisberger et al. (2012), and Li and Ling (2015) did recent empirical or theoretical studies on the RDP. Barberis (2013) describes the review on the prospect theory. ...
Full-text available
-The fact that any of a number of measurement mechanisms can be used to identify an invariable choice is called into question. It is suggested that documented preference reversals do not reflect an actual reversal of preference but rather an inadequate knowledge of what the preference is. One of the most perplexing paradoxical patterns of behavior reported in laboratory experiments in the area of decision making is the preference-reversal phenomenon. For more than two decades, economists and psychologists have been intrigued by this anomaly. The classic example of preference reversal involves the choice-pricing discrepancy in the evaluation of two lotteries with equal expected value. Subjects on the typical preference-reversal task tend to put a higher selling price on small probabihties of winning large amounts, a "$ Bet," but they tend to prefer the higher probability lottery, a "P Bet," when asked to choose between these two lotteries. A possible pair of such lotteries is as follows: Lottery A provides a 9/12 chance of winning $110 and a 3/12 chance of losing $10. (P Bet) Lottery B provides a 3/12 chance of winning $920 and a 9/12 chance of losing $200. ($ Bet)
The chronicle of cognitive-developmental theory is a tale of waxing and waning commitments to competing metaphors of the mind. Following Thorndike (1911), the stimulus-response connection was psychology’s preeminent mental metaphor for half a century. Over the years, it had profound influences on cognitive development (cf. Berlyne, 1970), with theories of children’s discrimination transfer (e.g., Kendler & Kendler, 1962) being the quintessential artifacts of those days. In the 1960s, interest shifted toward another metaphor, Piaget’s (e.g., 1953) logician in the mind, with much of the momentum coming from Flavell’s (1963) virtuoso exposition of Piaget’s work. By the end of the decade, cognitive development had become synonymous with logical development. Within adult psychology, however, Piagetian logicism never gained the foothold that it did in child development. There, S-R connectionism was first challenged by information theory (e.g., Broadbent, 1957) and then supplanted by its descendent, information-processing theory (e.g., Newell & Simon, 1972). Eventually, the formalist metaphor of information processing, which saw the mind as an abstract symbol-manipulating machine, seeped into cognitive development (e.g., Siegler, 1981). By the time of Piaget’s death, it had become the modal metaphor, although pockets of allegiance to logicism remain to this day (e.g., Chapman & Lindenberger, 1992).
Crime, smoking, drug use, alcoholism, reckless driving, and many other unhealthy patterns of behavior that play out over a lifetime often debut during adolescence. Avoiding risks or buying time can set a different lifetime pattern. Changing unhealthy behaviors in adolescence would have a broad impact on society, reducing the burdens of disease, injury, human suffering, and associated economic costs. Any program designed to prevent or change such risky behaviors should be founded on a clear idea of what is normative (what behaviors, ideally, should the program foster?), descriptive (how are adolescents making decisions in the absence of the program?), and prescriptive (which practices can realistically move adolescent decisions closer to the normative ideal?). Normatively, decision processes should be evaluated for coherence (is the thinking process nonsensical, illogical, or self-contradictory?) and correspondence (are the out-comes of the decisions positive?). Behaviors that promote positive physical and mental health outcomes in modern society can be at odds with those selected for by evolution (e.g., early procreation). Healthy behaviors may also conflict with a decision maker's goals. Adolescents' goals are more likely to maximize immediate pleasure, and strict decision analysis implies that many kinds of unhealthy behavior, such as drinking and drug use, could be deemed rational. However, based on data showing developmental changes in goals, it is important for policy to promote positive long-term outcomes rather than adolescents' short-term goals. Developmental data also suggest that greater risk aversion is generally adaptive, and that decision processes that support this aversion are more advanced than those that support risk taking. A key question is whether adolescents are developmentally competent to make decisions about risks. In principle, barring temptations with high rewards and individual differences that reduce self-control (i.e., under ideal conditions), adolescents are capable of rational decision making to achieve their goals. In practice, much depends on the particular situation in which a decision is made. In the heat of passion, in the presence of peers, on the spur of the moment, in unfamiliar situations, when trading off risks and benefits favors bad long-term outcomes, and when behavioral inhibition is required for good outcomes, adolescents are likely to reason more poorly than adults do. Brain maturation in adolescence is incomplete. Impulsivity, sensation seeking, thrill seeking, depression, and other individual differences also contribute to risk taking that resists standard risk-reduction interventions, although some conditions such as depression can be effectively treated with other approaches. Major explanatory models of risky decision making can be roughly divided into (a) those, including health-belief models and the theory of planned behavior, that adhere to a "rational" behavioral decision-making framework that stresses deliberate, quantitative trading off of risks and benefits; and (b) those that emphasize nondeliberative reaction to the perceived gists or prototypes in the immediate decision environment. (A gist is a fuzzy mental representation of the general meaning of information or experience; a prototype is a mental representation of a standard or typical example of a category.) Although perceived risks and especially benefits predict behavioral intentions and risk-taking behavior, behavioral willingness is an even better predictor of susceptibility to risk taking - and has unique explanatory power - because adolescents are willing to do riskier things than they either intend or expect to do. Dual-process models, such as the prototype/willingness model and fuzzy-trace theory, identify two divergent paths to risk taking: a reasoned and a reactive route. Such models explain apparent contradictions in the literature, including different causes of risk taking for different individuals. Interventions to reduce risk taking must take into account the different causes of such behavior if they are to be effective. Longitudinal and experimental research are needed to disentangle opposing causal processes - particularly, those that produce positive versus negative relations between risk perceptions and behaviors. Counterintuitive findings that must be accommodated by any adequate theory of risk taking include the following: (a) Despite conventional wisdom, adolescents do not perceive themselves to be invulnerable, and perceived vulnerability declines with increasing age; (b) although the object of many interventions is to enhance the accuracy of risk perceptions, adolescents typically overestimate important risks, such as HIV and lung cancer; (c) despite increasing competence in reasoning, some biases in judgment and decision making grow with age, producing more " irrational" violations of coherence among adults than among adolescents and younger children. The latter occurs because of a known developmental increase in gist processing with age. One implication of these findings is that traditional interventions stressing accurate risk perceptions are apt to be ineffective or backfire because young people already feel vulnerable and overestimate their risk. In addition, research shows that experience is not a good teacher for children and younger adolescents, because they tend to learn little from negative outcomes (favoring the use of effective deterrents, such as monitoring and supervision), although learning from experience improves considerably with age. Experience in the absence of negative consequences may increase feelings of invulnerability and thus explain the decrease in risk perceptions from early to late adolescence, as exploration increases. Finally, novel interventions that discourage deliberate weighing of risks and benefits by adolescents may ultimately prove more effective and enduring. Mature adults apparently resist taking risks not out of any conscious deliberation or choice, but because they intuitively grasp the gists of risky situations, retrieve appropriate risk-avoidant values, and never proceed down the slippery slope of actually contemplating tradeoffs between risks and benefits.
Many health and safety problems, including war and terrorism, are by-products of how people reason about risk. I describe a new approach to reasoning about risk that implements a modern dual-process model of memory called fuzzy-trace theory. This approach posits encoding of both verbatim and gist representations, with reliance on the latter whenever possible; dependence of reasoning on retrieval cues that access stored values and principles; and vulnerability of reasoning to processing interference from overlapping classes of events, which causes denominator neglect in risk or probability judgments. These simple principles explain classic and new findings, for example, the finding that people overestimate small risks but ignore very small risks. Fuzzy-trace theory differs from other dual-process approaches to reasoning in that it places intuition at the apex of development, considering fuzzy intuitive processing more advanced than precise computational processing (e.g., trading off risks and rewards). The theory supplies a conception of rationality that distinguishes degrees of severity of errors in reasoning. It also includes a mechanism for achieving consistency in reasoning, a hallmark of rationality, by explaining how a person can treat superficially different reasoning problems in the same way if the problems share an underlying gist.
"This is the classic work upon which modern-day game theory is based. What began more than sixty years ago as a modest proposal that a mathematician and an economist write a short paper together blossomed, in 1944, when Princeton University Press published Theory of Games and Economic Behavior. In it, John von Neumann and Oskar Morgenstern conceived a groundbreaking mathematical theory of economic and social organization, based on a theory of games of strategy. Not only would this revolutionize economics, but the entirely new field of scientific inquiry it yielded--game theory--has since been widely used to analyze a host of real-world phenomena from arms races to optimal policy choices of presidential candidates, from vaccination policy to major league baseball salary negotiations. And it is today established throughout both the social sciences and a wide range of other sciences. This sixtieth anniversary edition includes not only the original text but also an introduction by Harold Kuhn, an afterword by Ariel Rubinstein, and reviews and articles on the book that appeared at the time of its original publication in the New York Times, tthe American Economic Review, and a variety of other publications. Together, these writings provide readers a matchless opportunity to more fully appreciate a work whose influence will yet resound for generations to come.
Evidence favors the nested sets hypothesis, introduced by fuzzy-trace theory (FTT) in the 1990s to explain ???class-inclusion??? effects and extended to many tasks, including conjunction fallacy, syllogistic reasoning, and base-rate effects (e.g., Brainerd & Reyna 1990; Reyna 1991; 2004; Reyna & Adam 2003; Reyna & Brainerd 1995). Crucial differences in mechanisms distinguish the FTT and Barbey & Sloman (B&S) accounts, but both contrast with frequency predictions (see Reyna & Brainerd, in press).