Feature Fatigue Analysis Based on Behavioral Decision Making

Mingxing Wu1, Liya Wang1 1Department of Industrial Engineering and Logistics Management, Shanghai Jiao Tong University, Shanghai, China

(Email: xingxing@sjtu.edu.cn, wangliya@sjtu.edu.cn)

Abstract - Feature fatigue represents the phenomenon that customers prefer to choose high-feature products at the purchasing moment (before use); but once they start using the products (after use), they become overwhelmed by the complexity of these high-capability products and annoyed by the features they realize they don’t want or need. Feature fatigue will decrease customer satisfaction and negatively impact manufacturer’s long-term profit. In this paper, we propose a novel method based on behavioral decision making theory to analyze feature fatigue. We adopt six-dimensional perceived value model to analyze the effect of adding features on customer’s perceived value before and after use. Further, we propose an analysis model to analyze feature fatigue quantitatively.

Keywords - Behavioral decision making, Bounded

rationality, Feature fatigue, Perceived value

I. INTRODUCTION Previous research suggests that adding more features

to a product increases customer satisfaction, and then manufacturer’s revenue is expected to improve [1]. Therefore, manufacturers persist in offering customers an ever-increasing number of features aiming to make products more appealing and to increase their profit [2]. However, too many features will make products overwhelming for customers and difficult to use, which may result in customer frustration and ultimately make customers suffer from “feature fatigue” (FF) [1, 3, 4].

FF represents the phenomenon that customers prefer to choose high-feature products at the purchasing moment (before use); but once they start using the products (after use), they become overwhelmed by the complexity of these feature-rich products and annoyed by the features they realize they don’t want or need [1, 3]. In fact, customers’ utility functions for high-feature products before and after use are usually inconsistent [1]. They often give more weight to capability (features) and less weight to usability in their expected utility (before use) than experienced utility (after use) [1, 4] (see Fig. 1). Thus adding more features will increase the initial attractiveness of a product but decrease customer satisfaction with it after use because of the usability problems [1, 4], which ultimately leads to FF. Many cases have been reported to show this problem [3]. One famous example is the BMW 745 car, whose dashboard alone has more than 700 features. This high-capability car is truly attractive initially but, after use, most of the owners are frustrated by the multifunction displays and the complicated iDrive system [1, 3, 4].

FF will decrease customer satisfaction and negatively impact manufacturer’s long-term profit. Many customers will return the products, or take their business elsewhere in the future [4], inducing extra service cost and toxic Word-Of-Mouth, which will ultimately decrease customer loyalty and Customer Lifetime Value (CLV) (Fig. 2) [1].

The main reason for FF is that manufacturers use economic theory which is based on Rational Economic Man assumption to model the effect of adding features on customer’s utility of the product [1, 4], ignoring the fact that customers are bounded rational men: they have limited cognitive resources for handling (re)purchase decisions [5, 6]. Many studies show that customers are often not very good at making predictions about future events [7]. They often overestimate the utility of extra features prior to purchase [3], and give more weight to capability and less weight to usability before use than after use [1, 3, 4].

In this paper, we propose a novel method based on behavioral decision making (BDM) theory to analyze FF. We adopt six-dimensional perceived value (PV) model to analyze the effect of adding features on customer’s PV before and after use. Further, we propose an analysis model based on customer’s PV before and after use to analyze FF quantitatively.

The remainder of this paper is organized as follows. The next section reviews previous research on FF. In section III, we analyze FF by analyzing the effect of adding features on customer’s PV before and after use based on BDM theory. A quantitative FF analysis model based on customer’s PV is proposed in Section IV. Finally, conclusions and discussions are presented in section V.

Fig. 1 The relative weights of capability and usability in customers’ utility functions before and after use

Source: Gene Smith [8], adapted from Rust et al. [4].

978-1-4577-0739-1/11/$26.00 ©2011 IEEE 570

Fig. 2 The effect of high-feature products on CLV

II. LITERATURE REVIEW FF has been recognized as an important issue in many

fields [3]. Some efforts have been made to explain or “defeat” FF. Hamilton and Thompson [9] used construal level theory to explain the reason for FF. They found that direct and indirect product experiences result in different levels of mental construal and product preferences: indirect experience triggers more abstract mental construal and increases preference of high-desirability (capability) products, while direct experience triggers more concrete mental construal and increases high-feasibility (usability) products. Angelis [2] used schema-congruity theory to analyze the effect of adding features on customer’s utility of the product. They found that customer’s evaluation is affected by the cognitive elaboration associated with the product congruity of the added features. Customers perceive a benefit from increasing the number of features only when these features are congruent with the product. Gill [10] proposed that “the evaluation of convergent products (CPs) with a utilitarian versus hedonic base is subject to an asymmetric additivity effect”. Specifically, “whereas CPs with a utilitarian base gain more from adding an incongruent, hedonic functionality than a congruent, utilitarian one, CPs with a hedonic base gain less from an incongruent, utilitarian addition than a congruent, hedonic one”. These studies tell us why customer’s expected utility and experienced utility are inconsistent before and after use, but they do not point out how customers make (re)purchase decisions and how to “defeat” FF.

Thompson et al. [1] proposed an analytical model to analyze the influence of number of features on company’s profitability, but they just focused on the total number of features and ignored the differences between the features. Actually, product features and different combinations of the features may have different effect on customer’s evaluation [3]. Li and Wang [3] proposed a probability based method for FF analysis where Bayesian network technique was used to represent customer’s preferences to capacity and usability, but it is just an evaluation method that cannot provide much decision support for designers.

III. THE EFFECT OF ADDING FEATURES ON CUSTOMER’S PERCEIVED VALUE

A. Behavioral Decision making Theory

Classical decision making theory founded on Rational

Economic Man assumption suggests that decisions made by decision makers should lead to the same performance outcome [11]. Namely, expected utility will be the same with experienced utility. Conversely, BDM theory, which is founded on Bounded Rationality [5], argues that decisions made by decision makers result in superior micro or macro forecasts and performance outcomes [12]. That is, expected utility and experienced utility may be inconsistent. Actually, human decision making behavior often deviates from that predicted by the normative rational model [5]. Thus, using the normative model to predict customer’s decision making behavior may lead to error and ultimately make customers suffer from FF.

B. Six-dimensional Perceived Value Model

Customer behavior literature suggests that customers

make (re)purchase decisions based on their PV of the product [13]. Customers attempt to maximize their PV when they are making (re)purchase decisions [14]. PV is a subjective construct [15] configured by two perspectives, gain and cost. It comes from a trade-off between perceived benefits (positively impact PV) and perceived risks (negatively impact PV) [15].

Researchers have proposed multidimensional methods for measuring PV [13, 16]. In this paper, we adopt six-dimensional PV model for FF analysis. The six dimensions are: (1) financial, (2) performance, (3) psychological, (4) physical, (5) social, and (6) time. Based on the “net perceived return model” [16], we proposed a PV model as follows:

( ) ( )D

i ii

CPV CPB CPR G L= − = −∑ (1)

Where CPV = overall customer’s PV of the product; CPB = overall customer’s perceived benefits of the

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product; CPR = overall customer’s perceived risk of the product; iG and iL are the perceived gain (benefit) and perceived loss (risk) of the product of dimension i , respectively, 1,2, ,6i = … ; D = {Financial Value, Functional Value, Physical Value, Psychological Value, Social Value, Time Value}.

C. Customer’s Perceived Value before and after Use

Since PV will vary at different time [15], customer’s

PV of the product before and after use may be inconsistent. In this subsection, we analyze the effect of adding features on each dimension of customer’s PV before and after use (we just consider the effect of number of features on customer’s PV, ignoring quality, appearance, price and other factors that may affect customer’s PV).

(1) Financial dimension. Customers often like to compare the “cost-performance” of different products. When they form an overall impression of an alternative, they average the values of its features [17, 18]. Before use, high-feature products bring customers a sense of having high cost-performance or relative low price [19]. Thus, feature-rich products have higher financial value than those with fewer features. However, after use, customers realize many features they never or rarely use, because the features are useless or too complex to use. Therefore, they achieve more crucial perceived loss and lower cost-performance than they expected. Their perceived financial value after use gets lower than before use.

(2) Functional dimension. At the purchasing moment, customers are often uncertain about their own preferences because they are uncertain about their own tastes [17, 20]. They “look for reasons that justify a choice to themselves and to others and avoid alternatives that are susceptible to criticism” [17]. Therefore, customers often worried about that products with fewer features cannot meet their needs. Adding more features diminishes product’s performance uncertainty which will lower the functional risk, and will add greater benefits to customers [19]. Thus, adding more features to a product will increase the functional value before use. Nonetheless, in many cases, introducing new product features does not improve customer’s PV after use, because customers find that actually they do not always use all the features of the products they buy [2]. Moreover, the addition of features may even decrease the usability of a product by making it more complex [1, 2]. So their perceived loss after use is higher than that before use, and the perceived benefits after use are less than expected. Thus, customer’s perceived functional value after use is not as high as they expected before use.

(3) Physical dimension. Generally, since we just consider the effect of number of features on customer’s PV and ignore quality, appearance, price and other factors, the number of features has little influence on physical dimension value.

(4) Psychological dimension. At the moment of purchase, the number of features has not so much influence on psychological dimension value. However,

adding more features may produce psychological costs by generating overload and confusion in customer’s mind [1, 2]. When using the products in practice (after use), customers are overwhelmed by the complexity of these feature-overloaded products and annoyed by the features they realize they don’t want or need. They realize that they have made wrong decisions. According to Regret Theory, people will feel sad and sorrow every time they do mistakes on decision making [21, 22]. So the perceived psychological loss after use is higher than that before use. Therefore, their perceived psychological value after use gets lower.

(5) Social dimension. High-feature products may make customers easily achieve praise rather than derision. But when customers are overwhelmed by these products crowded with features, they receive derision rather than praise for their decision faults. So the perceived social value after use is lower than that before use.

(6) Time dimension. At the moment of purchase, customers may predict that high-feature products will be more complex than those with fewer features [1]. But overconfidence theory suggests that customers are overconfident while making decisions [12]. They are overconfident on their ability of dealing with the feature-caused complexities. However, after use, customers realize that actually they need more time to learn to use the all-inclusive products and to maintain these products than they expected. Thus, the perceived time value after use is lower than that before use.

D. Suffering Feature Fatigue

On summary, at the moment of purchase (before use),

feature-rich products have higher PV on financial, functional, psychological, social and time dimensions than those with fewer features. Therefore, the overall customer’s PV of the high-feature products is higher than those with fewer features. In comparison, after working with these feature-overloaded products, customers find lower perceived post purchase value achieved than expected. That means products are not as good as they predicted. In light of the Expectancy Disconfirmation Theory [23], customers evaluate the performance of the products they have purchased with reference to their prepurchase expectations. So when products fail to live up to expectations, customers will get dissatisfied [24], and ultimately suffer from FF.

Customer’s experience will affect their emotion and expectation of the brand they once bought [25, 26]. High levels of satisfaction might not increase retention, but high levels of dissatisfaction might have a large and deleterious impact on retention [27]. In the next purchase cycle, customers’ experiences of FF will decrease their PV of the former purchased brand [28]. They are in the worry of making wrong decisions, being traumatized [22]. In afraid of regression, customers may be hesitated and take their business to other competing brands [21]. Consequently, the former company’s long term profit will be negatively affected.

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IV. QUANTITATIVE FF ANALYSIS MODEL Thompson et al. [1] suggested that customer’s

inconsistency between expected utility and experienced utility causes FF. In this section we propose an analysis method based on customer’s PV before and after use to evaluate the degree of FF of product features.

A. Customer’s Perceived Value before Use

Suppose that the manufacturer adds n new features

to a product (or a product includes n more features than competitors), then the addition of the overall PV of the product before use can be calculated as follows (here we just considered the effect of the number of features on the overall customer’s PV):

( ) ( ) ( )( )

( ) ( )( ) ( ) ( )( ), , ,0 ,0

B B B

D DB n B n B B

i i i ii i

CPV CPB CPR

G L G L

Δ = Δ −

= − − −∑ ∑ (2)

Where the superscript B represents “before use”;

( )BCPVΔ = the addition of the overall customer’s PV of the product before use when adding n new features;

( )BCPB = overall customer’s perceived benefits before use; ( )BCPR = overall customer’s perceived risk before use;

( ),B niG and ( ),B n

iL are the gain (benefit) and the loss (risk) of the product with n new features on dimension i , respectively, 1,2, ,6i = … ; ( ),0B

iG and ( ),0BiL are the

gain and the loss of the product without new features on dimension i , respectively, 1,2, ,6i = … ; D = {Financial Value, Functional Value, Physical Value, Psychological Value, Social Value, Time Value}

B. Customer’s Perceived Value after Use

After use, the addition of the overall PV of adding n

new features can be calculated as follows:

( ) ( ) ( )( )( ) ( )( ) ( ) ( )( ), , ,0 ,0

A A A

D DA n A n A A

i i i ii i

CPV CPB CPR

G L G L

Δ = Δ − =

− − −∑ ∑ (3)

Where the superscript A is “After use”.

C. Feature Fatigue Index

We introduce Feature Fatigue Index ( FFI ) to

evaluate the degree of FF of product features. Considering customer’s PV before and after use, FFI is defined as follows:

( )

( )

A

B

CPVFFICPV

Δ=Δ

(4)

Where ( )BCPVΔ and ( )ACPVΔ is the addition of the overall customer’s PV of the product before and after use when adding n new features, respectively.

The FFI makes a trade-off between customer’s PV before and after use. The higher the FFI is, the lower probably the feature will make customers suffer from FF. Thus, the features with higher FFI will be incorporated into the product more preferentially than those with lower FFI . Manufacturers can make decisions based on a threshold (ϕ ) of the FFI : while FFI ϕ≥ , the feature should be integrated into the product. The threshold (ϕ ) can be determined by practitioners based on expertise.

D. A method for quantify the model

One effective tool for obtaining the FFI is the

Structural Equation Modeling (SEM) [29]. There are many applications such as LISREL, AMOS, EQS, and COSAN can help to analyze the data while using SEM.

The key step of SEM is the questionnaire conduction. The questions in the questionnaire should deal with all six dimensions of customer’s perceived benefits and risks. Since FF comes from incorporating too many features into a product, we should significantly pay more attention to the questions which can reflect the impacts of the added features.

V. CONCLUSIONS AND DISCUSSIONS The problem of FF has been recognized as an

important issue in many fields [3]. In this paper, a novel method based on BDM theory is proposed to analyze FF. We adopt six-dimensional PV model to analyze the effect of adding features on customer’s PV before and after use. Further, we present an analysis model where a FFI is introduced to analyze FF quantitatively.

The inconsistent PV affects customers’ (re)purchase decision making. This research shows that adding too many features to a product will increase financial, functional, psychological, social and time value at the moment of purchase. Thus, feature-rich products have higher overall customer’s PV than that with fewer features before use. So customers tend to purchase high-feature products. However, when they start working with these convergent products, their PV decreases because of the usability problems, which makes customers get frustrated and suffer from FF. In the next purchase cycle, the financial, functional, psychological, social and time value of this brand may decrease, because customer’s experiences of FF will affect their emotion and expectations of the brand. Therefore, they are more probably to take their business elsewhere in the future,

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which ultimately reduces company’s long-term profit. The analysis model is based on customer’s PV before

and after use, and the FFI is used to quantitatively evaluate the degree of FF of product features. One effective tool for obtaining the FFI is the Structural Equation Modeling.

Some limitations call for further research in the future. Firstly, experimental studies should be performed to validate the proposed method. Secondly, besides the six dimensions aforementioned, there may be other dimensions that independently impact customer’s PV.

ACKNOWLEDGMENT This research was supported by the National Natural

Science Foundation of China (Grant no. 71072061/G020801, 70932004/G0209).

REFERENCES [1] D. V. Thompson, R. W. Hamilton, and R. T. Rust, "Feature

fatigue: When product capabilities become too much of a good thing," Journal of Marketing Research, vol. 42, no. 4, pp. 431-442, 2005.

[2] M. D. Angelis, "The effect of adding features on product attractiveness: The role of product perceived congruity", Ph.D, Department of Management, University of Bologna, Bologna, 2008.

[3] M. Li and L. Wang, "Feature fatigue analysis in product development using bayesian networks," Expert Systems with Applications, vol. 38, no. 8, pp. 10631-10637, 2011.

[4] R. T. Rust, D. V. Thompson, and R. W. Hamilton, "Defeating feature fatigue," Harvard business review, vol. 84, no. 2, pp. 98-107, 2006.

[5] H. Simon, "A behavioral model of rational choice," The Quarterly Journal of Economics, vol. 69, no. 1, pp. 99-118, 1955.

[6] H. A. Simon, "Rational decision making in business organizations," The American Economic Review, vol. 69, no. 4, pp. 493-513, 1979.

[7] N. Mandel and S. M. Nowlis, "The effect of making a prediction about the outcome of a consumption experience on the enjoyment of that experience," Journal of consumer research, vol. 35, no. 1, pp. 9-20, 2008.

[8] G. Smith, Capability, usability and feature fatigue, http://atomiq.org/archives/2006/03/capability_usability_and_feature_fatigue.html, 2006

[9] R. W. Hamilton and D. V. Thompson, "Is there a substitute for direct experience? Comparing consumers' preferences after direct and indirect product experiences," Journal of Consumer Research, vol. 34, no. 4, pp. 546-555, 2007.

[10] T. Gill, "Convergent products:What functionalities add more value to the base?," Journal of Marketing, vol. 72, no. 2, pp. 46-62, 2008.

[11] R. Hogarth and M. Reder, "Introduction: Perspectives from economics and psychology," Rational choice: The contrast between economics and psychology, no., pp. 1-23, 1987.

[12] D. Kahneman and A. Tversky, "Prospect theory: An

analysis of decision under risk," Econometrica, vol. 47, no. 2, pp. 263-291, 1979.

[13] V. A. Zeithaml, "Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence," The Journal of Marketing, vol. 52, no. 3, pp. 2-22, 1988.

[14] W. J. Bilkey, "Psychic tensions and purchasing behavior," Journal of Social Psychology, vol. 41, no., pp. 247-257, 1955.

[15] A. Ravald and C. Gro�nroos, "The value concept and relationship marketing," European Journal of Marketing, vol. 30, no. 2, pp. 19-30, 1996.

[16] J. P. Peter and L. X. Tarpey, Sr., "A comparative analysis of three consumer decision strategies," The Journal of Consumer Research, vol. 2, no. 1, pp. 29-37, 1975.

[17] I. Simonson, Z. Carmon, and S. O'Curry, "Experimental evidence on the negative effect of product features and sales promotions on brand choice," Marketing Science, vol. 13, no. 1, pp. 23-40, 1994.

[18] N. H. Anderson, "Averaging versus adding as a stimulus-combination rule in impression formation," Journal of Experimental Psychology, vol. 70, no. 4, pp. 394-400, 1965.

[19] S. M. Nowlis and I. Simonson, "The effect of new product features on brand choice," Journal of Marketing Research, vol. 33, no. 1, pp. 36-46, 1996.

[20] J. G. March, "Bounded rationality, ambiguity, and the engineering of choice," The Bell Journal of Economics, vol. 9, no. 2, pp. 587-608, 1978.

[21] M. Zeelenberg, "Anticipated regret, expected feedback and behavioral decision making," Journal of Behavioral Decision Making, vol. 12, no. 2, pp. 93-106, 1999.

[22] D. Kahneman, "Experienced utility and objective happiness: A moment-based approach," no., 2006.

[23] R. E. Anderson, "Consumer dissatisfaction: The effect of disconfirmed expectancy on perceived product performance," Journal of Marketing Research, vol. 10, no. 1, pp. 38-44, 1973.

[24] S. Evans and A. Burns, "An investigation of customer delight during product evaluation: Implications for the development of desirable products," Proc. IMechE, Part B: J. Engineering Manufacture, vol. 221, no. 11, pp. 1625-1638, 2007.

[25] S. L. Wood and C. P. Moreau, "From fear to loathing? How emotion influences the evaluation and early use of innovations," Journal of Marketing, vol. 70, no. 3, pp. 44-57, 2006.

[26] R. Rust and R. Oliver, "Should we delight the customer?," Journal of the Academy of Marketing Science, vol. 28, no. 1, pp. 86-94, 2000.

[27] V. Mittal, W. T. Ross Jr, and P. M. Baldasare, "The asymmetric impact of negative and positive attribute-level performance on overall satisfaction and repurchase intentions," Journal of Marketing, vol. 62, no. 1, pp. 33-47, 1998.

[28] J. F. Petrick, "First timers' and repeaters' perceived value," Journal of Travel Research, vol. 43, no. 1, pp. 29-38, 2004.

[29] J. C. Anderson and D. W. Gerbing, "Structural equation modeling in practice: A review and recommended two-step approach," Psychological Bulletin, vol. 103, no. 3, pp. 411-423, 1988.

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