Int. J. Human-Computer Studies 108 (2017) 50–61

Contents lists available at ScienceDirect

International Journal of Human-Computer Studies

journal homepage: www.elsevier.com/locate/ijhcs

Why do users prefer the hedonic but choose the Utilitarian? Investigating

user dilemma of hedonic–utilitarian choice

Adarsh kumar Saiindarlal Kakar

Alabama State University, 915 S Jackson St, Montgomery, AL 36104, United States of America

a r t i c l e i n f o

Keywords:

Hedonic features

Utilitarian features

Hybrid feature

User satisfaction

a b s t r a c t

Research studies have shown that although the hedonic product feature is more valued by users it is the utilitarian

that is favored in acquisition choice situations. Further, the hedonic is more favored in forfeiture choice situa-

tions than the utilitarian. In this longitudinal study we examine this phenomenon with actual users of software.

The results of the study show that as expected in acquisition situations users explicitly prefer the utilitarian al-

though their implicit choice as reflected by the feature’s impact on their satisfaction is for the hedonic. However,

in forfeiture situations the explicit-implicit user preference is not based on hedonic–utilitarian considerations.

The results also suggest the reason for this observed phenomenon. Unlike utilitarian features which consistently

showed characteristics of Herzberg’s hygiene factors in acquisition-forfeiture situations, hedonic features demon-

strated characteristics of Herzberg’s motivators only during acquisition, while demonstrating characteristics of

hygiene factors during forfeiture. These findings offer interesting theoretical insights and have useful practical

implications for product managers of software products.

© 2017 Elsevier Ltd. All rights reserved.

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. Introduction

The journey of gaining satisfied users begins with effective captur-

ng, analyzing and understanding their genuine requirements. However,

an we take user responses at face value? Studies have shown that

hile users explicitly state their preference for new utilitarian features

heir ‘true ’ preference is for the hedonic ( Dhar and Wertenbroch, 2000;

iefenbach and Hassenzahl, 2011; Okada, 2005 ). An investigation of

his phenomenon is particularly important in the software context. If

alidated, it will call into question the efficacy of existing prioritiza-

ion techniques in accurately selecting software user requirements for

mplementation in the next release that reflect user preferences.

A review of literature shows that all the currently used software

ser requirements prioritization techniques such as the Analytic Hier-

rchy Process (AHP) ( Saaty, 1980 ), Theory W ( Boehm and Ross, 1989 ),

riority Groups Method ( Wiegers, 1999 ), Planning Game ( Beck, 2001 ),

00 Points Methods ( Leffingwell and Widrig, 2003 ), Binary Search Tree

Heger, 2004 ) and Value Oriented prioritization ( Azar et al., 2007 ) cap-

ure only the users ’ self-stated or explicit preferences. Yet, no study has

nvestigated the phenomenon of explicitly stated versus true preferences

n the context of software product features. Further, most studies in the

ast limited the subject choice to two alternatives involving hypotheti-

al situations. The limited choices do not reflect the complexities of real

hoice situations ( Okada, 2005 ). It is therefore not clear whether the

henomenon observed under such artificial created experimental con-

itions would be replicated in real life.

E-mail address: akakar@alasu.edu

ttp://dx.doi.org/10.1016/j.ijhcs.2017.07.003

eceived 5 May 2016; Received in revised form 10 May 2017; Accepted 6 July 2017

vailable online 12 July 2017

071-5819/© 2017 Elsevier Ltd. All rights reserved.

Additionally, past research has shown that users favor the retention

f the hedonic over the utilitarian in forfeiture situations ( Dhar and

ertenbroch, 2000; Diefenbach and Hassenzahl, 2011; Okada, 2005 ).

owever, while the reason of the preference for the utilitarian over he-

onic in acquisition situation has been empirically investigated and ex-

lained by the user difficulty in justifying the hedonic compared to the

tilitarian ( Dhar and Wertenbroch, 2000; Okada, 2005; Diefenbach and

assenzahl, 2011 ), the preference for the utilitarian over the hedonic in

orfeiture situation observed in these studies is rather contradicted by

his explanation. Further, software product features are not just hedonic

r utilitarian. They can be hybrid with varying degrees of hedonic and

tilitarian dimensions. So how do such Hybrid software features impact

sers ’ explicit and implicit choices? Hybrid features have elements that

rive users ’ true preference (the hedonic) as well as elements that help

n justifying this preference (the utilitarian).

Keeping this context in view, we investigate the phenomenon of

xplicit-implicit user choice of utilitarian, hedonic as well as hybrid

oftware product features. In the study we first perform a review of

ultidisciplinary research in the domain and develop a theory of the

henomenon based on concepts gleaned from the review. We use the

heory to hypothesize the expected user outcomes. We then conduct

n experiment with actual users of a software product in acquisition

nd forfeiture situations mimicking those in real life as closely as possi-

le. We compare the results of feature choices obtained from capturing

sers ’ explicit preferences with results obtained from capturing users ’

rue preferences derived implicitly from the impact of the choices on

A.k.S. Kakar Int. J. Human-Computer Studies 108 (2017) 50–61

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ser satisfaction. The technique for capturing users ’ ‘true ’ preference is

eveloped by adapting a well known method to the context of new soft-

are product features. Finally, we discuss the findings of the study and

heir implications for practice and future research.

. Literature review

In the literature review, we trace the historical origins of widely

ccepted typologies of product attributes and their predicted impacts

n user outcomes such as their satisfaction with the product. The con-

epts gleaned from the literature review will be used later in theory

evelopment to examine user choice of product attributes in acquisition-

orfeiture situations.

.1. Frederick Herzberg and the two factor theory

According to the Motivation-Hygiene theory ( Herzberg et al., 1959 )

here are two types of factors that impact job satisfaction. While mo-

ivating factors such as job recognition, achievement, work itself, ad-

ancement, and responsibility impact job satisfaction positively when

ulfilled, Hygiene factors such as salary, company policies, technical

ompetence, interpersonal relations and working conditions impact job

atisfaction negatively when not fulfilled. However, hygiene factors do

ot have the potential to cause user satisfaction when fulfilled and mo-

ivating factors the potential to cause user dissatisfaction when unful-

lled.

Investigators of consumer satisfaction have frequently adapted mod-

ls and techniques from studies of job satisfaction ( Pfaff, 1973; Czeip-

el et al., 1974 ). These adaptations have face validity because the con-

ept of satisfaction is common to both types of studies ( Maddox, 1981 ).

herefore, although the Motivation-Hygiene theory was developed by

erzberg et al., (1959) as an alternative to Maslow’s theory (1954) for

tudying job satisfaction, it has contributed to a body of knowledge on

ustomer satisfaction. Taking cues from job satisfaction literature, cus-

omer requirements were classified into two categories, those that cause

ustomer dissatisfaction if not fulfilled but no significant satisfaction if

ulfilled, and those that cause customer satisfaction if fulfilled but no

issatisfaction if not fulfilled.

.2. The three factor theory

Follow-up studies ( Swan and Combs, 1976; Maddox, 1981; Cadotte

nd Turgeon, 1988; Johnston and Selvestro, 1990 ) investigating cus-

omer requirements and their impact on customer satisfaction found

upport for Herzberg’s ( Herzberg et al., 1967 ) two factors theory. How-

ver, later studies ( Brandt, 1987; Brandt and Reffet, 1989; Stauss and

entschel, 1992 ; Johnston (1995) ; Anderson and Mittal, 2000 ) found

mpirical support for a three-factor theory, the third factor leading to

ustomer satisfaction as well as dissatisfaction when a customer require-

ent is fulfilled and not fulfilled respectively.

The acceptance of three factor theory has increased over the past 25

ears ( Lofgren and Witell, 2008 ). It has been applied in areas of prod-

ct development, business planning and service management ( Watson,

003 ). Empirical studies have shown the relevance of the three factor

heory to software products ( Zhang and von Dran, 2002; Lehtola and

auppinen, 2006; Zhao and Dholakia, 2009 ). The three factor theory

opular in quality literature as the “theory of attractive quality ” ( Kano

t al., 1984 ) extends Herzberg’s two factor theory. The three factors in

he three factor theory are:

Basic factors: They are prerequisites and must be satisfied first at

east at threshold levels for the product to be accepted. “The fulfillment

f basic requirements is a necessary but not a sufficient condition for

atisfaction ” ( Matzler et al., 2004 ). The user takes Basic requirements

or granted, and therefore does not explicitly ask for them. They are

imilar to Herzberg’s “Hygiene factors ” or “Dissatisfiers ”. They have

he potential to cause user dissatisfaction on non-implementation in

51

he product but do not enhance satisfaction on implementation. The

ther names used for Basic factors are Minimum Requirements ( Brandt,

988 ), Must-be requirements ( Kano et al., 1984 ). An example of non-

oftware Basic feature is air bags in automobiles ( Tontini, 2007 ). Pro-

iding an “air bag ” will not enhance customer satisfaction but if it is

ot present the customer may not buy that automobile. The following

re examples of Basic web site attributes – active links, no need to scroll

eb pages and up-to-date and accurate information ( von Dran et al.,

999 ).

Performance factors: These are requirements that the customer delib-

rately seeks to fulfill. They are uppermost in her consciousness. Fulfill-

ng these requirements leads to customer satisfaction and not fulfilling

hem leads to dissatisfaction. The other name for Performance factors

s One-dimensional requirements ( Kano et al., 1984 ). As “Performance

actors are typically directly connected to customers ’ explicit needs and

esires. Therefore, a company should be competitive with regard to per-

ormance factors ” ( Matzler et al., 2004 ). An example of non-software

erformance feature is gas mileage of automobiles ( Matzler and Hinter-

uber, 1998 ). The higher the gas mileage the more satisfied the customer

s and vice versa. The following are examples of Performance web site

ttributes – quick response and loading time, table of contents and ease

f use ( von Dran et al., 1999 ).

Excitement factors: Excitement features are those that the customer

id not expect. They surprise the user by adding unexpected value to the

roduct thereby delighting her. But not providing the Excitement fea-

ures do not cause user dissatisfaction. The Excitement factors are sim-

lar to Herzberg’s “Motivation factors ” or “Satisfiers ”. The other names

or Excitement features are Attractive features ( Kano et al., 1984 ), Value

nhancing requirements ( Brandt, 1988 ). Implementing excitement fea-

ures differentiate the product from the competition. An example of Ex-

itement feature of a non-software product is “no automobile engine

ound while accelerating ” ( Wolfindale et al., 2012 ). Customers found

his attribute to be unexpected and delightful compared to “no squeaks

nd rattles ” which they considered a Basic attribute and “overall interior

uietness ” which they regarded as a Performance attribute. The follow-

ng are examples of exciting web site attributes – variety of media for

ifferent learning styles, interesting information, and customization to

ndividual user preferences ( von Dran et al., 1999 ).

.3. Hedonic–utilitarian attributes

Parallel to the development of the three factor theory another ty-

ology of product features was taking shape. In the 1980 s consumer re-

earch was evolving from the cognitive bases of consumer decision mak-

ng to include the affective drivers ( Holbrook and Hirschman, 1982 ).

onsumption experience became the focus of investigation with the in-

reasing realization that a product may be valued by the consumer for

ts own sake rather than only as a means to an end ( Sweeney and Soutar,

001 ). Intrinsic factors such as emotional aspects of consumer behavior

ecame the object of investigations.

Earlier research in consumer decision making was based on utilitar-

an perspective of the product. The underlying assumption was that con-

umers are rational problem solvers ( Bettman, 1979 ). The Homo Eco-

omics view of consumers as a utility calculator was increasingly chal-

enged by the experiential view, Homo Ludens, of a consumer guided by

eeds and wants ( Rintamäki et al., 2006 ). The experiential view high-

ighted the influence of 3 Fs —fantasies, feelings and fun —representing

he “hedonic ” aspects of consumption, a term used to denote the doc-

rine that pleasure or happiness is the chief good in life ( Ogertschnig

nd Heijden, 2004; Merriam-Webster, 2003 ).

Other researchers supported the combined “thinking and feeling ” di-

ensions of consumer decision making by suggesting the relevance of

oth utilitarian and hedonic components of value derived by the user

rom the use of the product (e.g., Batra and Ahtola, 1990 ). MacKay

1999) noted that the appeal of a product is an “amalgam of rational

nd emotional factors ”. There was an increasing realization that users

A.k.S. Kakar Int. J. Human-Computer Studies 108 (2017) 50–61

Table 1

Essential differences between hedonic and utilitarian attributes.

Utilitarian attributes Hedonic attributes

Symbolize “shoulds ” ( Bazerman et al., 1998 ) Symbolize “wants ” ( Bazerman et al., 1998 )

Are practical and functional ( Stelmaszewska et al., 2004 ) Represent novelty, unexpectedness, fun, aesthetics ( Stelmaszewska et al., 2004 )

Serve Pain avoidance goals ( Higgins, 2001; Chernev, 2004; Higgins, 1997 ) Serve Pleasure seeking goals ( Higgins, 2001; Chernev, 2004; Higgins, 1997 )

Are extrinsic motivators i.e. users derived value is the outcome of but distinct

from the performance of the activity ( Davis et al., 1992 )

Are intrinsic motivators i.e. users derive value from the process of performing

the activity itself ( Davis et al., 1992 )

Represent means to an end ( Babin and Harris, 2011 ) Represent an end in itself ( Babin and Harris, 2011 )

Represent users ’ cognitive or reasoned preferences ( Bazerman et al., 1998 ) Represent users ’ affective or emotional preferences ( Bazerman et al., 1998 )

Can be Objectively appraised ( Chitturi, 2009 ) Are Subjective, Experiential ( Chitturi, 2009 )

Are Herzberg’s (1959) Hygiene factors ( Zhang and von Dran, 2002; Hassenzahl

et al., 2010 )

Are Herzberg’s (1959) Motivators ( Zhang and von Dran, 2002; Hassenzahl et al.,

2010 )

Serve Pain avoidance goals ( Higgins, 2001; Chernev, 2004; Higgins, 1997 ) Serve Pleasure seeking goals ( Higgins, 2001; Chernev, 2004; Higgins, 1997 )

Fulfill Preventive goals ( Higgins, 2001; Higgins, 1997 ) Fulfill Promotional goals ( Higgins, 2001; Higgins, 1997 )

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eek a complete experience with software products, an experience that

ot only includes achieving well-defined goals, but also involves the

enses and generates affective response ( Bly et al., 1998; Venkatesh and

rown, 2001 ).

A general consensus emerged over time that utilitarian and hedo-

ic dimensions of the product are indeed distinct and together capture

he essential facets of a product ( Batra and Ahtola, 1990; Block, 1995;

har and Wertenbroch, 2000; Mano and Oliver, 1993; Schmitt and Si-

onson, 1997; Strahilevitz and Myers, 1998; Veryzer, 1995 ). While the

roduct attributes which provide UV are “functional and goal oriented

nd generate cognitive response from the user ”, the product attributes

hich provide HV represent “novelty, aesthetics, unexpectedness, plea-

ure and fun and evoke affective user responses ” ( Strahilevitz and Myers,

998 ).

This conceptualization of hedonic and utilitarian product values

HV and UV) as distinct and independent constructs in consumer re-

earch literature (see Diefenbach et al., 2014 ), led to the introduction of

quivalent constructs of hedonic and pragmatic quality later in Human-

omputer Interaction (HCI) literature ( Hassenzhal et al., 2000 ; also

ee Diefenbach et al., 2014 ) and of perceived usefulness and perceived

njoyment in Technology Acceptance Model (TAM) literature ( Davis,

989; Venkatesh, 1999; Mun and Hwang, 2003; van der Heijden, 2004 ).

lthough perceived enjoyment is by far the most popular aspect of he-

onic value investigated in TAM, other operationalizations of hedonic

alue such as perceived playfulness and flow experience have also been

sed ( Gerow et al., 2013 ). From the perspective of self-determination

heory ( Deci and Ryan, 1985 ) while perceived usefulness provides ex-

rinsic motivation (means to an end) to its users to use the system, per-

eived enjoyment, playfulness and flow provide intrinsic motivation (an

nd in itself).

However, despite these different operationalizations, in general he-

onic and utilitarian attributes are understood in the same way across

iteratures. We summarize the essential differences between hedonic and

tilitarian product attributes in Table 1 .

. Theory development

Users in general prefer to make rational choices rather those influ-

nced by emotions ( Chitturi et al., 2007; Hsee et al., 2003; Kivetz and

imonson, 2002 ; O ’Curry and Strahilevitz, 2001 ; Okada, 2005 ). Past re-

earch shows that when users are faced with choice between a hedonic

nd a utilitarian alternative they “show ” their preference for the utilitar-

an ( Dhar and Wertenbroch, 2000; Diefenbach and Hassenzahl, 2011 ).

he reason underlying this phenomenon is termed as “lay rationalism

Lindgaard et al., 2006 ). People wish to make a rational choice. Hedonic

eatures are hard to justify rationally compared with utilitarian features

Diefenbach and Hassenzahl, 2009 ). Further hedonic attributes are as-

ociated with waste, luxury and guilt ( Kivetz and Simonson, 2002; Hsee

t al., 2003 ). Users wish to be seen as addressing utilitarian goals (points

f pain) before addressing their hedonic goals (pleasure needs) to avoid

52

uilt feelings ( Berry, 1994 ) although their true preference may be for

he latter ( Diefenbach and Hassenzahl, 2011 ). Thus the “explicit ” im-

ortance of user requirements obtained through self-report may reflect

socially acceptable ” or ‘politically correct ’ ’ response of users ( Oliver,

997 ). They are thus not likely to match the “derived ” or implicit re-

ponse when the effects of the aforementioned causes distorting the ex-

ression of “true ” user choice are mitigated. This leads us to the follow-

ng hypothesis:

ypothesis 1. The explicit user choice of software product feature will

iffer from her implicit choice of the user as reflected by its impact on

ser satisfaction with users preferring the utilitarian over the hedonic

n explicit choice and hedonic over utilitarian in implicit choice in ac-

uisition situations

IS (Information Systems) and HCI (Human Computer Interaction)

iteratures have conceptualized utilitarian product attributes as hygiene

actors and hedonic product attributes as Motivators ( Zhang and von

ran, 2002; Hassenzahl et al., 2010 ). However, hedonic and utilitarian

eatures represent ideal types. But product attributes are not generally

ound in pure form but vary along the hedonic–utilitarian dimension.

hus software product features can be categorized as predominantly

tilitarian, predominantly hedonic or hybrid. The hybrid features are

either predominantly hedonic nor predominantly utilitarian but are a

ix of both. For example, performance features which deliver a sur-

risingly high level of functionality can provide enjoyment (delight) to

he user ( Tontini et al., 2013 ). Enjoyment is an emotion that was found

o cross-load on hedonic as well as utilitarian value provided by soft-

are products attributes ( Ogertschnig and Heijden, 2004; van der Hei-

den, 2004 ). Also, hedonic features such as aesthetically pleasing user

nterface may promote utilitarian use ( Tractinsky, 1997; Tractinsky et

l., 2000 ) through reduced perception of effort ( Venkatesh, 2000 ). Such

ybrid features will therefore demonstrate characteristics of both utili-

arian and hedonic qualities.

We suggest that the predominantly utilitarian, the predominantly

edonic features and the hybrid features will exhibit characteristic sat-

sfaction responses as shown in Table 2 aligned with the arguments that

ollow for Hypotheses 2, 3 and 4.

The predominantly hedonic features are satisfiers (motivators)

Zhang and von Dran, 2002; Heizenzahl et al., 2010 ). By creating a

ositive experience for the user hedonic features create positive affect

Diefenbach and Hassenzahl, 2009; Diefenbach and Hassenzahl, 2011;

iefenbach et al., 2014; Mano and Oliver, 1993 ). Hedonic features are

ssociated with cheerfulness, excitement and delight – all promotion

pleasure seeking) related emotions ( Chitturi et al., 2008 ). While utili-

arian features are considered a means to an end (meeting practical goals

uch as increase in productivity and career progression), the intrinsically

ewarding hedonic features are considered by users as an end in itself

Ha et al., 2007 ). The positive experience generated by providing he-

onic features results in higher impact than the negative experience of

ot providing the hedonic feature resulting in asymmetric user response

A.k.S. Kakar Int. J. Human-Computer Studies 108 (2017) 50–61

Table 2

Impact of feature implementation on user satisfaction.

Feature types User satisfaction impact on implementation User satisfaction impact on non-implementation

Positive No impact Negative impact Positive No impact Negative

Utilitarian features √ √

Hybrid features √ √

Hedonic features √ √

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Diefenbach and Hassenzahl, 2009; Diefenbach and Hassenzahl, 2011;

iefenbach et al., 2014 ). While utilitarian features are entirely expected

or product functioning and taken for granted by the users, hedonic fea-

ures are considered an unexpected bonus. Therefore providing hedonic

eatures exceed expectations when provided for in the product thereby

nhancing user satisfaction but are not missed when not provided for in

he product. It is not surprising therefore to find that while hedonic prod-

ct attributes result in higher repurchase and positive word-of-mouth of

onsumers, but not providing them do not result in consumer abandon-

ent and negative word-of-mouth ( Chitturi et al., 2008 ). This leads us

o the hypotheses:

ypothesis 2. Hedonic features will impact user satisfaction positively

hen selected for implementation into the software product but will not

mpact user satisfaction negatively when not selected for implementa-

ion into the software product in acquisition situations

The predominantly utilitarian features by contrast are dissatis-

ers (hygiene) ( Zhang and von Dran, 2002; Heizenzahl et al., 2010 ).

hey prevent a negative user experience and thereby negative affect

Diefenbach and Hassenzahl, 2009; Diefenbach and Hassenzahl, 2011;

iefenbach et al., 2014; Mano and Oliver, 1993 ). Utilitarian features are

orrelated with security and confidence or prevention (pain avoidance)

elated emotions ( Chitturi et al., 2008 ). While hedonic features are con-

idered as “wants ”, utilitarian features are considered as “shoulds ” and

sers take it for granted that they will be provided for in the product

Bazerman et al., 1998 ). Thus, the negative experience generated by not

roviding utilitarian features results in higher impact than the positive

xperience of providing the utilitarian feature resulting in asymmet-

ic user response ( Diefenbach and Hassenzahl, 2009; Diefenbach and

assenzahl, 2011; Diefenbach et al., 2014 ). It is not surprising there-

ore to find that while not providing utilitarian features promotes nega-

ive word-of-mouth and adversely impacts repurchase intention of con-

umers, providing utilitarian features do not necessarily promote repur-

hase intention and positive word-of-mouth ( Chitturi et al., 2008 ). This

eads us to the hypothesis:

ypothesis 3. Utilitarian features will impact user satisfaction nega-

ively when not selected for implementation into the software product

ut will not impact user satisfaction positively when selected for imple-

entation into the software product in acquisition situation

The third type of features —hybrid —is neither predominantly hedo-

ic nor predominantly utilitarian but is a mix of both. Therefore provid-

ng these features generate positive affect as well as prevent negative

ffect. They will therefore be correlated with promotion related emo-

ions of cheerfulness, pleasure and delight as well as prevention related

motions of security and confidence. Users will therefore experience

igh arousal on both implementation of hybrid features as well non-

mplementation resulting in a symmetric user response. This leads us to

he hypothesis:

ypothesis 4. Hybrid features will impact user satisfaction positively

hen selected for implementation into the software product and impact

ser satisfaction negatively when not selected for implementation into

he software product in acquisition situation.

These three types of software features —utilitarian, hedonic and hy-

rid —and their predicted impacts on user satisfaction in the hypothesis

53

re consistent with both, Herzberg’s motivation-hygiene theory and the

heory of attractive quality. Hedonic features are similar to Satisfiers/

ttractive/ Excitement features while utilitarian features are similar to

issatisfiers/ Must-be/ Basic features and Hybrid features are similar to

ne–dimensional/ Performance features with respect to their impacts

n user satisfaction.

Additionally, products and their attributes often become part of the

xtended self of the user and reflect his self-identity ( Belk, 1988 ). This

s especially true for hedonic attributes ( Diefenbach and Hassenzahl,

011 ). Dhar and Wertenbroch (2000) observed higher endowment ef-

ect for hedonic goods compared to the utilitarian. “Once acquired, a

edonic good may quickly be considered as an appreciated part of one-

elf, which people naturally want to keep hold of, whereas utilitarian

oods always remain replaceable. ” ( Diefenbach and Hassenzahl, 2011 ).

ence we would expect hedonic features to be more favored for reten-

ion in a forfeiture situation than the utilitarian.

Further, hedonic features are more experiential and therefore diffi-

ult to justify in acquisition situations compared to the utilitarian fea-

ures which can be objectively appraised ( Chitturi, 2009 ). However,

nce acquired there is more spontaneous elaboration in case of hedonic

eatures than the utilitarian in a forfeiture situation ( Dhar and Werten-

roch, 2000 ). Hedonic attributes are more sensory and image provok-

ng ( Maclnnis and Price, 1987 ). Research has shown that easily imagin-

ble attributes become more salient during forfeiture choice situations

Keller and McGill, 1994; Sherman et al., 1985; Shiv and Huber, 1999 ).

osing hedonic features enhance negative affective consequences and

ead users to minimize negative emotions ( Roese, 1997; Sanna, 1999 )

hereby leading us to the following hypotheses:

ypothesis 5. The explicit user choice of software product feature will

ot differ from her implicit choice as reflected by their impact on user

atisfaction with users preferring the hedonic over the utilitarian in ex-

licit as well as implicit choice in forfeiture situations

Yet, taking in consideration their distinctive characteristics ( Table

) —hedonic features as satisfiers and utilitarian features as dissatisfiers

nd hybrid features as both satisfiers and dissatisfiers —lead us to the

ollowing hypothesis:

ypothesis 6. Hedonic features will impact user satisfaction positively

hen selected for retention in the software product (acquisition situa-

ion) but will not impact user satisfaction negatively when selected for

emoval from the software in forfeiture situations

ypothesis 7. Utilitarian features will not impact user satisfaction pos-

tively when selected for retention in the software product (acquisition

ituation) but will impact user satisfaction negatively when selected

rom removal from the software in forfeiture situations

ypothesis 8. Hybrid features will impact user satisfaction positively

hen selected for retention in the software product (acquisition situa-

ion) but will impact user satisfaction negatively when selected for re-

oval from the software in forfeiture situations

. Method

An Experimental method was adopted in the study to investigate user

esponse to software product features. Experimental research is useful

or examining cause and effect. It offers a methodical way of comparing

A.k.S. Kakar Int. J. Human-Computer Studies 108 (2017) 50–61

Table 3

Sample user feature requests used in the study.

Feature number Feature description

1 Choose from a calendar Allow dates to be chosen from a calendar

2 Shortcut to create tasks Enable users to create a shortcut to go directly into the task creation mode

3 Make quiet hours completely quiet Have a new option —“Super Quiet Hours ”—during which all reminders should be disabled

4 Purging completed tasks Provide a feature to purge all completed tasks

5 Color tasks based on priority Enable users to visually see task priority through a color coding scheme

6 Due date only The application should allow the user to bypass the due time

7 Creating tasks that repeat yearly Allow creation of yearly recurring tasks to remind users about important events such as birthdays, and anniversaries

8 Grocery shopping list Provide a feature to enable users to create and update a regular grocery list

9 New task default priority All tasks should be set to medium priority by default

10 Geolocation reminders Provide a feature to remind users of that they are passing through an important geolocation

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ifferences in the effect of treatments (type of product features) on the

ependent variable (user satisfaction).

.1. Experimental setting

Astrid Task Manager, a popular mobile app. with young users, was

hosen as a software product for investigation in the study. Astrid at

he time of the study had an active user forum where users could post

nd provide their comments on new feature requests. Actual users of

strid Task Manager provided their response to a pen and paper based

urvey in January 2012 for acquisition choice situation and then later in

anuary 2013 for forfeiture choice situation. In the acquisition situation

0 randomly selected feature requests (see Table 3 ) made by users of

strid Task Manager were used as the test instrument. In the forfeiture

ituation, 7 of these 10 feature requests which were implemented in

strid were used as the test instrument. The decision to have only 10

ser feature requests in the test instrument in the acquisition situation

as taken to mitigate the cognitive overload of the subjects participating

n the study. This decision was based on subject feedback during the

ebriefing session of the pilot study with Gmail where 15 use feature

equests were used.

.2. Subjects

We recruited a representative youth cohort as subjects. Youth are

ecognized as innovators and early adopters of the latest technologies

Ehrenberg et al., 2008 ). The subjects ’ age ranged between 19 and 24

ears and female students (69 out of a total of 122) outnumbered males

53 out of a total of 122). The average age of the subjects was 21.28

ears with the female subjects averaging 21.34 years and the male sub-

ects averaging 21.22 years. The sample size for the experiment was

etermined based on the effect size found during the pilot study. The

ilot study was conducted with 49 subjects who were users of Gmail.

ssuming a power of 0.8 and alpha = 0.05 (one tail), a look up of Co-

en’s power primer ( Cohen, 1992 ) gave the sample size. To account for

ortality rate in this longitudinal study, the figure from Cohen’s table

as inflated by 10% to get the required sample size of 70 subjects. In

he actual study data was obtained from 139 subjects in the first round

f the study, the analysis was restricted to responses of 122 subjects who

articipated in both rounds of the experiment.

.3. Study design and procedure

The study was conducted in 2 rounds in each of the two periods,

n January 2012 and in January 2013. A repeated measure design was

sed in the experiment i.e. all subjects participated in all conditions

f the experiment. The use of repeated measure design offers two ad-

antages. Variations in response due to individual variation such as in

erms of user efficacy, demographics and use experience are mitigated.

he design is therefore extremely sensitive to finding statistically sig-

ificant differences between the experimental conditions that users are

ubjected to. In addition fewer subjects are needed for the experiment.

54

.3.1. Period 1 (January 2012)

Subjects in Round 1 rated their choice of features using the com-

only used Binary Search tree method ( Heger, 2004 ) . Using this

ethod, s ubjects created a ranked list of feature request by first cre-

ting a node with the first feature request from the test instrument and

omparing the next feature with this node. If the feature is of lower pri-

rity it is placed on the left of the node else it is placed on the right of the

ode. This process continues with subsequent features in the feature set

ntil a ranked list of requirements is produced. The node at the extreme

ight is the feature of highest priority to that subject and the node at the

xtreme left is the feature of lowest priority. This ranked list of features

epresented the represented the rank order of users ’ explicit choices.

To arrive at the implicit choice of the users, subjects classified the

eatures in the test instrument into two categories, one included features

hich they want implemented in the software and the other included

eatures they are not interested in. Users also rated their overall sat-

sfaction with the implementation subset. User satisfaction is defined

s an affective user response to the degree to which the feature subset

nder consideration meets his specified requirements. A 7 point single

tem scale by Andrews and Withey (1976) with 1 —Terrible 2 —Unhappy

—Mostly Dissatisfied 4 —Neither Satisfied nor Dissatisfied 5 —Mostly

atisfied 6 —Pleased 7 —Delighted) was used by subjects for rating their

verall satisfaction level with the implementation subset. The implicit

hoice is then be determined statistically by regressing user satisfaction

n the user choice for implementation/ non-implementation of each fea-

ure in the feature set.

We adapted PRCA (Penalty Reward Contrast Analysis) a widely used

echnique developed by Brandt (1987) and used across product and

ervice domains (e.g. Alegre and Garau, 2009; Bartikowski and Llosa,

004; Busacca and Padula, 2005; Conklin et al., 2004; Ting and Chen,

002; Fuchs and Weiermair, 2003; Fuchs and Weiermair, 2004 ; Fuller et

l., 2006; Matzler et al., 2004a, 2004b; Matzler and Renzl, 2007; Fuller

nd Matzler, 2008; Mikuli ć and Prebe ž ac, 2011; Mikuli ć and Prebe ž ac,

010 ) for analyzing the data collected in Round 1 to determine the im-

licit choice of users. This method contrasts with explicit methods such

s the binary search tree which directly ask for user preference of each

eature. Such explicit choice methods which first determine user prefer-

nce of each feature and then assess the overall satisfaction potential of

feature set are subject to the biases such as users wanting the hedo-

ic but choosing the utilitarian. The PRCA method resolves this predica-

ent by working backwards by first getting the overall satisfaction with

feature set and then deriving the implicit satisfaction potential of each

eature statistically. By mitigating the hedonic–utilitarian bias the im-

licit satisfaction potential of each feature is expected to reflect the true

ser preference for that feature.

Using two sets of dummy variables representing each feature re-

uest with a value of (1, 0) indicating the user preference for its im-

lementation and a value of (0, 1) indicating the user choice for its

on-implementation, multiple regression analysis is conducted with the

verall user satisfaction with implementation of the feature subset as

he dependent variable. With this statistical approach, we get two re-

ression coefficients for each feature, one representing the user satisfac-

A.k.S. Kakar Int. J. Human-Computer Studies 108 (2017) 50–61

Table 4

Feature categorization based on hedonic–utilitarian ratings.

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ion with implementation of a feature and the other regression coeffi-

ient representing the user (dis) satisfaction with non implementation

f the same feature. The sum of the absolute values of the significant

egression coefficients for implementation and non-implementation of

feature represents the assessment of users ’ implicit importance of a

eature by providing a measure of the range of impact of a feature on

verall user satisfaction ( Mikuli ć and Prebe ž , 2008 ).

Subjects in round 2 conducted a week later rated each feature on

he hedonic–utilitarian measure ( Leclerc et al., 1994 ). Previous research

emonstrates that the temporal separation between measures reduces

otential effects due to Common Method Variance ( Sharma et al., 2009 ).

he ratings were anchored at 1 = not at all and 9 = very much. Aligned

ith previous use of the measure, the term hedonic was defined for

he user as “pleasant and fun, something that is enjoyable and appeals

o the senses “ and the term utilitarian was defined as “useful, practi-

al, functional, something that helps achieve a goal ” ( Strahilevitz and

yers, 1998; Dhar and Wertenbroch, 2000 ). Features were identified as

redominantly utilitarian (1 Standard Deviation above mean on the util-

tarian scale but 1 Standard deviation below mean on the hedonic scale)

nd predominantly hedonic (1 Standard Deviation above Mean on the

edonic scale but 1 Standard deviation below mean on the utilitarian

cale). The remaining features after excluding the predominantly utili-

arian as well as the predominantly hedonic were identified as Hybrid

see shaded area in Table 4 ).

The categorization of features is thus a reflection of how the target

roup feels overall about a feature even though individual assessments

ay vary. For example, if the majority of users assess a website fea-

ure such as “interesting information ” as more fun than useful (example

elebrity gossip) then it will be categorized as predominantly hedonic;

f the majority of users assess the information on the website as more

seful than fun (as in academic or professional websites) then it will get

ategorized as predominantly utilitarian; and if the majority of users

eel that the information is both useful and fun or neither then it will

et categorized as hybrid.

.3.2. Period 2 (January 2013)

In round 1, subjects provided their explicit choices using the afore-

entioned Binary Search tree method ( Heger, 2004 ). Using this method,

sers ranked the 7 features which were implemented out of the 10 fea-

ure requests in January 2012, in terms of their priority in which they

anted retained in the product in the forfeiture situation. Further they

lassified the features in the test instrument into two categories, one in-

luded features which they want retained in the software and the other

ncluded features they do not mind being removed from the software

roduct. Users also rated their overall satisfaction with the retained sub-

et in Round 1 using the Andrews and Whithey (1976) scale. The im-

55

licit choice of the users was determined by the aforementioned adapted

RCA method.

Subjects in round 2 rated each features on the hedonic–utilitarian

easure ( Leclerc et al., 1994 ). As in period 1, Round 2 in period 2

as conducted a week after Round 1 to reduce potential effects due to

ommon Method Variance ( Sharma et al., 2009 ). The user responses in

ound 2 were used to classify the features into hedonic, utilitarian and

ybrid categories using the procedure discussed in the previous section.

he study design and procedure is summarized in Fig. 1 below

. Results and analyses

.1. Acquisition situation —period 1

The results in Table 4 based on analysis of data obtained in round

show the features which were classified by the subjects in the pre-

ominately hedonic category {7, 10}, in the predominantly utilitarian

ategory {1, 4} and in the Hybrid category {2, 3, 5, 6, 8, 9}. The results

rom the implicit method ( Table 6 ) based on their expected user satisfac-

ion impacts ( Table 2 ) confirm the results of categorization —hedonic,

tilitarian and hybrid —obtained from explicit user responses ( Table 5 ).

The results of individual users explicit choices showed that the fea-

ures selected for implementation in the acquisition phase were found

o be positively correlated to utilitarian value (0.93) rated by individ-

al users. However, the users implicit choices for implementation were

ound to be positively related to hedonic value (0.96) thereby supporting

ypothesis 1. This can also be seen for the users ’ aggregate results pre-

ented in Tables 5 and 6 . Among the features, predominantly utilitarian

eatures 1 and 4 were ranked 2 and 1 respectively and predominantly

edonic features 7 and 10 were ranked 5 and 6 respectively. The hybrid

eatures 2 and 9 that were high in both utilitarian and hedonic values

ere ranked in-between at 4 and 3 respectively. Thus hypothesis 1 was

ully supported.

Further, the results of the regression analysis described in the method

f analysis section of data collected in rounds 1 and 2 ( Table 6 ) show

hat the hedonic features impacted user satisfaction positively when im-

lemented into the software product but did not impact user satisfaction

hen not implemented into the software product thus supporting Hy-

othesis 2. Additionally, utilitarian features impacted user satisfaction

egatively when not implemented into the product but did not impact

ser satisfaction when implemented into the product, thereby support-

ng Hypothesis 3. Of the Hybrid features {2, 3, 5, 6, 8, 9}, features {2, 9}

mpacted user satisfaction positively on implementation and negatively

n non-implementation thus supporting Hypothesis 4, but Hybrid fea-

ures {3, 5, 6,8} had a non-significant impact on both implementation

nd non-implementation. Thus, Hypothesis 4 was not fully supported.

A.k.S. Kakar Int. J. Human-Computer Studies 108 (2017) 50–61

Fig. 1. Study design and procedure.

Table 5

Feature wise rating on the hedonic–utilitarian scale (acquisition phase).

Feature Number (Feature type) 1 (U) 2 (B) 3 (B) 4 (U) 5 (B) 6 (B) 7 (H) 8 (B) 9 (B) 10 (H)

Hedonic rating 0.21 0.72 0.17 0.09 0.11 0.28 0.83 0.22 0.82 0.92

Utilitarian rating 0.81 0.69 0.14 0.78 0.09 0.14 0.11 0.13 0.79 0.28

Figures above are users ’ normalized mean hedonic–utilitarian ratings (between 0 and 1). Figures in bold are ratings that are 1

standard deviation above mean

B = hybrid; H = predominantly hedonic; U = predominantly utilitarian.

Table 6

Results of adapted PRCA in acquisition phase.

Table 7

Feature wise rating on the hedonic–utilitarian scale in forfeiture phase.

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.2. Forfeiture situation

The results of acquisition-forfeiture situations, ( Tables 5 and 7 ) show

hat the users ’ categorization of hedonic–utilitarian-hybrid remained

onsistent for each of the 7 common requirements during the two time

eriods. However, the individual users ’ explicit-implicit choice in forfei-

ure situation for retention-removal of Astrid features were uncorrelated

0.04) with the hedonic–utilitarian dimensions. This is also clear from

ables 7 and 8 which show that the two hedonic features 7 and 10 were a

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56

ot the preferred user choice for retention. They were ranked 4 and 2

espectively in the user choice. Thus hypothesis 5 was not supported.

Further, the results of the adapted PRCA of data collected in rounds

and 2 of forfeiture situation ( Table 8 ) show that one hedonic features

7} did not impact user satisfaction positively when implemented into

he product but impacted user satisfaction negatively when not imple-

ented into the product and the other hedonic feature {10} did impact

ser satisfaction positively when retained into the software product but

lso impacted user satisfaction negatively when removed from the soft-

are product, Thus Hypothesis 6 was not supported. Additionally, util-

A.k.S. Kakar Int. J. Human-Computer Studies 108 (2017) 50–61

Table 8

Results of adapted PRCA in forfeiture phase.

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tarian features {1, 4} impacted user satisfaction negatively when not

etained into the product but also impacted user satisfaction positively

hen retained in the product, thereby not supporting Hypothesis 7. Of

he Hybrid features {5, 6, 9}, feature {9} impacted user satisfaction pos-

tively when retained and negatively on removal thus supporting Hy-

othesis 8, but Hybrid features {5, 6} had a non-significant impact on

oth retention and removal. Thus, Hypothesis 8 was not fully supported.

. Discussion

Thus the results of the study while fully supporting Hypothesis 1, 2

nd 3 did not support Hypothesis 4, 5, 6, 7 and 8. In general, the results

ndicate that the suggested theory supported the predictions in the ac-

uisition phase but not in the forfeiture phase. Analyzing the results fur-

her we find that the reason for the forfeiture choice to be independent

f the hedonic–utilitarian characteristics lies in the anomalous behavior

f hedonic–utilitarian features in forfeiture situation. While the classifi-

ation of features between the two time periods in hedonic, hybrid and

tilitarian categories remained consistent (see Tables 5 and 7 ), hedo-

ic and utilitarian features demonstrate their distinctive characteristics

nly in the acquisition phase (see Table 6 ) but behaved differently in the

or feiture phase (see Table 8 ). Instead of showing the expected charac-

eristics of a satisfier, one hedonic feature (feature 7) demonstrated the

haracteristics of dissatisfier and another (feature 10) demonstrated the

haracteristic of satisfier as well as dissatisfier in the forfeiture situa-

ion. The utilitarian features (features 1 and 4) while demonstrating the

xpected characteristics of dissatisfier in acquisition situation, demon-

trated the characteristics of satisfier as well as a dissatisfier in forfeiture

ituations.

However, while the results provide a clue that the Hypotheses 5

nd 6 may not have been supported due to the anomalous behavior

f hedonic–utilitarian features in forfeiture situations, they do not pro-

ide the explanation for the change in their distinctive characteristics of

hese features in the two time periods. We therefore delved into existing

iterature again to find the answers. The theory of attractive quality sug-

ests that feature characteristics may not be durable and that they may

e subject to life cycles ( Kano, 2001 ). Kano (2001) gave the example of

remote control for television which was an excitement feature in 1983,

performance feature in 1989 and a basic feature in 1998. Löfgren and

itel (2008) in a longitudinal study confirmed the existence of feature

ife cycles for an e-service of ordering cinema tickets online.

Also, anecdotal evidence suggests that Graphical User Interface

GUI) was an excitement feature of a software product in the 1970 s.

y the 1980 s it became a performance feature, and by 1990 s it became

basic feature. Thus although GUI (and remote control of television)

tarted off as a satisfier (excitement feature), over time it transitioned

57

o became a satisfier as well as dissatisfier (performance feature) and

ventually became a dissatisfier (basic feature). Today, users and con-

umers will be extremely upset if their software did not have a GUI or

television did not provide for a remote control. They take the exis-

ence of these features in the product for granted even though at one

ime it was a novelty and users and consumers only aspired to possess

hem initially and later found them to be performance enhancing. Ap-

lying the concept of feature life cycles to the findings of the study we

an infer that during the time period of the study the hedonic features 7

nd 10 of Astrid may have similarly transited from the attractive to the

asic and performance categories respectively as can be seen from their

atisfaction impacts in Tables 5 and 7 .

From another perspective, the novelty and appeal of the features that

arlier provided them with enjoyment decreases with time. According

o the hedonic treadmill theory individuals react to deviations in one’s

urrent hedonic state based on automatic habituation processes by mak-

ng constant stimuli fade into the background ( Helson, 1948, 1964 ); and

hereby making the psychological resources available for dealing with

ovel stimuli ( Fredrick and Loewenstein, 1999 ). Further, studies have

hown that usefulness of a software product and its features are not re-

lized immediately as in the acquisition phase. In the short run, users

pend time in getting familiar with the various software product fea-

ures and how to use them. But with repetitive use the learning curve

ets in and in accordance with the classic learning curve model their

roductive use of the feature increases exponentially with experience

Ritter and Schooler, 2002 ). Through their unique knowledge derived

rom use of the product, users discover new ways of using existing fea-

ure as well as get exposed to new product features to improve their

ask performance ( Kristensson et al., 2004; Magnusson et al., 2003 ). As

hey discover new product functionality, users may also share them with

ther users ( Wu and Sukoco, 2010; Ardichvili et al., 2003 ).

This declining novelty and pleasure which a new feature provides

ut its increasing utility, may have resulted in hedonic feature 7 “Allow

reation of yearly recurring tasks to remind users about important events

uch as birthdays, and anniversaries ” and hedonic feature 10 “Provide

feature to remind users of that they are passing through an important

eolocation ” transiting over time from the attractive feature category

o behave like basic (dissatisfier) and performance (satisfier as well as

issatisfier) features respectively as can be seen from their user satis-

action impacts (see Tables 6 and 8 ). Also, due to increasing utility, the

tilitarian feature 1 “Allow dates to be chosen from a calendar ” and util-

tarian feature 4 “Provide a feature to purge all completed tasks ” may

ave transited from Basic to Performance category as can be evidenced

rom the user satisfaction impacts in Tables 6 and 8 . Thus explaining

hy the results of the study supported Hypotheses 1, 2, and 3 fully in

A.k.S. Kakar Int. J. Human-Computer Studies 108 (2017) 50–61

Fig. 2. Characteristic user satisfaction impacts of feature types in acquisition phase.

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he acquisition phase but did not support the parallel hypotheses 5, 6

nd 7 in the forfeiture phase.

The findings of the study also showed that in both acquisition-

orfeiture situations many hybrid features did not influence user sat-

sfaction significantly as predicted (see Table 2 ) thus not supporting hy-

otheses 4 and 8. Further analyses of the data in the acquisition phase

how that the hybrid features {3, 5, 6, 8} which did not impact user

atisfaction as predicted were 1 standard deviation below mean on both

edonic and utilitarian scales (see Tables 5 and 6 ). The Hybrid features

2, 9} that did not influence user satisfaction as predicted were 1 stan-

ard deviation above mean on both hedonic and utilitarian scales. Thus

hile some Hybrid features {2, 9} are as expected dissatisfiers as well as

atisfiers, other features categorized in the Hybrid category (3, 5, 6, 8}

re neither (see Table 6 ). We therefore put the Hybrid features such as

3, 5, 6, 8} in a separate category labeled Extraneous i.e. features that

sers are indifferent to. It does not matter to the user as reflected by

heir satisfaction impacts whether these features are provided for or ex-

luded from implementation in the software product. These are features

hat are below par on both utilitarian and hedonic scales.

Thus, the findings of the study provide designers and product man-

ger with a way to not only identifying hybrid features ( Table 6 ) but

lso as a way of separating out from among them those that the users

o not care about. Committing resources to implementing such extrane-

us features is non-remunerative, whereas not committing resources to

mplementing hybrid features is risky. Extraneous features do not im-

act user satisfaction positively on implementation nor negatively on

on-implementation whereas hybrid features do (see Fig. 2 ). They im-

act user satisfaction positively on implementation and negatively on

on-implementation. Thus unlike other types of features hybrid features

annot be ignored. This is not surprising considering that the sum of the

bsolute values of the significant regression coefficients for implementa-

ion and non-implementation of a feature in the software product using

RCA method represents the range of impacts of a feature on user satis-

action and thereby their importance to the users ( Mikuli ć and Prebe ž ,

008 ). In case of Hybrid features the sum of absolute values of regression

oefficients were consistently higher than the sum of absolute values of

egression coefficients obtained for utilitarian, hedonic and extraneous

eatures (see Tables 6 and 8 )

Fig. 2 by summarizing the characteristic user satisfaction impacts of

ll types of feature also illustrates how the proposed method adapted

rom PRCA of implicitly capturing user preferences can be used by de-

igners and product managers in managing user satisfaction with the

oftware product. Based on the findings of the study, if the goal in the

cquisition phase is to first preclude dissatisfaction then one should pri-

ritize on implementing requirements 9, 4, 2 and 1 in the decreasing

rder of priority depending on the availability of resources (see Table

). If the goal is to enhance user satisfaction then the software prod-

ct manager should focus on implementing features 10, 9, 7, 2 in the

58

ecreasing order of priority. Further, if the goal is to enhance hedo-

ic value of the product by making it more attractive and enjoyable

o use then the software product manager should focus on implement-

ng features 7 and 10. If the goal is to enhance the utilitarian value of

he software by improving user productivity and helping him meet his

unctional goals then the product manager should focus on implement-

ng features 1 and 4. Additionally, the study also accurately identified

xtraneous features {3, 5, 6, 8} that software managers can ignore as

hey neither add value to the user nor the software product.

. Limitations

The findings of the study are applicable to only utilitarian products.

n hedonic products users may not have to justify their choice of the

edonic attributes and hence their explicit and implicit choices during

cquisition phase may not be different. However, this is a matter of

nvestigation for future studies. Further, the subjects chosen for the em-

irical study are youth between 19–24 years of age. The rationale was to

et as homogenous a sample group of subjects as possible as the objec-

ive of the study was to control extraneous variables such as segmental

ifference in user preferences and mitigate alternative explanations for

he results obtained. This may limit the generalizability of the results.

lthough the study is an extension of a well researched phenomenon

rom other knowledge disciplines, for greater confidence in its empiri-

al findings and the proposed explanation, future research may examine

he user responses in acquisition-forfeiture situations for other user seg-

ents and other software products.

. Contribution

The users ’ hedonic-utility dilemma has been investigated since the

980 s. Research studies have shown that although the hedonic is more

alued by users it is the utilitarian that is favored in acquisition choice

ituations. ( Dhar and Wertenbroch, 2000; Diefenbach and Hassenzahl,

011; Okada, 2005 ). Further, the hedonic is more favored in forfeiture

hoice situations than the utilitarian. However, no study has examined

his phenomenon in the context of software products. In this longitu-

inal study we examined this phenomenon with users of a software

roduct. We designed the study to resemble the real-life choices among

ultiple alternatives with users of an actual software product in both

cquisition and forfeiture situations, instead of artificially created ex-

eriments between two imaginary choices in the studies conducted in

he past (see Okada, 2005 ). Further, to find out the difference between

mplicit and explicit choice of users in acquisition as well as forfeiture

ituations we adapted a widely accepted PRCA method to unravel the

mplicit choice of the users for comparing it with the explicitly stated

hoice.

The results of the study in general support those obtained in the past

tudies about preference for the hedonic in acquisition situations ( Dhar

nd Wertenbroch, 2000; Diefenbach and Hassenzahl, 2011; Okada,

005 ). While explicitly users preferred the utilitarian their implicit

hoice as reflected by the impact of the feature on their satisfaction was

or the hedonic. However, the results did not support the observations

f past studies about user choice in forfeiture situations that the explicit

hoice of the users is for the hedonic. They show that both explicit as

ell as implicit preference of the user is independent of the hedonic–

tilitarian considerations in forfeiture situations. The results also point

o the reason for this observed phenomenon. Predominantly hedonic

nd predominantly utilitarian features demonstrated characteristics of

atisfiers and dissatisfiers respectively only during the acquisition phase,

hile demonstrating characteristics of ‘dissatisfiers ’, and ‘satisfiers and

issatisfiers ’ respectively during forfeiture phase. A deeper analysis in-

icates that the underlying reason for this change in feature character-

stics may lie in the phenomenon of feature cycles. As elaborated in the

iscussion section, features types are not static constructs but vary with

ime and use experience. These findings and observations are a unique

A.k.S. Kakar Int. J. Human-Computer Studies 108 (2017) 50–61

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ontribution of the study. They open up new avenues for research that

ay have interesting theoretical and practical implications.

Past literature have emphasized the importance of involving users

s active contributors in the product development process rather than

s passive participants (e.g. Gardiner and Roth well, 1985; Leonard-

arton, 1995; Roth well, 1976; von Hipper, 1988; Wit ell, Lofgren

nd Gustafson, 2011 ). It has been suggested that the product evolution

hould be innovative in the users ’ frame of mind not the developers ’

Fellows and Hooks, 1998 ). Implementing new product features that do

ot resonate with the users may result in product investments that are

ounterproductive. Perhaps it is for this reason that prioritization meth-

ds for software user requirements have traditionally used user self re-

orts to identify features for implementation into the software product.

owever, the findings of this study questions whether we can take ex-

licit user responses to new product features at face value. In this study

e find evidence to suggest that we cannot take the user self report at

ace value in the acquisition phase. Users are hesitant to choose hedo-

ic features although they may prefer to acquire them. This was clear

hen both the predominantly utilitarian features were selected in ex-

licit choices made by users and yet the implicit choices suggest that

hey favored the hedonic.

Thus another contribution is the evidence found in the study that

apturing implicit user preferences through methods such as adaptation

f the widely accepted PRCA method has potential. The method could

ot only identify salient predominantly hedonic and predominantly util-

tarian features but also salient hybrid features that can impact user

atisfaction. Further the study also accurately identified extraneous fea-

ures that users do not care about. These findings are useful for practi-

ioners as they will help in managing user satisfaction and maximizing

eturn by prioritizing investment in implementing only those features

hat user’s value and eliminating those features which they do not value.

xtraneous features do not impact user satisfaction on implementation

s well as non-implementation, indicating that users do not care about

hem either way. Identifying extraneous feature is therefore important.

mplementing them in the software product has deleterious effect on

oth users and software providers. They can enhance user fatigue and

ncrease investment in the product without providing commensurate re-

urn for the resources invested by software provider in implementing

hem.

In a recent meta-analysis of TAM (Technology Adoption Model) stud-

es, Gerow et al., (2013) came up with a rather intriguing finding that

oth utilitarian and hedonic features are equally important to the users

f even utilitarian products. It is therefore important for software devel-

pment organizations to accurately identify salient utilitarian as well as

edonic features for implementation in their product. The study find-

ngs provide deeper insights into the merits of this observation. While

tilitarian features are important to prevent dissatisfaction, the hedonic

eatures are important for enhancing satisfaction. Thus if the goal is to

nhance user satisfaction then software providers should focus on hedo-

ic features. However, if the goal is to stem a decline in user satisfaction

hen software providers should focus on providing utilitarian features.

hus both predominantly utilitarian and hedonic features complement

ach other and do play an important role in meeting the satisfaction

oals of the software product.

Therefore, in general, features identified as predominantly hedonic

s well as features identified as predominantly utilitarian should be im-

lemented into the software product to maximize user satisfaction. How-

ver, hybrid features should be implemented only selectively as users

an be indifferent to hybrid features that are low in both hedonic and

tilitarian values. Investing in such extraneous features is a wasteful use

f scare organizational resources. Further, as discussed in the discus-

ion section, while utilitarian value of a feature is expected to increase

n time, the hedonic value of feature declines with time. Thus software

roduct managers should continually focus on introducing new hedonic

eatures to keep the users interested in the software product.

59

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