This article was downloaded by: [University of Illinois Chicago]On: 13 November 2014, At: 22:30Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House,37-41 Mortimer Street, London W1T 3JH, UK

Applied EconomicsPublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/raec20

Effects of consumer preferences on the convergenceof mobile telecommunications devicesYeonbae Kim , Jeong-Dong Lee & Daeyoung Koha Techno-Economics and Policy Program , Seoul National University , San 56-1, Shinlim-Dong, Kwanak-Ku, Seoul, 151-742, Korea Republicb Techno-Economics and Policy Program , Seoul National University , San 56-1, Shinlim-Dong, Kwanak-Ku, Seoul, 151-742, Korea Republic E-mail:Published online: 19 Aug 2006.

To cite this article: Yeonbae Kim , Jeong-Dong Lee & Daeyoung Koh (2005) Effects of consumer preferences on theconvergence of mobile telecommunications devices, Applied Economics, 37:7, 817-826, DOI: 10.1080/0003684042000337398

To link to this article: http://dx.doi.org/10.1080/0003684042000337398

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) containedin the publications on our platform. However, Taylor & Francis, our agents, and our licensors make norepresentations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of theContent. Any opinions and views expressed in this publication are the opinions and views of the authors, andare not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon andshould be independently verified with primary sources of information. Taylor and Francis shall not be liable forany losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoeveror howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use ofthe Content.

This article may be used for research, teaching, and private study purposes. Any substantial or systematicreproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in anyform to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

Effects of consumer preferences

on the convergence of mobile

telecommunications devices

Yeonbae Kim, Jeong-Dong Lee*, and Daeyoung Koh

Techno-Economics and Policy Program, Seoul National University,San 56-1, Shinlim-Dong, Kwanak-Ku, Seoul, 151-742, Korea Republic

Amidst the overall trend of convergence in information technology, deviceconvergence is noteworthy. This study looks at the possible direction ofdevice convergence based on consumer preferences for the main attributesof the mobile terminal of the future. Conjoint analysis and a mixed logitmodel using a Bayesian approach with Gibbs sampling are used to learnconsumer preferences. Results show that consumers generally prefera keyboard and a medium-sized display, although at present mostconsumers are indifferent to whether the terminal provides high-qualityInternet service and to whether it operates many kinds of applicationprograms or programs originally designed for personal computers. Giventhe heterogeneity of consumer preferences, partial, rather than perfect,device convergence is anticipated. Implications for the future of deviceconvergence and how it will affect other types of convergence are drawn.

I. Introduction

Convergence represents an important trend in infor-

mation technology (IT). Several particular types

of convergence are noted. For example, in service

convergence various services are incorporated into

one converged service. In network convergence sepa-

rate networks are incorporated into a converged

network; this has happened to the extent that the

distinction between wired and wireless networks

is no longer meaningful. In marketing and business

convergence, many value chains or business strategies

are changed to a newer and converged chain or

strategy. In interface and terminal convergence, also

referred to as device convergence, many kinds of

extant devices and terminals used for various means

are incorporated into a new, converged device that

enables consumers to use the converged services and

connect to the converged network.

Convergence itself is an important trend not only

in IT but also with respect to twenty-first century

society as a whole. As time goes by, given the rapid

progress in information technologies and chang-

ing consumer needs, the trend towards the over-

lapping and incorporating of application fields,

and the blurring of distinctions between previously

separate industries, will become ever more preva-

lent (Blackman, 1998). Convergence will be a new

driving force for IT industries suffering from market

saturation as it will create new demands, drastically

change market structures, require new policies and

regulations, inspire firms to set new research-and-

development or business strategies, and affect the

whole society.

*Corresponding author. E-mail: leejd@snu.ac.kr

Applied Economics ISSN 0003–6846 print/ISSN 1466–4283 online # 2005 Taylor & Francis Group Ltd 817

http://www.tandf.co.uk/journalsDOI: 10.1080/0003684042000337398

Applied Economics, 2005, 37, 817–826

Dow

nloa

ded

by [

Uni

vers

ity o

f Il

linoi

s C

hica

go]

at 2

2:30

13

Nov

embe

r 20

14

This study focuses on convergence with regard

to the mobile telecommunications terminal. Usingconjoint analysis, it estimates consumer preferences

regarding the future multiuse-converged mobileterminal, and predicts the future direction of deviceconvergence in mobile telecommunications.

The terminal or device convergence one sees inmobile telecommunications has its own importance –

first, in terms of the huge influence it will exerton the large-scale, fast-growing device and terminal

industries, and second, through a ‘feedback effect’,by which it will affect other convergences such asservice convergence and network convergence.

The existing literature1 on convergence generallyhas focused on service or network convergence or on

the convergence phenomenon as a whole. Authorshave seldom treated device convergence as a main

topic, and their analysis of the topic has been intuitiverather than based on quantitative data. This study

differs in that it analyses device convergence mainly,and the analysis is based on quantitative informationabout consumer preference.

Consumer preference is the most important factorin determining the direction device convergence will

take, because device convergence applies to theterminal or device the consumer uses directly in his

or her hands. Moreover, recently a fairly large part ofthe development of IT products has been dominatedby the pull of demand rather than the push of

technology. Yoffie (1997) refers to the example ofdevice convergence between the television and the

videocassette recorder, noting that although tech-nology may make the device convergence possible,

if there is not enough consumer demand, that deviceconvergence will fail in the end.

To estimate consumer preference, conjoint analysis

is used. Conjoint analysis is a method used to analyseconsumer preference for a new product that has not

appeared in the market. Some important attributesconcerned with future mobile terminal convergence

are chosen and hypothetical alternatives composedwith them. Those alternatives are shown to respon-

dents and ranked.This procedure yields information about consumer

preferences for the main attributes of the multiuse-

converged mobile terminal of the future, and it allows

one to identify attributes that figure critically in deviceconvergence. In addition, some implications ofdevice convergence are found for other convergences,such as service convergence and network convergence.

For statistical models, mixed logit using theBayesian approach with Gibbs sampling is estimated(Allenby and Rossi, 1999; Chiang et al., 1999; Huberand Train, 2001; Train, 2003). The mixed logit modelhas the advantage of not imposing restrictions onthe ordering of preferences so that coefficientsfor each attribute are the same across consumersas with a multinomial logit model (Layton, 2000;Calfee et al., 2001). Accordingly, it provides a flexiblemeans for representing the distribution of preferenceson each attribute change in the population.

The remainder of the paper is organized as follows:Section II: introduction to issues affecting mobiletelecommunications device convergence; Section III:methodology and survey details; Section IV:estimation results; Section V: implications; SectionVI: concluding remarks.

II. Issues Affecting MobileTelecommunicationsDevice Convergence

The first step in an analysis of mobile telecommuni-cations device convergence is the identification ofthe general factors that shape such convergence.Roughly, such issues can be categorized into three:one, the extent to which device convergence is likelyto occur; two, the specific attributes of the device; andthree, the effect of device convergence on other typesof convergence.

Extent of device convergence

Regarding this question there are at least two schoolsof thought. One is that technological progress and theneeds of consumers for converged services and manyfunctions will bring all kinds of terminals and devicestogether, and in the end they will be incorporated intoone device. According to this way of thinking, almostcomplete device convergence is likely. The secondschool holds that although service and network

1Blackman (1998) dealt with the convergence phenomenon as a whole, treating convergence between telecommunicationsand media, and showing some implications for new, appropriate regulatory frameworks. Messerschmitt (1996) dealt withconvergence between computing and telecommunications technologies, specifically in terms of networked computing applica-tions, and emphasized the importance of user-to-user applications in convergence. Mueller (1999) also dealt with convergenceas a whole. In his working paper, in regard to device convergence, he insisted that because of the new media ecologyand technological improvements, devices and applications would become more diverse and specialized while becomingmore interoperable. Yoffie (1997) referred to past examples of device convergence such as between the television and thevideocassette recorder and emphasized knowing whether consumer needs exist or not to predict success of device convergence.

818 Y. Kim et al.

Dow

nloa

ded

by [

Uni

vers

ity o

f Il

linoi

s C

hica

go]

at 2

2:30

13

Nov

embe

r 20

14

convergence will become more and more likely,because of the diversity of consumer demandsand tastes the device will evolve into many types.Mueller (1999) posits that rather than a perfectlevel of device convergence being achieved, it ismore probable that current terminals and deviceswill evolve into some application-specific devicesthat can be used regardless of the service or networkthey are connecting to. That is to say, partial deviceconvergence is the most likely outcome.

Naturally it cannot be said for sure which theorywill prevail. But is there not a method by whichone might predict which outcome is the more likely?To find that method, one should, above all, identifythe factors that will affect the extent of deviceconvergence. The two theories presented aboveshare the idea that consumer needs and preferenceswill be key factors in device convergence. Therefore,knowing the extent of the heterogeneity of con-sumers’ preferences can help tell one which outcomeis more probable. In this study, the mixed logitmodel is adopted, which can reflect the heterogeneityof consumers’ preferences well.

Specific attributes of the future mobile device

Regarding attributes, there are many issues. But thisstudy focuses on the following attributes because,for example, even though colour or design may beimportant factors that attract consumers to a device,it is thought that they are not very significant factorsbearing on device convergence and therefore are notof profound importance here:

. How large should the display be, and howportable should the future mobile device be?

. What kind of input equipment will most likelybe adopted?

. What level of Internet service quality should thefuture mobile device provide?

. Is it desirable for a future multiuse mobileterminal to provide very diverse applicationprograms, or is it desirable that the terminal becompatible with a personal computer (PC)?

The preceding issues can be separated into twocategories: issues directly related to device conver-gence and issues not directly related to the deviceconvergence phenomenon, but having significance.The second and the fourth questions above aredirectly related to device convergence. The thirdquestion is not directly related to device convergenceitself; however, it may affect the future directionof device convergence very much, and it alsoholds important meaning for service convergencesin mobile telecommunications. The first question is

not directly related to the device convergencephenomenon, either. However, it has bearing ondevice convergence because portability and displaysize are key factors when determining the form factorof the future multiuse-converged mobile terminal,and the form factor comes into play when consumerschoose devices.

Effect of device convergence on othertypes of convergence

How do the changing preferences of consumersfor devices affect convergence in services and net-works? This feedback effect is another issue thatis considered.

III. Methodology

Because this study focuses on mobile device conver-gences that are not yet on the market, conjoint ana-lysis is used for demand analysis. Conjoint analysisis a stated-preference technique that has been widelyapplied in marketing (Huber and Train, 2001);transportation research (Carlsson, 2003); healtheconomics (Bryan and Parry, 2002; San Miguelet al., 2000); and environmental economics (Roeet al., 1996; Layton, 2000); among others. Using theconjoint technique, levels of attributes or featuresdescribing a good, service, or policy are combinedto build descriptions of hypothetical bundles.Individuals are asked to state their preferences forhypothetical alternatives, and their responses arethen analysed using statistical models (Alvarez-Farizo and Hanley, 2002).

The survey used in this study asks respondents torank order a set of alternatives. The reason for usingcontingent-ranking results is that the method canreduce sampling costs substantially because moreinformation is recovered from each respondent thancan be gotten when respondents provide only a singleresponse, that of their most preferred choice (as ina referendum) (Layton, 2000).

In contingent-ranking conjoint analysis, a rank-ordered logit model (Layton, 2000; Calfee et al.,2001) is generally used for estimation. Althoughthis model has an advantage in that ranking (choice)probability has a simple closed form, it imposesrestrictions on the ordering of preferences in such amanner that coefficients for each attribute are thesame across consumers.

The mixed logit model captures preference varia-tion by introducing stochastic terms into the coeffi-cients caused by deviations from mean preferencesand allowing these terms to be correlated acrosseach other. With this method, the stochastic

The future of the mobile telecommunications terminal 819

Dow

nloa

ded

by [

Uni

vers

ity o

f Il

linoi

s C

hica

go]

at 2

2:30

13

Nov

embe

r 20

14

component of utility is correlated across alternativesthrough the model’s attributes – that is, the modeldoes not impose an ‘independent of irrelevant alter-natives’ property (Calfee et al., 2001).

Procedures for estimating the mixed logit modelhave been developed within both the classical andthe Bayesian traditions. The classical methods ofestimation are generally based on the maximumlikelihood estimation. Applications of the mixedlogit model using a classical approach are givenin Brownstone and Train (1999), Layton (2000),Calfee et al. (2001), and Carlsson (2003).

Allenby and Rossi (1999), Chiang et al. (1999),Huber and Train (2001), and Train (2003) have devel-oped Bayesian approaches to the mixed logit model.This method constructs a Markov chain Gibbssampler that can be used to draw directly fromthe exact posterior distribution and perform finitesample likelihood inference to any degree of accuracy(McColluch and Rossi, 1994).

These Bayesian procedures have certain advan-tages over the classical procedure. First, one canavoid direct evaluation of the nontrivial likelihoodfunction and the associated problems of approximat-ing choice probabilities that are required with classi-cal methods. In addition, the mathematical propertiesof the multinomial model do not guarantee conver-gence of maximum likelihood estimation to a globalmaximization, and the solution obtained by a non-linear programming optimizer may depend criticallyon the location of the starting point.

Second, desirable estimation properties, such asconsistency and efficiency, can be attained undermore relaxed conditions using Bayesian procedurescompared with classical methods. Maximum simu-lated likelihood is consistent only if the number ofdraws used in a simulation is considered to rise withsample size; efficiency is attained only if the numberof draws rises more quickly than the square rootof the sample size. In contrast, Bayesian estimatorsare consistent for a fixed number of draws usedin a simulation and are efficient if the numberof draws rises, at any rate, with the sample size(Train, 2003).

Third, the results of Bayesian procedures can beinterpreted simultaneously from both the Bayesianand classical perspectives, drawing on the insightsafforded by each tradition. The Bernstein von Misestheorem establishes that, under conditions main-tained in this study’s methods, the mean of theBayesian posterior is a classical estimator that isasymptotically equivalent to the maximum likelihood

estimator. The theorem also establishes that thecovariance of the posterior is the asymptotic covari-ance of this estimator (Train and Sonnier, 2003).

Model specification

Assume that individual i faces a choice among Jalternatives in each of T choice sets in a surveyand the individual is asked to rank the alterna-tives in order of preference. One can represent theperson’s utility from alternative j in choice set t asfollows:

Uijt ¼ �0ixijt þ "ijt ð1Þ

where xijt is the vector of attributes associated withalternative j, �i is a vector of unknown parameters(the coefficients of attribute vector xijt), and "ijt is arandom disturbance.

The random disturbance ("ijt) is assumed to havean independent and identical extreme value distri-bution. The coefficients vector, �i, is assumed to bedistributed normally across the population withmean vector b and variance-covariance matrix W(unbounded normal distribution).

In this contingent-ranking conjoint analysis, onecan adopt the same procedure as in Train (2003)for Bayesian estimation. The only change is thatone should calculate the probability of the individ-ual’s sequence of rankings, which is used in theMetropolis-Hasting (M-H) algorithm, instead of theprobability based on the response of the most pre-ferred choice as in Train (2003).2 The probability ofperson i’s observed sequence of rankings is

L ri ¼ ri1, . . . , riT� �

���� �

¼YT

t¼1

YJ�1

j¼1

e�0xijt

Pk¼Jk¼j e

�0xiktð2Þ

where rit ¼ fri1t, ri2t, . . . , riJtg is the vector of individ-ual i’s ranking responses of the choice sets in descend-ing order of preference in period t.

The unbounded normal distribution for a pricecoefficient has some inappropriate properties. Anormal distribution for a price coefficient impliesthat some share of the population actually prefershigher prices. The existence of price coefficientswith the wrong sign renders the model unusable forcalculation of willingness to pay and other welfaremeasures, and if the distribution of price coefficientsoverlaps zero, then the willingness to pay isunboundedly large for some customers (Train andSonnier, 2003). In this application, we assume thatthe price coefficient has a log-normal distribution.

2 For details, see Train (2003, pp. 302–6).

820 Y. Kim et al.

Dow

nloa

ded

by [

Uni

vers

ity o

f Il

linoi

s C

hica

go]

at 2

2:30

13

Nov

embe

r 20

14

This distribution has beneficial properties in that itrestricts the price coefficient for all respondents tohaving the same sign, and the price coefficient cannothave a value of zero. This distribution can beobtained as simple transformations of normal (�),C ¼ expð�Þ.

Unbounded normal distribution is also inappro-priate for the coefficient of a desirable attribute thatis valued (or, at worst, ignored) by all customers. It isimplausible that there are users who dislike the higherquality of service, that is, users who choose lowerquality of service, even though doing so actuallycosts the same as choosing the higher-quality service.Accordingly, we assume that the coefficients of attrib-utes such as ‘quality of Internet’ and ‘diversity andcompatibility of application programs’ have normaldistribution censored from below at zero. The trans-formation for normal censored from below at zerois C ¼ maxð0, �Þ. This transformation is useful forcoefficients of an attribute to which some customersare indifferent and other customers find desirable(Train and Sonnier, 2003).

When a transformation is used for bounded distri-butions of a coefficient, utility is specified as follows:

Uijt ¼ C �ið Þ0xijt þ "ijt ð3Þ

where C is a transformation.There are minor changes in the estimation proce-

dure with this transformation. The probability of theindividual’s sequence of ranking, which is used in theM-H algorithm, should be changed based on trans-formed �i (Train and Sonnier, 2003).

Survey and data

For this study, respondents were surveyed who haveat least one kind of mobile terminal such as a note-book PC, mobile phone, smart phone, personaldigital assistant (PDA), satellite phone, and so on.The survey was first conducted among 500 respon-dents; 55 of those were later discarded because oflack of some information. The survey was restrictedto adults ranging in age from 20 to 60 in May 2003,in Seoul, Korea. To improve the reliability of thecomplex conjoint survey, a one-to-one direct inter-view method by well-trained interview specialistswas used. Critical attributes and levels were selectedfrom a pilot test before the real test. Table 1 showsthe attributes and their levels used in the actualsurvey. They reflect the important issues for deviceconvergence discussed in Section II.

Note that preference for portability was notincluded as an attribute in the real survey despiteits importance to the form factor of the device.Preference for portability can be inferred from

preferences for other attributes, and one shouldavoid double-counting bias. It is assumed that pref-erence for the display size can almost directly revealpreference for portability. Drawings of squares areincluded in real size corresponding to attribute levelsettings to make respondents consider portabilitywhen they considered their preference for display size.

On the other hand, to characterize preference forportability as mainly associated with the display size,due notice had to be given in the interview processthat the size of the keyboard is not overly related toportability. To do so, the interviewer was asked toexplain carefully to respondents that the keyboardcan be an attachable, foldable or modifiable typethat does not affect portability very much.

Because the number of alternative cards was toolarge, fractional factorial design was used to reducethat number, and 15 alternative cards were finallyarrived at. Because respondents tend to evaluatethree or four higher-ranking alternatives seriouslyand evaluate the lower-ranking alternatives trivially,the 15 alternative cards were divided into threesub-alternative card sets consisting of five cardseach. Thus, respondents were asked to rank thosefive cards three times. (For details on the surveycards, see the Appendix.)

Table 1. Attributes and attribute levels

Attributes Attribute levels

Pricea 400 000 won550 000 won700 000 won

Type of inputequipment

Keypad (0)Keyboard (with trackball mouse)Touchscreen

Quality ofInternet

Low-quality Internet: able to embodycontents- and letter-based Internetonly (e.g., CDMA2000) (0)

High-quality Internet: able to embodydesktop PC-like Internet(e.g., wireless LAN)

Diversity andcompatibilityof applicationprograms

Low level: only application programsspecially designed for the terminalcan be operated (0)

High level: many application programscan be operated; also, manyapplication programs for PC can beoperated compatibly

Display size Small display (small mobile phone –large-display mobile phone)

Medium display (large-display mobilephone – PDA)

Large display (PDA – compactnotebook PC) (0)

aAt the time of the survey (May 2003), 1199.8 Korean wonapproximately equals 1 US dollar.

The future of the mobile telecommunications terminal 821

Dow

nloa

ded

by [

Uni

vers

ity o

f Il

linoi

s C

hica

go]

at 2

2:30

13

Nov

embe

r 20

14

IV. Estimation Results

Except for price, which is a quantity variable, to

estimate the coefficients of dummy variables for the

attributes, keypad, low-quality Internet, low-level

diversity and compatibility of application programs,

and large display were chosen as the base attribute

levels. Thus the estimated coefficients signify the

relative preferences for these base attribute levels.

It is assumed that the coefficients of high-quality

Internet and high-level diversity and compatibility

of application programs have censored normal dis-

tribution. In addition, log-normal distribution was

assumed for the coefficients of price.

This application generated 20 000 draws with

Gibbs sampling. The first 10 000 were discarded, and

the draws in every tenth iteration of the second 10 000

draws were retained; the 1000 retained draws were

used to conduct inference. The means of the 1000

draws of b and those of the diagonal elements of W

are shown in Table 2.

From the Bayesian perspective, these are posterior

means of b and the diagonal elements of W; from

the classical perspective, these represent the estimated

mean and variance of the �i in the population. The

research interpreted these results from the classical

viewpoint. From the estimation results of Table 2,

the means of the coefficients are significant in general,

and the variances of all coefficients are large in

magnitude and significant as well. This means that

a lot of heterogeneity exists in consumers’ preferences

for the critical attributes of the future mobile

telecommunications terminal. This result justifies

the use of the mixed logit model rather than the

standard logit model, and it helps one to answer the

question ‘To what extent is device convergence likely

to happen?’.

Because b and W are the mean and the variance

of the �i in the population according to the classical

perspective, the distribution of the coefficient for

each variable is obtained through simulation on the

estimated values of b and W. Two thousand draws of

�i were taken from a normal distribution with mean

equal to the estimated value of b and variance equal

to the value of W. Each draw of �i was then trans-

formed to obtain a draw of coefficients. Table 3

shows means and variances of these coefficients.

The result shows that variance of price is very

large, which means that very different patterns of

response to price will be found among consumers.

From Table 3, consumers prefer a medium-sized

display, a keyboard, high-quality Internet, and a high

Table 2. Estimation results

AttributesMean of� (b)

Standarddeviation

Varianceof � (W )

Standarddeviation

Pricea �4.7169* (0.6578) 7.0460* (2.1857)Keyboard 0.4291* (0.0721) 0.4393* (0.0939)Touchscreen �0.1627* (0.0741) 0.4173* (0.0779)High-quality Internet �0.9597 (0.5026) 2.5462* (1.1332)High-level diversity and compatibility �1.5821* (0.5198) 3.8201* (1.3570)Small display 0.0560 (0.0871) 0.7572* (0.1647)Medium display 2.1273* (0.1177) 1.7267* (0.3300)

aThe price variable is entered as the negative of price so that the distribution of the coefficient (transformeddistribution) for price is everywhere positive.*Significant at the 5% level.

Table 3. Means and variances of draws for the coefficients

Attributes Attribute levels Mean Variance Median

Price �0.2781 6.7529 �0.0085Input equipment Keyboard 0.4192 0.4244 0.4218

Touchscreen �0.1421 0.4528 �0.1187Quality of Internet High-quality Internet 0.2653 0.3619 0.0000Diversity and compatibility ofapplication programs

High-level diversity andcompatibility

0.2360 0.4053 0.0000

Display size Small display 0.0699 0.7037 0.0778Medium display 2.0731 1.6988 2.0520

822 Y. Kim et al.

Dow

nloa

ded

by [

Uni

vers

ity o

f Il

linoi

s C

hica

go]

at 2

2:30

13

Nov

embe

r 20

14

level of diversity and compatibility of applicationprograms on average.

To analyse the distributions of coefficients further,the share of respondents who prefer some attributelevels or who are indifferent was estimated, as shownin Table 4.

Diversity and compatibility of applicationprograms, used as an index for the performance ofthe terminal, are not valued much by most consumers(79.25%). This means that for most people, the futuremobile telecommunications terminal need not havehigh-level performance and specifications if thosecost more.

High-quality Internet is not valued much by manyconsumers (71.6%) either. This means that whenInternet service is to be used on the mobile terminal,consumers are not willing to pay more for high-quality Internet service rather than low-qualityInternet service.

Surprisingly, these last two results run counter tothe general intuition that such characteristics areones that should attract consumers very much andthat should be included in any future mobile tele-communications terminal. It is infered that this isbecause the empirical results are reflecting the‘current’ consumers’ needs. Therefore, for the time

being, until consumer needs change significantly,these implications should hold.

An additional advantage of using the mixedlogit model is that it can represent the correlationbetween coefficients or marginal utilities of theattribute levels so that it can also represent a morerealistic substitution pattern. Table 5 shows thosecorrelations.

From Table 5 it can be seen that preference forability to embody high-quality Internet and prefer-ence for high-level diversity and compatibility ofapplication programs have a high positive correla-tion. This result coincides with expectations.

V. Discussion and Implications

How large should the display be? How portableshould the future mobile device be?

As mentioned earlier, the preceding two questionsare strongly correlated. Up to a point, display sizeis the most critical factor affecting the form factorof the mobile device. Accordingly, it affects portabil-ity almost linearly. Therefore, the two are analysedat the same time. From the estimation results,

Table 4. Shares of the population preferring each attribute level the most

Attributes Preferences Share (%)

Type of input equipment Keypad*>keyboard, touchscreen 21.85Keyboard>keypad*, touchscreen 62.45Touchscreen>keypad*, keyboard 15.70

Quality of Internet High-quality Internet>low-quality Internet* 28.40High-quality Internet¼ low-quality Internet* 71.60

Diversity and compatibility ofapplication programs

High level>low level 20.75High level¼ low level* 79.25

Display size Small display>medium display, large display* 9.25Medium display>small display, large display* 88.85Large display*>small display, medium display 1.90

*Base attribute levels for the estimation.

Table 5. Correlations among the coefficients

Price 1Keyboard �0.0115 1Touchscreen �0.1249 0.0153 1High-quality Internet �0.0452 �0.0601 0.2885 1High-level diversityand compatibility

�0.0383 �0.1117 0.2225 0.5654 1

Small display �0.0100 �0.0295 0.2163 0.1837 0.2765 1Medium display �0.0018 0.2279 �0.2202 �0.0864 �0.0646 �0.0606 1

The future of the mobile telecommunications terminal 823

Dow

nloa

ded

by [

Uni

vers

ity o

f Il

linoi

s C

hica

go]

at 2

2:30

13

Nov

embe

r 20

14

a kind of consensus is found in the preference fora medium-sized display (88.85%), which means thatthere exists a certain optimum level for the displaysize and, at the same time, for the volume (portabil-ity) of the device. This result is significant for thefuture direction of device convergence. It impliesthat this kind of optimum display size and volumecan be expected to be one of the major future direc-tions of device convergence. Consequently, it can bepresumed that the future mobile telecommunicationsterminal will have a volume, or size, similar to thatof a current PDA or mobile phone while adoptinga considerably large display.

Based on this, research and development shouldfocus on increasing the number of pixels per areasince that is one way to improve recognizabilitywhile maintaining portability. In the future, ahologram display or foldable display would be goodresearch-and-development alternatives.

What kind of input equipment will most likelybe adopted?

A keyboard with a trackball mouse is the most pre-ferred kind of input equipment. This implies that aclear need exists for a convenient way to inputletters directly. Accordingly, device convergencemust be directed towards that. It also means thatfor input equipment, device convergence betweenthe mobile terminal and the PC is preferable. Onthe other hand, compared with other input equip-ment, a keyboard entails a trade-off betweenportability and convenient inputting. Therefore, theconvergent device needs to incorporate a keyboardwhile maintaining or improving portability. As usedin the survey, a trackball mouse or infrared-ray-sensor mouse can be a very good means of supportbecause either device improves the convenience ofinputting data and of command while maintainingportability at almost the same level. In the future,a hologram keyboard will be an attractive alterna-tive if the cost and technological problems can bemanaged.

Surprisingly, a touchscreen, the main inputequipment of PDAs and smart phones now andconsidered one of the more probable types ofinput equipment of the future, is on average notpreferred.

Is it desirable for a future multiuse mobile terminalto support very diverse application programs? Is itdesirable for it to be compatible with a PC?

The study survey deals with the preceding twoquestions at the same time. The diversity and

compatibility of application programs, used as anindex for the performance and computing abilityof the terminal, were not valued much by mostrespondents (79.25%). This means that for mostpeople, the future mobile telecommunications term-inal does not need to have high-level performanceand or run diverse application programs if thoseamenities would cost more.

For device convergence, this implies that perfectconvergence between the PC and the mobile deviceis not so desirable, and, as mentioned earlier, ratherthan perfect device convergence to one device, partialdevice convergence towards application-specificdevices with only essential specifications for con-verged services will be more prevalent.

What quality of Internet service should the futuremobile device provide?

This issue does not have much implication for deviceconvergence itself, but a low level of preference forhigh-quality Internet service has important implica-tions for other convergences. We discuss this latermore in detail.

To what extent will device convergence take place?

Judging by the estimation results, a certain consensusfor an optimum display size and volume (portability)exists. This may well be one of the major future direc-tions of device convergence.

However, a large amount of heterogeneity inconsumers’ preferences is found for other attributes,as shown in Section IV. Therefore, it can be inferredthat partial device convergence is more probablethan perfect device convergence. The stronger likeli-hood of partial device convergence means that theworld may well see a main trend in portability anddisplay size, but at the same time many diverse attrib-utes meeting the various preferences of consumersmay be embodied separately in many kinds of mobiledevices. In that case, application-specific devices thatare fitted to consumers’ diverse needs will be preva-lent in the market.

What effects will device convergence have on otherconvergences?

Based on the finding of a low level of preference forhigh-quality Internet service, many kinds of servicesbased on wire and wireless convergence, such aswireless LAN–mobile Internet roaming service,2.3-gigahertz portable Internet service, and 3G Wideband Code-Division Multiple Access (WCDMA)mobile Internet service, may not quickly penetrate

824 Y. Kim et al.

Dow

nloa

ded

by [

Uni

vers

ity o

f Il

linoi

s C

hica

go]

at 2

2:30

13

Nov

embe

r 20

14

the market upon introduction if they do not showconsumers a clear difference from former Internetservices. In addition, this finding suggests that ifNext Generation communication Network (NGcN),a kind of network convergence, is aimed to providemainly high-quality Internet service, introducingit too quickly to the market is not desirable.Rather, a gradual introduction after wire–wirelessconverged services are set down firmly will be moreappropriate.

Given the finding of a higher preference fora medium-sized display, telecommunications–broadcasting convergence should prepare servicecontent that is fitted to a medium-sized display onmobile devices. Although content can be realizedvery well on a large display, this service convergencewill fail to attract consumers if it cannot guaranteegood service quality on a medium display.

In light of the low level of preference for highdevice specifications, service convergence shouldbe targeted to provide service content that can berealized at even low specifications. The oppositecase also holds.

In addition, the extent of device convergence willaffect service convergence and network convergence.A partial device convergence, which is very likely, willrequire service convergence to be targeted to provideservice contents that can be easily transformed orstandardized in order to be realized easily on manykinds of application-specific devices. This can haveanother meaning for network convergence. Now,NGcN is expected to be made based on an Internetprotocol (IP) system. If it turns out to be the case,all application-specific devices can be operated com-patibly under that system, and service content can beprovided with ease.

VI. Conclusion

This study analyses how device convergence inmobile telecommunications will progress based onconsumer preference estimates. For demand analysis,conjoint analysis and a mixed logit model were usedto acquire information about the heterogeneity ofconsumers’ preferences.

The estimation results suggest that quite a lot ofheterogeneity exists among consumers’ preferences,although a kind of consensus also exists for anoptimum display size and degree of portability.

The results also show that rather than perfectdevice convergence toward one dominant device,one is more likely to see partial device convergencetowards application-specific devices with an optimumdisplay size and portability. In addition to the effect

consumers’ preferences will exert on the main

attributes of the device, they will significantly influ-

ence service convergence and network convergence

as well.

References

Allenby, G. M. and Rossi, P. E. (1999) Marketing modelsof consumer heterogeneity, Journal of Econometrics,89, 57–78.

Alvarez-Farizo, B. and Hanley, N. (2002) Using conjointanalysis to quantify public preferences over theenvironmental impacts of wind farms: an examplefrom Spain, Energy Policy, 30, 107–16.

Batt, C. E. and Katz, J. E. (1997) A conjoint model ofenhanced voice mail services: implications for newservice development and forecasting, Telecommunica-tions Policy, 21, 743–60.

Batt, C. E. and Katz, J. E. (1998) Consumer spendingbehavior and telecommunications service, Telecommu-nications Policy, 22, 23–46.

Blackman, C. (1998) Convergence between telecommunica-tions and other media: how should regulation adapt?,Telecommunications Policy, 22, 163–70.

Brownstone, D. and Train, K. (1999) Forecasting newproduct penetration with flexible substitution patterns,Journal of Econometrics, 89, 109–29.

Bryan, S. and Parry, D. (2002) Structural reliability ofconjoint measurement in health care: an empiricalinvestigation, Applied Economics, 34, 561–7.

Calfee, J., Winston, C. and Stempski, R. (2001) Econometricissues in estimating consumer preferences from statedpreference data: a case study of the value of auto-mobile travel time, Review of Economics and Statistics,83, 699–707.

Carlsson, F. (2003) The demand for intercity publictransport: the case of business passengers, AppliedEconomics, 35, 41–50.

Chiang, J., Chib, S. and Narasimhan, C. (1999) Markovchain Monte Carlo and models of considerationset and parameter heterogeneity, Journal ofEconometrics, 89, 223–48.

Hensher, D. (2001) The valuation of commuter travel timesavings for car drivers: evaluating alternative modelspecifications, Transportation, 28, 257–67.

Huber, J. and Train, K. (2001) On the similarity of classicaland Bayesian estimates of individual mean partworths,Marketing Letters, 12, 257–67.

Layton, D. F. (2000) Random coefficient models forstated preference surveys, Journal of EnvironmentalEconomics and Management, 40, 21–36.

McColluch, R. and Rossi, P. E. (1994) An exact likelihoodanalysis of the multinomial probit model, Journal ofEconometrics, 64, 207–40.

Messerschmitt, D. (1996) The convergence of telecommu-nications and computing: what are the implicationstoday?, IEEE, 84, 1167–86.

Mueller, M. (1999) Digital convergence and its conse-quences, Working Paper, Syracuse University.

Roe, B., Boyle, K. J. and Teisl, M. F. (1996) Using conjointanalysis to derive estimates of compensating variation,Journal of Environmental Economics and Management,31, 145–59.

The future of the mobile telecommunications terminal 825

Dow

nloa

ded

by [

Uni

vers

ity o

f Il

linoi

s C

hica

go]

at 2

2:30

13

Nov

embe

r 20

14

San Miguel, F., Ryan, M. and McIntosh, E. (2000)Applying conjoint analysis in economic evaluations:an application to menorrhagia, Applied Economics,32, 832–3.

Train, K. (2003) Discrete Choice Method with Simulation,Cambridge University Press, Cambridge.

Train, K. and Sonnier, G. (2003) Mixed logit with boundeddistribution of partworths, Working Paper, Universityof California, Berkeley and Los Angeles.

Yoffie, D. (1997) Competing in the Age of DigitalConvergence, Harvard Business School Press,Cambridge, MA.

Appendix: Example of the Alternative Card Used in the Real Survey

First Alternative Set

No.Inputequipment

Quality ofInternet

Diversity and compatibility ofapplication programs

Displaysize Price

1 Keypad Low-quality Internet Low level Small 400 000 won2 Touchscreen Low-quality Internet High level Small 700 000 won3 Keyboard Low-quality Internet High level Large 550 000 won4 Keyboard High-quality Internet Low level Small 400 000 won5 Keypad High-quality Internet Low level Medium 700 000 won

826 Y. Kim et al.

Dow

nloa

ded

by [

Uni

vers

ity o

f Il

linoi

s C

hica

go]

at 2

2:30

13

Nov

embe

r 20

14