Selas Turkiye Motivations Behind The Adoption Of New Mobile Ict Products By Alexandre Fleury

Motivations behind the adoption of new mobile ICT productsEssay following the PhD summer school on Political Economy of ICT

Next Generation Mobile Media

Alexandre FleuryAalborg [email protected]

September 30, 2009

Abstract

This document elaborates on the issue of motivational factors introduced partly in the paperI presented during the course. I examine here the most common theoretical models and theirapplication to explain the individual factors that drive consumer adoption and usage of technologyin general and of next generation mobile media services in particular. The Technology AcceptanceModel appears to be the most popular approach, although its limitations might call for the designof new models adapted to the emerging mobile media landscape.

1 Introduction

Knowing consumer incentives for adopting and using mobile technologies and services is crucial inorder to understand or anticipate the success or failure thereof. This is an important aspect ofmy PhD thesis, which aims at studying the end user experience with mobile rich media services.Understanding the motivations driving the adoption and usage of mobile media services as wellas the constraints preventing their use would indeed help explain the reasons behind a positive ornegative experience.

During the summer school on the political economy of next generation mobile media, lecturersand participants presented and discussed their perspective on the definition of the new environ-ment surrounding Information and Communication Technologies (ICTs) and the role of the variouscontributors to the system in influencing this environment. Insights were provided from variousperspectives, ranging from infrastructure considerations to regulation issues. Eventually the ac-tors’ decisions and actions in the environment has an influence on the final consumer’s decision toadopt and use ICT systems. It is therefore essential to investigate the personal factors affectingindividuals and how these factors relate to the overall environment.

1.1 Definition considerations

According to W. Melody’s presentation on “Political Economy of ICT”, the driving forces andinteractions at play in the new ICT environment are organized according to Figure 1. It should benoted that end users were originally not included in the diagram. One of the reason for omittingexplicit mention of end users is that their place on the diagram depends on the interest of theperson who interprets it. For instance, this paper considers end users as mobile media servicesconsumers hence interacting primarily with the technological applications, while being indirectlyinfluenced by economic and regulative forces.

Additionally, W. Melody uses the notion of knowledge economy to describe the type of economythat relies on the interdependence between information and communication. As described in [16],knowledge economies include the following characteristics:

Information infrastructure facilitating next generation knowledge economy activities

Information content generation and use, including Intellectual Property Rights (IPR) issues

Human capital as the principal producer, repository, disseminator and applier of informationand knowledge

Applications increasing productivity by improving capability, reducing transaction costs andstimulating structural changes within organizations, industries and markets

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Structure and efficiency of new knowledge economy markets

International trade and its significance to a global knowledge economy

Furthermore, M. Fransman proposed the analogy of an ecosystem to represent the new ICTeconomy illustrated in Figure 2. The four actors depicted in the diagram are either creators orusers of knowledge, and can alternatively be organized in layers, represented by the number aboveeach actor. The analogy between ICT and biology can be justified by the symbiotic nature of therelationships between the various actors of the system.

Technologies

Policies

Economy

Applications

Regulations

Prod/Services

Figure 1: The driving forces and their interaction,adapted from W. Melody

4consummers

3platform, content &

applications providers

1networked element providers

2network

operators

Figure 2: The four players and six symbiotic relation-ships in the new ICT ecosystem (source: [9])

1.2 Contribution scope

Numerous factors impact the consumers’ decision of adopting and using technology. The emphasisof the work presented here is put on the individual motivations that influence consumer adoptionof technologies in general and next generation mobile media services in particular. Economic andregulative drivers are eclipsed as it would require further research to integrate them in the broaderpicture of the full new ICT ecosystem.

Additionally, in the perspective of including this work as part of my PhD thesis, my personalinterest on the end user point of view influenced the choice of focus for this work.

1.3 Outline

This document first presents in Section 2 a review of user acceptance theories and how each couldbe applied to measure the acceptability of mobile rich media services. Then these methods areevaluated in Section 3 in the light of empirical studies where their application has been reportedand assessed. Finally, Section 4 concludes on the combined results of those studies and open forpossible further research.

2 Theories exploring user adoption of technology

An extensive literature originating from sociology, psychology and social psychology has describedtheoretical methods designed for investigating the drivers of technology adoption by consumers.From the Theory of Reasoned Action to the Unified Theory of Action and Use of Technology, thissection briefly summarizes six popular approaches in use since the 1960s. Benefits and limitationsof the theories are also presented.

2.1 Theory of Reasoned Action

As depicted in Figure 3, the Theory of Reasoned Action (TRA) postulates that individual behavioris driven by a behavioral intention, which is the composition of an attitude towards the behaviorand a subjective norm ([7]).

The attitude toward a behavior is defined by the individual’s positive or negative feelings aboutperforming the behavior. Determining this attitude consists in assessing the consequences of the

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behavior and the desirability of reaching these consequences. The attitude towards behavior cantherefore be measured as the sum of the products of consequences by their desirability.

The subjective norm corresponds to the assessment of how much the individual believes closepeers think the behavior should be performed. Additionally, subjective norm depends on theindividual’s willingness to satisfy the close peers’ wishes. The subjective norm can therefore beexpressed as the product of the individual perception by the willingness to please close peers,summed for all peers considered.

Attitude towards act or behavior

Behavioral intention

Subjective norm

Behavior

Figure 3: Representation of the Theory of Reasoned Action, adapted from [8]

The first main limitation of TRA is the difficulty to actually distinguish attitude from norm, asit has been argued that attitudes can be perceived as norms. The second critic expressed againstTRA concerns its assumption that behavioral intention comes with freedom to act whereas inpractice act is usually constrained. Additionally, TRA has not been designed specifically with ISin mind and focuses on individual only; it does not consider economy-related factors.

2.2 Theory of Planned Behavior

The Theory of Planned Behavior (TPB) reproduces most of TRA, although in TPB behavioralintention is also function of perceived behavioral control. This addition aims at removing theassumption of freedom to act associated with behavioral intention in TRA and other models ([7]).

In this model, behavioral control describes the individual perception of the difficulty to performa behavior. Additionally and contrary to TRA, the three components of behavioral intentioninfluence each other.

Attitude toward act or behavior


Perceived behavioral control

BehaviorSubjective norm

Figure 4: Representation of the Theory of Planned Behavior (source: [1])

TPB is a more complete model than TRA and is more management relevant as it focuses onspecific factors influencing user adoption and usage. However, like TRA it is not IS oriented anddoes not consider economy-related factors.

2.3 Technology Acceptance Model

The Technology Acceptance Model (TAM) is an adaptation of the TRA for the field of InformationSystems. As depicted in Figure 5, it discards the attitude construct and focuses on extrinsic(perceived usefulness) and intrinsic (perceived ease of use) factors to determine the behavioralintention of use, which leads to the actual system use ([3]).

According to the theory’s originating authors, perceived ease of use directly impacts both thebehavioral intention of use and the perceived usefulness.

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Since its publication TAM has been widely adapted to fit various experimental conditions byintroducing additional or alternative factors to the original model presented here. Examples ofspecific adaptations are provided in section 3.

Perceived usefulness

Behavioral intention to use

Perceived ease of use

Actual system use

Figure 5: Representation of the Technology Acceptance Model (source: [4])

TAM offers several advantages over the other models. For instance it appears more robust andparsimonious in explaining IS adoption behavior. Additionally, it has been the most influentialmodel as hundreds of empirical studies used the TAM or an adaptation of it to their own evalua-tion conditions [14]. Nevertheless, TAM includes the same shortcoming assumption on behavioralintention implying freedom of act as TRA. Additionally, TAM does not consider economy-relatedfactors and excludes the possibility of influence from institutional, social and personal control fac-tors. Finally, TAM has been criticized on its applicability outside the workplace environment forwhich it has been originally created.

2.4 Motivation Theories

Psychological research has contributed to the attempt of understanding individuals’ motivations foradopting information technology. The approaches figuring on the long list of models developed byresearchers generally distinguish between intrinsic and extrinsic factors. On the one hand intrinsicmotivation focuses on the process of performing an activity. Perceived enjoyment, perceived funand perceived playfulness are common sources of intrinsic motivations. On the other hand extrinsicmotivation put the emphasis on the performance of the activity in helping achieving outcomesconsidered valuable. Perceived usefulness is the most commonly used extrinsic motivation factor([5]).

Additionally, motivation relies on the concept of self-control (or self-motivation) which measuresthe willingness of an individual to gather intellectual faculties to perform a given task. For instance[24] describes the expectancy theory that defines motivation as a combination of:

Valence the value of the perceived outcome

Instrumentality the belief that completing some actions enables achieving the outcome

Expectancy the belief of being able to complete the actions

Another interesting and relevant concept is the affect perseverance, described in [22]. It pos-tulates that an emotional preference continues even though the source of the original emotion hasbeen removed. This concept is particularly useful when adapting a system which already receivedpositive feedbacks; its users may be willing to remain positively attached to it, although somefunctions have been modified or removed.

2.5 Diffusion of Innovations Theory

As depicted in Figure 6, the Diffusion of Innovation Theory (DOI) segments a population accordingto its willingness to adopt innovations. From innovators to laggards, each group presents a set ofdistinctive characteristics, as follows:

Innovators venturesome, educated, multiple info sources

Early adopters social leaders, popular, educated

Early majority deliberate, many informal social contacts

Late majority skeptical, traditional, lower socio-economic status

Laggards neighbors and friends are main info sources, fear of debt

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Early majority (34%)

Late majority (34%)

Early adopters (13.5%)

Laggards (16%)

Innovators (2.5%)

Figure 6: Population segmentation according to the Diffusion of Innovations Theory (source: [21])

Additionally, DOI establishes diffusion as the process of communicating innovation throughcertain channels over time among the members of a social system. This process follows the followingfive steps:

Knowledge person becomes aware of an innovation and has some idea of how it functions

Persuasion person forms a favorable or unfavorable attitude toward the innovation

Decision person engages in activities that lead to a choice to adopt or reject the innovation

Implementation person puts an innovation into use

Confirmation on evaluates the results of an innovation-decision already made

DOI claims that IS implementation success depends on three factors: technical compatibility,technical complexity and relative advantage. Further research adapted this model by adding vol-untariness, image, result demonstrability, visibility and trialability ([21, 17]) as factors impactingthe adoption of IS. However, research has later demonstrated that the three factors used in theoriginal model (depicted in Figure 7) exert the most influence over adoption.

Technical compatibility

IS implementation success

(adoption, infusion)

Relative advantage (perceived need)

Technical complexity

(ease of use)

Figure 7: Representation of the Diffusion of Innovations Theory (source: [21])

2.6 Unified Theory of Action and Use of Technology

The Unified Theory of Action and use of Technology (UTAUT) is an attempt to explain both userintention to use an Information System and subsequent usage behavior. It was developed from thereview of eight models previously used to describe IS usage behavior. In addition to the modelspresented in this document, the models are a combined theory of planned behavior/technologyacceptance model, the model of PC utilization and the social cognitive theory ([23]).

According to this theory, an individual usage behavior is driven by behavioral intention andfacilitating conditions, where the latter is very close to perceived behavioral control from TPB anddepicts the degree to which individual believes that an infrastructure exists to support use of thesystem of interest.

As depicted in Figure 8, this model describes behavioral intention as function of three con-structs: performance expectancy, effort expectancy and social influence. Moreover, in addition tothe facilitating conditions, the impact of these constructs are mediated by the individual’s gender,age, experience and voluntariness of use.

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Performance expectancy


Social influence

Use behavior

Effort expectancy

Facilitating conditions

Gender Age ExperienceVoluntariness of

use

Figure 8: Representation of the Unified Theory of Action and Use of Technology (source: [23])

UTAUT can be considered as a more elaborate model as it results from the careful study ofprevious theories. Additionally, it presents the advantage of being IS oriented, although it does notconsider economy-related factors.

3 Empirical studies and results

This section focuses on the application of the models discussed in Section 2 to the field of mobileservices. The focus is first put on how these models have been adapted to better fit a specific fieldor experimental conditions. Additionally the section provides empirical results from the studiesintroduced. The figures illustrating the models used in these studies are available in Appendix A.

3.1 General studies

The first study of interest concerns the assessment of factors that drive consumers’ usage behavior,which has been reported in [10]. It relies on an adaptation of the decomposed version of TPB (seeFigure 10) to demonstrate that:

∙ attitude, social influence, media influence, perceived mobility and perceived monetary valueimpact consumers’ intention to continue using mobile data services

∙ perceived ease of use, perceived usefulness and perceived enjoyment influence consumers’attitude toward continued usage of mobile data services

∙ individual usage context influences consumers’ behavior

Focusing on the Chinese market, [19] demonstrated that mobile voice service and innovationexperience influence greatly adoption of mobile data services while perceived ease of use and brandexperience affected it largely. The study, which relied on an adapted TAM (see Figure 11), demon-strated that promotion of data services is best achieved when associated with perfect voice serviceexperience. This illustrates the importance of the affect perseverance concept and the need topropose new services on top of existing successes.

Another adapted version of TAM described in [25] and depicted in Figure 12 presents interestingand somewhat surprising findings. The results from the study show that consumers’ intention toadopt and use mobile commerce services is significantly influenced by perceived risk, cost, com-patibility and perceived usefulness. Perceived ease of use does however not significantly affectbehavioral intention directly but impacts perceived usefulness.

3.2 Study of specific factors

Some studies have put their focus on studying more uncommon drivers of user adoption or usage.This section presents two of such noteworthy studies.

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The first study ([2]) investigated the role of design aesthetics in driving users’ intention ofusing mobile services. The study used an augmented version of TAM and found that aestheticsdid significantly influence perceived usefulness, perceived ease of use and perceived enjoyment(see Figure 13). Furthermore, the study revealed that loyalty is significantly impacted by bothperceived usefulness and perceived enjoyment. Loyalty is another important factor highly tight toaffect perseverance, which has been shown to be function of perceived value early in the product’slife cycle in [12].

While studying the impact of intrinsic and extrinsic motivational factors on performing anactivity, the study reported in [6] demonstrated that money generally decreases intrinsic motivation,while verbal motivation increases it. The study argues that money is usually associated withperformance among children, employees and students. Therefore if large monetary rewards maylead to increased performance, the individual feeling of depending on money will most likely decreaseintrinsic motivation.

3.3 The social influence

Few studies have focused their attention on the role played by the social environment on an indi-vidual usage of ICTs. The authors of [11] used a modified version of TAM (see Figure 14) whichintegrates social influence theory to explain the usage of MMS among pre-adopters (potentialusers) and post-adopters (users). According to their findings, pre-adopters are mainly motivatedby technological factors while post-adopters are mostly influenced by social determinants. Thisdemonstrates the need for considering both types of drivers when releasing mobile media services.

Furthermore, [15] investigated the effect of a critical mass of users as a key element of user accep-tance of groupware systems. Such system imply a high level of collaboration and cooperation amongusers. The authors implemented a modified version of TAM (see Figure 15) and demonstrated astrong effect (both direct and indirect through perceived ease of use and perceived usefulness) ofperceived critical mass on the intention to use groupware. This reveals the importance of knowingthat peers already use a system for an individual to consider using the system too.

3.4 Application to mobile video/TV

A growing number of authors has reported studies measuring specifically the individual driversbehind mobile media consumption. For instance [18] classified the main social motivations behindmobile video consumption. The study, which also demonstrated that mobile usage influences con-tent choice (users prefer less focus demanding content), concluded that users would consume mobilevideos according to the following scheme of reasons:

1. Individual viewing

Managing solitude To appear purposeful rather than alone in socially uncomfortable sit-uations

Disengaging from others To avoid possibilities of social contact or control the acousticenvironment

Managing transitions between spaces To kill time

2. Coordinating mobile experiences with family life

Juggling commitments As time-shifting solution to deal with family related commitmentswithout sacrificing airings of video material

Coordinating content with family To satisfy all family members in case of conflictingpreferences in terms of content choice

3. Watching at homeAllows greater flexibility in terms of location for watching, alone or in company of other familymembers

4. Sharing the experience

Watching together To engage in discussing content, preferences, etc.

Showing video to others As a starting point for discussions, for generating humor or shar-ing experiences

When it comes to mobile television consumption, [13] argues that such service holds a highhedonic component that needs to be carefully considered. The authors examined the influenceof cognitive concentration (also referred as flow experience) and media content on the consumers’

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acceptance of mobile TV. An extended version of TAM (see Figure 16) was applied to demon-strate that flow experience impacts significantly consumers’ intention to use hedonic informationtechnology and that content critically influences cognitive concentration.

Finally, the results from the first set of tests conducted in the CAMMP project1 demonstratethat the two main drivers for mobile TV consumption in a social environment are the desireto keep up-to-date (with news) and kill time. The study participants mentioned the similaritybetween common “mobile” media consumption practices (listening to music/podcasts and readingnewspapers/books/magazines) and watching video content, thus reinforcing their intention of usingsuch mobile video service. Additionally, participants suggested that a high level of informationquality would be the prime motivator for online participation with user generated content, whilehigh scores would constitute an important driving forces when competition is involved in a mobileservice.

4 Conclusions and future work

This document reported and discussed theoretical models and their empirical application to variousproducts examined under various circumstances. This work constitutes the basis for a deeperanalysis of the field of motivation evaluation, relevant as part of my PhD thesis. The emphasis hasbeen strongly put on individual drivers for technology adoption and usage, which calls for furtheranalysis of other factors influencing such behavior, such as economical or regulatory forces. In thecontinuity of the work within CAMMP on individual motivations for contributing to cooperativeand/or competitive mobile services, a future step could be to apply one of the models discussed hereto measure the determinants of cooperative/collaborative behaviors with mobile online services.

As a result of the review process of theoretical models and their empirical applications introducedin this document, TAM appears to be the most popular model for evaluating individual motivationsbehind various types of ICT services. Despite several attempts to offer more flexible, robust ordedicated approaches, many authors grounded their evaluation of service acceptance on extrinsicand intrinsic behavioral drivers originated by the originating authors of TAM.

Nevertheless, in a attempt to decrease the difficulty of adapting TAM to emerging mobile ICTservices, the authors of [20] propose a reviewed model (depicted in Figure 9). This model drawson previous research and integrates factors relevant to the challenges specific to the new mobileICT landscape. However, it is noticeable that most empirical studies cited here have validatedtheir model through quantitative analysis (mainly large scale surveys) involving populations ofvarying demographics. Validating this new model would therefore require extensive efforts in furtheranalysis in order to validate its determinants, propositions and assumptions.


Attitude

Social influence



Facilitating conditions

Gender/AgeUser predisposition

KnowledgeCompatibility

Behavioral controlImage

Personal innovativenessPerceived enjoyment

Interpersonal influenceExternal influence

PromotionPerceived securityPerceived privacy

Figure 9: Compound model introduced in [20], better adapted to the new ICT ecosystem?

1http://www.cammp.aau.dk/

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[15] H. Lou, W. Luo, and D. Strong. Perceived critical mass effect on groupware acceptance. Eur.J. Inf. Syst., 9(2):91–103, 2000.

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[17] G. C. Moore and I. Benbasat. Development of an instrument to measure the perceptions ofadopting an information technology innovation. Information Systems Research, 2(3):192–222,September 1991.

[18] K. O’Hara, A. S. Mitchell, and A. Vorbau. Consuming video on mobile devices. In M. B.Rosson and D. J. Gilmore, editors, CHI ’07: Proceedings of the SIGCHI conference on Humanfactors in computing systems, pages 857–866, New York, NY, USA, 2007. ACM.

[19] J. Qi, L. Li, Y. Li, and H. Shu. An extension of technology acceptance model: Analysisof the adoption of mobile data services in china. Systems Research and Behavioral Science,26(3):391–407, February 2009.

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[23] V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis. User acceptance of informationtechnology: Toward a unified view. MIS Quarterly, 27(3):425–478, 2003.

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[25] J.-H. Wu and S.-C. Wang. What drives mobile commerce? an empirical evaluation of therevised technology acceptance model. Inf. Manage., 42(5):719–729, 2005.

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A Research models used in cited literature

This appendix includes the models used in the empirical studies presented in Section 3. Thediagram includes the level of significance of the factors of interest.

Normative beliefs

Attitudinal beliefs


Attitude

Perceived enjoyment

*** ***


***

Intention to continue using mobile media

service

Media influence

Social influence

Perceived mobility

Perceived behavioral control

***

***

***

***

Perceived monetary value

***

Figure 10: Research model used in [10] (all variables significant at ***: p < 0.001, but significance level variesdepending on the service considered)

TAM


Attitude



*

*

**

*

Brand experience Innovation Voice service

Flow experience

**

**

*

*

Figure 11: Research model used in [19] (*: p < 0.05)

10




Actual system use** **

**

Compatibility Cost Perceived risk

** ** * **

n.s.

Figure 12: Research model used in [25] (n.s.: not significant, *: p < 0.05, **: p < 0.01)


Design aestheticsPerceived ease of

use

Perceived enjoyment

*

*

***

***

Loyalty

* ***

***

Figure 13: Research model used in [2] (*: p < 0.05, ***: p < 0.001)

Technology context


Intention to use

Perceived user resource

***/** */


***/

Social context

Social norm Social emotion

/*** ***/***

Perceived critical mass

**/

Figure 14: Research model used in [11] (*: p < 0.05, **: p < 0.01, ***: p < 0.001 for pre/post adopters)

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Perceived critical mass


*

*

Intention to use groupware

* *

*

*

Figure 15: Research model used in [15] (*: p < 0.05)


Intention to use mobile TV


**

*

n.s.

Content

Cognitive concentration

**

**

**

**

Figure 16: Research model used in [13] (n.s.: not significant, *: p < 0.05, **: p < 0.01)

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Selas Turkiye Motivations Behind The Adoption Of New Mobile Ict Products By Alexandre Fleury