Scholarly article on topic 'Determinants of User Behavior Intention (BI) on Mobile Services: A Preliminary View'

Determinants of User Behavior Intention (BI) on Mobile Services: A Preliminary View Academic research paper on "Economics and business"

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{"Technology Acceptance Model" / "behavior intention" / "mobile application" / "mobile services" / determinant}

Abstract of research paper on Economics and business, author of scientific article — Kamarudin Shafinah, Noraidah Sahari, Riza Sulaiman, Mohd Soyapi Mohd Yusoff, Mohammad Mohd Ikram

Abstract The use of mobile services is upraising worldwide due to the positive progression of mobile technologies. In contrast, less attention are given on matters concerning the user Behavior Intention (BI) on mobile application and services. The purpose of this article is to shed light on selected models that deals with user BI studies. This study views influential determinants which had been proposed in previous works and that were focused on mobile application and services. The outcome of this article is considered important providing a baseline for future works pertaining to mobile applications and services for accessing the user needs.

Academic research paper on topic "Determinants of User Behavior Intention (BI) on Mobile Services: A Preliminary View"

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Procedia Technology 11 (2013) 127-133

The 4th International Conference on Electrical Engineering and Informatics (ICEEI 2013)

Determinants of User Behavior Intention (BI) on Mobile Services:

A Preliminary View

Kamarudin Shafinaha,c'* , Noraidah Saharia, Riza Sulaimanb, Mohd Soyapi Mohd

Yusoffb, Mohammad Mohd Ikram

aCenter of Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi,

Selangor, Malaysia.

b Institute of Visual Informatics, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia. c Faculty of Agriculture and Food Sciences, Universiti Putra Malaysia Bintulu Sarawak Campus, Nyabau Road, P.O. Box 396,97008 Bintulu,

Sarawak, Malaysia.

dMalaysian Rubber Board, RRIMResearch Station, 47000 Sungai Buloh, Selangor, Malaysia.

Abstract

The use of mobile services is upraising worldwide due to the positive progression of mobile technologies. In contrast, less attention are given on matters concerning the user Behavior Intention (BI) on mobile application and services. The purpose of this article is to shed light on selected models that deals with user BI studies. This study views influential determinants which had been proposed in previous works and that were focused on mobile application and services. The outcome of this article is considered important providing a baseline for future works pertaining to mobile applications and services for accessing the user needs.

© 2013 The Authors.Published byElsevier Ltd.

Selection andpeer-reviewunder responsibility ofthe FacultyofInformationScience&Technology,UniversitiKebangsaan Malaysia.

Keywords: Technology Acceptance Model; behavior intention; mobile application; mobile services; determinant;

* Corresponding author. Tel.: +6-019-651-3467; fax: +6-003-8927-7089 E-mail address: fienah2000@yahoo.com

2212-0173 © 2013 The Authors. Published by Elsevier Ltd.

Selection and peer-review under responsibility of the Faculty of Information Science & Technology, Universiti Kebangsaan Malaysia. doi:10.1016/j.protcy.2013.12.171

1. Introduction

Mobile applications are being extensively used in various fields including commerce, healthcare, marketing, finance and entertainment. Much of this is owing to the persistent and tremendous growth of mobile devices like smart phones, iPad, iPhone, etc. [1]. Furthermore, it is estimated that over 250000 mobile applications are available in an assortment of stores and marketplaces [2]. Besides that, many new terms have been created within the domain area to which the mobile applications were applied, such as m-Government, m-Health and m-Commerce, where mis usually referred to as mobile.

In most areas where mobile applications are used (m-Government, m-Health, etc.), mobile services (m-Services) were identified as part of the new term's dimension. For instance, in m-Government, m-Services can be referred to as a channel for communication between citizens with the government. In addition, in m-Government, the m-Services is competent in providing a form of m-Transactions and m-Payment for their citizens [3]. The m-Services is defined as an application services accessible from mobile phones via wireless and mobile communication networks [4]. The users are able to use the m-Services to search for information, perform financial transaction, give opinions regarding certain matters, obtain advise, etc. [4;5]. Hence, m-Services has been recapitulated as one of the beneficial technologies that is capable of adjusting to user-specific circumstances [6].

On one hand, several of the m-Services have been triumphant and successful in its development and implementation where the usage tends to increase to great potential. On the other hand, there are few m-Services which often fail to thrive in regards to its development and implementation due to a number of reasons such as the users' requirement are not met and taken for consideration [1; 4; 6; 7; 8; 9]. The prevalence of m-Services can be denoted as not only being dependent on technology enhancement, but user adoption or user Behavior Intention (BI) is also required. Technology Acceptance Model (TAM) and its extended models are much related with the user BI studies. Therefore, this article aims to provide a brief understanding of TAM and its extended models as well as in determining additional determinants which had been suggested in previous works for m-Services.

2. Theoretical Background

Theory of Reasoned Action (TRA), Theory of Planned Behavior (TPB), Innovation Diffusion Theory (IDT) and Social Cognitive Theory (SCT) are among the theories used in the development of a model to access the user BI towards new technology [10]. Table 1 shows the determinants involved for each of the aforementioned theories.

Table 1. Theories related with user behaviour intention models.

Theory of Reasoned Action (TRA) Theory of Planned Behaviour (TPB) Innovation Diffusion Theory (IDT) Social Cognitive Theory (SCT)

• Attitude Toward Behavior • Subjective Norm • Attitude Toward Behavior • Subjective Norm • Perceived Behavioral Control • Relative Advantage • Ease of Use • Image • Visibility • Compatibility • Results Demonstrability • Voluntaries of Use • Outcome Expectation-Performance • Outcome Expectations-Personal • Self-efficacy • Affect • Anxiety

The TRA which is a theory related to actual behaviour of a person, provided the basis for the development of Technology Acceptance Model (TAM) in the 1980's. TAM is a well-known accepted model in accessing the users' requirement of new technology [5; 8] such as e-mail, spreadsheets, the World Wide Web and electronic commerce [11; 12]. According to Wu et al. [10], TAM was gradually adopted by researchers because TAM concentrated on users' perception to infer BI directly and this method was simple, clear and straightforward. In addition, TAM primary determinants were User Beliefs, Attitudes and Behaviour Intention to use new system or technology [12]. Fig. 1 depicts TAM where the basic determinants of TAM are shown as Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). These determinants are used to indicate a person's attitude towards the use of an actual new system or technology. Variables Xi, X2, .., Xn in Fig. 1 are designated as the external factors or measurement items, mainly used to evaluate PU and PEOU.

Fig. 1. The Technology Acceptance Model (Adopted from [13])

Although TAM is a distinguished model, yet TAM remains incomplete. TAM did not concern on social influence, economic factors and outside influences in the adoption of new technology [14; 15]. Apart from that, TAM also lacked in task focus [14]. Subsequently, TAM was adapted by few researchers that later proposed extended models of TAM like Technology Acceptance Model 2 (TAM2), Unified Theory of Acceptance and Use Technology Model (UTAUT), and Technology Acceptance Model 3 (TAM3). The extended models of TAM added several determinants mainly to provide a more comprehensive model structure [12; 16]. The comparison of the determinants for these models is shown in Table 2. Definitions to those determinants can be found in the literatures [7; 10; 13; 17; 20; 18; 19; 20; 21; 22; 23]. According to Table 2, the additional determinants for TAM extended models were that of which have been adopted, adapted or integrated with other theories aside from TRA. For example, C-TAM-TPB model is the combination between TAM and TPB. Therefore, the determinant of Perceived Behavioral Control (PBC) model was added to the original TAM.

Table 2. The determinants of TAM and its extended models.

TAM[17] TAM2[16] UTAUT[18] TAM3[19] C-TAM-TPB [18]

• Actual system • Actual used • Actual used • Use behavior • Attitude toward

used • Behavior intention • Behavioral intention • B ehavior intention behavior

• Attitude toward • Perceived • Performance • Perceived usefulness • Subjective norm

using usefulness expectancy • Perceived ease of use • Perceived behavioral

• Perceived • Perceived ease of • Effort expectancy • Subjective norm control

usefulness use • Social influence • Image • Perceived usefulness

• Perceived ease of • Subjective norm • Facilitating • Job relevance

use • Image Conditions • Output quality

• Job relevance • Gender • Results demonstrability

• Output quality • Age • Computer self-efficacy

• Results • Experience • Perceptions of external

demonstrability • Voluntariness control

• Computer anxiety

• Computer playfulness

• Perceived enjoyment

• Objective Usability

• Experience

• Voluntariness

3. Critical Analysis Output: Determinants of m-Services

To ensure the successfulness of m-Services, the understanding of why and how an individual intends to use m-Services is quite often a challenge. For that reason, a document analysis was conducted in this present work to determine the additional determinants which were added for the understanding of user BI on m-Services. These additional determinants were needed to complement the basic theories, TAM and TAM's extended models as well as being appropriate for m-Services innovations. This article is expected to deliver a basic guideline for researchers in proposing a more comprehensive user BI model suitable for their m-Services areas.

Table 3. The determinants of m-Services.

Source Year

Focus/ Object

Basic determinant

Additional determinant

¿o J5

PQ O "O "O

S & o a

QcuHH.i3.i3

24 2013 Mobile government V V V V V V V V V V V

25 2012 Mobile commerce V V V V V V V V V

9 2012 Mobile services through social media V V V V V V V

26 2010 Mobile entertainment for rural people V V V V V V V V V

21 2010 Mobile payment services V V V V V V

5 2012 Mobile government V V V V V V V V V

27 2011 Mobile data services V V V V V V V V

4 2010 Mobile services V V V V

28 2009 Mobile payment V V V V V V V V

29 2009 Mobile ticketing on public transportation V V V

30 2008 Mobile services V V V V

11 6 11 10 8 244 2 423555331222111111

Based on Table 3, the determinants of Behavior Intention, Perceived Usefulness/Use and Perceived Ease of Use are shown to be the most common basic determinants in accessing the user BI for m-Services. The determinant of Perceived Behavioral Control is a determinant suggested in TPB. Other basic determinants (Image/Perceived Status Benefit, Self-Efficacy, Perceived Enjoyment, Moderating Effects, and Facilitating Condition) can be found in TAM extended models except for Compatibility, where this determinant was adopted from IDT.

This preliminary assessment found that 15 additional determinants were suggested in the previous works related to BI on m-Services (Table 3). Five previous works had constantly highlighted and mentioned on three of the

additional determinants: (1) Perceived Cost, (2) Perceived Risk/Security and (3) Trust. The determinant of Perceived Cost in m-Services may include the initial fees, subscription fees, transaction fees and communication fees [5; 25]. In certain cases, cost also included the capability of an individual to buy mobile devices [5; 26]. Accordingly, the determinant of Perceived Cost was suggested for identifying the users concern about cost matters which is required to be paid for use of the m-Services. Perceived Cost is defined as "the extent to which an individual perceives that using a particular service is costly" [25].

The determinants of Perceived Risk/Security and Trust were slightly of similar objectives. Users may be confronted with several risks namely fraud, product quality, and other illegal activities. This leads to feelings of anxiety about those risks. The Perceived Risk/Security can be defined as "the degree to which an individual believes that they are exposed to a certain type of risk (financial, social, psychological, physical or time)" [25]. In the meantime, the determinant of Trust is important to help users overcome the feelings of uncertainty towards m-Services [25] and this being much associated with trustworthy sources [24]. Trust is defined as "the degree to which an individual believes to a specific service that can be regarded as having no threats of security and privacy" [25]. Both Perceived Risk/Security and Trust are presumed to be the most crucial determinants in areas like m-commerce or m-services whereby requiring the users to provide their confidential and personal information.

Further, the determinants of External/Media Influence and Interpersonal Influences can be included as sub-determinants to support the determinant of Subjective Norm/Social Influence [24] since these determinants underpin the basic definitions for the Subjective Norm determinant, "perceptions of the preferences of others is significant regarding the worth of engaging in a specific behavior" in TPB [24; 30].

In view that m-Services is to have ubiquitous characteristics, Mobility was also one of the concerns among researchers. The determinant of Mobility is used to measure the degree to which an individual perceives benefits especially from the context of time and place, service access and use [29]. Other than Mobility, the determinant of Context has also been suggested due to the ubiquitous characteristics. Context can be defined as a dynamic determinant, which comprises detailed information about a person, place or even an object [9]. For example, m-Services for business social media should provide information on location, identity, the state of people and mobile device details [9].

Perceived Novelty/Innovative is "the degree to which an individual or other unit of adoption was relatively earlier in adopting new ideas in comparison to other members of a social system" [25; 31]. In addition, Perceived Novelty/Innovative can also be referred to as the degree to which an individual accepts a novel product and services [26]. This determinant is suitable for pre adoption studies of potential users. This determinant was adapted from IDT, where all determinants in IDT are linked to innovations [25].

The determinant of Technology and Service Quality deals on quality and the convenience of using a particular service and is comprised of determinants that exists in IDT (e.g. output quality, trail ability, etc.) [26]. It has been postulated in most studies that good quality technology and service is expected to gain significant effects on BI towards m-Services [5; 26]. However, in certain works, the results for the Technology and Service Quality determinant portrayed a contrasting result that deterred the BI [26]. Further, the determinant of Transparency has been proposed to access the degree to which an individual believes that the services provided should be transparent [9]. This determinant can be included as a part of external determinants to support the Technology and Service Quality determinant since Transparency can be put into term as having similar objectives with the Visibility determinant in IDT.

Interactivity, Simplicity and Perceived Flexibility Benefit are the determinants much associated on the functions provided by m-Services to its user. For Interactivity, it is believed that users will gain a positive BI towards a particular m-Services given that the functions provided by the m-Services have higher interactivity [24]. Meanwhile, the determinant of Simplicity is connected to the users comfortness or such as in having a lower complexity level while using the functions provided in the m-Services [32]. Therefore, it is assumed that given a much simpler function to m-Services, it will considerably increase the BI among users seeing that the users feel comfortable in using the particular services [26].

The determinant of Perceived Flexibility Benefit is mainly referred to as flexibility, which is the ability to perform a task quickly, of quality, productiveness and effectiveness. Perceived Flexibility Benefit is defined as the degree to which an individual believes that higher flexibility of m-Services will increase user BI on m-Services [30].

Informal Communication is a unique determinant that is appropriate for services involving communication activities such as social media. This determinant can be determined as the degree to which an individual realizes (with/without intention) of what is happening in their surroundings and sharing this knowledge with everyone along with updating ones' own [9]. Lastly, the determinant of User Satisfaction is suggested for use rather than the determinant of Attitude Toward Use for the measuring of post adoption behavior [4].

4. Conclusion

This article briefly views and describes on several theories, TAM and TAM extended models that were narrated with BI studies. Beyond the most common determinants such as Behavior Intention, Perceived Usefulness and Perceived Ease of Use, many other determinants were added to complement the existing models as well as to suit m-Services needs. Interestingly, it was shown that the determinants of Perceived Cost, Perceived Risk/Security and Trust were of major concern in the previous works for the additional determinants. These determinants were evermore compulsory for m-Commerce or particularly in areas where users are required to provide confidential and personal information. It is hoped that the additional determinants suggested in those previous works might be put into consideration for future BI studies towards other m-Services areas for instance agriculture and rural studies.

Acknowledgements

This work is supported by the Malaysian Ministry of Higher Education and the research grant from Universiti Kebangsaan Malaysia (Grant no.: HEJIM-FTSM-FKAB-MTDC-101101005). The authors wish to acknowledge sincere gratitude to Siti Munirah Mohd (Universiti Kebangsaan Malaysia), Fatihah Ramli (Universiti Malaysia Sarawak), other colleagues as well as anonymous reviewers rendering their support, assistance, as well as their insightful and constructive comments.

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