## Abstract

Mobile banking services are one of the most promising recent technological innovations. In this study, we developed a conceptual model to explore mobile banking services for user behavior in the financial banking industry in intention adoption. The aim of this study is to explore the effect of user behavior and guidance on the mobile banking services intention adoption structure model among customers based on decomposed theory of planned behavior and trust-related behaviors based on the knowledge of experts. In this study, we use a new hybrid model, the multiple attribute decision making (MADM) model, which combines decision making trial and evaluation laboratory (DEMATEL) for building an influential network relationship map (INRM), DANP (DEMATEL-based ANP) for determining the influential weights of criteria, and the VIKOR method using the influential weights to evaluate and integrate the criteria in the gaps and reduce the gaps to satisfy the users’ behavior needs based on INRM. An empirical case of Taiwan’s financial banking industry is used as an example to demonstrate the application of the proposed hybrid MADM model and its efficiency. In the results, we find that the proposed user behavior framework can offer a deeper understanding of the variables/criteria that influence the interrelationship for the intention adoption of mobile banking services by DEMATEL technique. We can also combine the influential weights of DANP with weighting gaps using the VIKOR method to evaluate how to reduce these gaps and provide the best improvement strategies to satisfy the mobile banking services for users’ behavior needs.

This is a preview of subscription content, access via your institution.

## References

Ajzen I (1991) The theory of planned behavior. Organ Behav Hum Decis Process 50(2):179–211

Ajzen I (2005) Attitudes, personality, and behavior, 2nd edn. Open University Press, Maidenhead

Ajzen I (2012) The theory of planned behavior. In: Lange PAM, Kruglanski AW, Higgins ET (eds) Handbook of theories of social psychology, vol 1. Sage, London, pp 438–459

Andersson P, Heinonen K (2002) Acceptance of mobile services: insights from the Swedish market for mobile telephony. Working paper, Stockholm School of Economics, Stockholm, October

Bandura A (1986) Social foundations of thought and action: a social cognitive theory. Prentice-Hall, Englewood Cliffs

Beiginia AR, Besheli AS, Soluklu ME, Ahmadi M (2011) Assessing the mobile banking adoption based on the decomposed theory of planned behavior. Eur J Econ Financ Adm Sci 28:7–15

Chen FH, Hsu TS, Tzeng GH (2011) A balanced scorecard approach to establish a performance evaluation and relationship model for hot spring hotels based on a hybrid MCDM model combining DEMATEL and ANP. Int J Hosp Manag 30(4):908–932

Chiu WY, Tzeng GH, Li HL (2013) A new hybrid MCDM model combining DANP with VIKOR to improve e-store business. Knowl Based Syst 37:48–61

Davis F (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13(3):319–340

Devaraj S, Fan M, Kohli R (2002) Antecedents of B2C channel satisfaction and preference: validating e-commerce metrics. Inf Syst Res 13(3):316–334

Gefen D, Karahanna E, Straub DW (2003) Inexperience and experience with online stores: the importance of TAM and trust. IEEE Trans Eng Manag 50(3):307–321

Grazioli S, Jarvenpaa SL (2000) Perils of internet fraud: An empirical investigation of deception and trust with ex testing experienced internet. IEEE Trans Syst Man Cybern Part A Syst Hum 30(4):395–410

Hanafizadeha P, Behboudi M, Koshksaray AA, Jalilv Marziyeh, Shirkhani Tabar MJS (2014) Mobile-banking adoption by Iranian bank clients. Telemat Inf 31(1):62–74

Herzberg A (2007) Payments and banking with mobile personal devices. Commun ACM 46(5):53–58

Hsbollah HM, Idris KM (2009) E-learning adoption: the role of relative advantages, trial ability and academic specialization. Campus-Wide Inf Sys 26(1):54–70

Hsu MH, Chiu CM (2004) Predicting electronic service continuance with a decomposed theory of planned behavior. Behav Inf Technol 23(5):359–373

Hu SK, Lu MT, Tzeng GH (2014) Exploring smart phone improvements based on a hybrid MCDM model. Expert Syst Appl 41(9):4401–4413

Huang CY, Shyu JZ, Tzeng GH (2007) Reconfiguring the innovation policy portfolios for Taiwan’s SIP mall industry. Technovation 27(12):744–765

Kazemi A, Nilipour A, Kabiry N, Hoseini MM (2013) Factors affecting Isfahanian mobile banking adoption based on the decomposed theory of planned behavior. Int J Acad Res Bus Social Sci 3(7):230–245

Kim HW, Chan HC, Gupta S (2007) Value-based adoption of mobile internet: an empirical investigation. Decis Support Syst 43(1):111–126

Laukkanen T (2007) Internet vs mobile banking: comparing customer value perceptions. Bus Process Manag J 13(6):788–797

Lean OK, Zailani S, Ramayah T, Fernando Y (2009) Factors influencing intention to use e-government services among citizens in Malaysia. Int J Inf Manag 29(6):458–475

Liao S, Shao YP, Wang H, Chen A (1999) The adoption of virtual banking: an empirical study. Int J Inf Manag 19(1):63–74

Lin HF (2011) An empirical investigation of mobile banking adoption: the effect of innovation attributes and knowledge-based trust. Int J Inf Manag 31(3):252–260

Liou JJJ, Tzeng GH (2012) Comments on “Multiple criteria decision making (MCDM) methods in economics: an overview”. Technol Econ Dev Econ 18(4):672–695

Liu CH, Tzeng GH, Lee MH (2012) Improving tourism policy implementation: the use of hybrid MCDM models. Tour Manag 33(2):413–426

Liu CH, Tzeng GH, Lee MH, Lee PY (2013) Improving metro-airport connection service for tourism development: using hybrid MCDM models. Tour Manag Perspect 6:95–107

Lu MT, Lin SW, Tzeng GH (2013) Improving RFID adoption in Taiwan’s healthcare industry based on a DEMATEL technique with a hybrid MCDM model. Decis Support Syst 56:259–269

Luarn P, Lin H (2005) Toward an understanding of the behavioral intention to use mobile banking. Comput Hum Behav 21(6):873–891

Luo X, Li H, Zhang J, Shim JP (2010) Examining multi-dimensional trust and multi-faceted risk in initial acceptance of emerging technologies: an empirical study of mobile banking services. Decis Support Syst 49(2):222–234

Mallat N, Rossi M, Tuunainen VK (2004) Mobile banking services. Commun ACM 47(5):42–46

McKnight DH, Chervany NL (2002) What trust means in e-commerce customer relationships: An interdisciplinary conceptual typology. Int J Electron Commer 6(2):35–59

McKnight DH, Choudhury V, Kacmar C (2002) Developing and validating trust measures for e-commerce: an integrative typology. Inf Syst Res 13(3):344–359

Moon J, Kim Y (2001) Extending the TAM for a world-wide-web context. Inf Manag 38(4):217–230

Moore GC, Benbasat I (1991) Development of an instrument to measure the perceptions of adopting an information technology innovation. Inf Syst Res 2(3):192–222

Ong CS, Laia JY, Wang YS (2004) Factors affecting engineers’ acceptance of asynchronous e-learning systems in high-tech companies. Inf Manag 41(6):795–804

Opricovic S (1998) Multicriteria optimization of civil engineering systems. Faculty of Civil Engineering, Belgrade (in Serbian)

Opricovic S, Tzeng GH (2002) Multicriteria planning of post-earthquake sustainable reconstruction. Comput Aided Civil Infrastruct Eng 17(3):211–220

Opricovic S, Tzeng GH (2003) Fuzzy multicriteria model for post-earthquake land-use planning. Nat Hazards Rev 4(2):59–64

Opricovic S, Tzeng GH (2004) Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS. Eur J Oper Res 156(2):445–455

Opricovic S, Tzeng GH (2007) Extended VIKOR method in comparison with outranking methods. Eur J Res 178(2):514–529

Ou Yang YP, Shieh HM, Leu JD, Tzeng GH (2008) A novel hybrid MCDM model combined with DEMATEL and ANP with applications. Int J Oper Res 5(3):160–168

Papies D, Clement M (2008) Adoption of new movie distribution services on the Internet. J Media Econ 21(3):131–157

Pedersen PE (2005) Adoption of mobile internet services: an exploratory study of mobile commerce early adopters. J Organ Comput 15(2):203–222

Pikkarainen T, Pikkarainen K, Karjaluoto H, Pahnila S (2004) Consumer acceptance of online banking: an extension of the technology acceptance model. Internet Res 14(3):224–235

Ratten V (2011) Mobile banking innovations and entrepreneurial adoption decisions. Int J E-Entrep Innov 2(2):27–38

Riivari J (2005) Mobile banking: a powerful new marketing and CRM tool for financial services companies all over Europe. J Financ Serv Mark 10(1):11–20

Rogers EM (1962) Diffusion of innovations. The Free Press of Glencoe, Macmillan Company, New York

Rogers EM (1983) Diffusion of innovations. Free Press, New York

Rogers E (1995) Diffusion of innovation. Free Press, New York

Saaty TL (1996) Decision making with dependence and feedback: analytic network process. RWS Publications, Pittsburgh, New York

Scornavacca E, Hoehle H (2007) Mobile banking in Germany: a strategic perspective. Int J Electron Financ 1(3):304–320

Shen YC, Huang CY, Chu CH, Hsu CT (2010) A benefit-cost perspective of the consumer adoption of the mobile banking system. Behav Inf Technol 29(5):497–511

Shih YY, Fang K (2004) The use of a decomposed theory of planned behavior to study Internet banking in Taiwan. Internet Res 14(3):213–223

Shiue YM (2007) Investigating the sources of teachers’ instructional technology use through the decomposed theory of planned behavior. J Educ Comput Res 36(4):425–453

Smarkola C (2008) Efficacy of a planned behavior model: beliefs that contribute to computer usage intentions of student teachers and experienced teachers. Comput Hum Behav 24(3):1196–1215

Suoranta M, Mattila M (2004) Mobile banking and consumer behavior: new insights into the diffusion pattern. J Financ Serv Mark 8(4):354–366

Tan FB, Sutherland P (2004) Online consumer trust: a multi-dimensional model. J Electron Commer Organ 2(3):40–58

Tan YH, Thoen W (2001) Toward a generic model of trust for electronic commerce. Int J Electron Mark 5(2):61–74

Taylor S, Todd P (1995) Decomposition and crossover effects in the theory of planned behavior: a study of consumer adoption intentions. Int J Res Mark 12(2):137–155

Teo TSH, Pok SH (2003) Adoption of WAP-enabled mobile phones among Internet users. Omega Int J Manag Sci 31(6):483–498

Teo TSH, Lim VKG, Lai RYC (1999) Intrinsic and extrinsic motivation in internet usage. Omega Int J Manag Sci 27(1):25–37

Tzeng GH, Huang JJ (2011) Multiple attribute decision making: methods and applications. CRC Press, Taylor and Francis Group, A Chapman and Hall Book, London

Tzeng GH, Lin CW, Opricovic S (2005) Multi-criteria analysis of alternative-fuel buses for public transportation. Energy Policy 33(11):1373–1383

Venkatesh V (2000) Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model. Inf Syst Res 11(4):342–365

Venkatesh V, Davis FD (1996) A model of the antecedents of perceived ease of use: development and test. Decis Sci 27(3):451–481

Vijayasarathy LR (2004) Predicting consumer intentions to use on-line shopping: the case for an augmented technology acceptance model. Inf Manag 41(6):747–762

Yan Z, Liu C, Niemi V, Yu G (2013) Exploring the impact of trust information visualization on mobile application usage. Personal Ubiquitous Comput 17(6):1295–1313

Yu PL (1973) A class of solutions for group decision problems. Manag Sci 19(8):936–946

## Author information

### Authors and Affiliations

### Corresponding author

## Appendices

### Appendix 1: DEMATEL technique

DEMATEL technique is used to build an influence relationship matrix for dimensions/criteria to measure the cause and effect on each element. This technique is widely used in various types of complex studies to understand the intricacies of the problem structure. The DEMATEL technique contains five steps.

*The first step* confirms the number of elements in a system, *n*, and develops scales for measuring the influential relationship in each element, comparing contexts/criteria by pair-wise comparison, using a scale of 0–4 to represent a complete lack of influence (0), low influence (1), medium influence (2), high influence (3), and extremely high influence (4) by natural language.

*The second step* identifies an initial influence matrix, comparing influence interaction degree pairs to directly obtain the influence matrix \( \varvec{Z} = [z_{ij} ]_{n \times n} \), where *z*
_{
ij
} represents the degree that criterion *i* affects criterion *j*. If the *i*th criterion directly affects the *j*th criterion, then *z*
_{
ij
} ≠ 0; otherwise, *z*
_{
ij
} = 0.

*The third step* normalizes the direct influence matrix to obtain the matrix \( \varvec{A} \) from Eqs. (1) and (2). The diagonal term of matrix \( \varvec{A} \) is zero, and the maximum sum of any row or column is 1.

where

*The fourth step* obtains the total influence matrix \( \varvec{T} \) from Eq. (3):

where \( \varvec{A} = [a_{ij} ]_{n \times n} \), \( 0 \le a_{{ij}} < 1,0 < \sum\limits_{{j = 1}}^{n} {a_{{ij}} \le 1} ,\;0 < \sum\limits_{{i = 1}}^{n} {a_{{ij}} \le 1} . \)If the total of at least one row or column is equal to 1 (but not all) in ∑
^{n}_{
j=1}
*a*
_{
ij
} and ∑
^{n}_{
i=1}
*a*
_{
ij
}, then we can guarantee \( \lim_{h \to \infty } \varvec{A}^{h} = [0]_{n \times n} \), and \( \varvec{I} \) is the identity matrix.

*The fifth step* obtains prominence and relation. By totaling each row and column of the total influence matrix \( \varvec{T = }[t_{ij} ] \), we obtain the sum of all row and column vectors as follows:

The value *d*
_{
i
}, the sum of all rows in the total influence matrix ** T**, represents the degree that the criterion directly or indirectly affects all other criteria. The value

*s*

_{ j }, the sum of all columns in

**, represents the degree that the criterion is affected by all other criteria. According to the definition, when**

*T**j*=

*i*, then

*d*

_{ i }+

*s*

_{ i }represents the degree of the total influence relationship of

*i*criterion which denotes to include

*i*criterion affects all other criteria and is affected by all other criteria, meaning “prominence”;

*d*

_{ i }−

*s*

_{ i }represents the degree of the effect on and from other criteria, showing the “net influence relationship”. If (

*d*

_{ i }−

*s*

_{ i }) is positive, then criterion

*i*affects other criteria, and if (

*d*

_{ i }−

*s*

_{ i }) is negative, then criterion

*i*is influenced by other criteria.

### Appendix 2: influential weights of DANP

We can use the DEMATEL technique to not only build the interacting relationships among the factors/criteria but also obtain the most accurate influential weights. We improve the traditional ANP to solve the interrelationship of dependence and feedback problems among factors/criteria. Therefore, we use the basic concept of ANP (Saaty 1996) as a basis with total influence matrix of DEMATEL technique to solve the influential weights. Thus, DANP (DEMATEL-based ANP) contains the following steps.

*The first step* develops the expert influence questionnaire structure of DEMATEL technique. The questionnaires are clearly described and broken down into components.

*The second step* develops an unweighted supermatrix \( W = \text{(}\varvec{T}_{c}^{\alpha } )^{\prime} \), transposing each normalized dimension (or called context/cluster) with the total degree of influence \( \varvec{T}_{c}^{\alpha } \) obtained from the total influence matrix *T*
_{
c
} using the DEMATEL technique, as shown in Eq. (4) from Eq. (3).

The normalized *T*
_{
c
}, with a total degree of influence, provides \( \varvec{T}_{c}^{\alpha } \) from the dimensions (contexts/clusters) shown in Eq. (5).

We use \( \varvec{T}_{c}^{\alpha 11} \) to demonstrate the basic concept as example in Eqs. (6) and (7).

We normalize the total influence matrix *T*
_{
c
} into the normalized total influence matrix \( \varvec{T}_{c}^{\alpha } \) using its contexts; then, the unweighted supermatrix \( \varvec{W} \) is obtained by transposing \( \varvec{T}_{c}^{\alpha } \), i.e., \( W = \text{(}\varvec{T}_{c}^{\alpha } )^{\prime} \), according to the basic concept of ANP in an unweighted supermatrix \( \varvec{W} \), as shown in Eq. (8).

In addition, Eq. (9) produces matrix \( {\varvec{W}}^{11} \). If the groups or criteria are independent, then the corresponding entry in the matrix is blank or zero. Matrix \( {\varvec{W}}^{nn} \) is obtained in a similar manner.

*The third step* obtains the weighting supermatrix, contextualizing the total influence relationship matrix \( {\varvec{T}}_{D} \), as in Eq. (10). Let each context of matrix \( {\varvec{T}}_{D} \) be normalized with the total degree of influence to obtain \( {\varvec{T}}_{D}^{\alpha } \). Eq. (11) shows the following result:

Multiplying the normalized matrix \( {\varvec{T}}_{D}^{\alpha } \) by the unweighted supermatrix \( \varvec{W} \) gives the normalized supermatrix \( \varvec{W}^{\alpha } \), as shown in Eq. (12).

*The fourth step* obtains the normalized supermatrix \( \varvec{W}^{\alpha } \). We can obtain the supermatrix limit by multiplying the normalized spuermatrix \( \varvec{W}^{\alpha } \) by itself several times until the supermatrix has converged and become a long-term stable supermatrix to a sufficiently large power *g*. Therefore, the weights of the influence of each criterion are obtained by \( \mathop {\lim }\limits_{g \to \infty } ({\varvec{W}}^{\alpha } )^{g} \), where *g* represents any number for the power/exponent. We use these processes to obtain the weights of influence.

### Appendix 3: VIKOR method

The VIKOR method, developed by Opricovic and Tzeng (2003, 2004, 2007), solves the issues of conflicting criteria experienced by MADM. This method is based on the positive-ideal (or the desired level) and negative-ideal (or the least-desired) solutions, with a preference for staying close to the positive-ideal point. This is basic concept according to traditional thinking. The gap concept measures the proximity to the positive-ideal point. We describe the VIKOR method below.

*The first step* determines the values *x*
^{*}_{
j
}
and *x*
^{−}_{
j
}
in the quality criterion assessment criteria. The value *x*
^{*}_{
j
}
represents the positive-ideal point (desired levels or aspiration level in each criterion), which is the best score in criterion *j*. The value *x*
^{−}_{
j
}
represents the negative-ideal point, which is the worst score in criterion *j*. The development of the VIKOR method began with the following form of the *L*
_{
p
} metric:

where 1 ≤ *p* ≤ ∞; *k* = 1, 2, …, *m*, and the influential weight *w*
_{
j
} is derived from DANP. In the article, we use the new concepts of Eqs. (14) and (15) to obtain the following results for the improvement gaps of each context/criterion based on interdependence and feedback problems:

In basic concept of this new approach, we use the performance scores from 0 to 10 (complete dissatisfaction (0), ← 0, 1, 2, 3,…, 7, 8, 9, 10 → extreme satisfaction (10)) in questionnaires; therefore, that aspiration level can be set at 10 score and the worst value at zero score. Therefore, in this study, we set *x*
^{*}_{
j
}
= 10, *j* = 1, 2,…, *n* as the aspiration level and *x*
^{−}_{
j
}
= 0, *j* = 1,2,…,*n* as the worst value, which differs from traditional approach. In this approach, we set *x*
^{*}_{
j
}
as the aspiration level and *x*
^{−}_{
j
}
as the worst value because this approach allow us to avoid “Choose the best among inferior choices/options/alternatives (i.e., pick the best apple among a barrel of rotten apples).”

*The second step* calculates the minimal mean of the group utility *F*
_{
k
} (minimal average gap) and maximal regret *Q*
_{
k
} (maximal gap for all criteria or for each context of criteria to give improvement priority).

where *r*
_{
kj
} = (|*x*
^{*}_{
j
}
− *x*
_{
kj
}|)/(|*x*
^{*}_{
j
}
− *x*
_{
kj
}|)(|*x*
^{*}_{
j
}
− *x*
^{−}_{
j
}
|)(|*x*
^{*}_{
j
}
− *x*
^{−}_{
j
}
|) represents the gap ratio (on a normalization scale) and *F*
_{
k
} represents the ratios of the average gap from the aspiration level *x*
^{*}_{
j
}
to the performance value *x*
_{
kj
} in criterion *j* of alternative *k*. In this article, we focus on minimizing the gap *r*
_{
kj
} for all criteria *j* = 1, 2,…,*n*. Then, *w*
_{
j
} represents the relative influential weight of criterion *j*; *w*
_{
j
} can be obtained from DANP based on the DEMATEL technique. *Q*
_{
k
} represents the maximum gap in all criteria (or the context of each criterion of the *k*-th alternative) for prioritizing improvement.

*The third step* provides the comprehensive indicator *R*
_{
k
} and its ranked results. Equation (17) computes these values. From Eq. (17), we observe how mobile banking for user behavior implementation can be improved to reduce the gaps for achieving the aspiration level based on the influential network relation map.

Using the values derived from *S*
^{*} = min_{
k
}
*S*
_{
k
} (traditional approach) or *F*
^{*} = 0 (achieving the aspiration level where the gap is zero, our approach), *F*
^{−} = max_{
k
}
* F*
_{
k
} (traditional approach) or *F*
^{−} = 1 (the worst situation, our approach); *Q*
^{*} = min_{
k
}
* Q*
_{
k
} (traditional approach) or *Q*
^{*} = 0 (achieving the desired level, our approach), *Q*
^{−} = max_{
k
}
* Q*
_{
k
} (traditional approach) or *Q*
^{−} = 1 (the worst situation, our approach). Thus, in our approach, the gap for *S*
^{*} = 0 and *F*
^{−} = 1, and *Q*
^{*} = 0, and *Q*
^{−} = 1, we can re-write Eq. (17) as *R*
_{
k
} = *vF*
_{
k
} + (1 − *v*)*Q*
_{
k
}(i.e., weighting two objectives *F*
_{
k
} and *Q*
_{
k
}). The weight *v* = 1 only considers how we can minimize the average gap (average regret), and the weight *v* = 0 only determines how to select the maximum gap for improvement. In general, *v* = 0.5, but this value can be adjusted depending on the situation.

Based on the above concepts, we can easily determine how to improve the gaps *r*
_{
kj
} (*k* = 1, 2,…,* m*; *j* = 1, 2,…,* n*) and the improvement priority according to the influential network relationship map for the achieving aspiration level.

## Rights and permissions

## About this article

### Cite this article

Lu, MT., Tzeng, GH., Cheng, H. *et al.* Exploring mobile banking services for user behavior in intention adoption: using new hybrid MADM model.
*Serv Bus* **9**, 541–565 (2015). https://doi.org/10.1007/s11628-014-0239-9

Received:

Accepted:

Published:

Issue Date:

DOI: https://doi.org/10.1007/s11628-014-0239-9