Analyzing the factors that affect the adoption of mobile services in Taiwan

Download (0)

全文

(1)

Analyzing the factors that affect the adoption of mobile services in Taiwan

Lon-Fon Shieh

a

, Tien-Hsiang Chang

b

, Hsin-Pin Fu

c,

, Sheng-Wei Lin

d

, Ying-Yen Chen

c

a

Department of Business Management, National United University, Miaoli, Taiwan, ROC

b

Department of Information Management, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan, ROC

c

Department of Marketing and Distribution Management, National Kaohsiung First University of Science and Technology, Kaohsiung, Taiwan, ROC

d

Department of Marketing Management, Takming University of Science and Technology, Taipei, Taiwan, ROC

a r t i c l e i n f o

a b s t r a c t

Article history:

Received 2 February 2013

Received in revised form 6 November 2013 Accepted 11 November 2013

Available online xxxx

Most previous research studies on the general factors related to the adoption of mobile services have used multiple regression methods (the technology acceptance mode or structural equation modeling) and some have used MCDM tools. However, these studies still have some shortcomings, so they cannot provide the enough precise information about these factors and their weightings. This information is needed by firms so that they can allocate their limited resources to the most important factors and draw appropriate strategies to improve the content and quality of their mobile services. This study reviewed the literature and constructed a three-layer hierarchical table of the factors that affect consumers' adoption of mobile services. A pair-wise questionnaire was then designed and distributed to managers who are familiar with the mobile services of Chunghwa Telecom, a leading telecommunication company. From the data collected, the weight of each factor was directly calculated, using the fuzzy analytic hierarchy process (FAHP). The paper analyzes the importance of factors and three implications are also discussed. Hopefully, this knowledge will enable firms to better utilize their limited resources, by devising management strategies based on the weights of these factors, and therefore, to meet consumer demand at lower cost and with greater efficiency.

© 2013 Elsevier Inc. All rights reserved.

Keywords: Mobile service Adoption factors FAHP

1. Introduction

Since the introduction of mobile broadband wireless access (MBWA) technologies, such as high-speed downlink packet access (HSDPA), wide band code division multiple access (WCDMA), CDMA2000, time division-synchronous code division multiple access (TD-SCDMA), worldwide interoperability for microwave access (WiMAX), and long term evolution (LTE), many mobile service operators have started to provide mobile services to their customers. MBWAs are emerging wireless telecommunication technol-ogies that provide users with high-speed Internet access.

More and more customers are now using smart phones to access mobile services, and mobile service is developing rapidly. However, more than 50% of the users of mobile services are still not satisfied with the services they receive in Taiwan[17]. This indicates that the mobile service market still has considerable opportunities for growth. Mobile service operators or handset makers could increase their competitiveness if they were able to improve their perfor-mance in meeting consumer demand. Consequently, it is very important that they understand the requirements of mobile service users and the relative weight of each factor that determines the consumers' needs.

A great deal of research has been done in order to fully understand these factors and their significance. However, most previous research in this area has focused on the general factors related to the adoption of mobile services, using multiple regression as the research method[9,11,24,29]. Although the beta value in multiple regression can be expressed as the relative

Technological Forecasting & Social Change xxx (2013) xxx–xxx

⁎ Corresponding author at: Department of Marketing and Distribution Management, National Kaohsiung First University of Science and Technol-ogy, 2, Jhuoyue Rd., Nanzih District, Kaohsiung City 811, Taiwan, ROC. Tel.: +886 7 6011000x4221; fax: +886 7 6011043.

E-mail address:hpfu@nkfust.edu.tw(H.-P. Fu).

TFS-17892; No of Pages 9

0040-1625/$– see front matter © 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.techfore.2013.11.004

Contents lists available atScienceDirect

Technological Forecasting & Social Change

Please cite this article as: L.-F. Shieh, et al., Analyzing the factors that affect the adoption of mobile services in Taiwan, Technol. Forecast. Soc. Change (2013),http://dx.doi.org/10.1016/j.techfore.2013.11.004

(2)

weights of the factors, its value is obtained indirectly through testing. Also, due to the measurement errors of independent variables and dependent variables, prediction errors can occur between real dependent value and predicted dependent value, and collinearity problems between independent variables can ensue. Moreover, a minus value of beta could be obtained, making it difficult to judge the importance of the resulting value. Therefore, few studies have used the beta value to measure the weights of the factors.

An MCDM tool can be used to analyze different factors and determine their ranking in order of their importance to the adoption of new services, because factor selection is a multi-criteria decision-making (MCDM) problem. Some independent studies have applied MCDM tools directly, in order to obtain the relative weight of the factors from the viewpoint of experts. Jeng and Bailey[23]also used ANP to find the factors of customer retention in the Canadian mobile telecom market and found that well-financed foreign en-trants pose a risk to the major domestic carriers. Thus, successful promotional strategies will require strong leverage of their existing price and quality advantages.

Büyüközkan[7]used the fuzzy analytic hierarchy process (FAHP) to propose an analytic framework based on mobile commerce (m-Commerce) users and area experts and to provide practitioners with a more effective and efficient model for prioritizing m-Commerce requirements. Nikou and Mezei[33] also used AHP, based on students' opinions to understand the adoption of mobile services. However, these studies lack an understanding of the adopted factors from a business perspective. Büyüközkan [7] also suggested that further research could apply FAHP to study the adoption of m-Commerce from the perspective of enterprises.

Moreover, we found that previous studies of using MCDM tools only focus on mobile communications services[10], and thus, lack the factors of handset equipment and the psychology of consumers, and only a fraction of the relevant factors (less than ten) were surveyed[7,10,23].

Also, previous studies provided an imperfect hierarchy structure, in which faulty weightings could occur in the streaming of the weighting if there is different number of sub-criteria under each criterion [7,10,33]. For example, faulty weightings can occur if there are two criteria in the two layer hierarchy table, but criteria 1 has two sub-criteria and criteria 2 has four. The sub-criteria in criteria 1 could have a higher ranking than sub-criteria in criteria 2, even though the weighting of criteria 2 is greater than that of criteria 1, because the weighting of each sub-criterion in criteria 2 has to be shared by more sub-criteria.

Moreover, the AHP method does not truly reflect human cognitive processes— especially in the context of problems that are not fully defined and/or problems involving uncertain data (the so-called,‘fuzzy’ problems)[33]. Based on the above shortcomings, these previous studies cannot provide precise information about enough factors and their weightings. This information is required by firms to allocate their limited resources to the most important factors and draw appropriate strategies to improve the content and quality of their mobile services. To address this problem in a different way than in previous studies[7,10,23,33], this study was expanded to include a large amount of literature related to the adoption factors of mobile services and constructed a

three-layer hierarchical table of these factors, in which the weighting of each upper layer criterion has been shared by the same number (three) of down-layer criteria, so that the conflict in the ranking of the upper-layer criteria and the sub-criteria layer will not occur. A pair-wise questionnaire was then designed and distributed to managers who are familiar with the mobile services of Chunghwa Telecom, which has a 40% market share of the mobile service users in Taiwan. The weightings of factors were calculated by using the FAHP. The resulting implications for marketing are discussed, based on the results of this study, with the goal of enabling service providers to increase their competitive-ness by meeting consumer demand more effectively. 2. Literature review

Many researchers have examined the adoption of mobile services, and most of the related studies can be classified into three categories: those that look at the equipment (hard-ware) and at general mobile services, those that focus on specific applications, and those that study the psychology of mobile users.

Regarding handsets, the perceived utility of the new handset directly stimulates consumers to purchase 3G mobile phones, which Teng et al.[43] and Pagani[34]think will satisfy the service-expectations of the users. However, the users then require service advantage, such as bandwidth, transmission speed, security and privacy. Biel et al.[3]indicated that users need stable equipment to access mobile applications. Thus, high quality telecommunication equipment is necessary. In addition, Boyd and Mason [5] have verified that the manufacturer's reputation affects the attractiveness of the product and purchase intention.

Besides this requirement for basic equipment, innovation, knowledge, technological cluster, age, gender, perceived usefulness, perceived enjoyment, perceived ease of use, price, and speed of use have been found to be the key factors affecting the adoption of mobile services[21,34,47]. Chong et al. [11]

concluded that the perceived advantages, the perceived ease of use, the variety of services and social influence are factors that can influence the adoption of mobile services in Malaysia. Kwak and Yoo[27]also indicated that data rates, the quality of communications service, the number of broadcasting channels, video-on-demand (VOD) service, and supplementary services are attributes of consumers' willingness to pay for fourth generation (4G) technology.

Moreover, Blechar et al.[4]revealed how users' choices of mobile services in Denmark are influenced by their reference situations and the reference prices of the services offered. In addition to the technologies and mobile services offered, standards, infrastructure and content are also key factors influencing the adoption of mobile services[2].

In the special applications of mobile services, most mobile service applications focus on business-to-consumers (B2C). In the applications of mobile payment service, Schierz et al.[41]

found that compatibility, individual mobility, and subjective norms are significant factors. In the mobile learning aspect, Chong et al.[12]indicated that perceived ease of use, perceived usefulness, quality of services, and cultural aspects have significant and positive effects on the adoption of mobile learning in Malaysia. In applications of mobile ticketing, Mallat

2 L.-F. Shieh et al. / Technological Forecasting & Social Change xxx (2013) xxx–xxx

Please cite this article as: L.-F. Shieh, et al., Analyzing the factors that affect the adoption of mobile services in Taiwan, Technol. Forecast. Soc. Change (2013),http://dx.doi.org/10.1016/j.techfore.2013.11.004

(3)

mobile service providers have gradually developed a higher transfer rate for standard mobile communica-tions, enabling mobile phone users to watch the news, enjoy the videos and multimedia messages, access the Internet and other services. For computer users, Internet service providers also continue to think about the advantages of the high transmission-rate of wireless computer networks, so WiMAX, LTE and 802.lln techni-cal standards have emerged to meet the needs of these mobile service users.

(2) The rise of wireless sensor networking technology includes the ubiquitous family, security, care, and envi-ronmental monitoring. This development means that the device needs to have the following functions: sensing, identification, location and connection, in addition to its other features. Therefore, the results obtained in this study will have a greater impact in the future, under the changing demands for consumer and corporate mobile services, in combination with the development of communication technology. Recently, retail and distribu-tion industries have begun to promote these mobile services, hoping to gain more consumers. Mobile services in the future will create greater economic value by being extended to enterprises, for use as a management tool and providing mobile services for industry will generate higher profits. However, these mobile service companies need to understand business and business strategy. They can use this study to determine which factors should be given priority so that they can plan effectively and use their resources more effectually.

6. Conclusions and future study 6.1. Conclusions

The hierarchy constructed in this study uses the factors that experts consider to be the most important factors in the selection and adoption of a mobile service. The analysis of the relative weightings of these factors reveals the emphasis placed on each factor. This approach differs from the examinations of the adoption of mobile services by previous scholars, because it recognizes that the optimal allocation of resources to build an effective business model is a multi-criteria decision-making (MCDM) problem. Although other studies have considered the priority of the criteria to adopt mobile services, these criteria focused on communications services and did not include the criteria of handset equipment and the psychology of consumers. To address this shortcoming, this study also collected the factors related to the selection of handset equipment and the psychol-ogy of consumers when they are selecting mobile services, and determined the weighting of each factor using FAHP. Based on the factor weightings, three implications were found that will help mobile service providers to construct more robust business models. If enterprises can better understand the degree of importance of any given factor, they can then develop a more competitive model, resulting in lower costs and greater efficiency. Thus, if operators promote their mobile services by following the results of this study, they will be able to attract more users to adopt them. In addition, this research verified that a MCDM tool can be used to determine and analyze the factors affecting the adoption of mobile service.

6.2. Limitations and future study

This study has been carried out from the viewpoint of the mobile service operator. The mobile service model continues to grow, so the new issues affecting a consumer's decisions to select and adopt a mobile service can be considered by researchers and explored in the future. Also, this study did not consider government policy and relevant regulations in the collection of factors, so research covering government policy and relevant regulations can be undertaken in the future. Moreover, due to the limited time and research environment, the samples were collected from experts of Chunghwa telecom. The results did not include the input of other mobile service operators, and future research could include their input. In addition, the factor table was con-structed after the factors were extracted from a review of the literature and the factors were then weighted using FAHP. Even though the researchers tried to collect all of the relevant factors, the mobile service industry continues to grow and develop, and the factors affecting the adoption of mobile services will change constantly. Therefore, it is possible that a more complete hierarchical factor table can be constructed for future study.

Acknowledgment

This research was supported by the National Science Council (NSC) of the Executive Yuan, Taiwan, R.O.C. (NSC 101-2410-H-151-003).

Appendix A

If your company intends to adopt mobile service, please compare in pairs the relative weight among three factors that are “mobile telecommunications equipment,” “mobile telecommunications services” and “consumer psychological factors.”

References

[1] J. Aguarón, J.M. Moreno-Jiménez, The geometric consistency index: approximated thresholds, Eur. J. Oper. Res. 147 (1) (2003) 137–145. [2] S.J. Barnes, The mobile commerce value chain: analysis and future

developments, Int. J. Inf. Manag. 22 (2) (2002) 91–108.

[3] B. Biel, T. Grill, V. Gruhn, Exploring the benefits of the combination of a software architecture analysis and a usability evaluation of a mobile application, J. Syst. Softw. 83 (11) (2010) 2031–2044.

[4] J. Blechar, I.D. Constantiou, J. Damsgaard, Exploring the influence of reference situations and reference pricing on mobile service user behaviour, Eur. J. Inf. Syst. 15 (3) (2006) 285–291.

[5] T.C. Boyd, C.H. Mason, The link between attractiveness of“extra brand” attributes and the adoption of innovations, J. Acad. Market. Sci. 27 (3) (1999) 306–319.

[6] J.J. Buckley, Fuzzy hierarchical analysis, Fuzzy Sets Syst. 17 (3) (1985) 233–247.

[7] G. Büyüközkan, Determining the mobile commerce user requirements using an analytic approach, Comp. Stand. Interfaces 31 (1) (2009) 144–152.

8 L.-F. Shieh et al. / Technological Forecasting & Social Change xxx (2013) xxx–xxx

Please cite this article as: L.-F. Shieh, et al., Analyzing the factors that affect the adoption of mobile services in Taiwan, Technol. Forecast. Soc. Change (2013),http://dx.doi.org/10.1016/j.techfore.2013.11.004

(4)

[8] H. Chen, J.C. Ho, D.F. Kocaoglu, A strategic technology planning framework: a case of Taiwan's semiconductor foundry industry, IEEE Trans. Eng. Manag. 56 (1) (2009) 4–15.

[9] L. Chen, T.O. Meservy, M. Gillenson, Understanding information systems continuance for information-oriented mobile applications, Commun. Assoc. Inf. Syst. 30 (2012) 127–146.

[10] P.T. Chen, J.Z. Cheng, Unlocking the promise of mobile value-added services by applying new collaborative business models, Technol. Forecast. Soc. Chang. 77 (4) (2010) 678–693.

[11] A.Y.L. Chong, N. Darmawan, K.B. Ooi, B. Lin, Adoption of 3G services among Malaysian consumers: an empirical analysis, Int. J. Mob. Commun. 8 (2) (2010) 129–149.

[12] J.L. Chong, A.Y.L. Chong, K.B. Ooi, B. Lin, An empirical analysis of the adoption of m-learning in Malaysia, Int. J. Mob. Commun. 9 (1) (2011) 1–18. [13] T.Y. Chou, S.C.T. Chou, G.H. Tzeng, Evaluating IT/IS investments: a fuzzy

multi-criteria decision model approach, Eur. J. Oper. Res. 173 (3) (2006) 1026–1046.

[14] R. Csutora, J.J. Buckley, Fuzzy hierarchical analysis: the Lambda-Max method, Fuzzy Sets Syst. 120 (2) (2001) 181–195.

[15] A. Dias Jr., P.G. Ioannou, Company and project evaluation model for privately promoted infrastructure projects, J. Constr. Eng. Manag. 122 (1) (1996) 71–82.

[16] J.M. Duke, R. Aull-Hyde, Identifying public preferences for land preservation using the analytic hierarchy process, Ecol. Econ. 42 (1–2) (2002) 131–145. [17] FIND, Telecommunications platform application development plan-ning, available at:http://www.find.org.tw/2010(accessed 2012/8/8). [18] J.L. Funk, The future of mobile shopping: the interaction between lead

users and technological trajectories in the Japanese market, Technol. Forecast. Soc. Chang. 74 (3) (2007) 341–356.

[19] N. Gerdsri, D.F. Kocaoglu, Applying the Analytic Hierarchy Process (AHP) to build a strategic framework for technology roadmapping, Math. Comput. Model. 46 (7–8) (2007) 1071–1080.

[20] W.H. Hsiao, T.S. Chang, M.S. Huang, Y.C. Chen, Selection criteria of recruit-ment for information systems employees: using the analytic hierarchy process (AHP) method, Afr. J. Bus. Manag. 5 (15) (2011) 6201–6209. [21] S.R. Hill, I. Troshani, Factors influencing the adoption of personalisation

mobile services: empirical evidence from young Australians, Int. J. Mob. Commun. 8 (2) (2010) 150–168.

[22] S. Jaganathan, J.J. Erinjeri, J. Ker, Fuzzy analytic hierarchy process based group decision support system to select and evaluate new manufacturing technologies, Int. J. Adv. Manuf. Technol. 32 (11–12) (2007) 1253–1262. [23] D.J.F. Jeng, T. Bailey, Assessing customer retention strategies in mobile telecommunications: hybrid MCDM approach, Manag. Decis. 50 (9) (2012) 1570–1595.

[24] D.C. Karaiskos, D.A. Drossos, A.S. Tsiaousis, G.M. Giaglis, K.G. Fouskas, Affective and social determinants of mobile data services adoption, Behav. Inform. Technol. 31 (3) (2012) 209–219.

[25] S.M. Keaveney, M. Parthasarathy, Customer switching behavior in online services: an exploratory study of the role of selected attitudinal, behavioral, and demographic factors, J. Acad. Market. Sci. 29 (4) (2001) 374–390. [26] H.W. Kim, H.C. Chan, S. Gupta, Value-based adoption of mobile internet:

an empirical investigation, Decis. Support. Syst. 43 (1) (2007) 111–126. [27] S.Y. Kwak, S.H. Yoo, Ex-ante evaluation of the consumers' preference for the 4th generation mobile communications service, Technol. Forecast. Soc. Chang. 79 (7) (2012) 1312–1318.

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

[29] J. Lu, J.E. Yao, C.S. Yu, Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology, J. Strateg. Inf. Syst. 14 (3) (2005) 245–268.

[30] J. Lu, C.S. Yu, C. Liu, Facilitating conditions, wireless trust and adoption intention, J. Comput. Inf. Syst. 46 (1) (2005) 17–24.

[31] N. Mallat, M. Rossi, V.K. Tuunainen, A. Öörni, The impact of use context on mobile services acceptance: the case of mobile ticketing, Inf. Manag. 46 (3) (2009) 190–195.

[32] J.M. Moreno-Jiménez, W. Polasek, E-democracy and knowledge. A multi-criteria framework for the new democratic era, J. Multi-Criteria Decis. Anal. 12 (2–3) (2003) 163–176.

[33] S. Nikou, J. Mezei, Evaluation of mobile services and substantial adoption factors with Analytic Hierarchy Process (AHP), Telecommun. Policy (2012),http://dx.doi.org/10.1016/j.telpol.2012.09.007. [34] M. Pagani, Determinants of adoption of third generation mobile

mulitimedia services, J. Interact. Mark. 18 (3) (2004) 46–59. [35] T.R. Prabhu, K. Vizayakumar, Technology choice using FHDM— a case of

iron-making technology, IEEE Trans. Eng. Manag. 48 (2) (2001) 209–222.

[36] L. Radcliffe, M.J. Schiederjans, Trust evaluation: an AHP and multi-objective programming approach, Manag. Decis. 41 (5/6) (2003) 587–595.

[37] C. Ranganathan, D.B. Seo, Y. Babad, Switching behavior of mobile users: do users' relational investments and demographics matter? Eur. J. Inf. Syst. 15 (3) (2006) 269–276.

[38] T.L. Saaty, The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation, McGraw-Hill, New York, NY, 1980.

[39] S. Sarker, J.D. Wells, Understanding mobile handheld device use and adoption, Commun. ACM 46 (12) (2003) 35–40.

[40] R. Scheepers, H. Scheepers, O.K. Ngwenyama, Contextual influences on user satisfaction with mobile computing: findings from two healthcare organizations, Eur. J. Inf. Syst. 15 (3) (2006) 261–268.

[41] P.G. Schierz, O. Schilke, B.W. Wirtz, Understanding consumer accep-tance of mobile payment services: an empirical analysis, Electron. Commer. Res. Appl. 9 (3) (2010) 209–216.

[42] H. Shinno, H. Yoshioka, S. Marpaung, S. Hachiga, Quantitative SWOT analysis on global competitiveness of machine tool industry, J. Eng. Des. 17 (3) (2006) 251–258.

[43] W.C. Teng, H.P. Lu, H.J. Yu, Exploring the mass adoption of third-generation (3G) mobile phones in Taiwan, Telecommun. Policy 33 (10–11) (2009) 628–641.

[44] T.S.H. Teo, S.H. Pok, Adoption of WAP-enabled mobile phones among Internet users, Omega 31 (6) (2003) 483–498.

[45] D. Thakur, Market competition and the distributional consequences of mobile phones in Canada, Technol. Forecast. Soc. Chang. 79 (2) (2012) 223–230.

[46] P.J.M. van Laarhoven, W. Pedrycz, A fuzzy extension of Saaty's priority theory, Fuzzy Sets Syst. 11 (1–3) (1983) 199–227.

[47] K.C.C. Yang, Exploring factors affecting the adoption of mobile commerce in Singapore, Telematics Inform. 22 (3) (2005) 257–277. [48] R.M. Yusuff, K.P. Yee, M.S.J. Hasmi, A preliminary study on the potential

use of the analytical hierarchical process (AHP) to predict advanced manufacturing technology (AMT) implementation, Robot. Comput. Integr. Manuf. 17 (5) (2001) 421–427.

LON-FON SHIEH is an Assistant Professor in the Department of Business Management at National United University (NUU), Taiwan. He received his Ph.D. degree in business management at the National Taiwan University of Science and Technology (NTUST) in 2004. He is a chief of information industry section in the Industry Development Bureau in Taiwan. His research interest is in industry strategy application and management.

TIEN-HSIANG CHANG is a professor at the Department of Information Management, National Kaohsiung University of Applied Sciences. She holds a B.S. from National Chiao Tung University (Taiwan) in Transportation and Engineering Management, M.S. from the University of Missouri-Columbia, USA, in Industrial Engineering and Ph. D. from the Department of Industrial Management, National Taiwan University of Science and Technology. Her current research interests are in operation research, stochastic and international business management. Dr. Chang has published articles in International Journal of System Science, Industrial Management and Data Systems and Industrial Marketing Management.

HSIN-PIN FU currently serves as a distinguished professor of the Depart-ment of Marketing and Distribution ManageDepart-ment at National Kaohsiung First University of Science and Technology. He holds a B.S. from Chung Yuan Christian University, Taiwan, M.S. from the University of Missouri-Columbia, USA and Ph. D. from National Chiao Tung University, Taiwan, all in Industrial Engineering. His current research interests are in electronic business and operation management in industrial applications. Mr. Fu has published over 50 articles in International Journals.

SHENG-WEI LIN is an assistant professor at the Department of Marketing Management, Takming University of Science and Technology, Taipei, Taiwan, ROC. He holds a Ph. D. from the Department of Marketing and Distribution Management at National Kaohsiung First University of Science and Technology, Taiwan, ROC. His current research interests are in marketing management and tourism management.

YINGYEN CHEN holds a MS from the Department of Marketing and Distribution Management at National Kaohsiung First University of Science and Technology. Her research interests are in retailing marketing and distribution management.

9 L.-F. Shieh et al. / Technological Forecasting & Social Change xxx (2013) xxx–xxx

Please cite this article as: L.-F. Shieh, et al., Analyzing the factors that affect the adoption of mobile services in Taiwan, Technol. Forecast. Soc. Change (2013),http://dx.doi.org/10.1016/j.techfore.2013.11.004

數據

Updating...

參考文獻