Abstract
Prior research has provided valuable insight into how and why individual make a decision on the acceptance and use of information technologies (ITs) in the organizations. In practice, however, the diversity of IT and managerial task make these theoretical models not always work. To reinforce understanding about the drive mechanism of users’ IT usage, we draw from the representative researches on the technology acceptance model (TAM), and: (i) review and develop an integrated model of determinants of individual level IT usage from both cognitive and emotional perspectives; (ii) discuss the moderating effects of experience, commitment to use, task complexity, network externalities and instrumental on the relationships between determinants and user behavior.
This research is supported by the Natural Science Foundation (G2011202064, G2011202154) and Social Science Foundation (HB12GL050) of Hebei Province.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
This research is supported by the Natural Science Foundation (G2011202064, G2011202154) and Social Science Foundation (HB12GL050) of Hebei Province.
References
Ajzen I (1991) The theory of planned behavior. Organ Behav Hum Decis Process 50(2):179
Ajzen I (2002) Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. J Appl Soc Psychol 30(4):665–683
Amoako K, Salam A (2004) An extension of the technology acceptance model in an ERP implementation environment. Inf Manag 41(6):731–745
Bolt MA, Killough LN, Koh WC (2001) Testing the interaction effects of task complexity in computer training using the social cognitive model. Decis Sci 32(1):1–20
Choo CW (1996) How organizations use information to construct meaning, create knowledge and make decisions. Int J Inf Manag 16(5):329–340
Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13(3):319–340
Davis FD, Bagozzi RP, Warshaw PR (1989) User acceptance of computer technology. Manag Sci 35(8):982–1003
DeLone WH, McLean ER (1992) Information systems success: the quest for the dependent variable. Inf Syst Res 3(1):60–95
Dickinger A, Arami M, Meyer D (2008) The role of perceived enjoyment and social norm in the adoption of technology with network externalities. Eur J Inf Syst 17(1):4–11
Fishbein M, Ajzen I (1975) Belief, attitude, intention, and behavior. Addison-Wesley, New Jersey
Fredrickson BL (1998) What good are positive emotions. Rev Gen Psychol 2(3):300–319
French J, Raven BH (1975) The bases of social power. In: Cartwright D (ed) Studies of social power. Institute for Social Research, Ann Arbor
Goodhue DL, Thompson RL (1995) Task-technology fit and individual performance. MIS Q 19(2):213–236
Hartwick J, Barki H (1994) Explaining the role of user participation in information system use. Manag Sci 40(4):440–465
Hu Wei-peng, Shi Kan (2004) A review of the research about organizational commitment. Adv Psychol Sci 12(1):103–110 (in Chinese)
Karahanna E, Straub DW (1999) The psychological origins of perceived usefulness and ease-of-use. Inf Manag 35(4):237–250
Lazarus RS (1991) Emotion and adaptation. In: Pervin LA (ed) Handbook of personality: theory and research. Oxford University Press, New York, pp 609–637
Long Li-rong, Fang Li-luo, Ling Wen-quan, Li Ye (2000) Theory and measurement of career commitment. J Dev Psychol 8(4):39–45 (in Chinese)
Meyer JP, Allen NJ (1991) A three-component conceptualisation of organizational commitment. Hum Resour Manag Rev 1(1):61–89
Meyer JP, Becker TE, Van Dick R (2006) Social identities and commitments at work: toward an integrative model. J Organ Behav 27(5):665–683
Qi Zhen-jiang, Zhu Ji-ping (2007) The theory of organizational commitment and its research development. J Zhejiang Univ 37(6):90–98 (in Chinese)
Salancik GR (1977) Commitment is too easy. Organ Dyn 6(1):62–80
Seo M-G, Bartunek JM, Barrett LF (2010) The role of affective experience in work motivation: test of a conceptual model. J Organ Behav 31(7):951–968
Strader TJ, Ramaswami SN, Houle PA (2007) Perceived network externalities and communication technology acceptance. Eur J Inf Syst 16:54–65
Sun Yuan (2010) The unified technology acceptance model based on task-technology fit theory. PhD dissertation, Zhejiang University, Hangzhou (in Chinese)
Taylor S, Todd PA (1995) Understanding information technology usage: a test of competing models. Inf Syst Res 6(2):144–176
Venkatesh V, Bala H (2008) Technology acceptance model 3 and a research agenda on interventions. Decis Sci 39(2):273–315
Venkatesh V, Davis FD (2000) A theoretical extension of the technology acceptance model. Manag Sci 46(2):186–204
Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology. MIS Q 27(3):425–478
Xiaowen F, Chan S, Brzezinski J, Shuang XU (2006) Moderating effects of task type on wireless technology acceptance. J Manag Inf Syst 22(3):123–157 (in Chinese)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lv, Rj., He, Lj., Chen, Xc., Zhao, Z. (2013). A Driven Model of IT Usage: Determinants and Moderating Effects of Situational Variables. In: Qi, E., Shen, J., Dou, R. (eds) The 19th International Conference on Industrial Engineering and Engineering Management. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37270-4_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-37270-4_30
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37269-8
Online ISBN: 978-3-642-37270-4
eBook Packages: Business and EconomicsBusiness and Management (R0)