Examining Factors that Influence Intent to Adopt Data Science

References

• Abbasi A, Sarker S, Chiang R. Big data research in information systems: toward an inclusive research agenda. J Assoc Inf Syst. 2016;17(2):3.
   Web of Science ®Google Scholar
• Agarwal R, Dhar V. Editorial—Big data, data science, and analytics: the opportunity and challenge for IS research. Infor Syst Res. 2014;25(3):443–448.
   Web of Science ®Google Scholar
• Ajzen I. The theory of planned behavior. Organ Behav Hum Dec. 1991;50(2):179–211.
   Web of Science ®Google Scholar
• Ajzen I, Fishbein M. Attitude-behavior relations: a theoretical analysis and review of empirical research. Psychol Bull. 1977;84(5):888.
   Web of Science ®Google Scholar
• Al-Debei MM, Al-Lozi E, Papazafeiropoulou A. Why people keep coming back to Facebook: explaining and predicting continuance participation from an extended theory of planned behaviour perspective. Decis Support Sys. 201355(1):43–54.
   Google Scholar
• Aparicio M, Bacao F, Oliveira T. Cultural impacts on e-learning systems’ success. Internet High Educ. 2016;31:58–70.
   Web of Science ®Google Scholar
• Brynjolfsson E. The contribution of information technology to consumer welfare. Inform Syst Res. 1996;7(3):281–300.
   Web of Science ®Google Scholar
• Chang YP, Zhu DH. Understanding social networking sites adoption in China: a comparison of pre-adoption and post-adoption. Comput Hum Behav. 2011;27(5):1840–1848.
   Web of Science ®Google Scholar
• Chau PY, Hu PJH. Information technology acceptance by individual professionals: a model comparison approach. Decis Sci. 2001;32(4):699–719.
   Web of Science ®Google Scholar
• Chen CD, Fan YW, Farn CK. Predicting electronic toll collection service adoption: an integration of the technology acceptance model and the theory of planned behavior. Transp Res C-Emer. 2007;15(5):300–311.
   Web of Science ®Google Scholar
• Chen H, Chiang RH, Storey VC. Business intelligence and analytics: from big data to big impact. MIS Quart. 2012;36(4):1165–1188.
   Web of Science ®Google Scholar
• Cheon J, Lee S, Crooks SM, Song. An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Comput Educ. 2012;59(3):1054–1064.
   Google Scholar
• Clemons EK, Reddi SP, Row MC. The impact of information technology on the organization of economic activity: the “move to the middle” hypothesis. J Manage Inf Syst. 1993;10(2):9–35.
   Google Scholar
• Clemons EK, Row MC. Limits to interfirm coordination through information technology: results of a field study in consumer packaged goods distribution. J Manage Inform Syst. 1993;10(1):73–96.
   Google Scholar
• Davenport TH, Patil D. Data scientist. Harvard Bus Rev. 2012;90:70–76.
   PubMed Web of Science ®Google Scholar
• Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quart. 1989;13(3):319–340.
   Google Scholar
• DeLone WH, McLean ER. Information systems success: the quest for the dependent variable. Inform Syst Res. 1992;3(1):60–95.
   Web of Science ®Google Scholar
• DeLone WH, McLean ER. Information systems success revisited. In: System Sciences, 2002. HICSS. Proceedings of the 35th Annual Hawaii International Conference on,IEEE; 2002. pp. 2966–2976.
   Google Scholar
• Delone WH, McLean ER. The DeLone and McLean model of information systems success: a ten-year update. J Manage Inform Syst. 2003;19(4):9–30.
   Web of Science ®Google Scholar
• Fishbein M. A theory of reasoned action: some applications and implications. Neb Symp on Motiv. 1979;27:65–116.
   Google Scholar
• Fishbein M, Ajzen I. Belief, attitude, intention, and behavior: an introduction to theory and research. Philos and Rhet. 1977;10(2):130–132.
   Google Scholar
• Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. J Market Res. 1981;18(1):39–50.
   Web of Science ®Google Scholar
• Garcia-Smith D, Effken JA. Development and initial evaluation of the clinical information systems success model (CISSM). Int J Med Inform. 2013;82(6):539–552.
   PubMed Web of Science ®Google Scholar
• Gefen D, Karahanna E, Straub DW. Trust and TAM in online shopping: an integrated model, MIS Quart. 2003;27(1):51–90.
   Google Scholar
• Henseler J, Ringle CM, Sinkovics RR. The use of partial least squares path modeling in international marketing. Adv Int Mark. 2009;20(1):277–319.
   Google Scholar
• Hitt L, Brynjolfsson E. The three faces of IT value: theory and evidence. ICIS 1994 Proceedings; 1994. p. 69.
   Google Scholar
• Hsu MH, Yen CH, Chiu CM, Chang CM. A longitudinal investigation of continued online shopping behavior: an extension of the theory of planned behavior. Int J Hum-Comput St. 2006;64(9):889–904.
   Web of Science ®Google Scholar
• Kim DJ, Ferrin DL, Rao HR. A trust-based consumer decision-making model in electronic commerce: the role of trust, perceived risk, and their antecedents. Decis Support Sys. 2008;44(2):544–564.
   Web of Science ®Google Scholar
• Lee MC. Explaining and predicting users’ continuance intention toward e-learning: an extension of the expectation–confirmation model. Comput Educ. 2010;54(2):506–516.
   Web of Science ®Google Scholar
• Lee MC, Tsai TR. What drives people to continue to play online games? An extension of technology model and theory of planned behavior. Int. J Hum-Comput Interact. 2010;26(6):601–620.
   Web of Science ®Google Scholar
• Liao C, Chen JL, Yen DC. Theory of planning behavior (TPB) and customer satisfaction in the continued use of e-service: an integrated model. Comput Human Behav. 2007;23(6):2804–2822.
   Web of Science ®Google Scholar
• Lin J, Chan HC, Wei KK. Understanding competing web application platforms: an extended theory of planned behavior and its relative model. IEEE T Eng Manage. 2011;58(1):21–35.
   Web of Science ®Google Scholar
• Mathieson K. Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Inform Syst Res. 1991;2(3):173–191.
   Web of Science ®Google Scholar
• Oliver RL. Effect of expectation and disconfirmation on postexposure product evaluations: an alternative interpretation. J Appl Psychol. 1977;62(4):480.
   Web of Science ®Google Scholar
• Oliver RL. A cognitive model of the antecedents and consequences of satisfaction decisions. J Market Res. 1980;17(4):460–469.
   Web of Science ®Google Scholar
• Pavlou PA, Fygenson M. Understanding and predicting electronic commerce adoption: an extension of the theory of planned behavior. MIS Quart. 2006;30(1):115–143.
   Web of Science ®Google Scholar
• Peace AG, Galletta DF, Thong JY. Software piracy in the workplace: a model and empirical test. J Manage Inform Syst. 2003;20(1):153–177.
   Web of Science ®Google Scholar
• Phillips-Wren G, Iyer LS, Kulkarni U, Ariyachandra T. Business analytics in the context of big data. Commun Assoc Inform Syst. 2015;37:448–472.
   Web of Science ®Google Scholar
• Rai A, Maruping LM, Venkatesh V. Offshore information systems project success: the role of social embeddedness and cultural characteristics. MIS Quart. 2009;33(3):617–641.
   Web of Science ®Google Scholar
• Riemenschneider CK, Harrison DA, Mykytyn PP. Understanding IT adoption decisions in small business: integrating current theories. Inform Manage. 2003;40(4):269–285.
   Web of Science ®Google Scholar
• Rouse M. Guide to big data analytics, tools, trends and best practices [Internet]. 2012 [cited 2017 Feb 20]. Available from http://searchbusinessanalytics.techtarget.com/definition/big-data-analytics
   Google Scholar
• Seddon PB. A respecification and extension of the DeLone and McLean model of IS success. Inform Syst Res. 1997;8(3):240–253.
   Web of Science ®Google Scholar
• Shaikh AA, Karjaluoto H. Making the most of information technology & systems usage: a literature review, framework and future research agenda. Comput Hum Behav. 2015;49:541–566.
   Web of Science ®Google Scholar
• van der Aalst WM. Data scientist: the engineer of the future. In: Enterprise Interoperability VI. Springer; 2014. pp. 13–26.
   Google Scholar
• Varian H. How the Web challenges managers. McKinsey&Company Publication; 2009. Available from: http://www mckinsey com/insights/innovation/hal_varian_on_how_the_web_challen ges_managers
   Google Scholar
• Venkatesh V, Davis F. A theoretical extension of the technology acceptance model: four longitudinal field studies. Manage Sci. 2000;46(2):186–204.
   Web of Science ®Google Scholar
• Venkatesh V, Morris M, Davis G, Davis F, User acceptance of information technology: toward a unified view. MIS Quart. 2003;27(3):425–478.
   Web of Science ®Google Scholar
• Wang YS, Wang HY, Shee DY. Measuring e-learning systems success in an organizational context: scale development and validation. Comput Hum Behav. 2007;23(4):1792–1808.
   Web of Science ®Google Scholar
• Wang YS, Lin HH, Luarn P. Predicting consumer intention to use mobile service. Inform Syst J. 2006;16(2):157–179.
   Web of Science ®Google Scholar
• Wimmer H, Powell LM. A comparison of open source tools for data science. J Inform Syst Appl Res. 2016;9(2):4.
   Google Scholar
• Wu JH, Wang YM. Measuring KMS success: a respecification of the DeLone and McLean’s model. Inform Manage. 2006;43(6):728–739.
   Web of Science ®Google Scholar
• Wu L, Chen JL. An extension of trust and TAM model with TPB in the initial adoption of on-line tax: an empirical study. Int J Hum-Comput St. 2005;62(6):784–808.
   Web of Science ®Google Scholar