The influence of smartphones on academic performance: The development of the technology-to-performance chain model

Purpose The purpose of this paper is to investigate the factors that influence college students’ smartphone use for academic purposes by identifying the task-technology fit (TTF) of smartphones. A research model is proposed to explain how TTF of smartphones affects college students’ perceived academic performance and smartphone use. Design/methodology/approach Online surveys were administered to college students at a South Korean university that has offered online academic services for more than five years, and 1,923 valid responses were analyzed. The study used partial least squares path modeling to evaluate the measurement model, and the bootstrapping technique to test the significance of the hypotheses. Findings The findings highlight that the TTF of smartphones has a direct influence on students’ perceptions of performance impact and an indirect influence on smartphone use through a precursor of utilization, such as attitude toward smartphone use, social norms and facilitating conditions. Research limitations/implications Despite a reasonably large sample, a single cross-sectional survey has a likelihood of selection bias in the sample. Practical implications This study applies the TTF model to smartphone use among college students and suggests an effective way to motivate them to use mobile technologies for their academic activities. Originality/value The present study develops an empirical model to assess the adoption of smartphones and its effect on college students’ academic performance. Above all, the study identifies a causal relationship among TTF, precursor of utilization, smartphone use and a perceived impact on academic performance based on the development and validation of the TTF constructs of smartphones.

[1]  Yi-Shun Wang,et al.  Measuring e-learning systems success in an organizational context: Scale development and validation , 2007, Comput. Hum. Behav..

[2]  Innovation and organisational trust: study of firms in Poland , 2011 .

[3]  D. Sandy Staples,et al.  Testing the Technology-to-Performance Chain Model , 2004, J. Organ. End User Comput..

[4]  Shahriar Akter,et al.  Application of the task-technology fit model to structure and evaluate the adoption of E-books by Academics , 2013, J. Assoc. Inf. Sci. Technol..

[5]  R. Bagozzi A Field Investigation of Causal Relations among Cognitions, Affect, Intentions, and Behavior , 1982 .

[6]  David C. Yen,et al.  The acceptance and diffusion of the innovative smart phone use: A case study of a delivery service company in logistics , 2009, Inf. Manag..

[7]  Yifan Li,et al.  A framework of students' reasons for using CMC media in learning contexts: A structural approach , 2011, J. Assoc. Inf. Sci. Technol..

[8]  Gordon B. Davis,et al.  User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..

[9]  Dale L. Goodhue,et al.  The model underlying the measurement of the impacts of the IIC on the end‐users , 1997 .

[10]  Alison J. Head,et al.  Lessons Learned: How College Students Seek Information in the Digital Age. Project Information Literacy Progress Report. , 2009 .

[11]  Jaeki Song,et al.  An investigation of mobile learning readiness in higher education based on the theory of planned behavior , 2012, Comput. Educ..

[12]  Hyunseung Choo,et al.  Smartphones as smart pedagogical tools: Implications for smartphones as u-learning devices , 2011, Comput. Hum. Behav..

[13]  Christer Carlsson,et al.  Factors driving the adoption of m-learning: An empirical study , 2010, Comput. Educ..

[14]  E. Rogers,et al.  Diffusion of innovations , 1964, Encyclopedia of Sport Management.

[15]  Kuo-Lun Hsiao,et al.  Android smartphone adoption and intention to pay for mobile internet: Perspectives from software, hardware, design, and value , 2013, Libr. Hi Tech.

[16]  Elke M. Leeds,et al.  Digital Video Presentation and Student Performance: A Task Technology Fit Perspective , 2010, Int. J. Inf. Commun. Technol. Educ..

[17]  Michael M. Grant,et al.  Mobile computing devices in higher education: Student perspectives on learning with cellphones, smartphones & social media , 2013, Internet High. Educ..

[18]  Dale Goodhue,et al.  Task-Technology Fit and Individual Performance , 1995, MIS Q..

[19]  Mark T. Dishaw,et al.  Extending the Technology Acceptance Model , 1997 .

[20]  Waiman Cheung,et al.  Determinants of the intention to use Internet/WWW at work: a confirmatory study , 2001, Inf. Manag..

[21]  Jane Klobas,et al.  LMS use and Instructor Performance: The Role of Task-technology Fit , 2011 .

[22]  Viswanath Venkatesh,et al.  Technology adoption decisions in the household: A seven‐model comparison , 2014, J. Assoc. Inf. Sci. Technol..

[23]  Yoonmo Sang,et al.  Exploring Koreans' smartphone usage: An integrated model of the technology acceptance model and uses and gratifications theory , 2013, Comput. Hum. Behav..

[24]  Sang Yup Lee,et al.  Examining the factors that influence early adopters' smartphone adoption: The case of college students , 2014, Telematics Informatics.

[25]  Camille Johnson-Yale,et al.  Academic work, the Internet and U.S. college students , 2008, Internet High. Educ..

[26]  Namkee Park,et al.  Factors influencing smartphone use and dependency in South Korea , 2013, Comput. Hum. Behav..

[27]  Hassan M. Selim,et al.  Critical success factors for e-learning acceptance: Confirmatory factor models , 2007, Comput. Educ..

[28]  Tanya J. McGill,et al.  How students and instructors using a virtual learning environment perceive the fit between technology and task , 2007, J. Comput. Assist. Learn..

[29]  Aaron Smith,et al.  U.S. Smartphone Use in 2015 , 2015 .

[30]  Jane E. Klobas,et al.  A task-technology fit view of learning management system impact , 2009, Comput. Educ..

[31]  Eden Dahlstrom,et al.  ECAR Study of Undergraduate Students and Information Technology, 2014. , 2014 .

[32]  Daejoong Kim,et al.  The Integrated Model of Smartphone Adoption: Hedonic and Utilitarian Value Perceptions of Smartphones Among Korean College Students , 2012, Cyberpsychology Behav. Soc. Netw..

[33]  Hyunjoo Lee,et al.  Determining the factors that influence college students' adoption of smartphones , 2014, J. Assoc. Inf. Sci. Technol..

[34]  Fred D. Davis Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[35]  Chao-Min Chiu,et al.  Examining the integrated influence of fairness and quality on learners’ satisfaction and Web‐based learning continuance intention , 2007, Inf. Syst. J..

[36]  Eric W. T. Ngai,et al.  Empirical examination of the adoption of WebCT using TAM , 2007, Comput. Educ..

[37]  I. Ajzen,et al.  Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research , 1977 .

[38]  Jengchung V. Chen,et al.  Acceptance and adoption of the innovative use of smartphone , 2007, Ind. Manag. Data Syst..

[39]  Erik M. van Raaij,et al.  The acceptance and use of a virtual learning environment in China , 2008, Comput. Educ..

[40]  M. Treacy,et al.  Utilization as a dependent variable in MIS research , 2015, DATB.

[41]  Christopher M. Lopez,et al.  What motivates college students to adopt 4G smartphone technology?: A uses and gratifications perspective , 2013 .