Nothing Special   »   [go: up one dir, main page]

Skip to main content

Advertisement

Log in

Examining the infusion of mobile technology by healthcare practitioners in a hospital setting

  • Published:
Information Systems Frontiers Aims and scope Submit manuscript

Abstract

While mobile Health (mHealth) holds much potential, the infusion of mHealth is still in its infancy and has yet to achieve sufficient attention in the Information Systems field. As a result, the objective of this paper is to identify the (a) determinants for successful infusion of mHealth by healthcare practitioners and (b) benefits healthcare practitioners perceive from infusing mHealth. A sequential mixed methods approach (case study and survey) is employed to achieve this objective. The study contributes to IS theory and practice by: (1) developing a model with six determinants (Availability, Self-Efficacy, Time-Criticality, Habit, Technology Trust, and Task Behaviour) and three individual performance-related benefits associated with mHealth infusion (Effectiveness, Efficiency, and Learning), (2) exploring undocumented determinants and relationships, (3) identifying conditions that both healthcare practitioners and organisations can employ to assist with mHealth infusion and (4) informing healthcare organisations and vendors as to the performance of mHealth in post-adoptive scenarios.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Abdinnour-Helm, S., & Saeed, K. (2006). Examining Post Adoption Usage: Conceptual Development and Empirical Assessment. Paper presented at the AMCIS 2006 Proceedings, Paper 148.

  • Agarwal, R., & Venkatesh, V. (2002). Assessing a firm’s web presence: a heuristic evaluation procedure for the measurement of usability. Information Systems Research, 13(2), 168–186.

    Article  Google Scholar 

  • Aitken, M., & Gauntlett, C. (2013). IMS Institute for healthcare informatics patient apps for improved healthcare: from novelty to mainstream. http://obroncology.com/imshealth/content/IIHI%20Apps%20report%20231013F_interactive.pdf.

  • Ang, W. J. J., Hopkins, M. E., Partridge, R., Hennessey, I., Brennan, P. M., Fouyas, I., et al. (2014). Validating the use of smartphone-based accelerometers for performance assessment in a simulated neurosurgical task. Neurosurgery, 10, 57–65.

    Google Scholar 

  • Anglada-Martinez, H., Riu-Viladoms, G., Martin-Conde, M., Rovira-Illamola, M., Sotoca-Momblona, J., & Codina-Jane, C. (2015). Does mHealth increase adherence to medication? Results of a systematic review. International Journal of Clinical Practice, 69(1), 9–32.

    Article  Google Scholar 

  • Basole, R. C. (2004). The value and impact of mobile information and communication technologies. In Proceedings of the 2004 International Federation of Automatic Control Symposium, Atlanta, GA, USA, 2004,(pp. 1-7)

  • Becker, S., Brandl, C., Meister, S., Nagel, E., Miron-Shatz, T., Mitchell, A., et al. (2015). Demographic and health related data of users of a mobile application to support drug adherence is associated with usage duration and intensity. PloS One, 10(1), e0116980.

    Article  Google Scholar 

  • Bergeron, F., Raymond, L., Rivard, S., & Gara, M. (1995). Determinants of EIS use: testing a behavioral model. Decision Support Systems, 14(2), 31–146.

    Article  Google Scholar 

  • Black, A. D., Car, J., Pagliari, C., Anandan, C., Cresswell, K., Bokun, T., et al. (2011). The impact of eHealth on the quality and safety of health care: a systematic overview. PLoS Medicine, 8(1), e1000387.

    Article  Google Scholar 

  • Boell, S. K., & Cecez-Kecmanovic, D. (2014). A hermeneutic approach for conducting literature reviews and literature searches. Communications of the Association for Information Systems, 34(1), 257–286.

    Google Scholar 

  • Cavaye, A. L. M. (1996). Case study research: a multi-faceted research approach for IS. Information Systems Journal, 6(3), 227–242.

    Article  Google Scholar 

  • Cavus, N., & Munyavi, R. M. (2015). An assessment of the effects of widespread use of mobile applications in the health sector: an exploratory study of its success and failures. Global Journal on Advances in Pure & Applied Sciences, 7, 145–149.

    Google Scholar 

  • Chen, H., Hailey, D., Wang, N., & Yu, P. (2014). A review of data quality assessment methods for public health information systems. International Journal of Environmental Research and Public Health, 11(5), 5170–5207.

    Article  Google Scholar 

  • Chin, W. W. (1998). The partial least squares approach for structural equation modeling. In M. G. E. (Ed.), (Ed.), Modern Methods for Business Research (pp. 295–336). Mahwah: Lawrence Erlbaum Associates.

    Google Scholar 

  • Chiu, T. M., & Eysenbach, G. (2010). Stages of use: consideration, initiation, utilization, and outcomes of an internet-mediated intervention. BMC Medical Informatics and Decision Making, 10(1), 73.

    Article  Google Scholar 

  • Choi, A. L., Lai, D. A., & Lai, T. L. (2016). Health analytics, economics and medicine toward a twenty-first century health care system. Health, 8(05), 428.

    Article  Google Scholar 

  • Chung, W. Y., & Guinan, P. J. (1994). Effects of participative management on the performance of software development teams. In Proceedings of the 1994 computer personnel research conference on Reinventing IS: managing information technology in changing organizations: managing information technology in changing organizations. (pp. 252–260): ACM.

  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences Hillsdale. NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: development of a measure and initial test. MIS Quarterly, 19(2), 189–211.

    Article  Google Scholar 

  • Cooper, R. B., & Zmud, R. W. (1990). Information technology implementation research: a technological diffusion approach. Management Science, 36(2), 123–139.

    Article  Google Scholar 

  • Craig, K., Tams, S., Clay, P., & Thatcher, J. (2010). Integrating trust in technology and computer self-efficacy within the post-adoption context: An empirical examination.

    Google Scholar 

  • Daniels, N., & Sabin, J. E. (2002). Setting limits fairly: Can we learn to share medical resources? Oxford University Press. http://EconPapers.repec.org/RePEc:oxp:obooks:9780195149364.

  • Darke, P., Shanks, G., & Broadbent, M. (1998). Successfully completing case study research: combining rigour, relevance and pragmatism. Information Systems Journal, 8(4), 273–289. doi:10.1046/j.1365-2575.1998.00040.x.

    Article  Google Scholar 

  • Davenport, T. H., & Prusak, L. (1998). Working Knowledge. How Organizations Manage What They Know. Boston, Mass: Harvard Business School Press 1998.

    Google Scholar 

  • Dehzad, F., Hilhorst, C., de Bie, C., & Claassen, E. (2014). Adopting health apps, What’s hindering doctors and patients? Health, 2014.

  • Deng, X., & Chi, L. (2012). Understanding postadoptive behaviors in information systems use: a longitudinal analysis of system use problems in the business intelligence context. Journal of Management Information Systems, 29(3), 291–326.

    Article  Google Scholar 

  • DesRoches, C. M., Campbell, E. G., Rao, S. R., Donelan, K., Ferris, T. G., Jha, A., et al. (2008). Electronic health Records in Ambulatory Care — a National Survey of physicians. New England Journal of Medicine, 359(1), 50–60. doi:10.1056/NEJMsa0802005.

    Article  Google Scholar 

  • Devaraj, S., & Kohli, R. (2000). Information technology payoff in the health-care industry: alongitudinal study. Journal of Management Information Systems, 16(4), 41–67.

    Article  Google Scholar 

  • Diamantopoulos, A., & Siguaw, J. A. (2006). Formative versus reflective indicators in organizational measure development: a comparison and empirical illustration. British Journal of Management, 17(4), 263–282.

    Article  Google Scholar 

  • Dwivedi, Y. K., Shareef, M. A., Simintiras, A. C., Lal, B., & Weerakkody, V. (2016). A generalised adoption model for services: a cross-country comparison of mobile health (m-health). Government Information Quarterly, 33(1), 174–187.

    Article  Google Scholar 

  • Eady, A., Glasziou, P., & Haynes, B. (2008). Less is more: where do the abstracts in the EBM journal come from? Evidence-Based Medicine, 13(1), 3.

    Article  Google Scholar 

  • Ebell, M. H. (2009). How to find answers to clinical questions. American Family Physician, 79(4), 293–296.

    Google Scholar 

  • El Morr, C., & Subercaze, J. (2010). Knowledge Management in Healthcare. MM Cunha, A. Tavares, & R. Simões, Handbook of Research on Developments in e-Health and Telemedicine: Technological and Social Perspectives. IGI Global.

  • Fadel, K. (2006). Individual Infusion of Information Systems: The Role of Adaptation and Individual Cognitions. Paper presented at the AMCIS 2006, Proceedings Paper 38.

  • Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: algebra and statistics. Journal of Marketing Research, 18(3), 382–388.

    Article  Google Scholar 

  • Franz-Vasdeki, J., Pratt, B. A., Newsome, M., & Germann, S. (2015). Taking mHealth solutions to scale: enabling environments and successful implementation. Journal of Mobile Technology in Medicine, 4(1), 35–38.

    Article  Google Scholar 

  • Free, C., Phillips, G., Watson, L., Galli, L., Felix, L., Edwards, P., et al. (2013). The effectiveness of mobile-health technologies to improve health care service delivery processes: a systematic review and meta-analysis. PLoS Medicine, 10(1), e1001363.

    Article  Google Scholar 

  • Gagnon, M.-P., Ngangue, P., Payne-Gagnon, J., & Desmartis, M. (2016). M-health adoption by healthcare professionals: a systematic review. Journal of the American Medical Informatics Association, 23(1), 212–220.

    Article  Google Scholar 

  • Galliers, R. D. (1992). Choosing information systems research approaches. In R. D. Galliers (Ed.), Information systems research: Issues, methods and practical guidelines (pp. 144–162). Alfred Waller Ltd: Henley-on-Thames.

    Google Scholar 

  • Gallivan, M., & Srite, M. (2005). Information technology and culture: identifying fragmentary and holistic perspectives of culture. Information and Organization, 15(4), 295–338.

    Article  Google Scholar 

  • Garfield, M. J., & Dennis, A. R. (2012). Toward an integrated model of group development: disruption of routines by technology-induced change. Journal of Management Information Systems, 29(3), 43–86.

    Article  Google Scholar 

  • Gebauer, J., & Tang, Y. (2007). Applying the theory of task-technology fit to mobile information systems: the role of user mobility. In Management of Mobile Business, 2007. ICMB 2007. International Conference on the, 2007 (pp. 38–38): IEEE.

  • Gefen, D., & Straub, D. (2005). A practical guide to factorial validity using PLS-graph: tutorial and annotated example. Communications of the Association for Information Systems, 16(1), 91–109.

    Google Scholar 

  • Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213–236.

    Article  Google Scholar 

  • Hackman, J. R., & Oldham, G. R. (1976). Motivation through the design of work: test of a theory. Organizational Behavior and Human Performance, 16(2), 250–279.

    Article  Google Scholar 

  • Hamine, S., Gerth-Guyette, E., Faulx, D., Green, B. B., & Ginsburg, A. S. (2015). Impact of mHealth chronic disease management on treatment adherence and patient outcomes: a systematic review. Journal of Medical Internet Research, 17(2), e52.

    Article  Google Scholar 

  • Hasan, H., Linger, H., Kim, H.-W., Chan, H. C., & Gupta, S. (2016). Examining information systems infusion from a user commitment perspective. Information Technology & People, 29(1), 173–199.

    Article  Google Scholar 

  • Hsia, T.-L., Lin, L.-M., Wu, J.-H., & Tsai, H.-T. (2006). A framework for designing nursing knowledge management systems. Interdisciplinary Journal of Information, Knowledge, and Management, 1(1), 13–23.

    Article  Google Scholar 

  • Hsiao, J.-L., & Chen, R.-F. (2012). An investigation on task-technology fit of mobile nursing information Systems for Nursing Performance. Computers, Informatics, Nursing, 30(5), 265–273.

    Article  Google Scholar 

  • Hsieh, J. J. P. A., & Wang, W. (2007). Explaining employees’ extended use of complex information systems. European Journal of Information Systems, 16(3), 216–227.

    Article  Google Scholar 

  • Hsieh, J. P.-A., & Zmud, R. (2006). Understanding Post-Adtopive Usage Behaviors: A Two-Dimensional View. DIGIT 2006 Proceedings. Paper 3. http://aisel.aisnet.org/digit2006/3.

  • Igbaria, M., & Iivari, J. (1995). The effects of self-efficacy on computer usage. Omega, 23(6), 587–605.

    Article  Google Scholar 

  • Jahns, R. H. P. (2014). Mobile health market report 2013–2017 (Vol3). Research2Guidace Report. http://research2guidance.com/wp-content/uploads/2015/08/Mobile-Health-Market-Report-2013-2017-Graphical-Package-Preview.pdf.

  • Jarvenpaa, S. L., & Lang, K. R. (2005). Managing the paradoxes of mobile technology. Information Systems Management, 22(4), 7–23.

    Article  Google Scholar 

  • Jasperson, J., Carter, P. E., & Zmud, R. W. (2005). A comprehensive conceptualization of post-adoptive behaviors associated with information technology enabled work systems. MIS Quarterly, 29(3), 525–557.

    Article  Google Scholar 

  • Junglas, I., Abraham, C., & Ives, B. (2009). Mobile technology at the frontlines of patient care: understanding fit and human drives in utilization decisions and performance. Decision Support Systems, 46(3), 634–647. doi:10.1016/j.dss.2008.11.012.

    Article  Google Scholar 

  • Kahn, J. G., Yang, J. S., & Kahn, J. S. (2010). ‘Mobile’health needs and opportunities in developing countries. Health Affairs, 29(2), 252–258.

    Article  Google Scholar 

  • Kane, B., & Luz, S. (2009). Achieving diagnosis by consensus. Computer Supported Cooperative Work, 18(4), 357–392. doi:10.1007/s10606-009-9094-y.

    Article  Google Scholar 

  • Katz, J. E., & Rice, R. E. (2009). Public views of mobile medical devices and services: a US national survey of consumer sentiments towards RFID healthcare technology. International Journal of Medical Informatics, 78(2), 104–114.

    Article  Google Scholar 

  • Ke, W., Tan, C.-H., Sia, C.-L., & Wei, K.-K. (2012). Inducing intrinsic motivation to explore the enterprise system: the supremacy of organizational levers. Journal of Management Information Systems, 29(3), 257–290.

    Article  Google Scholar 

  • Kim, H.-W., Chan, H. C., & Lee, S.-H. (2012). A User Commitment Approach to Information Systems Infusion. In PACIS 2012 Proceedings, Paper 101.

  • Kim, S., Shim, B., Kim, J. A., & Cho, I. S. (2010). SW architecture for access to medical information for knowledge execution. Security-Enriched Urban Computing and Smart Grid, 574–580.

  • Kwon, T. H., & Zmud, R. W. (1987). Unifying the fragmented models of information systems implementation. In Critical issues in information systems research (pp. 227–251): Wiley.

  • Labrique, A. B., Vasudevan, L., Kochi, E., Fabricant, R., & Mehl, G. (2013). mHealth innovations as health system strengthening tools: 12 common applications and a visual framework. Global Health: Science and Practice, 1(2), 160–171.

    Google Scholar 

  • Liang, H., Peng, Z., Xue, Y., Guo, X., & Wang, N. (2015). Employees’ exploration of complex systems: an integrative view. Journal of Management Information Systems, 32(1), 322–357.

    Article  Google Scholar 

  • Lim, S. Y., Jarvenpaa, S. L., & Lanham, H. J. (2015). Barriers to Interorganizational knowledge transfer in post-hospital care transitions: review and directions for information systems research. Journal of Management Information Systems, 32(3), 48–74.

    Article  Google Scholar 

  • Limayem, M., Hirt, S. G., & Cheung, C. M. K. (2007). How habit limits the predictive power of intention: the case of information systems continuance. MIS Quarterly, 31(4), 705–737.

    Article  Google Scholar 

  • Makinen, J., & Jaakkola, H. (2000) Representing infusion of mobile phones. In Engineering Management Society, 2000. Proceedings of the 2000 IEEE (pp. 476–480): IEEE.

  • Marshall, C., & Rossman, G. B. (1989). Designing Qualitative Research: Sage Publications.

  • McKnight, D. H., Carter, M., Thatcher, J. B., & Clay, P. F. (2011). Trust in a specific technology: an investigation of its components and measures. ACM transactions on Management Information Systems, 2(2), 1–25. doi:10.1145/1985347.1985353.

    Article  Google Scholar 

  • McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing and validating trust measures for e-commerce: an integrative typology. Information Systems Research, 13(3), 334–359. doi:10.1287/isre.13.3.334.81.

    Article  Google Scholar 

  • Muessig, K. E., Pike, E. C., LeGrand, S., & Hightow-Weidman, L. B. (2013). Mobile phone applications for the care and prevention of HIV and other sexually transmitted diseases: a review. Journal of Medical Internet Research, 15(1), e1.

    Article  Google Scholar 

  • Nair, P., & Bhaskaran, H. (2014). The emerging interface of healthcare system and mobile communication technologies. Health and Technology, 1–7.

  • Neupane, S., Odendaal, W., Friedman, I., Jassat, W., Schneider, H., & Doherty, T. (2014). Comparing a paper based monitoring and evaluation system to a mHealth system to support the national community health worker programme, South Africa: an evaluation. BMC Medical Informatics and Decision Making, 14(1), 69.

    Article  Google Scholar 

  • Ng, E. H., & Kim, H. W. (2009). Investigating information systems infusion and the moderating role of habit: A user empowerment perspective. Paper presented at the Proceedings of International Conference of Information Systems,

    Google Scholar 

  • Nilsson, E. G. (2009). Challenges for mobile solutions for emergency response. In Position paper at the workshop for Mobile Information Technology for Emergency Response at the ISCRAM 2009 conference.

  • Nimkar, S. (2016). Promoting individual health using information technology: trends in the US health system. Health Education Journal. doi:10.1177/0017896916632790.

    Article  Google Scholar 

  • O' Connor, Y., O' Donoghue, J., & O' Reilly, P. Understanding mobile technology post-adoption behaviour: impact upon knowledge creation and individual performance. In 2011 Tenth International Conference on Mobile Business (ICMB), 20–21 June 2011 2011 (pp. 275–282).

  • O' Connor, Y., O’Raghailligh, P. J., & O' Donoghue, J. (2012). Individual Infusion of M-Health Technologies: Determinants and Outcomes. In ECIS 2012 Proceedings, Paper 164.

  • Oakley, R., & Palvia, P. A study of the impact of mobile self-efficacy and emotional attachment on mobile device infusion. In AMCIS 2012, Paper 15, 2012.

  • Owen, J. E., Jaworski, B. K., Kuhn, E., Makin-Byrd, K. N., Ramsey, K. M., & Hoffman, J. E. (2015). mHealth in the wild: using novel data to examine the reach, use, and impact of PTSD coach. JMIR Mental Health, 2(1), e7.

    Article  Google Scholar 

  • Patton, M. Q. (2001). Qualitative research & evaluation methods: Sage publications, Incorporated.

    Google Scholar 

  • Pearce, J. L., & Gregersen, H. B. (1991). Task interdependence and extrarole behavior: A test of the mediating effects of felt responsibility. Journal of Applied Psychology, 76(6), 838–844.

    Article  Google Scholar 

  • Peijian, S., & Lihua, H. Innovative use of information technology: a role identity perspective. In Proceedings of the First China Summer Workshop on Information Management, Shanghai, China., 2007

  • Peng, G., Dey, D., & Lahiri, A. (2014). Healthcare IT adoption: an analysis of knowledge transfer in socioeconomic networks. Journal of Management Information Systems, 31(3), 7–34.

    Article  Google Scholar 

  • Prgomet, M., Georgiou, A., & Westbrook, J. I. (2009). The impact of mobile handheld technology on hospital physicians’ work practices and patient care: a systematic review. Journal of the American Medical Informatics Association, 16(6), 792–801.

    Article  Google Scholar 

  • Rahurkar, S., Vest, J. R., & Menachemi, N. (2015). Despite the spread of health information exchange, there is little evidence of its impact on cost, use, and quality of care. Health Affairs, 34(3), 477–483.

    Article  Google Scholar 

  • Ramamurthy, K., Sen, A., & Sinha, A. P. (2008). Data warehousing infusion and organizational effectiveness. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, 38(4), 976–994.

    Article  Google Scholar 

  • Ranallo, P. A., Kilbourne, A. M., Whatley, A. S., & Pincus, H. A. (2016). Behavioral health information technology: from chaos to clarity. Health Affairs, 35(6), 1106–1113.

    Article  Google Scholar 

  • Rossi, M., Tuunainen, V. K., & Pesonen, M. (2007). Mobile technology in field customer service: big improvements with small changes. Business Process Management Journal, 13(6), 853–865.

    Article  Google Scholar 

  • Saeed, K. A., & Abdinnour-Helm, S. (2008). Examining the effects of information system characteristics and perceived usefulness on post adoption usage of information systems. Information and Management, 45(6), 376–386.

    Article  Google Scholar 

  • Safran, C., & Goldberg, H. (2000). Electronic patient records and the impact of the internet. International Journal of Medical Informatics, 60(2), 77–83.

    Article  Google Scholar 

  • Saga, V. L., & Zmud, R. W. (1994). The nature and determinants of IT acceptance, routinization and infusion. Diffusion Transfer and Implementation of Information Technology, 45(A-45), 67–86.

    Google Scholar 

  • Sousa, R. D., & Goodhue, D. L. (2003). Understanding exploratory use of ERP systems. In AMCIS 2003 Proceedings. (Vol. Paper 62).

  • Strauss, A. L., & Corbin, J. (1990). The basics of qualitative analysis: Grounded theory procedures and techniques. Newbury Park: Sage Publications Inc., California.

    Google Scholar 

  • Sundaram, S., Schwarz, A., Jones, E., & Chin, W. (2007). Technology use on the front line: how information technology enhances individual performance. Journal of the Academy of Marketing Science, 35(1), 101–112. doi:10.1007/s11747-006-0010-4.

    Article  Google Scholar 

  • Sundin, P., Callan, J., & Mehta, K. (2016). Why do entrepreneurial mHealth ventures in the developing world fail to scale? Journal of Medical Engineering & Technology, 1–14.

  • Tanoglu, I., & Basoglu, N. (2006). Information technology (IT) diffusion: An analysis of user behavior in the exploitation of IT. In Technology Management for the Global Future, 2006. PICMET 2006, 8–13 July 2006 (Vol. 4, pp. 1735–1741). doi:10.1109/picmet.2006.296747.

  • Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: a test of competing models. Information Systems Research, 6(2), 144–176.

    Article  Google Scholar 

  • Tenenhaus, M., Vinzi, V. E., Chatelin, Y. M., & Lauro, C. (2005). PLS path modeling. Computational Statistics and Data Analysis, 48(1), 159–205.

    Article  Google Scholar 

  • Tennant, V., Mills, A., & Chin, W. (2011). Investigating information system infusion at the individual level: re-conceptualisation and operationalization paper presented at the PACIS 2011 Proceedings. Paper 189.

  • Thatcher, J. B., McKnight, D. H., Baker, E. W., Arsal, R. E., & Roberts, N. H. (2011). The role of Trust in Postadoption IT exploration: an empirical examination of knowledge management systems. Engineering Management, IEEE Transactions on, 58(1), 56–70.

    Article  Google Scholar 

  • Torkzadeh, G., Chang, J. C. J., & Hardin, A. M. (2011). Usage and impact of technology enabled job learning. European Journal of Information Systems, 20(1), 69–86.

    Article  Google Scholar 

  • Torkzadeh, G., & Doll, W. J. (1999). The development of a tool for measuring the perceived impact of information technology on work. Omega, 27(3), 327–339.

    Article  Google Scholar 

  • Trice, A. W., & Treacy, M. E. (1988). Utilization as a dependent variable in MIS research. SIGMIS Database, 19(3–4), 33–41. doi:10.1145/65766.65771.

    Article  Google Scholar 

  • Tu, H. (2015). Failed cases of mobile healthcare apps: why Kanchufang has failed. mHealth, 1(1).

  • Turner, T., Spruijt-Metz, D., Wen, C., & Hingle, M. (2015). Prevention and treatment of pediatric obesity using mobile and wireless technologies: a systematic review. Pediatric Obesity, 10(6), 403–409.

    Article  Google Scholar 

  • Urquhart, C. (2001). An encounter with grounded theory: tackling the practical and philosophical issues. Qualitative research in IS: Issues and trends, 104–140.

  • Venkatesh, V., Brown, S. A., Maruping, L. M., & Bala, H. (2008). Predicting different conceptualizations of system use: the competing roles of behavioral intention, facilitating conditions, and behavioral expectation. MIS Quarterly, 32(3), 483–502.

    Article  Google Scholar 

  • Weingart, S. N., Massagli, M., Cyrulik, A., Isaac, T., Morway, L., Sands, D. Z., et al. (2009). Assessing the value of electronic prescribing in ambulatory care: a focus group study. International Journal of Medical Informatics, 78(9), 571–578.

    Article  Google Scholar 

  • White, A., Allen, P., Goodwin, L., Breckinridge, D., Dowell, J., & Garvy, R. (2005). Infusing PDA technology into nursing education. Nurse Educator, 30(4), 150–154.

    Article  Google Scholar 

  • Yin, R. K. (1994). Case study research, design and methods. Newbury Park: Sage Publications.

    Google Scholar 

  • Zhang, N., Guo, X., Wang, F., Chen, G., & Wei, Q. (2011).Task-Technology Fit in Mobile Work: Exploring the Links between Task Attributes and Technology Characteristics. In Mobile Business (ICMB), 2011 Tenth International Conference on, 20–21 June 2011 (pp. 268–274). doi:10.1109/icmb.2011.47.

  • Zmud, R. W., & Apple, L. E. (1992). Measuring technology incorporation/infusion. Journal of Product Innovation Management, 9(2), 148–155.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yvonne O’ Connor.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

O’ Connor, Y., O’ Reilly, P. Examining the infusion of mobile technology by healthcare practitioners in a hospital setting. Inf Syst Front 20, 1297–1317 (2018). https://doi.org/10.1007/s10796-016-9728-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10796-016-9728-9

Keywords

Navigation