TECHNOLOGY SUPPORT AND POST-ADOPTION IT SERVICE USE: EVIDENCE FROM THE CLOUD

被引:18
作者
Retana, German F. [1 ]
Forman, Chris [2 ]
Narasimhan, Sridhar [3 ]
Niculescu, Marius Florin [3 ]
Wu, D. J. [3 ]
机构
[1] INCAE Business Sch, Informat Syst & Digital Transformat, Alajuela, Costa Rica
[2] Cornell Univ, SC Johnson Coll Business, Dyson Sch Appl Econ & Management, Ithaca, NY 14850 USA
[3] Georgia Inst Technol, Scheller Coll Business, 800 West Peachtree NW, Atlanta, GA 30303 USA
关键词
IT service; organizational learning; IT use; cloud computing; infrastructure-as-a-service; technology support; service strategies; PANEL-DATA; DYNAMIC-MODELS; ASSIMILATION; INNOVATION; DIFFUSION; BUSINESS;
D O I
10.25300/MISQ/2018/13064
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Does a provider's technology support strategy influence its buyers' post-adoption IT service use? We study this question in the context of cloud infrastructure services. The provider offers two levels of support, basic and full. Under basic support, the provider handles simple service quality issues. Under full support, the provider also offers education, training, and personalized guidance through two-way interactions with buyers. Using unique data on public cloud infrastructure services use by 22,179 firms from March 2009 to August 2012, we find that buyers who receive full support use the service 34.85 percent more than other users. We further show that buyers who have full support deploy infrastructure services more efficiently, increasing the fraction of servers they run in parallel by 3.53 percentage points relative to those who do not. Furthermore, buyers who drop full support and switch back to basic support continue using 15.01 percent more of the service and have a proportion of servers running in parallel that is 2.82 percentage points higher compared to buyers who have never received full support. These findings provide suggestive evidence of buyer learning as a result of provider support.
引用
收藏
页码:961 / 978
页数:25
相关论文
共 43 条
  • [1] ESTIMATION OF DYNAMIC-MODELS WITH ERROR-COMPONENTS
    ANDERSON, TW
    HSIAO, C
    [J]. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1981, 76 (375) : 598 - 606
  • [2] Angrist JD, 2009, MOSTLY HARMLESS ECONOMETRICS: AN EMPIRICISTS COMPANION, P1
  • [3] [Anonymous], 2007, 2007 IEEE International Parallel and Distributed Processing Symposium, IEEE, DOI DOI 10.1109/IPDPS.2007.370631
  • [4] [Anonymous], 2011, NIST DEFINITION CLOU
  • [5] [Anonymous], RIGHTSCALE 2016 STAT
  • [6] Deriving the Pricing Power of Product Features by Mining Consumer Reviews
    Archak, Nikolay
    Ghose, Anindya
    Ipeirotis, Panagiotis G.
    [J]. MANAGEMENT SCIENCE, 2011, 57 (08) : 1485 - 1509
  • [7] SOME TESTS OF SPECIFICATION FOR PANEL DATA - MONTE-CARLO EVIDENCE AND AN APPLICATION TO EMPLOYMENT EQUATIONS
    ARELLANO, M
    BOND, S
    [J]. REVIEW OF ECONOMIC STUDIES, 1991, 58 (02) : 277 - 297
  • [8] ANOTHER LOOK AT THE INSTRUMENTAL VARIABLE ESTIMATION OF ERROR-COMPONENTS MODELS
    ARELLANO, M
    BOVER, O
    [J]. JOURNAL OF ECONOMETRICS, 1995, 68 (01) : 29 - 51
  • [9] A View of Cloud Computing
    Armbrust, Michael
    Fox, Armando
    Griffith, Rean
    Joseph, Anthony D.
    Katz, Randy
    Konwinski, Andy
    Lee, Gunho
    Patterson, David
    Rabkin, Ariel
    Stoica, Ion
    Zaharia, Matei
    [J]. COMMUNICATIONS OF THE ACM, 2010, 53 (04) : 50 - 58
  • [10] Sunk costs and the depth and probability of technology adoption
    Åstebro, T
    [J]. JOURNAL OF INDUSTRIAL ECONOMICS, 2004, 52 (03) : 381 - 399