Asia pacific journal of information systems

The Korea Society of Management Information Systems (한국경영정보학회)
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Factors Affecting HR Analytics Adoption: A Systematic Review Using Literature Weighted Scoring Approach

  • Suchittra Pongpisutsopa (Information Technology Management Division, Faculty of Engineering, Mahidol University) ;
  • Sotarat Thammaboosadee (Information Technology Management Division, Faculty of Engineering, Mahidol University ) ;
  • Rojjalak Chuckpaiwong (Information Technology Management Division, Faculty of Engineering, Mahidol University)
  • Received : 2020.08.06
  • Accepted : 2020.12.08
  • Published : 2020.12.31
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Abstract

In the era of disruptive change, a data-driven approach is vital to Human Resource Management (HRM) of any leading organization, for it is used to gain a competitive advantage. HR analytics (HRA) has emerged as innovative technologies since advanced analytics, i.e., predictive or prescriptive analytics, were widely used in the High Performing Organizations (HPOs). Therefore, many organizations elevate themselves to become HPOs through Data Science on the "people side." This paper proposes a systematic literature review using the Literature Weighted Scoring (LWS) to develop a conceptual framework based on three adoption theories, which are the Technology-Organization-Environment (TOE), Diffusion of Innovation (DOI), and Unified Theory of Acceptance and Use of Technology (UTAUT). The results show that a total of 13 theory-derived factors are determined as influential factors affecting HRA adoption, and the top three factors are "Quantitative Self-Efficacy," "Top Management Support," and "Data Availability." The conceptual framework with hypotheses is proposed to provide a foundation for further studies on organizational HRA adoption.

Keywords

Acknowledgement

The authors would like to acknowledge the full financial support by the State Railway of Thailand (Ph.D. Full-time Scholarship), and the partial financial support from two institutes: the King Prajadhipok and Queen Rambhai Barni Memorial Foundation (Research Scholarships for Graduate Students of Universities in Thailand;) and Faculty of Graduate Studies, Graduate Studies of Mahidol University Alumni Association (Partial Funding for Graduate Student Thesis).

References

  1. Abraham, R., Schneider, J., and Vom Brocke, J. (2019). Data governance: A conceptual framework, structured review, and research agenda. International Journal of Information Management, 49, 424-438.  https://doi.org/10.1016/j.ijinfomgt.2019.07.008
  2. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.  https://doi.org/10.1016/0749-5978(91)90020-T
  3. Alamelu, R., Nalini, R., Motha, L. C. S., Amudha, R., and Bowiya, S. (2017). Adoption factors impacting human resource analytics among employees. International Journal of Economic Research, 14(6), 417-423.
  4. Alhammadi, A., Stanier, C., and Eardley, A. (2015). The determinants of cloud computing adoption in Saudi Arabia. In 2nd International Conference on Computer Science and Engineering. 
  5. Almarri, K., Aljarman, M., and Boussabaine, H. (2019). Emerging contractual and legal risks from the application of building information modelling. Engineering, Construction and Architectural Management, 26(10), 2307-2325.  https://doi.org/10.1108/ECAM-06-2018-0224
  6. Anturaniemi, K. (2018). Human capital analytics: Maturity evaluation and development proposal for a case company. Metropolia Ammattikorkeakoulu. 
  7. Awa, H. O., and Ojiabo, O. U. (2016). A model of adoption determinants of ERP within TOE framework. Information Technology & People, 29(4), 901-930.  https://doi.org/10.1108/ITP-03-2015-0068
  8. Awa, H. O., Ojiabo, O. U., and Orokor, L. E. (2017). Integrated technology-organization-environment(TOE) taxonomies for technology adoption. Journal of Enterprise Information Management, 30(6), 893-921.  https://doi.org/10.1108/JEIM-03-2016-0079
  9. Bangert-Drowns, R. L., and Rudner, L. M. (1990). Meta-analysis in educational research. Practical Assessment, Research, and Evaluation, 2(1), 8. 
  10. Barriere, J. M. (2016). The influence of trust on attitude of employees towards HR Analytics in organisations. University of Twente. 
  11. Bassi, L. (2011). Raging debates in HR analytics. People and Strategy, 34(2), 14-17. 
  12. Batarliene, N., Ciziuniene, K., Vaiciute, K., Sapalaite, I., and Jarasuniene, A. (2017). The impact of human resource management on the competitiveness of transport companies. Procedia Engineering, 187, 110-116.  https://doi.org/10.1016/j.proeng.2017.04.356
  13. Becker, B., and Gerhart, B. (1996). The impact of human resource management on organizational performance: Progress and prospects. Academy of Management Journal, 39(4), 779-801.  https://doi.org/10.2307/256712
  14. Bingham, R. D., and McNaught, T. P. (1976). The adoption of innovation by local government. Lexington Books. 
  15. Bushman, B. J., and Wang, M. C. (1994). Vote-counting procedures in meta-analysis. In The handbook of research synthesis (Vol. 236, pp. 193-213). Russell Sage Foundation. 
  16. Cascio, W., and Boudreau, J. (2011). Investing in people: Financial impact of human resource initiatives. Ft Press. 
  17. Compeau, D. R., and Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189-211.  https://doi.org/10.2307/249688
  18. Damanpour, F., and Gopalakrishnan, S. (1998). Theories of organizational structure and innovation adoption: The role of environmental change. Journal of Engineering and Technology Management, 15(1), 1-24.  https://doi.org/10.1016/S0923-4748(97)00029-5
  19. Daradkeh, M. K. (2019). Determinants of visual analytics adoption in organizations. Information Technology & People, 32(3), 668-695.  https://doi.org/10.1108/ITP-10-2017-0359
  20. Davenport, T. H. (2006). Competing on analytics. Harvard Business Review, 84(1), 98. 
  21. Davenport, T. H. (2019). Is HR the most-analytics driven function. Harvard Business Review (Digital Article, April 2019), Retrieved from 19 April 2019. 
  22. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.  https://doi.org/10.2307/249008
  23. Deloitte University (2017). 2017 global human capital trends. Retrieved from 10 August 2018 https://www2.deloitte.com/content/dam/Deloitte/global/Documents/About-Deloitte/central-europe/ce-global-human-capital-trends.pdf 
  24. Deloitte University (2018). 2018 global human capital trends. Retrieved from 1 December 2019 https://www2.deloitte.com/insights/us/en/focus/human-capital-trends.html 
  25. Delone, W. H., and McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9-30.  https://doi.org/10.1080/07421222.2003.11045748
  26. Distler, V., Lallemand, C., and Bellet, T. (2018). Acceptability and acceptance of autonomous mobility on demand: The impact of an immersive experience. Paper Presented at the Proceedings of the 2018 Chi Conference on Human Factors in Computing Systems. 
  27. Dooren, J. V. (2012). HR analytics in practice: An overview of the influence of contingency factors on the applicability of HR analytics in Dutch organizations. Retrieved from November 1 2018 http://arno.uvt.nl/show.cgi?fid=127276 
  28. Etukudo, R. U. (2019). Strategies for using analytics to improve human resource management. Ph.D. Dissertation, Walden University. 
  29. Falletta, S. (2014). In search of HR intelligence: Evidence-based HR analytics practices in high performing companies. People and Strategy, 36(4), 28-37. 
  30. Falletta, S., and Combs, W. (2020). The HR analytics cycle: A seven-step process for building evidence-based and ethical HR analytics capabilities. Journal of Work-Applied Management. doi: 10.1108/JWAM03-2020-0020 
  31. Fiocco, E. (2017). HR analytics at work Exploring diffusion of innovation within a Swedish-based MNC. University of Gothenberg. 
  32. Friedman, T. L. (2017). Thank you for being late: An optimist's guide to thriving in the age of accelerations (Version 2.0, with a new afterword). Picador/Farrar Straus and Giroux. 
  33. Gartner (2018). Gartner IT glossary. Retrieved from October 30 2018 https://www.gartner.com/it-glossary/prescriptive-analytics 
  34. George, L., and Kamalanabhan, T. (2016). A study on the acceptance of HR analytics in organisations. International Journal of Innovative Research and Development, 5(2). 
  35. Grant, M. J., and Booth, A. (2009). A typology of reviews: An analysis of 14 review types and associated methodologies. Health Information & Libraries Journal, 26(2), 91-108.  https://doi.org/10.1111/j.1471-1842.2009.00848.x
  36. Grant, N. C. (2020). Factors influencing willingness to adopt advanced analytics in small businesses. Cleveland State University. 
  37. Gupta, S. (2017). Overview of HR analytics-past, present and future. Journal of Research Articles in Management Science and Allied Areas(Refereed), 10(2), 1-6. 
  38. Gustafsson, D. (2012). Business intelligence, analytics and human capital: current state of workforce analytics in Sweden. University of Skovde. 
  39. Halper, F., and Stodder, D. (2014). TDWI analytics maturity model guide. Retrieved from https://tdwi.org/~/media/545E06D7CE184B19B269E929B0903D0C 
  40. Halper, F., and Stodder, D. (2015). TDWI analytics maturity model guide. 
  41. Hameed, M. A., Counsell, S., and Swift, S. (2012). A conceptual model for the process of IT innovation adoption in organizations. Journal of Engineering and Technology Management, 29(3), 358-390.  https://doi.org/10.1016/j.jengtecman.2012.03.007
  42. Harada, Y., and Sengoku, S. (2019). The key success factors of biotech start-up firms: Characteristics and attributes of the management teams of high-performing biotech start-ups. Paper Presented at the PICMET 2019-Portland International Conference on Management of Engineering and Technology: Technology Management in the World of Intelligent Systems, Proceedings. 
  43. Harvard Business Review (2014). HR joins the analytics revolution. Harvard Business School Publishing. 
  44. Hmoud, B. I., and Varallyai, L. (2020). Artificial intelligence in human resources information systems: Investigating its trust and adoption determinants. International Journal of Engineering and Management Sciences, 5(1), 749-765.  https://doi.org/10.21791/IJEMS.2020.1.65
  45. Hosseini, M. R., Banihashemi, S., Chileshe, N., Namzadi, M. O., Udeaja, C., Rameezdeen, R., and McCuen, T. (2016). BIM adoption within Australian Small and Medium-sized Enterprises (SMEs): An innovation diffusion model. Construction Economics and Building, 16(3), 71-86.  https://doi.org/10.5130/AJCEB.v16i3.5159
  46. Hunter, J. E., and Schmidt, F. L. (2004). Methods of meta-analysis: Correcting error and bias in research findings. Sage. 
  47. Huselid, M. A. (2018). The science and practice of workforce analytics: Introduction to the HRM special issue. Human Resource Management, 57(3), 679-684.  https://doi.org/10.1002/hrm.21916
  48. Johnston, A. C. (2006). An empirical investigation of the influence of fear appeals on attitudes and behavioral intentions associated with recommended individual computer security actions. ProQuest Information & Learning. 
  49. Ka, H. K., and Kim, J. S. (2014). An empirical study on the influencing factors for big data intented adoption: Focusing on the strategic value recognition and TOE framework. Asia Pacific Journal of Information Systems, 24(4), 443-472.  https://doi.org/10.14329/apjis.2014.24.4.443
  50. Kamal, M. M. (2006). IT innovation adoption in the government sector: Identifying the critical success factors. Journal of Enterprise Information Management, 19(2), 192-222.  https://doi.org/10.1108/17410390610645085
  51. Kapoor, B., and Kabra, Y. (2014). Current and future trends in human resources analytics adoption. Journal of Cases on Information Technology(JCIT), 16(1), 50-59.  https://doi.org/10.4018/jcit.2014010105
  52. Khatri, V., and Brown, C. V. (2010). Designing data governance. Communications of the ACM, 53(1), 148-152.  https://doi.org/10.1145/1629175.1629210
  53. Kim, H. J., and Bretschneider, S. (2004). Local government information technology capacity: An exploratory theory. Paper Presented at the System Sciences, 2004. Proceedings of the 37th Annual Hawaii International Conference. 
  54. King, W., and He, J. (2005, 13/10/2008). Understanding the role and methods of meta-analysis in IS research. Retrieved from https://doresearch.wordpress.com/ 
  55. Kuan, K. K., and Chau, P. Y. (2001). A perception-based model for EDI adoption in small businesses using a technology-organization-environment framework. Information & Management, 38(8), 507-521.  https://doi.org/10.1016/S0378-7206(01)00073-8
  56. Kulik, J. A., and Kulik, C. L. C. (1989). The concept of meta-analysis. International Journal of Educational Research, 13(3), 227-340.  https://doi.org/10.1016/0883-0355(89)90052-9
  57. Kumar, A., Krishnamoorthy, B., and Kamath, D. B. (2020). Key themes for multi-stage business analytics adoption in organizations. Asia Pacific Journal of Information Systems, 30(2), 397-419.  https://doi.org/10.14329/apjis.2020.30.2.397
  58. Ladley, J. (2012). Data governance: How to design, deploy and sustain an effective data governance program. Waltham, MA: Morgan Kaufmann. 
  59. Lakshmi, P., and Pratap, P. (2016). HR analytics-a strategic approach to HR effectiveness. International Journal of Human Resource Management and Research, 6(3), 21-28. 
  60. Lee, J., Pee, L., and Min, J. (2016). New perspectives on the development, adoption, and application of information systems. Asia Pacific Journal of Information Systems, 26(3), 385-392.  https://doi.org/10.14329/apjis.2016.26.3.385
  61. Levenson, A. (2011). Using targeted analytics to improve talent decisions. People and Strategy, 34(2), 1-22. 
  62. Looi, H. C. (2005). E-commerce adoption in Brunei Darussalam: A quantitative analysis of factors influencing its adoption. Communications of the Association for Information Systems, 15(1), 3. 
  63. Lydgate, X. K. M. (2018). Human resource analytics: Implications for strategy realization and organizational performance. Portland State University. 
  64. Malini, N. (2018). Understanding the adoption of HR analytics in Indian corporations: A case study on selected Indian private multinational company. City College, Retrieved from http://www.cityinstitution.com/understanding-the-adoption-of-hr-analytics-in-indian-corporations/ 
  65. Marais, A., Grobbelaar, S. S., and Kennon, D. (2020). A systematic review of healthcare innovation platforms towards a conceptual framework. Paper Presented at the 26th International Association for Management of Technology Conference, IAMOT 2017. 
  66. Marler, J., and Boudreau, J. W. (2017). An evidence-based review of HR Analytics. International Journal of Human Resource Management, 28(1), 3-26. doi: 10.1080/09585192.2016.1244699 
  67. Marler, J., and Fisher, S. (2013). An evidence-based review of e-HRM and strategic human resource management. Human Resource Management Review, 23(1), 18-36.  https://doi.org/10.1016/j.hrmr.2012.06.002
  68. Masese, O. F., and UttaM, M. K. (2016). Creating value of human resource through analytics. Asia Pacific Journal of Research, 1(19), 97-103. 
  69. Molefe, M. (2014). From data to insights: HR analytics in organisations. University of Pretoria. 
  70. Momin, W. Y. M., and Mishra, K. (2015). HR Analytics as a strategic workforce planning. IJAR, 1(4), 258-260. 
  71. Mukundan, S. (2017). Analytics in HR a snapshot view. International Journal of Engineering Technology, Management and Applied Sciences, 5(7), 597-601. 
  72. Nadal, C., Doherty, G., and Sas, C. (2019). Technology acceptability, acceptance and adoption-definitions and measurement. Paper Presented at the 2019 CHI Conference on Human Factors in Computing Systems. 
  73. Nemati, H., and Udiavar, A. (2012). Organizational readiness for implementation of supply chain analytics. AMCIS 2012 Proceedings, 26. 
  74. Oh, I. S. (2020). Beyond meta-analysis: Secondary uses of meta-analytic data. Annual Review of Organizational Psychology and Organizational Behavior, 7, 125-153. doi: 10.1146/annurev-orgpsych-012119-045006 
  75. Oliveira, T., and Martins, M. F. (2009). Determinants of information technology adoption in Portugal. Paper Presented at the ICE-B. 
  76. Oliveira, T., Thomas, M., and Espadanal, M. (2014). Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors. Information & Management, 51(5), 497-510.  https://doi.org/10.1016/j.im.2014.03.006
  77. Ozgen, K. (2013). Self-efficacy beliefs in mathematical literacy and connections between mathematics and real world: The case of high school students. Journal of International Education Research, 9(4), 305-316.  https://doi.org/10.19030/jier.v9i4.8082
  78. Pan, M. J., and Jang, W. Y. (2008). Determinants of the adoption of enterprise resource planning within the technology-organization-environment framework: Taiwan's communications industry. Journal of Computer Information Systems, 48(3), 94-102. 
  79. Pare, G., and Kitsiou, S. (2017). Methods for literature reviews. In Handbook of eHealth evaluation: An evidence-based approach[Internet]. University of Victoria. 
  80. Phaosathianphan, N., and Leelasantitham, A. (2019). Understanding the adoption factors influence on the use of Intelligent Travel Assistant(ITA) for eco-tourists: An extension of the UTAUT. International Journal of Innovation and Technology Management, 16(8), 1950060. 
  81. Premkumar, G., and Ramamurthy, K. (1995). The role of interorganizational and organizational factors on the decision mode for adoption of interorganizational systems. Decision Sciences, 26(3), 303-336.  https://doi.org/10.1111/j.1540-5915.1995.tb01431.x
  82. Pudjianto, B., Zo, H., Ciganek, A. P., and Rho, J. J. (2011). Determinants of e-government assimilation in Indonesia: An empirical investigation using a TOE framework. Asia Pacific Journal of Information Systems, 21(1), 49-80. 
  83. Puklavec, B., Oliveira, T., and Popovic, A. (2018). Understanding the determinants of business intelligence system adoption stages. Industrial Management & Data Systems, 118(1), 236-261.  https://doi.org/10.1108/IMDS-05-2017-0170
  84. Reddy, P. R., and Lakshmikeerthi, P. (2017). 'HR analytics'-An effective evidence based HRM tool. International Journal of Business and Management Invention, 6(7), 23-34. 
  85. Renaud, K., and Van Biljon, J. (2008). Predicting technology acceptance and adoption by the elderly: A qualitative study. Paper Presented at the Proceedings of the 2008 Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists on IT Research in Developing Countries: Riding the Wave of Technology. 
  86. Rice, R., Grant, A., Schmitz, J., and Torobin, J. (1990). Individual and network influences on the adoption and perceived outcomes of electronic messaging. Social Networks, 12(1), 27-55.  https://doi.org/10.1016/0378-8733(90)90021-Z
  87. Rogers, E. M. (1983). Diffusion of innovations. The Free Press. 
  88. Rogers, E. M. (1995). Diffusion of innovations. New York. 
  89. Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Simon and Schuster. 
  90. Rogers, E. M. (2010). Diffusion of innovations. New York, NY: Simon and Schuster. 
  91. Ruder-Hook, M. (2018). Organizational adoption of AI through a sociocultural lens. Ph.D. Dissertation, University of Michigan. 
  92. Ruohonen, S. (2015). Business benefits of leveraging predictive analytics in HR. Master's Thesis, Aalto University. 
  93. Saraswathy, R., and Premakumari, P. (2016). A snapshot of HR analytics in practice in organizations in Tiruchirappalli. Paper Presented at the National Conference on Emerging Business Strategies in Economic Development. 
  94. Saraswathy, R., Vaijayanthi, P., and Shreenivasan, K. A. (2017). A snapshot of HR analytics-An overview of the influence of contingency factors on the applicability of HR analytics in INDIAN organizations. International Journal of Applied Business and Economic Research, 15(11). 
  95. Sivathanu, B., and Pillai, R. (2019). Technology and talent analytics for talent management-a game changer for organizational performance. International Journal of Organizational Analysis, 28(2), 457-473.  https://doi.org/10.1108/IJOA-01-2019-1634
  96. Sjoerd, V. D. H., and Tanya, B. (2017). The rise (and fall?) of HR analytics: A study into the future application, value, structure, and system support. Journal of Organizational Effectiveness: People and Performance, 4(2), 157-178.  https://doi.org/10.1108/JOEPP-03-2017-0022
  97. Smith, T. (2013). HR analytics: The what, why and how. Numerical Insights LLC. 
  98. Spahic, J. (2015). Exploring HR intelligence practices in fortune 1000 and select global firms. Drexel University. 
  99. Stuart, A. (2005). Finance is from Mars, HR is from Venus. Retrieved from September 8 2018 ww2.cfo.com/human-capital-careers/2005/11/finance-is-from-mars-hr-is-from-venus/ 
  100. Teo, T. S., Tan, M., and Buk, W. K. (1997). A contingency model of Internet adoption in Singapore. International Journal of Electronic Commerce, 2(2), 95-118.  https://doi.org/10.1080/10864415.1997.11518310
  101. Thong, J. Y. (1999). An integrated model of information systems adoption in small businesses. Journal of Management Information Systems, 15(4), 187-214.  https://doi.org/10.1080/07421222.1999.11518227
  102. Tornatzky, L. G., Fleischer, M., and Chakrabarti, A. K. (1990). Processes of technological innovation. Lexington Books. 
  103. Tushman, M. L., and Anderson, P. (1986). Technological discontinuities and organizational environments. Administrative Science Quarterly, 31(3), 439-465.  https://doi.org/10.2307/2392832
  104. Vargas, R. (2015). Adoption factors impacting human resource analytics among human resource professionals. Nova Southeastern University. 
  105. Vargas, R., Yurova, Y. V., Ruppel, C. P., Tworoger, L. C., and Greenwood, R. (2018). Individual adoption of HR analytics: A fine grained view of the early stages leading to adoption. The International Journal of Human Resource Management, 29(22), 1-22.  https://doi.org/10.1080/09585192.2018.1437982
  106. Venkatesh, V., and Brown, S. A. (2001). A longitudinal investigation of personal computers in homes: Adoption determinants and emerging challenges. MIS Quarterly, 25(1), 71-102.  https://doi.org/10.2307/3250959
  107. Venkatesh, V., Morris, M. G., Davis, G. B., and Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.  https://doi.org/10.2307/30036540
  108. Vihari, N., and Raoa, M. (2013). Analytics as a predictor for strategic and sustainable human resource function: An integrative literature review. IIT, Roorkee. 
  109. Wang, X., White, L., Chen, X., Gao, Y., Li, H., and Luo, Y. (2015). An empirical study of wearable technology acceptance in healthcare. Industrial Management & Data Systems, 115(9), 1704-1723.  https://doi.org/10.1108/IMDS-03-2015-0087
  110. Wang, Y. M., and Wang, Y. C. (2016). Determinants of firms' knowledge management system implementation: An empirical study. Computers in Human Behavior, 64, 829-842.  https://doi.org/10.1016/j.chb.2016.07.055
  111. Wanyoto, K. (2016). A diagnostic model for adoption of human resources analytics in local subsidiaries: Study focused on multi-national enterprises (MNEs) in Sub-Saharan Africa. University of Pretoria. 
  112. Williams, I. (2011). Organizational readiness for innovation in health care: Some lessons from the recent literature. Health Services Management Research, 24(4), 213-218.  https://doi.org/10.1258/hsmr.2011.011014
  113. Witte, L. (2016). We have HR analytics! So what?: An exploratory study into the impact of HR analytics on strategic HRM. University of Twente. 
  114. Xiao, Q., and Cooke, F. L. (2020). Towards a hybrid model? A systematic review of human resource management research on Chinese state-owned enterprises(1993-2017). International Journal of Human Resource Management, 31(1), 47-89. doi: 10.1080/09585192.2019.1682030 
  115. Zheng, D. (2014). The adoption of green information technology and information systems: An evidence from corporate social responsibility. Paper Presented at the PACIS. 
  116. Zhu, K., Dong, S., Xu, S. X., and Kraemer, K. L. (2006). Innovation diffusion in global contexts: Determinants of post-adoption digital transformation of European companies. European Journal of Information Systems, 15(6), 601-616.  https://doi.org/10.1057/palgrave.ejis.3000650
  117. Zhu, K., Kraemer, K., and Xu, S. (2002). A cross-country study of electronic business adoption using the technology-organization-environment framework. ICIS 2002 Proceedings, 31. 
  118. Zhu, K., Kraemer, K., and Xu, S. (2003). Electronic business adoption by European firms: A cross-country assessment of the facilitators and inhibitors. European Journal of Information Systems, 12(4), 251-268.  https://doi.org/10.1057/palgrave.ejis.3000475
  119. Zhu, K., and Kraemer, K. L. (2005). Post-adoption variations in usage and value of e-business by organizations: Cross-country evidence from the retail industry. Information Systems Research, 16(1), 61-84.  https://doi.org/10.1287/isre.1050.0045
  120. Zhu, K., Kraemer, K. L., and Dedrick, J. (2004). Information technology payoff in e-business environments: An international perspective on value creation of e-business in the financial services industry. Journal of Management Information Systems, 21(1), 17-54.  https://doi.org/10.1080/07421222.2004.11045797
  121. Zhu, K., Kraemer, K. L., and Xu, S. (2006). The process of innovation assimilation by firms in different countries: A technology diffusion perspective on e-business. Management Science, 52(10), 1557-1576. https://doi.org/10.1287/mnsc.1050.0487