2,902
Views
18
CrossRef citations to date
0
Altmetric
Articles

Impact of data-driven decision-making in Lean Six Sigma: an empirical analysis

, & ORCID Icon

References

  • Akinbobola, O. I., & Adeleke, A. A. (2016). External variables as antecedents of users perception in virtual library usage. Interdisciplinary Journal of Information, Knowledge, and Management, 11, 73–87.
  • Alavi, M., & Leidner, D. E. (2001). Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS Quarterly, 25(1), 107–136.
  • Albliwi, S. A., Albliwi, S. A., Antony, J., Antony, J., Arshed, N., Arshed, N., & Ghadge, A. (2017). Implementation of Lean Six Sigma in Saudi Arabian organisations: Findings from a survey. International Journal of Quality & Reliability Management, 34(4), 508–529.
  • Albliwi, S. A., Antony, J., & Lim, S. A. H. (2015). A systematic review of Lean Six Sigma for the manufacturing industry. Business Process Management Journal, 21(3), 665–691.
  • Ambler, T. (2000). Marketing metrics. Business Strategy Review, 11(2), 59–66.
  • Antony, J. (2004). Six sigma in the UK service organisations: Results from a pilot survey. Managerial Auditing Journal, 19(8), 1006–1013.
  • Antony, J., Krishan, N., Cullen, D., & Kumar, M. (2012). Lean Six Sigma for higher education institutions (HEIs) challenges, barriers, success factors, tools/techniques. International Journal of Productivity and Performance Management, 61(8), 940–948.
  • Arpaci, I. (2016). Understanding and predicting students’ intention to use mobile cloud storage services. Computers in Human Behavior, 58, 150–157.
  • Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191.
  • Best, R. J. (2010). Getting started using marketing metrics. White Paper, Marketing Metrics Handbook.
  • Bhatt, G. D. (2000). Organizing knowledge in the knowledge development cycle. Journal of Knowledge Management, 4(1), 15–26.
  • Boonsiritomachai, W., McGrath, M., & Burgess, S. (2014). A research framework for the adoption of business intelligence by Small and Medium-sized enterprises. Paper presented at the 27th Annual Seaanz Conference in Small Enterprise Association of Australia and New Zealand, Sydney, Australia.
  • Bradford, M., & Florin, J. (2003). Examining the role of innovation diffusion factors on the implementation success of enterprise resource planning systems. International Journal of Accounting Information Systems, 4(3), 205–225.
  • Brown, B., Chui, M., & Manyika, J. (2011). Are you ready for the era of ‘big data’? McKinsey Quarterly, 4(1), 24–35.
  • Burton-Jones, A., & Hubona, G. S. (2006). The mediation of external variables in the technology acceptance model. Information & Management, 43(6), 706–717.
  • Chang, S. C., & Tung, F. C. (2008). An empirical investigation of students’ behavioral intentions to use the online learning course websites. British Journal of Educational Technology, 39(1), 71–83.
  • Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188.
  • Chen, L. D., & Tan, J. (2004). Technology adaptation in E-commerce: Key determinants of virtual stores acceptance. European Management Journal, 22(1), 74–86.
  • Clark, B. H., & Ambler, T. (2001). Marketing performance measurement: Evolution of research and practice. International Journal of Business Performance Management, 3(2–4), 231–244.
  • Davenport, T. H. (2006). Competing on analytics. Harvard Business Review, 84(1), 98.
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319–340.
  • Davis, F. D. (1993). User acceptance of information technology; system characteristics, user perceptions, and behavioral impacts. International Journal of Man Machine Studies, 38, 475–487.
  • De Groot, R. S., Alkemade, R., Braat, L., Hein, L., & Willemen, L. (2010). Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making. Ecological Complexity, 7(3), 260–272.
  • DeVellis, R. F. (2016). Scale development: Theory and applications (Vol. 26). San Diego: Sage.
  • Dillman, D. A. (2007). Mail and internet surveys: The tailored design method (2nd ed.). Hobeken,NJ: Wiley.
  • Dishaw, M. T., & Strong, D. M. (1999). Extending the technology acceptance model with task–technology fit constructs. Information & Management, 36(1), 9–21.
  • Dubey, R., Gunasekaran, A., Childe, S. J., Fosso Wamba, S., & Papadopoulos, T. (2016). Enablers of Six Sigma: Contextual framework and its empirical validation. Total Quality Management & Business Excellence, 27(11–12), 1346–1372.
  • Ferguson, D. (2007). Lean and Six Sigma: The same or different. Management Services, 51(3), 12–13.
  • Fishbein, M., & Ajzen, I. (1980). Attitudes and voting behavior: An application of the theory of reasoned action. In Stephenson G. M., & Davis J. M. (Eds.), Progress in applied social psychology (Vol. 1, pp. 253–313). London: Wiley.
  • Flanagin, A. J. (2000). Social pressures on organizational website adoption. Human Communication Research, 26(4), 618–646.
  • Furterer, S. L. (2016). Lean Six Sigma in service: Applications and case studies. Boca Raton,FL: CRC Press.
  • Garbelli, M. E. (2008). Market-driven management, competitive markets, and performance metrics. Retrieved from http://symphonya.unimib.it/article/view/2008.1.07garbelli.
  • Godoe, P., & Johansen, T. (2012). Understanding adoption of new technologies: Technology readiness and technology acceptance as an integrated concept. Journal of European Psychology Students, 3, 38–52.
  • Griffith, T. L., Sawyer, J. E., & Neale, M. A. (2003). Virtualness and knowledge in teams: Managing the love triangle of organizations, individuals, and information technology. MIS Quarterly, 27, 265–287.
  • Gruca, T. S., & Rego, L. L. (2005). Customer satisfaction, cash flow, and shareholder value. Journal of Marketing, 69(3), 115–130.
  • Gyung Kim, M., and A. S.Mattila. (2013). Does a surprise strategy need words? The effect of explanations for a surprise strategy on customer delight and expectations. Journal of Services Marketing, 27(5), 361–370.
  • Hilton, R. J., & Sohal, A. (2012). A conceptual model for the successful deployment of Lean Six Sigma. International Journal of Quality & Reliability Management, 29(1), 54–70.
  • Hoerl, R. W., & Gardner, M. M. (2010). Lean Six Sigma, creativity, and innovation. International Journal of Lean Six Sigma, 1(1), 30–38.
  • Hong, W., & Zhu, K. (2006). Migrating to internet-based e-commerce: Factors affecting e-commerce adoption and migration at the firm level. Information & Management, 43(2), 204–221.
  • Jang, W. Y., Lin, C. I., & Pan, M. J. (2009). Business strategies and the adoption of ERP: Evidence from Taiwan's communications industry. Journal of Manufacturing Technology Management, 20(8), 1084–1098.
  • Jeffery, M. (2010). Data-driven marketing: The 15 metrics everyone in marketing should know. Hoboken,NJ: John Wiley & Sons.
  • Jugulum, R., & Samuel, P. (2010). Design for Lean Six Sigma: A holistic approach to design and innovation. New York,NY: John Wiley & Sons.
  • Kim, Y., Park, Y., & Choi, J. (2017). A study on the adoption of IoT smart home service: Using value-based adoption model. Total Quality Management & Business Excellence, 28(9-10), 1–17.
  • King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740–755.
  • Kock, N. (2013). Using WarpPLS in E-collaboration studies: Descriptive statistics, settings. Interdisciplinary Applications of Electronic Collaboration Approaches and Technologies, 62, 1–17.
  • Koronios, A., Lin, S., & Gao, J. (2005). ‘A data quality model for asset management in engineering organisations’. Proceedings of the 10th international conference on information quality (ICIQ 2005), 4–6 November, Cambridge, MA, USA, pp. 27–51.
  • Kureshi, N., Qureshi, F., & Sajid, A. (2010). Current health of quality management practices in service sector SME – A case study of Pakistan. The TQM Journal, 22(3), 317–329.
  • LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT Sloan Management Review, 52(2), 21.
  • Lawshe, C. H. (1975). A quantitative approach to content validity I. Personnel Psychology, 28(4), 563–575.
  • Lee, S. J., & You, Y. Y. (2016). The influences of B2B service quality on the relationship satisfaction, brand performance and relationship performance – an application of the IMP interaction model. Indian Journal of Science and Technology, 9(43), 1–7.
  • Levitin, A. V., & Redman, T. C. (1998). Data as a resource: Properties, implications, and prescriptions. MIT Sloan Management Review, 40(1), 89.
  • Liao, Y., Deschamps, F., Loures, E. D. F. R., & Ramos, L. F. P. (2017). Past, present and future of industry 4.0 – a systematic literature review and research agenda proposal. International Journal of Production Research, 55(12), 3609–3629.
  • Mandinach, E. B., Honey, M., & Light, D. (2006, April). A theoretical framework for data-driven decision making. Annual meeting of the American Educational Research Association, San Francisco, CA.
  • McAfee, A., & Brynjolfsson, E. (2012). Big data: The management revolution: Exploiting vast new flows of information can radically improve your company’s performance. But first you will have to change your decision making culture. Harvard Business Review, 90(10), 61–67.
  • Mendelson, H. (2000). Organizational architecture and success in the information technology industry. Management Science, 46(4), 513–529.
  • Mintz, O., & Currim, I. S. (2013). What drives managerial use of marketing and financial metrics and does metric use affect performance of marketing-mix activities? Journal of Marketing, 77(2), 17–40.
  • Näslund, D. (2008). Lean, six sigma and lean sigma: Fads or real process improvement methods? Business Process Management Journal, 14(3), 269–287.
  • Neslin, S. A., & Shankar, V. (2009). Key issues in multichannel customer management: Current knowledge and future directions. Journal of Interactive Marketing, 23(1), 70–81.
  • Nicolaou, A. I., & McKnight, D. H. (2006). Perceived information quality in data exchanges: Effects on risk, trust, and intention to use. Information Systems Research, 17(4), 332–351.
  • Pan, M. J., & 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.
  • Petzer, D. J., & Steyn, T. F. J. (2006). Customer retention: A theoretical perspective of service failure and service recovery in the hotel industry. Acta Commercial, 6(1), 162–172.
  • Pituch, K. A., & Lee, Y. K. (2006). The influence of system characteristics on e-learning use. Computers & Education, 47(2), 222–244.
  • Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big Data, 1(1), 51–59.
  • Psychogios, A. G., Atanasovski, J., & Tsironis, L. K. (2012). Lean Six Sigma in a service context: A multi-factor application approach in the telecommunications industry. International Journal of Quality & Reliability Management, 29(1), 122–139.
  • Ramamurthy, K. R., Sen, A., & Sinha, A. P. (2008). An empirical investigation of the key determinants of data warehouse adoption. Decision Support Systems, 44(4), 817–841.
  • Ramanathan, S., & Sarulatha, N. (2013). Big data: A marketer’s perspective of emerging marketing approach. International Journal of Management Research and Reviews, 3(5), 2872.
  • Riggins, F. J., & Wamba, S. F. (2015 January 5–8). Research directions on the adoption, usage, and impact of the internet of things through the use of big data analytics. In 48th Hawaii International Conference on System Sciences (HICSS), Kauai, HI, USA (pp. 1531–1540). IEEE.
  • Rogers, E. M. (2004). A prospective and retrospective look at the diffusion model. Journal of Health Communication, 9(S1), 13–19.
  • 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 & Management, 45(6), 376–386.
  • Sahay, B. S., & Ranjan, J. (2008). Real-time business intelligence in supply chain analytics. Information Management & Computer Security, 16(1), 28–48.
  • Snee, R. D. (2010). Lean Six Sigma – getting better all the time. International Journal of Lean Six Sigma, 1(1), 9–29.
  • Sreedharan, V. R., Raju, R., Rajkanth, R., & Nagaraj, M. (2016). An empirical assessment of Lean Six Sigma awareness in manufacturing industries: Construct development and validation. Total Quality Management & Business Excellence. Advance online publiation. doi: 10.1080/14783363.2016.1230470
  • Sreedharan, V. R., Raju, R., & Srivatsa Srinivas, S. (2017). A review of the quality evolution in various organisations. Total Quality Management & Business Excellence, 28(3–4), 351–365.
  • Srinivas, S. S., & Sreedharan, V. R. (in press). Failure analysis of automobile spares in a manufacturing supply chain distribution centre using Six sigma DMAIC framework. International Journal of Services and Operations Management.
  • Strong, D. M., Lee, Y. W., & Wang, R. Y. (1997). Data quality in context. Communications of the ACM, 40(5), 103–110.
  • Thong, J. Y., Hong, W., & Tam, K. Y. (2002). Understanding user acceptance of digital libraries: What are the roles of interface characteristics, organizational context, and individual differences? International Journal of Human-Computer Studies, 57(3), 215–242.
  • Tsironis, L. K., & Psychogios, A. G. (2016). Road towards Lean Six Sigma in service industry: A multi-factor integrated framework. Business Process Management Journal, 22(4), 812–834.
  • Venkatesh, V., & Brown, S. A. (2001). A longitudinal investigation of personal computers in homes: Adoption determinants and emerging challenges. MIS Quarterly, 25(1), 71–102.
  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.
  • Venkatesh, V., Davis, F. D., & Morris, M. G. (2007). Dead or alive? The development, trajectory, and future of technology adoption research. Journal of the Association for Information Systems, 8(4), 267.
  • Vince, G. (2008). Lean six. Management Services, 52(1), 22–23.
  • Vinodh, S., & Vimal, K. E. K. (2012). Thirty criteria based leanness assessment using fuzzy logic approach. The International Journal of Advanced Manufacturing Technology, 60(9–12), 1185–1195.
  • Wang, F. K., & Chen, K. S. (2010). Applying Lean Six Sigma and TRIZ methodology in banking services. Total Quality Management, 21(3), 301–315.
  • Wang, R. Y., & Strong, D. M. (1996). Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems, 12(4), 5–33.
  • Zhu, K., Kraemer, K. L., & 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.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.