Analysis Of Socio-Technical Factors In Business Intelligence Framework Case Study Of Higher Learning Institution

The contradictory pressures and features of socio-technical factors in an organization that relates to people, processes, and technologies, create data value on organizational strategic performance using Business Intelligence (BI) engine within different conceptual frameworks and their impact on st...

Full description

Saved in:

Bibliographic Details
Main Author:	Jayakrishnan, Mailasan
Format:	Thesis
Language:	English English
Published:	2018
Subjects:	H Social Sciences (General)
Online Access:	http://eprints.utem.edu.my/id/eprint/23317/1/Analysis%20Of%20Socio-Technical%20Factors%20In%20Business%20Intelligence%20Framework%20Case%20Study%20Of%20Higher%20Learning%20Institution.pdf http://eprints.utem.edu.my/id/eprint/23317/2/Analysis%20Of%20Socio-Technical%20Factors%20In%20Business%20Intelligence%20Framework%20Case%20Study%20Of%20Higher%20Learning%20Institution.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utem-ep.23317
record_format	uketd_dc
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
topic	H Social Sciences (General) H Social Sciences (General)
spellingShingle	H Social Sciences (General) H Social Sciences (General) Jayakrishnan, Mailasan Analysis Of Socio-Technical Factors In Business Intelligence Framework Case Study Of Higher Learning Institution
description	The contradictory pressures and features of socio-technical factors in an organization that relates to people, processes, and technologies, create data value on organizational strategic performance using Business Intelligence (BI) engine within different conceptual frameworks and their impact on strategy development and implementation for strategic performance management. Existing studies have not sufficiently designated the interactions between all seven (7) socio-technical factors or their influences on BI utilization. The specific features of Knowledge Management (KM) and BI in this study have been outlined as a guideline for research in viewing the big picture in decision making processes when implementing organizational performance diagnostics framework. The research goal of this study was to develop an organizational performance framework of socio-technical factors that influence on BI utilization. This study identified sociotechnical factors with observing MIT90s and McKinsey seven (7) S’s framework of people (staff, skills, and style), processes (strategy and structure) and technologies (systems and shared values) and their influences on BI utilization. The data needed for the study was collected from 474 current administration and academic staff of University A with the help of a 25-item questionnaire-based survey were developed for this research literature-based proposed model. The study utilized reliability analysis results to analyze 7 usable socio-technical factors. Data analysis was conducted with SPSS and results confirmed that shared values-oriented factors predicted knowledge seeking and contributing in BI utilization. Furthermore, reliability measurement was confirmed between 7 usable socio-technical factors including people (staff - 0.810, skills – 0.801 and style – 0.796), processes (strategy – 0.771 and structure – 0.780) and technologies (systems – 0.790 and shared values – 0.850). These findings extend the relevance and statistical power of existing studies on BI usage for displaying an organizational performance indicator.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Jayakrishnan, Mailasan
author_facet	Jayakrishnan, Mailasan
author_sort	Jayakrishnan, Mailasan
title	Analysis Of Socio-Technical Factors In Business Intelligence Framework Case Study Of Higher Learning Institution
title_short	Analysis Of Socio-Technical Factors In Business Intelligence Framework Case Study Of Higher Learning Institution
title_full	Analysis Of Socio-Technical Factors In Business Intelligence Framework Case Study Of Higher Learning Institution
title_fullStr	Analysis Of Socio-Technical Factors In Business Intelligence Framework Case Study Of Higher Learning Institution
title_full_unstemmed	Analysis Of Socio-Technical Factors In Business Intelligence Framework Case Study Of Higher Learning Institution
title_sort	analysis of socio-technical factors in business intelligence framework case study of higher learning institution
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty Of Information And Communication Technology
publishDate	2018
url	http://eprints.utem.edu.my/id/eprint/23317/1/Analysis%20Of%20Socio-Technical%20Factors%20In%20Business%20Intelligence%20Framework%20Case%20Study%20Of%20Higher%20Learning%20Institution.pdf http://eprints.utem.edu.my/id/eprint/23317/2/Analysis%20Of%20Socio-Technical%20Factors%20In%20Business%20Intelligence%20Framework%20Case%20Study%20Of%20Higher%20Learning%20Institution.pdf
_version_	1747834034386370560
spelling	my-utem-ep.233172022-02-07T14:56:12Z Analysis Of Socio-Technical Factors In Business Intelligence Framework Case Study Of Higher Learning Institution 2018 Jayakrishnan, Mailasan H Social Sciences (General) HD Industries. Land use. Labor The contradictory pressures and features of socio-technical factors in an organization that relates to people, processes, and technologies, create data value on organizational strategic performance using Business Intelligence (BI) engine within different conceptual frameworks and their impact on strategy development and implementation for strategic performance management. Existing studies have not sufficiently designated the interactions between all seven (7) socio-technical factors or their influences on BI utilization. The specific features of Knowledge Management (KM) and BI in this study have been outlined as a guideline for research in viewing the big picture in decision making processes when implementing organizational performance diagnostics framework. The research goal of this study was to develop an organizational performance framework of socio-technical factors that influence on BI utilization. This study identified sociotechnical factors with observing MIT90s and McKinsey seven (7) S’s framework of people (staff, skills, and style), processes (strategy and structure) and technologies (systems and shared values) and their influences on BI utilization. The data needed for the study was collected from 474 current administration and academic staff of University A with the help of a 25-item questionnaire-based survey were developed for this research literature-based proposed model. The study utilized reliability analysis results to analyze 7 usable socio-technical factors. Data analysis was conducted with SPSS and results confirmed that shared values-oriented factors predicted knowledge seeking and contributing in BI utilization. Furthermore, reliability measurement was confirmed between 7 usable socio-technical factors including people (staff - 0.810, skills – 0.801 and style – 0.796), processes (strategy – 0.771 and structure – 0.780) and technologies (systems – 0.790 and shared values – 0.850). These findings extend the relevance and statistical power of existing studies on BI usage for displaying an organizational performance indicator. 2018 Thesis http://eprints.utem.edu.my/id/eprint/23317/ http://eprints.utem.edu.my/id/eprint/23317/1/Analysis%20Of%20Socio-Technical%20Factors%20In%20Business%20Intelligence%20Framework%20Case%20Study%20Of%20Higher%20Learning%20Institution.pdf text en public http://eprints.utem.edu.my/id/eprint/23317/2/Analysis%20Of%20Socio-Technical%20Factors%20In%20Business%20Intelligence%20Framework%20Case%20Study%20Of%20Higher%20Learning%20Institution.pdf text en validuser http://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=112731 mphil masters Universiti Teknikal Malaysia Melaka Faculty Of Information And Communication Technology 1. Ackoff, R., 2012. Idealized design: Creative corporate visioning. Omega, 21(4), pp. 401– 410. doi: 10.1016/0305-0483(93)90073-T. 2. Adair, J., 2009. Effective Leadership. Pan Books. 3. Ahmad, K. and Zabri, S. M., 2016. The Application of Non-Financial Performance Measurement in Malaysian Manufacturing Firms. Procedia Economics and Finance. Elsevier B.V., 35(October 2015), pp. 476–484. doi: 10.1016/S2212-5671(16)00059-9. 4. Aithal, P. S. and Kumar, P. M. S., 2016. Organizational Behaviour in 21 st Century – “ Theory A ” for Managing People for Performance. IOSR Journal of Business and Management (IOSR-JBM), 18(7), pp. 126–134. doi: 10.9790/487X-180704126134. 5. Aldairi, J. S., Khan, M. K. and Munive-Hernandez, J. E., 2016. A Hybrid KnowledgeBased Lean Six Sigma Maintenance System for Sustainable Buildings. Transactions on Engineering Technologies. Singapore: Springer Singapore, pp. 355–369. doi: 10.1007/978-981-10-1088-0_27. 6. Amarilli, F., 2014. A Framework for Business IT Alignment in Turbulent Environments. Athens Journal of Technology Engineering, 1(2 (June)), pp. 103–118. Available at: http://www.atiner.gr/journals/technology/2014-1-2-2-Amarilli.pdf. 7. Anderson-Lehman, R., 2004. Continental Airlines Flies High With Real-Time Business Intelligence. MIS Quarterly Executive, 3(4), pp. 163–176. doi: 10.1201/1078.10580530/45769.23.1.20061201/91768.2. 8. Anderson, C. and Kennedy, J. A., 2012. Micropolitics: A New Model of Status Hierarchies in Teams. Blackwell Business, pp. 49–80. doi: 10.1108/S1534-109 0856(2012)0000015006. 9. Andjelkovic Pesic, M. and Dahlgaard, J. J., 2013. Using the Balanced Scorecard and the European Foundation for Quality Management Excellence model as a combined roadmap for diagnosing and attaining excellence. Total Quality Management & Business Excellence, 24(5–6), pp. 652–663. doi: 10.1080/14783363.2013.791109. 10. Armstrong, M., 2008. How to Be an Even Better Manager: A Complete A-Z of Proven Techniques and Essential Skills. Oxford University Press. 11. Ave, R. Y., 2017. Pathology of Organizational Training in Universities of Iran Medical Sciences based on McKinsey 7S Model. MIS quarterly, 1(2), pp. 80–86. 12. Baesens, B., 2016. Transformational issues of big data and analytics in networked business. MIS quarterly, 38(2), pp. 629–631. Available at: http://www.airccse.org/journal/ijgca/papers/3212ijgca03.pdf. 13. Bagozzi, R., 1994. Principles of Marketing Research. Blackwell Business. 14. Bailey, D., 2015. The Metrology of Organizational Performance: How Baldrige Standards Have Become the Common Language for Organizational Excellence Around the World. Oxford University Press, (July), pp. 1–17. 15. Balgobind, V., 2002. The impact of Transformational Leadership on Subordinate Job satisfaction. University of South Africa. 16. Bass, B. M., & Avolio, B. J., 1994. Improving organizational effectiveness through transformational leadership. Thousand Oaks: CA: Sage Publications. 17. Basu, R., 2016. Critical linkages between quality management practices and performance110 from Indian IT enabled service SMEs. Total Quality Management & Business Excellence, pp. 1–39. doi: 10.1080/14783363.2016.1252259. 18. Bell, E. and Bryman, A., 2015. The Ethics of Management Research: An Exploratory Content Analysis. British Journal of Management, 18(1), pp. 63–77. doi: 10.1111/j.1467-8551.2006.00487.x. 19. Bernstein and Joerres, 2016. Globalization, robots, and the future of work. Harvard Business Review, 94(10), pp. 74–79. 20. Bestman, A. E., 2016. The Efficacy of the Big Data Syndrome and Organizational Information Governance. Journal of Information Engineering and Applications, 6(4), pp. 31–42. 21. Bolden, R., 2011. Distributed leadership in organizations: A review of theory and research. International Journal of Management Reviews, 13(3), pp. 251–269. doi: 10.1111/j.1468-2370.2011.00306.x. 22. Boyle, S., 2007. Impact Of Changes In Organizational Structure On Selected Key Performance Indicators For Cultural Organizations. International Journal of Cultural Policy, 13(3), pp. 319–334. doi: 10.1080/10286630701556506. 23. Bryman, A., 2008. Social Research Method. 3rd edn. Oxford University Press. 24. Brynjolfsson, E., Hitt, L. M. and Kim, H. H., 2011. Strength in Numbers: How Does Data-Driven Decisionmaking Affect Firm Performance. SSRN Electronic Journal. doi: 10.2139/ssrn.1819486. 25. Calvo-Mora, A., 2015. Contextual and mediation analysis between TQM critical factors and organisational results in the EFQM Excellence Model framework. International111 Journal of Production Research, 53(7), pp. 2186–2201. doi: 10.1080/00207543.2014.975859. 26. Caroline, A., 2018. Kajian Konsep MIT 90’s Sebagai Salah Satu Kerangka Kerja untuk Membangun Sistem Informasi Bisnis. SSRN Electronic Journal, 4(April), pp. 93–102. 27. Cerne, M., 2014. What Goes Around Comes Around: Knowledge Hiding, Perceived Motivational Climate, and Creativity. Academy of Management Journal, 57(1), pp. 172– 192. doi: 10.5465/amj.2012.0122. 28. Chen, C.-K., Shie, A.-J. and Yu, C.-H., 2012. A customer-oriented organisational diagnostic model based on data mining of customer-complaint databases. Expert Systems with Applications, 39(1), pp. 786–792. doi: 10.1016/j.eswa.2011.07.074. 29. Chin, W. Y., 2017. Analyzing the factors that influencing the success of post graduates in achieving graduate on time (GOT) using analytic hierarchy process (AHP). SSRN Electronic Journal, p. 040009. doi: 10.1063/1.5012197. 30. Chourides, P., Longbottom, D. and Murphy, W., 2003. Excellence in knowledge management: an empirical study to identify critical factors and performance measures. Measuring Business Excellence, 7(2), pp. 29–45. doi: 10.1108/13683040310477977. 31. Connelly, C. E., 2012. Knowledge hiding in organizations. Journal of Organizational Behavior, 33(1), pp. 64–88. doi: 10.1002/job.737. 32. Cooper, B. L., 2000. Data Warehousing Supports Corporate Strategy at First American Corporation. MIS Quarterly, 24(4), p. 547. doi: 10.2307/3250947. 33. Counihan, A., Finnegan, P. and Sammon, D., 2002. Towards a framework for evaluating112 investments in data warehousing. Information Systems Journal, 12(4), pp. 321–338. doi: 10.1046/j.1365-2575.2002.00134.x. 34. Creswell, J. W., 2013. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, Research design Qualitative quantitative and mixed methods approaches. Journal of Organizational Behavior. doi: 10.1007/s13398-014-0173-7.2. 35. Daft, R. L., 2006. Understanding the theory and design of organizations. Thomson SouthWestern, Mason, USA. 36. Davenport, T. H., 2006. Competing on analytics. Harvard Business Review, 84(5), p. 150. 37. Dawson, J. F., 2014. Moderation in Management Research: What, Why, When, and How. Journal of Business and Psychology, 29(1), pp. 1–19. doi: 10.1007/s10869-013- 9308-7. 38. Despont-Gros, C., Mueller, H. and Lovis, C., 2005. Evaluating user interactions with clinical information systems: A model based on human–computer interaction models. Journal of Biomedical Informatics, 38(3), pp. 244–255. doi: 10.1016/j.jbi.2004.12.004. 39. Diop, S. and Yorote, A., 2014. Human Resources Management in Industrial Sector of Mali. International Journal of Business and Commerce, 3(8), pp. 82–90. 40. Dobre, O., 2013. Employee motivation and organizational performance. Review of applied socio-economic research, 5(1), pp. 53–60. 41. Du, W., Pan, S. L. and Zuo, M., 2013. How to Balance Sustainability and Profitability in Technology Organizations: An Ambidextrous Perspective. IEEE Transactions on Engineering Management, 60(2), pp. 366–385. doi: 10.1109/TEM.2012.2206113.113 42. Dwivedi, Y. K., 2015. Research on information systems failures and successes: Status update and future directions. Information Systems Frontiers, 17(1), pp. 143–157. doi: 10.1007/s10796-014-9500-y. 43. Earl, M. J., 1989. Management strategies for information technology. Hemel Hempstead, UK: Prentice Hall. 44. Ebrahimi, M. and Sadeghi, M., 2013. Quality management and performance: An annotated review. International Journal of Production Research, 51(18), pp. 5625–5643. doi: 10.1080/00207543.2013.793426. 45. Ebrahimi, Z. F., Wei, C. C. and Rad, R. H., 2015. The impact of the conceptual total quality management model on role stressors. Total Quality Management & Business 46. Excellence, 26(7–8), pp. 762–777. doi: 10.1080/14783363.2014.884302. 47. Eckerson, W. W., 2012. Performance Dashboards: Measuring, Monitoring, and Managing Your Business. Business Book Summaries, 2(1), pp. 22–48. 48. Edmondson, A. C., 2011. Strategies for learning from failure. Harvard business review, 89(4), pp. 48–55. 49. Elayyan, A. and Shraah, M. Al., 2015. The Impact of Decision Making Styles on Organizational Learning: An Empirical Study on the Public Manufacturing Companies in Jordan. International Journal of Business and Social Science, 6(4), pp. 54–62. 50. Elbashir, M. Z., Collier, P. A. and Sutton, S. G., 2011. The Role of Organizational Absorptive Capacity in Strategic Use of Business Intelligence to Support Integrated Management Control Systems. The Accounting Review, 86(1), pp. 155–184. doi:114 10.2308/accr.00000010. 51. Emery, J. C. and Morton, M. S. S., 1972. Management Decision Systems: ComputerBased Support for Decision Making. Administrative Science Quarterly, 17(1), p. 142. doi: 10.2307/2392104. 52. Fan, X. and Lu, D., 2014. Re-balancing the excellence frameworks with individualistic logic. Total Quality Management & Business Excellence, 25(5–6), pp. 478–493. doi: 10.1080/14783363.2013.799333. 53. Foley, É. and Guillemette, M. G., 2010. What is Business Intelligence?. International Journal of Business Intelligence Research, 1(4), pp. 1–28. doi: 10.4018/jbir.2010100101. 54. Forster, N. S. and Rockart, J. F., 1989. Critical Success Factors: An annotated bibliography. MIT Sloan Review, (191), pp. 3041–3089. 55. Foster, K., 2015. Business Intelligence Competency Center: Improving Data and Decisions. Information Systems Management, 32(3), pp. 229–233. doi: 10.1080/10580530.2015.1044343. 56. Frank, O. L., 2015. The indispensability of good operation & amp; maintenance (O&M) manuals in the operation and maintenance of low carbon buildings. Sustainable Cities and Society, 14, pp.9. doi: 10.1016/j.scs.2014.06.002. 57. Franta, D., 2012. SOF as a learning organization. Journal of Organizational Behavior. 58. Garry and Michael S. Morton., 1971. A Framework For Management Information Systems. Journal of Organizational Behavior, 71(51), pp. 458–470. 59. Gadu, M. and El-khameesy, P. N., 2014. A Knowledge Management Framework Using115 Business Intelligence Solutions. Journal of Organizational Behavior, 11(5), pp. 102– 107. 60. Givens, R. J., 2008. Transformational Leadership : The Impact on Organizational and Personal Outcomes. Emerging Leadership Journey, 1, pp. 4–24. 61. Glover, W. J., 2011. Critical success factors for the sustainability of Kaizen event human resource outcomes: An empirical study. International Journal of Production Economics. Elsevier, 132(2), pp. 197–213. doi: 10.1016/j.ijpe.2011.04.005. 62. Gökdeniz, İ., Kartal, C. and Kömürcü, K., 2017. Strategic Assessment based on 7S McKinsey Model for a Business by Using Analytic Network Process (ANP). International Journal of Academic Research in Business and Social Sciences, 7(6), pp. 2222–6990. doi: 10.6007/IJARBSS/v7-i6/2967. 63. Gulati, R. and Soni, T., 2015. Digitization: A Strategic Key to Business. Journal of Advances in Business Management, 1(2), pp. 60–67. 64. Hannula, M. and Pirttimäki, V., 2003. Business intelligence empirical study on the top 50 Finnish companies. Journal of American Academy of Business, pp. 593–599. Available at: http://www.umsl.edu/~lacitym/bi1.pdf. 65. Hawking, P. and Sellitto, C., 2015. Business Intelligence Strategy. International Journal of Enterprise Information Systems, 11(1), pp. 1–12. doi: 10.4018/ijeis.2015010101. 66. Henderson, J. C. and Venkatraman, H., 1993. Strategic alignment: Leveraging information technology for transforming organizations. IBM Systems Journal, 32(1), pp. 472–484. doi: 10.1147/sj.382.0472.116 67. Heyden, M. L. M., 2013. Perceived Environmental Dynamism, Relative Competitive Performance, and Top Management Team Heterogeneity: Examining Correlates of Upper Echelons Advice-Seeking. Organization Studies, 34(9), pp. 1327–1356. doi: 10.1177/0170840612470229. 68. Hoang, D. T., Igel, B. and Laosirihongthong, T., 2013. The impact of total quality management on innovation. International Journal of Quality & Reliability Management, 23(9), pp. 1092–1117. 69. Hodgkinson, S. L., 1996. The role of the corporate IT function in the federal IT organization in Information management: The organizational dimension. Oxford, UK: Oxford University Press, pp. 247–269. 70. Holger Kisker., Boris Evelson., M. B., 2015. Business Intelligence Playbook. 71. Hollweck, T., 2016. Case Study Research Design and Methods (5th ed.). Journal of Organizational Behavior, 1(2014), pp. 108–110. doi: 10.3138/cjpe.30.1.108. 72. Horkoff, J., 2012. Making data meaningful: The business intelligence model and its formal semantics in description logics. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7566 LNCS(PART 2), pp. 700–717. doi: 10.1007/978-3-642-33615-7_17. 73. Hussain Ali Bekhet and Shorouq Fathi Kamel Eletter., 2012. Credit Risk Management for the Jordanian Commercial Banks: A business Intelligence Approach. Australian Journal of Basic and Applied Sciences, 6(9), pp. 188–195. 74. Hussain, S., 2014. Consumer Innovativeness Leading to Innovation Adoption. Journal of Organizational Behavior, 6(27), pp. 229–236.117 Inmon, W. H., 2005. Building the data warehouse. Journal of Organizational Behavior. 75. Jackson, S. E., Schuler, R. S. and Jiang, K., 2014. An Aspirational Framework for Strategic Human Resource Management. The Academy of Management Annals, 8(1), pp. 1–56. doi: 10.1080/19416520.2014.872335. 76. Jaques, E., 2017. Requisite organization: A total system for effective managerial organization and managerial leadership for the 21st century. Journal of Organizational Behavior. 77. Jarzabkowski, P. and Whittington, R., 2008. A Strategy-as-Practice Approach to Strategy Research and Education. Journal of Management Inquiry, 17(4), pp. 282–286. doi: 10.1177/1056492608318150. 78. Kimball, R. and Ross, M., 2002. The Data Warehouse Toolkit. Second Edi. Edited by Robert Elliott. Canada: John Wiley & Sons, Inc. 79. Kirkwood, A. and Price, L., 2014. Technology-enhanced learning and teaching in higher education: what is “enhanced” and how do we know? A critical literature review. Learning, Media and Technology, 39(1), pp. 6–36. doi: 10.1080/17439884.2013.770404. 80. Kleine, C. and Lawton, R., 2015. Meeting the Needs of Linguistically Diverse Students at the College Level. College Reading & Learning Association, (November), pp. 1–37. 81. Kohtamäki, M. and Farmer, D., 2017. Real-time Strategy and Business Intelligence. Journal of Organizational Behavior, pp. 11–37. doi: 10.1007/978-3-319-54846-3. 82. Kok, S. K. and McDonald, C., 2017. Underpinning excellence in higher education – an investigation into the leadership, governance and management behaviours of high-118 performing academic departments. Studies in Higher Education, 42(2), pp. 210–231. doi: 10.1080/03075079.2015.1036849. 83. Kothari, C., Kumar, R. and Uusitalo, O., 2014. Research Methodology, New Age International. Journal of Organizational Behavior, doi: http://196.29.172.66:8080/jspui/bitstream/123456789/2574/1/Research%20Methodology .pdf. 84. Krishnan, V. R., 2005. Transformational leadership and outcomes: role of relationship duration. Leadership & Organization Development Journal, 26(6), pp. 442–457. doi: 10.1108/01437730510617654. 85. Kumari, N., 2013. Business intelligence in a nutshell. International Journal of Innovative research in computer and communications engineering, 1(4), pp. 969–975. 86. Kuo, L. J. and Chen, Z., 2016. The Impact of Bilingual Experience on The Literacy Development of Struggling Readers. Journal of Childhood & Developmental Disorders, 02(02). doi: 10.4172/2472-1786.100017. 87. Landseadel, P., 1995. Methodologies in user interface design. Ieee aerospace and electronic systems magazine, 8(10), pp. 15–20. Available at: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=406817. 88. Laumer, S., Maier, C. and Eckhardt, A., 2014. The impact of business process management and applicant tracking systems on recruiting process performance: an empirical study. Journal of Business Economics, 85(4), pp. 421–453. doi: 10.1007/s11573-014-0758-9. 89. Lee M., 2014. Dilemmas in defining HRD. In Chalofs. Hoboken, NJ: Wiley.: Handbook119 of human resource development. 90. Lee, V. H. and Ooi, K. B., 2015. Applying the Malcolm Baldrige National Quality Award criteria: an approach to strengthen organisational memory and process innovation. Total Quality Management & Business Excellence, 26(11–12), pp. 1373–1386. doi: 10.1080/14783363.2014.934519. 91. Leonard, J. W., 1998. Smart Thinking for Crazy Times: The Art of Solving the Right Problems. Academy of Management Perspectives, 12(3), pp. 93–94. doi: 10.5465/AME.1998.1109060. 92. Leong, T. K., 2014. Using Project Performance to Measure Effectiveness of Quality Management System Maintenance and Practices in Construction Industry. The Scientific World Journal, 2014, pp. 1–9. doi: 10.1155/2014/591361. 93. Lim D. H., Song J. H., 2014. Trends and issues in integrating knowledge management and organizational learning for workplace performance improvement. Hoboken, NJ: Wiley: Handbook of human resource development, pp. 369–385. 94. Lindström, Å., 2006. A survey on CIO concerns-do enterprise architecture frameworks support them?. Information Systems Frontiers, 8(2), pp. 81–90. doi: 10.1007/s10796- 006-7972-0. 95. Loveman, G., 2003. Diamonds in the datamine. Harvard Business Review, 81(5), pp. 109–113. 96. Lowy, A. and Hood, P., 2002. The Power of the 2 x 2 Matrix: Using 2 x 2 Thinking to Solve Business Problems and Make Better Decisions. Academy of Management Perspectives, 17(1), pp. 134–138. doi: 10.1002/mds.5555.120 97. Luftman, J. N., 2003. Competing in the Information Age. Oxford University Press. doi: 10.1093/0195159535.001.0001. 98. Magd, E., 2015. TQM and strategic alliances: development and validation in the context of Egyptian manufacturing sector. International Journal of Strategic Business Alliances, 4(1), p. 39. doi: 10.1504/IJSBA.2015.069309. 99. Maghrabi, R. O., 2011. The Role of Business intelligence (Bi) in Service Innovation: an Ambidexterity Perspective’. Available at: http://aisel.aisnet.org/cgi/viewcontent.cgi?article=1292&context=amcis2011_submissions 100. Maier, C., 2015. Giving too much social support: social overload on social networking sites. European Journal of Information Systems, 24(5), pp. 447–464. doi: 10.1057/ejis.2014.3. 101. Malhotra, N., 2007. Marketing Research : An Applied Orientation. Pearson. March, S. T. and Hevner, A. R., 2007. Integrated decision support systems: A data warehousing perspective. Decision Support Systems, 43(3), pp. 1031–1043. doi: 10.1016/j.dss.2005.05.029. 102. Marks, G. N., 2015. Are school-SES effects statistical artefacts? Evidence from longitudinal population data. Oxford Review of Education, 41(1), pp. 122–144. doi: 10.1080/03054985.2015.1006613. 103. Martin, A., 2012. Enterprise IT Architecture in Large Federated Organizations: The Art of the Possible. Information Systems Management, 29(2), pp. 137–147. doi: 10.1080/10580530.2012.662103.121 104. Martin, D., 2007. Good Organisational Reasons for “Bad” Software Testing: An Ethnographic Study of Testing in a Small Software Company. 29th International Conference on Software Engineering (ICSE’07) IEEE, pp. 602–611. doi: 10.1109/ICSE.2007.1. 105. Martins, E. C. and Terblanche, F., 2003. Building organisational culture that stimulates creativity and innovation. European Journal of Innovation Management, 6(1), pp. 64–74. doi: 10.1108/14601060310456337. 106. Massaro, M., Dumay, J. and Garlatti, A., 2015. Public sector knowledge management: a structured literature review. Journal of Knowledge Management, 19(3), pp. 530–558. doi: 10.1108/JKM-11-2014-0466. 107. Mayer, I., 2012. Towards a Comprehensive Methodology for the Research and Evaluation of Serious Games. Procedia Computer Science, 15, pp. 233–247. doi: 10.1016/j.procs.2012.10.075. 108. McPhee, W., 2014. A new sustainability model: engaging the entire firm. Journal of Business Strategy, 35(2), pp. 4–12. doi: 10.1108/JBS-11-2013-0106. 109. Mina, A., Bascavusoglu-Moreau, E. and Hughes, A., 2014. Open service innovation and the firm’s search for external knowledge. Research Policy, 43(5), pp. 853–866. doi: 10.1016/j.respol.2013.07.004. 110. Ministry of Education Malaysia., 2017. Quick Facts 2017 Malaysia Educational Statistics. (July), pp. 1–50. Available at: https://www.moe.gov.my/images/Terbitan/Buku-informasi/QUICK-FACTS- 2017/20170809_QUICKFACTS_2017_FINAL5_interactive.pdf.122 111. Ministry of Education Malaysia (MoE)., 2015. Malaysia Education Blueprint 2015-2025 (Higher Education). Ministry of Education Malaysia, 2025, p. 40. doi: 10.5923/j.ijis.20120206.05. 112. Mistry, V., 2008. Benchmarking e‐learning: trialling the “MIT90s” framework. Benchmarking: An International Journal, 15(3), pp. 326–340. doi: 10.1108/14635770810876629. 113. Misuraca, G. and Viscusi, G., 2014. Digital governance in the public sector. Proceedings of the 8th International Conference on Theory and Practice of Electronic Governance - ICEGOV ’14. New York, USA: ACM Press, pp. 146–154. doi: 10.1145/2691195.2691286. 114. Mitchell, R. K., 2016. Stakeholder Agency and Social Welfare. Pluralism and Decision Making in the Multi-Objective Corporation, 41(2), pp. 252–275. 115. Moore, A., 2018. The value of consensus in rapid organisation assessment: wildlife programmes and the Conservation Excellence Model. Total Quality Management & Business Excellence, pp. 1–15. doi: 10.1080/14783363.2018.1444472. 116. Moshonsky, M., Serenko, A. and Bontis, N., 2014. Examining the Transfer of Academic Knowledge to Business Practitioners. International Journal of Knowledge Management, 10(3), pp. 70–95. doi: 10.4018/ijkm.2014070105. 117. Murugesan, M. and Karthikeyan, K., 2016. Business Intelligence Market Trends and Growth in Enterprise Business. International Journal on Recent and Innovation Trends in Computing and Communication, 4(3), pp. 188–192. 118. Musenze, I. A., Munene, J. C. and Joseph M., 2013. Communication Practices and123 Quality Service Delivery Tradition: Uganda’s Local Government Perspective. International Journal of Innovation and Applied Studies, 4(2), pp. 413–423. Available at: http://www.issr-journals.org/ijias/abstract.php?article=IJIAS-13-202-01. 119. Noran, O. and Bernus, P., 2017. Business cloudification: An enterprise architecture perspective. 19th International Conference on Enterprise Information Systems, ICEIS 2017, 3, pp. 353–360. doi: 10.5220/0006248603530360. 120. Nsights, I., Sawy, O. A. El and Pavlou, P. A., 2013. Digital Business Strategy: Toward A Next Generation Of Insights. Journal of Knowledge Management, 37(2), pp. 471–482. 121. Oeilly, C. A., 2014. The Promise and Problems of Organizational Culture. Group & Organization Management, 39(6), pp. 595–625. doi: 10.1177/1059601114550713. 122. Olsen, D. H. and Trelsgård, K., 2016. Enterprise Architecture Adoption Challenges: An exploratory Case Study of the Norwegian Higher Education Sector. Procedia Computer Science. Elsevier Masson SAS, 100(1877), pp. 804–811. doi: 10.1016/j.procs.2016.09.228. 123. Oviatt, S., 1999. Ten myths of multimodal interaction. Communications of the ACM, 42(11), pp. 74–81. doi: 10.1145/319382.319398. 124. Pandey, S. C. and Dutta, A., 2013. Role of knowledge infrastructure capabilities in knowledge management. Journal of Knowledge Management, 17(3), pp. 435–453. doi: 10.1108/JKM-11-2012-0365. 125. Parnell, J. A., 2005. Strategic philosophy and management level. Management Decision, 43(2), pp. 157–170. doi: 10.1108/00251740510581894.124 126. Petrillo, A., 2018. Fourth Industrial Revolution: Current Practices, Challenges, and Opportunities. Digital Transformation in Smart Manufacturing, pp. 1–20. doi: 10.5772/intechopen.72304. 127. Phillips-wren, G., 2015. Business Analytics in the Context of Big Data: A Roadmap for Research. Journal of Knowledge Management. 128. Pollanen, R., 2017. Relationships between strategic performance measures, strategic decision-making, and organizational performance: empirical evidence from Canadian public organizations. Public Management Review, 19(5), pp. 725–746. doi: 10.1080/14719037.2016.1203013. 129. Popova-Nowak, I. V. and Cseh, M., 2015. The Meaning of Organizational Learning. Human Resource Development Review, 14(3), pp. 299–331. doi: 10.1177/1534484315596856. 130. Purcell, J. and Boxal, P., 2003. Strategy and Human Resource Management. Management, Work and Organisations UK: Palgrave Macmillan. 131. Qady, M. and Kandil, A., 2015. Automatic Classification of Project Documents on the Basis of Text Content. Journal of Computing in Civil Engineering, 29(3), p. 40-43. doi: 10.1061/(ASCE)CP.1943-5487.0000338. 132. Quarterly, S., Summer, N. and Bartholomew, O., 2014. Strategic environmental sustainability management: highlighting the need and opportunities to recognize environmentally hidden economic sectors. International Journal of Business Insights & Transformation, 37(2), pp. 264–265. 133. Quigley, T. J., Hall, B. and Hall, B., 2014. Strategic management. Journal of Knowledge125 Management, pp. 1–21. 134. Rabbanikhah, F., 2016. Analyzing Effective Factors in Efficiency of Organizational Trainings. A Case Study : Employees of Ministry of Health and Medical Education, pp. 2136–2154. 135. Rahim, F. T. and Zainuddin, D. Y., 2015. Study on the Impact of Technological Innovation Capabilities on Competitive Advantage and Firm Performance : an Empirical Study in the Automotive Industry in Malaysia. Journal of Scientific Research and Development, 2(13), pp. 194–202. 136. Rahman, A., Zamri, S., and Leong, K. E., 2017. A Meta-Analysis Study of Satisfaction and Continuance Intention to Use Educational Technology. International Journal of Academic Research in Business and Social Sciences, 7(4), pp. 1059–1072. doi: 10.6007/IJARBSS/v7-i4/2915. 137. Ramakrishnan, T., Jones, M. C. and Sidorova, A., 2012. Factors influencing business intelligence (BI) data collection strategies: An empirical investigation. Decision Support Systems, 52(2), pp. 486–496. doi: 10.1016/j.dss.2011.10.009. 138. Ramamurthy, K., Sen, A. and Sinha, A. P., 2008. An empirical investigation of the key determinants of data warehouse adoption. Decision Support Systems, 44(4), pp. 817–841. doi: 10.1016/j.dss.2007.10.006. 139. Rausch, P., Sheta, AF. & Ayesh, A., 2013. Business Intelligence and Performance Management Theory. Systems and Industrial Applications. 140. Río-Rama., 2017. Influence of quality on employee results: the case of rural accommodations in Spain. Total Quality Management & Business Excellence, 28(13–126 14), pp. 1489–1508. doi: 10.1080/14783363.2016.1150171. 141. Roberts, N., Campbell, D. E. and Vijayasarathy, L. R., 2016. Using Information Systems to Sense Opportunities for Innovation: Integrating Postadoptive Use Behaviors with the Dynamic Managerial Capability Perspective. Journal of Management Information Systems, 33(1), pp. 45–69. doi: 10.1080/07421222.2016.1172452. 142. Rosenberg, M., 2003. Nonviolent Communication: A Language of Life. Encinitas, CA: Puddle Dancer Press. 143. Ross, J. W., Weill, P., & Robertson, D. C., 2006. Enterprise architecture as strategy. Boston, MA: Harvard Business School Press. 144. Ross, J. W., 2003. Creating a Strategic IT Architecture Competency: Learning in Stages. SSRN Electronic Journal. doi: 10.2139/ssrn.416180. 145. Rutendo Mushore., 2017. Leveraging Business Intelligence and Analytics to Improve Decision-making and Organisational Success. University of Cape Town. Available at: http://hdl.handle.net/11427/27408. 146. Saadat, V. and Saadat, Z., 2016. Organizational Learning as a Key Role of Organizational Success. Procedia - Social and Behavioral Sciences, 230(May), pp. 219– 225. doi: 10.1016/j.sbspro.2016.09.028. 147. Saarela, M., 2016. Expert-based versus citation-based ranking of scholarly and scientific publication channels. Journal of Informetrics, 10(3), pp. 693–718. doi: 10.1016/j.joi.2016.03.004. 148. Sadeh, E. and Garkaz, M., 2015. Explaining the mediating role of service quality127 between quality management enablers and students’ satisfaction in higher education institutes: the perception of managers. Total Quality Management & Business Excellence, 26(11–12), pp. 1335–1356. doi: 10.1080/14783363.2014.931065. 149. Saunders, M., Lewis, P. and Thornhill, A., 2005. For Business Students. Journal of Knowledge Management, p. 649. 150. Saunders, M., Lewis, P. and Thornhill, A., 2009. Business Students. Boston, MA: Harvard Business School Press. 151. Savva, S., 2016. Re-imagining Schooling: Weaving the Picture of School as an Affinity Space for Twenty-First Century Through a Multiliteracies Lens. Reimagining the Purpose of Schools and Educational Organisations: Springer International Publishing, pp. 49–64. doi: 10.1007/978-3-319-24699-4_5. 152. Schermerhorn, J., 2001. Management. New York, USA: John Wiley & Sons. Scott Morton, M., 1991. The Corporation of the 1990s: Information Technology and Organisational Transformation. Oxford: Oxford University Press. 153. Seeck, H., 2010. Management Paradigms in Finnish Management Research. Journal of Management History, 16(2), pp. 174–194. 154. Sekaran, U., and Bougie, R., 2016. Research Methods for Business: A Skill Building Approach. Research methods for business. doi: 10.1017/CBO9781107415324.004. 155. Serenko, A. and Bontis, N., 2013. Global ranking of knowledge management and intellectual capital academic journals. Journal of Knowledge Management, 17(2), pp. 307–326. doi: 10.1108/13673271311315231.128 156. Serenko, A. and Bontis, N., 2017. Global ranking of knowledge management and intellectual capital academic journals: 2017. Journal of Knowledge Management, 21(3), pp. 675–692. doi: 10.1108/JKM-11-2016-0490. 157. Shahrabi, J., Hadavandi, E. and Salehi Esfandarani, M., 2013. Developing a hybrid intelligent model for constructing a size recommendation expert system in textile industries. International Journal of Clothing Science and Technology, 25(5), pp. 338– 349. doi: 10.1108/IJCST-04-2012-0015. 158. Shahriari, B., 2016. Strategic Management. European Journal of Management and Marketing Studies Discussing The Effective Factors, pp. 99–109. 159. Singh, A., 2013. A study of role of McKinsey’s 7S framework in achieving organizational excellence. Organizational Development Journal, 31(3), pp. 39–50. 160. Siva, V., 2016. The support of Quality Management to sustainable development: a literature review. Journal of Cleaner Production, 138, pp. 148–157. doi: 10.1016/j.jclepro.2016.01.020. 161. Smith, A. M., 2012. Research Methodology: A Step-by-step Guide for Beginners. Nurse Education in Practice, 12(3), p. e25. doi: 10.1016/j.nepr.2011.11.008. 162. Smith, J., 2014. A Road Map for the Future. Quality Magazine, pp. 2–3. 163. Starbuck, W. H., 2016. 60th Anniversary Essay. Administrative Science Quarterly, 61(2), pp. 165–183. doi: 10.1177/0001839216629644. 164. Starbuck, W. H., 2017. Organizational learning and unlearning. The Learning Organization, 24(1), pp. 30–38. doi: 10.1108/TLO-11-2016-0073.129 165. Steenbergen, M., van den Berg, M. and Brinkkemper, S., 2008. A Balanced Approach to Developing the Enterprise Architecture Practice. Boston, MA: Harvard Business School Press, in, pp. 240–253. doi: 10.1007/978-3-540-88710-2_19. 166. Sunder M. and Antony, J., 2018. A conceptual Lean Six Sigma framework for quality excellence in higher education institutions. International Journal of Quality & Reliability Management, 35(4), pp. 857–874. doi: 10.1108/IJQRM-01-2017-0002. 167. Swanson, S. R. and Hsu, M. K., 2011. The Effect of Recovery Locus Attributions and Service Failure Severity on Word-of-Mouth and Repurchase Behaviors in the Hospitality Industry. Journal of Hospitality & Tourism Research, 35(4), pp. 511–529. doi: 10.1177/1096348010382237. 168. Tadajewski, M., 2016. Academic labour, journal ranking lists and the politics of knowledge production in marketing. Journal of Marketing Management, 32(1–2), pp. 1– 18. doi: 10.1080/0267257X.2015.1120508. 169. Teh, D. and Corbitt, B., 2015. Building sustainability strategy in business. Journal of Business Strategy, 36(6), pp. 39–46. doi: 10.1108/JBS-08-2014-0099. 170. They, H. and Up, S., 2015. The Forrester Wave: Agile Business Intelligence Key takeaways. Boston, MA: Harvard Business School Press. Uhmavaara, R., 2016. How to validate Design. Boston, MA: Harvard Business School Press. 171. Vaus, D., 2002. Surveys in social research. New South Wales: Allen and Unwin. 172. Vernadat, F. B., 2010. Technical, semantic and organizational issues of enterprise130 interoperability and networking. Annual Reviews in Control, 34(1), pp. 139–144. doi: 10.1016/j.arcontrol.2010.02.009. 173. Vinodh, S. and Dinesh Kumar, C., 2012. Development of computerized decision support system for leanness assessment using multi grade fuzzy approach. Journal of Manufacturing Technology Management, 23(4), pp. 503–516. doi: 10.1108/17410381211230457. 174. Vukomanovic, M., Radujkovic, M. and Nahod, M., 2014. EFQM excellence model as the TQM model of the construction industry of southeastern Europe. Journal of Civil Engineering and Management, 20(1), pp. 70–81. doi: 10.3846/13923730.2013.843582. 175. Vukšić, V. B., Bach, M. P. and Popovič, A., 2013. Supporting performance management with business process management and business intelligence: A case analysis of integration and orchestration. International Journal of Information Management, 33(4), pp. 613–619. doi: 10.1016/j.ijinfomgt.2013.03.008. 176. Ward, J., 2002. Strategic planning for information systems. 3rd edn. Chichester: Wiley. 177. Watson, H. J. and Wixom, B. H., 2007. The Current State of Business Intelligence. Computer, 40(9), pp. 96–99. doi: 10.1109/MC.2007.331. 178. Whitworth, Brian and Ahmad, A., 2013. The Social Design of Technical Systems: Building technologies for communities. Aarhus, Denmark: The Interaction Design Foundation. 179. Wilkins, A. S., 2017. To Lag or Not to Lag?: Re-Evaluating the Use of Lagged Dependent Variables in Regression Analysis. Political Science Research and Methods, pp. 1–19. doi: 10.1017/psrm.2017.4.131 180. Wilson, J. R. and Carayon, P., 2014. Systems ergonomics: Looking into the future – Editorial for special issue on systems ergonomics/human factors. Applied Ergonomics, 45(1), pp. 3–4. doi: 10.1016/j.apergo.2013.08.007. 181. Wixom, B. and Watson, H., 2010. The BI-Based Organization. International Journal of Business Intelligence Research, 1(1), pp. 13–28. doi: 10.4018/jbir.2010071702. 182. Yao, Z., 2013. Knowledge complementarity, knowledge absorption effectiveness, and new product performance: The exploration of international joint ventures in China. International Business Review, 22(1), pp. 216–227. doi: 10.1016/j.ibusrev.2012.04.002. 183. Yermish, I., 2010. Business Plus Intelligence Plus Technology Equals Business Intelligence. International Journal of Business Intelligence Research, 1(1), pp. 48–63. doi: 10.4018/jbir.2010071704. 184. Yu-Yun Lee., 2015. Survey on Operations and Service Management. Institute of Technology Management National Chung Hsing University, pp. 1–46.

Analysis Of Socio-Technical Factors In Business Intelligence Framework Case Study Of Higher Learning Institution

Similar Items