3,775
Views
17
CrossRef citations to date
0
Altmetric
Research Article

Talent development model for a career in Islamic banking institutions: A SEM approach

ORCID Icon, ORCID Icon & | (Reviewing Editor)
Article: 1186259 | Received 04 Nov 2015, Accepted 28 Apr 2016, Published online: 19 Jul 2016

References

  • Afthanorhan, W. M. A. B. W., & Ahmad, S. (2014). Path analysis in covariance-based structural equation modeling with Amos 18.0. European Journal of Business and Social Sciences, 2, 10.
  • Amat Taap, M. (2014). Finance accreditation agency (FAA) talent development survey 2014 in collaboration with Islamic finance news. Retrieved September 7, 2016, from http://www.faa.org.my
  • Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103, 411–423.
  • Andrews, T., & Powell, D. (2009). 5.2 collaborative teaching & learning centres at the University of Queensland. Learning spaces in higher education: Positive outcomes by design (p. 45). Brisbane.
  • Arbuckle, J. L. (1995). AMOS for windows, analysis of moment structures (version 3.5). Chicago, IL: SmallWaters.
  • Aziz, Y., & Wahab, A. (2014). Human capital development with competitive advantage for UITM undergraduates in banking industries (Master Thesis). University of Technology MARA, Dungun.
  • Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16, 74–94.
  • Baird, A., Furukawa, M. F., & Raghu, T. S. (2012). Understanding contingencies associated with the early adoption of customer-facing web portals. Journal of Management Information Systems, 29, 293–324.
  • Balkundi, P., & Kilduff, M. (2006). The ties that lead: A social network approach to leadership. The Leadership Quarterly, 17, 419–439.
  • Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238–246.
  • Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25, 351–370.
  • Bollen, K. A., & Pearl, J. (2013). Eight myths about causality and structural equation models. In S. L. Morgan (Ed.), Handbook of causal analysis for social research (Chapter 15, pp. 301–328). Dordrechtc: Springer.
  • Cobley, S., & McKenna, J. (2011). Implementation concerns for Gagné’s vision of academic talent development. Talent Development & Excellence, 3, 33–36.
  • Cohen, J. (1988). Statistical power analysis: A computer program. New Jersey, NJ: Routledge.
  • Colangelo, N., Assouline, S. G., & Gross, M. U. (2004). A nation deceived: How schools hold back America’s brightest students. In C. Belin & N. Jacqueline. (Eds.), The Templeton national report on acceleration (Vol. 2, p. 10). IA: Blank International Center for Gifted Education and Talent Development (NJ1).
  • Cummins, R. A., & Gullone, E. (2000, March). Why we should not use 5-point Likert scales: The case for subjective quality of life measurement (pp. 74–93). In Proceedings, Second International Conference on Quality of Life in Cities, Singapore.
  • Cunningham, W. A., Preacher, K. J., & Banaji, M. R. (2001). Implicit attitude measures: Consistency, stability, and convergent validity. Psychological Science, 12, 163–170.
  • Dawes, J. G. (2008). Do data characteristics change according to the number of scale points used? An experiment using 5 point, 7 point and 10 point scales. International Journal of Market Research, 51, 61–104.
  • Earthman, G. I. (2002). School facility conditions and student academic achievement. UCLA’s Institute for Democracy, Education, & Access, Stanford, CA.
  • Economic Transformation Programme. (2014). Malaysia economic transformation programme annual report 2014. Retrieved from http://www.etp.pemandu.gov.my
  • Elliott, K. M., & Healy, M. A. (2001). Key factors influencing student satisfaction related to recruitment and retention. Journal of Marketing for Higher Education, 10(4), 1–11.
  • Farouq, A. (2015, August 12–13). Corporate governance in Islamic financial institutions. Paper presented at the 6th Asia Islamic Banking Conference, Kuala Lumpur.
  • Finance Accreditation Agency. (2014). Talent development survey 2014. Retrieved from http://www.faa.org.my
  • Finnie, R., & Usher, A. (2005). Measuring the quality of post-secondary education: Concepts, current practices and a strategic plan. Ottawa: CPRN = RCRPP.
  • Fornell, C., & Bookstein, F. L. (1982). Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 19, 440–452.
  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39–50.
  • Gagne, F. (1998). A proposal for subcategories within gifted or talented populations. Gifted Child Quarterly, 42, 87–95.
  • Gagne, F. (2000). A differentiated model of giftedness and talent (DMGT): Update. Montreal: Universite du Quebec a Montreal.
  • Gagne, F. (2003). Transforming gifts into talents: The DMGT as a developmental theory. In N. Colangelo & G. Davis (Eds.), Handbook of gifted education (3rd ed., pp. 60–74). New York, NY: Pearson Education.
  • Gagne, F. (2007). Ten commandments for academic talent development. Gifted Child Quarterly, 51, 93–118.
  • Gagne, F. (2004). Transforming gifts into talents: The DMGT as a developmental theory. High Ability Studies, 15, 119–147.
  • Gagne, F. (2005). From gifts to talents: The DMGT as a developmental model. In R. J. Sternberg & J. E. Davidson (Eds.), Conceptions of giftedness (2nd ed., pp. 98–119). New York, NY: Cambridge University Press.
  • Gow, G. (1999). Shifts in the campus planning and development paradigm. Paper presented at ATEM Conference, Wellington.
  • Hair, Jr., J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: A global perspective (7th ed.). Upper Saddle River, NJ: Pearson Prentice Hall.
  • Hair, Jr., J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2013). A primer on partial least squares structural equation modeling (PLS-SEM) (pp. 7–10). Thousands Oaks, CA: Sage.
  • Henderson, A. T., & Mapp, K. L. (2002). A new wave of evidence: The impact of school, family, and community connection on student achievement. Annual synthesis 2002. Austin, TX: National Center for Family and Community Connections with Schools.
  • Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural equation modelling: Guidelines for determining model fit. Articles, 2, 53.
  • Hoyle, R. H. (Ed.). (1995). Structural equation modeling: Concepts, issues, and applications. Thousands Oaks, CA: Sage.
  • Kemple, J. J., Herlihy, C. M., & Smith, T. J. (2005). Making progress toward graduation: Evidence from the talent development high school model. New York, NY: MDRC.
  • Kya, L. T., Ngor, P. Y., & Awang, Z. (2012). Statistic for UiTM (3rd ed., pp. 22–23). Oxford Fajar Sdn. Bhd.
  • Lowry, P. B., & Gaskin, J. (2014). Partial least squares (PLS) structural equation modeling (SEM) for building and testing behavioral causal theory: When to choose it and how to use it. IEEE Transactions on Professional Communication, 57, 123–146.
  • Luan, T. K. (2013). Editors’ Note. Banker’s Journal Malaysia, 139. Institute of bankers Malaysia. KDN PP 3781/05/2013 (032406) No. 139-2012. (p. 2).
  • MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1, 130–149.
  • Malaysian International Islamic Financial Centre. (2013). Insight: Human capital development sustaining the growth of Islamic finance. Kuala Lumpur: Bank Negara Malaysia Publications.
  • ManpowerGroup. (2013). The great talent shortage awakening: Actions to take for a sustainable workforce. Retrieved from http://www.manpowergroup.com
  • Marimuthu, S. J. (2015, August 12–13). Human capital in Islamic finance: What initiatives exist to develop training programmes for Islamic financial services? Paper presented at the 6th Asia Islamic Banking Conference, Kuala Lumpur.
  • McLagan, P., & Suhadolink, D. (1989). Models for HRD practice: The research report. Alexandria, VA: American Society for Training and Development.
  • McLaughlin, P., & Faulkner, J. (2012). Flexible spaces … what students expect from university facilities. Journal of Facilities Management, 10, 140–149.
  • Mcvay, G. J., Murphy, P. R., & Wook Yoon, S. (2008). Good practices in accounting education: Classroom configuration and technological tools for enhancing the learning environment. Accounting Education, 17, 41–63.
  • Moon, T. R., Brighton, C. M., & Callahan, C. M. (2003). State standardized testing programs: Friend or foe of gifted education? Roeper Review, 25, 49–60.
  • Morton, L. (2004, January). Integrated and integrative talent management: A strategic HR framework. New York, NY: Conference Board.
  • Nabi, G., & Holden, R. (2008). Graduate entrepreneurship: Intentions, education and training. Education+Training, 50, 545–551.
  • Norman, G. (2010). Likert scales, levels of measurement and the “laws” of statistics. Advances in Health Sciences Education, 15, 625–632.
  • Nunally, J. C., & Bernstein, I. H. (1994). Psychonometric theory. Kuala Lumpur: Federal Publication.
  • Quek, C. G. (2005). A national study of scientific talent development in Singapore. Williamsburg, VA: Faculty of the School of Education, The College William and Mary.
  • Reid, B. D. (1992). Research needs in gifted education: A study of practitioners’ perceptions. Paper Presented at annual meeting of America Education Research Association, San Fransisco, CA.
  • Reis, S. M., Schader, R., Milne, H., & Stephens, R. (2003). Music & minds: Using a talent development approach for young adults with Williams syndrome. Exceptional Children, 69, 293–313.
  • Renzulli, J. S. (1992). Setting an agenda: Research priorities for the gifted and talented through the year 2000. Storr, CT: National Research Center on the Gifted and Talented.
  • Renzulli, J. S. (1994). Schools for talent development: A practical plan for total school improvement. Mansfield Center, CT: Creative Learning Press.
  • Renzulli, J. S., & Reis, S. M. (1998). Talent development through curriculum differentiation. NASSP Bulletin, 82, 61–74.
  • Riehl, C., & Sipple, J. W. (1996). Making the most of time and talent: Secondary school organizational climates, teaching task environments, and teacher commitment. American Educational Research Journal, 33, 873–901.
  • Rubin, R. S., & Dierdorff, E. C. (2009). How relevant is the MBA? Assessing the alignment of required curricula and required managerial competencies. Academy of Management Learning & Education, 8, 208–224.
  • Smart, B. D. (2005). Topgrading: How leading companies win by hiring, coaching, and keeping the best people. New York, NY: Penguin.
  • Syed Othman, A. (2013). Yurizk Global Islamic Finance Education Report 2013. Philosophy of Education in Islamic Finance (pp. 48–49). Delaware.
  • Uline, C., & Tschannen-Moran, M. (2008). The walls speak: The interplay of quality facilities, school climate, and student achievement. Journal of Educational Administration, 46, 55–73.
  • VanTassel-Baska, J. (1994). Comprehensive curriculum for gifted learners. Boston, MA: Allyn & Bacon.
  • VanTassel-Baska, J., & Brown, E. F. (2007). Toward best practice: An analysis of the efficacy of curriculum models in gifted education. Gifted Child Quarterly, 51, 342–358.
  • Venter, E. (2001). A constructivist approach to learning and teaching. South African Journal of Higher Education, 15, 86–92.
  • Walton, R. (2014, December). Mapping MI to the DMGT: A theoretical framework. Australasian Journal of Gifted Education, 23, 37–44.
  • Zainudin, A. (2015). SEM made simple. Bangi, Selangor: MPWS Publisher.
  • Zikmund, W., Babin, B., Carr, J., & Griffin, M. (2012). Business research methods. Oklohama, OK: Cengage Learning.