Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Determinants of stakeholders’ intention to adopt pharmacogenomic

Abstract

Pharmacogenomics (PGx) testing, which aims to identify the genes that affect our responses to drugs, has been favoured by healthcare professionals as a means of maximising drug efficacy and improving the safety and cost-effectiveness of healthcare. Support from the public is needed to determine the successful development of this technology and its implementation in society. Therefore, the objective of this paper was to analyse factors that influence stakeholders’ intentions to adopt pharmacogenomic testing in Malaysia. A validated instrument was administered through face-to-face interviews with a total of 421 adult respondents who were stratified according to 2 stakeholder groups: healthcare providers (n = 221) and patients/family members (n = 200). The data were then analysed using SPSS® version 24 software and the advanced multivariate statistical approach of Partial Least Square (PLS) path modelling in order to analyse the complex relationships among variables. Results of the studies indicated that the Malaysian stakeholders had a high amount of trust in the key players (mean score of 5.31), perceived high benefits (mean score of 5.53) and claimed to have high intentions of adopting PGx (mean score of 5.39). The majority of the predictors have significant direct relationships with the intention to adopt PGx, with the exception of moral concerns. Perceived benefits appeared to be the most important direct predictor of the intention to adopt PGx testing (ß = 0.371, P < 0.001) followed by trust in the key players (ß = 0.312, P < 0.001), engagement (ß = 0.272, P < 0.001) and religiosity (ß = 0.133, P < 0.01). In addition, perceived risks also had a direct negative association with the intention to adopt PGx (ß = −0.096, P < 0.05). At the same time, the perceived benefits also served as a mediator for all the other factors except risk. The results provide insights into the multidimensional nature of the determinants of the intention to adopt PGx testing in Malaysia. Although the results showed that the stakeholders in Malaysia were very positive towards PGx testing, they were also cautious about it. The predictors identified in this study can serve as indicators for social acceptance of PGx testing to facilitate the clinical research and implementation of PGx.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Research framework of stakeholders’ intention and its predicting factors to adopt pharmacogenomic.
Fig. 2: Structural equation model of factors influencing stakeholders’ intention to adopt Pharmacogenomics (PGx) showing interrelationships among variables.

Similar content being viewed by others

References

  1. Almomani B, Hawwa AF, Goodfellow NA, Millership JS, McElnay JC. Pharmacogenetics and the print media: What is the public told? BMC Med Genet. 2015;16:1–10.

    CAS  Google Scholar 

  2. Balasopoulou A, Mooy F-M, Baker DJ, Mitropoulou C, Skoufas E, Bulgiba A, et al. Advancing global precision medicine: an overview of genomic testing and counseling services in Malaysia. Omi J Integr Biol. 2017;21:733–40.

    CAS  Google Scholar 

  3. Sudia J. Exploring Barriers to the Adoption of Pharmacogenomic Technology in the Clinical Setting by Clinical Healthcare Providers. 2016. https://scholarship.shu.edu/cgi/viewcontent.dissertations.

  4. Wolf CR, Smith G, Smith RL. Pharmacogenetics. BMJ. 2000;320:987–90.

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Hudson J, Orviska M. European attitudes to gene therapy and pharmacogenetics. Drug Discov Today. 2011;16:843–7.

    PubMed  Google Scholar 

  6. Bailey DS, Bondar A, Furness LM. Pharmacogenomics—it’s not just pharmacogenetics. Curr Opin Biotechnol. 1998;9:595–601.

    CAS  PubMed  Google Scholar 

  7. Crews KR, Hicks JK, Pui CH, Relling MV, Evans WE. Pharmacogenomics and individualized medicine: translating science into practice. Clin Pharmacol Ther. 2012;92:467–75.

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Lemke AA, Hulick PJ, Wake DT, Wang C, Sereika AW, Yu KD, et al. Patient perspectives following pharmacogenomics results disclosure in an integrated health system. Pharmacogenomics 2018;19:321–31.

    CAS  PubMed  Google Scholar 

  9. Evans WE, Relling MV. Moving towards individualized medicine with pharmacogenomics. Nature 2004;429:464.

    CAS  PubMed  Google Scholar 

  10. Cuffe S, Hon H, Qiu X, Tobros K, Wong CKA, De Souza B, et al. Cancer patients acceptance, understanding, and willingness-to-pay for pharmacogenomic testing. Pharmacogenet Genom. 2014;24:348–55.

    CAS  Google Scholar 

  11. Lachance K, Korol S, O’meara E, Ducharme A, Racine N, Liszkowski M, et al. Opinions, hopes and concerns regarding pharmacogenomics: a comparison of healthy individuals, heart failure patients and heart transplant recipients. Pharmacogenom J. 2015;15:13.

    CAS  Google Scholar 

  12. Trinidad SB, Coffin TB, Fullerton SM, Ralston J, Jarvik GP, Larson EB. “Getting off the Bus Closer to Your Destination”: patients’ views about pharmacogenetic testing. Perm J. 2015;19:31.

    Google Scholar 

  13. Daud ANA, Bergsma EL, Bergman JEH, De Walle HEK, Kerstjens-Frederikse WS, Bijker BJ, et al. Knowledge and attitude regarding pharmacogenetics among formerly pregnant women in the Netherlands and their interest in pharmacogenetic research. BMC Pregnancy Childbirth. 2017;17:120.

    PubMed  PubMed Central  Google Scholar 

  14. Chang MT, McCarthy JJ, Shin J. Clinical application of pharmacogenetics: focusing on practical issues. Pharmacogenomics. 2015;16:1733–41.

    CAS  PubMed  Google Scholar 

  15. Luzum JA, Pakyz RE, Elsey AR, Haidar CE, Peterson JF, Whirl-Carrillo M, et al. The pharmacogenomics research network translational pharmacogenetics program: outcomes and metrics of pharmacogenetic implementations across diverse healthcare systems. Clin Pharmacol Ther. 2017;102:502–10.

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Bank PC, Swen JJ, Guchelaar HJ. A nationwide cross-sectional survey of pharmacy students on pharmacogenetic testing in The Netherlands. Pharmacogenomics. 2018;19:311–9.

    CAS  PubMed  Google Scholar 

  17. Just KS, Steffens M, Swen JJ, Patrinos GP, Guchelaar HJ, Stingl JC. Medical education in pharmacogenomics—results from a survey on pharmacogenetic knowledge in healthcare professionals within the European pharmacogenomics clinical implementation project Ubiquitous Pharmacogenomics (U-PGx). Eur J Clin Pharmacol. 2017;73:1247–52.

    PubMed  PubMed Central  Google Scholar 

  18. Amin L, Azad MAK, Ahmad Azlan NA, Zulkifli F. Factors influencing stakeholders’ attitudes toward cross-kingdom gene transfer in rice. New Genet Soc. 2014;33:370–99.

    Google Scholar 

  19. Amin L, Hashim H, Mahadi Z, Che Ngah A, Ismail K. Determinants of stakeholders’ attitudes to xenotransplantation. Xenotransplantation. 2018;22:e12430.

    Google Scholar 

  20. Bredahl L. Determinants of consumer attitudes and purchase intentions with regard to genetically modified foods—results of a cross-national survey. J Consum Policy. 2001;1:23–61.

    Google Scholar 

  21. Fishbein M, Ajzen I. Belief, attitude, intention, and behavior: an introduction to theory and research. Philos Rhetoric. 1975;10:130–2.

    Google Scholar 

  22. Master Z, Resnik DB. Hype and public trust in science. Sci Eng Ethics. 2013;19:321–35.

    PubMed  Google Scholar 

  23. Amin L, Hashim H, Mahadi Z, Ibrahim M, Ismail K. Determinants of stakeholders’ attitudes towards biodiesel. Biotechnol Biofuels. 2017;10:219.

    PubMed  PubMed Central  Google Scholar 

  24. Gaskell G, Allum N, Stares S. Europeans and biotechnology in 2002. Eurobarometer 58.0. A report to the EC Directorate General for research from the project “Life Sciences in European Society” QLG7-CT-1999-00286. 2nd ed. Brussel: European Commission; 2003.

  25. Pardo R, Midden C, Miller JD. Attitudes toward biotechnology in the European Union. J Biotechnol. 2002;98:9–24.

    CAS  PubMed  Google Scholar 

  26. Stilgoe J, Lock SJ, Wilsdon J. Why should we promote public engagement with science? Public Underst Sci. 2014;23:4–15.

    PubMed  PubMed Central  Google Scholar 

  27. Samuel GN, Farsides B. Genomics England’s implementation of its public engagement strategy: Blurred boundaries between engagement for the United Kingdom’s 100,000 Genomes project and the need for public support. Public Underst Sci. 2018;27:352–64.

    PubMed  Google Scholar 

  28. Frewer LJ, Howard C, Hedderley D, Shepherd R. What determines trust in information about food-related risks? Underlying psychological constructs. Risk Anal. 1996;16:473–86.

    CAS  PubMed  Google Scholar 

  29. Hansen J, Holm L, Frewer L, Robinson P, Sandøe P. Beyond the knowledge deficit: recent research into lay and expert attitudes to food risks. Appetite. 2003;4:111–21.

    Google Scholar 

  30. Horst M, Kuttschreuter M, Gutteling JM. Perceived usefulness, personal experiences, risk perception and trust as determinants of adoption of e-government services in The Netherlands. Comput Hum Behav. 2007;23:1838–52.

    Google Scholar 

  31. Slovic P. The perception of risk: risk, society and policy. London (UK): Earthscan; 2000. p. 473.

  32. Trumbo CW, McComas KA. The function of credibility in information processing for risk perception. Risk Anal. 2003;23:343–53.

    PubMed  Google Scholar 

  33. Pin RR. Perceptions of nutrigenomics: affect, cognition & behavioral intention. Enschede: University of Twente; 2009. https://doi.org/10.3990/1.9789036528207.

  34. Stewart-Knox B, Kuznesof S, Robinson J, Rankin A, Orr K, Duffy M, et al. Factors influencing European consumer uptake of personalised nutrition. Results of a qualitative analysis. Appetite. 2013;66:67–74.

    PubMed  Google Scholar 

  35. Frewer LJ, Bergmann K, Brennan M, Lion R, Meertens R, Rowe G, et al. Consumer response to novel agri-food technologies: implications for predicting consumer acceptance of emerging food technologies. Trends Food Sci Technol. 2011;22:442–56.

    CAS  Google Scholar 

  36. Frewer LJ. Consumer acceptance and rejection of emerging agrifood technologies and their applications. Eur Rev Agric Econ. 2017;44:683–704.

    Google Scholar 

  37. Ghasemi S, Karami E, Azadi H. Knowledge, attitudes and behavioral intentions of agricultural professionals toward genetically modified (GM) foods: a case study in Southwest Iran. Sci Eng Ethics. 2013;19:1201–27.

    PubMed  Google Scholar 

  38. Amin L, Hashim H, Mahadi Z, Ismail K. Determinants of the willingness to participate in biobanking among Malaysian stakeholders in the Klang Valley. BMC Med Res Methodol. 2018;18:163.

    PubMed  PubMed Central  Google Scholar 

  39. Ateeq-ur-Rehman, Shabbir MS. The relationship between religiosity and new product adoption. J Islam Mark. 2010;1:63–9.

    Google Scholar 

  40. Chen MF, Li HL. The consumer’s attitude toward genetically modified foods in Taiwan. Food Qual Prefer. 2007;18:662–74.

    Google Scholar 

  41. Amin L, Ahmad J, Jahi J, Nor AR, Osman M, Mahadi NM. Factors influencing malaysian public attitudes to agro-biotechnology. Public Underst Sci. 2011;20:674–89.

    PubMed  Google Scholar 

  42. The Canadian Trade Commissioner Service. Welcome kit—Malaysia. 2016; 2019. http://www.infoexport.gc.ca/en.

  43. Gaskell G, Allum N, Wagner W, Kronberger N, Torgersen H, Hampel J, et al. GM foods and the misperception of risk perception. Risk Anal. 2004;24:185–94.

    PubMed  Google Scholar 

  44. Ronteltap A, van Trijp JCM, Renes RJ, Frewer LJ. Consumer acceptance of technology-based food innovations: lessons for the future of nutrigenomics. Appetite. 2007;49:1–7.

    CAS  PubMed  Google Scholar 

  45. Verbeke W, Frewer LJ, Scholderer J, De Brabander HF. Why consumers behave as they do with respect to food safety and risk information. Anal Chim Acta. 2007;586:2–7.

    CAS  PubMed  Google Scholar 

  46. Poínhos R, Van Der Lans IA, Rankin A, Fischer ARH, Bunting B, Kuznesof S, et al. Psychological determinants of consumer acceptance of personalised nutrition in 9 European countries. PLoS ONE. 2014;9:e110614.

    PubMed  PubMed Central  Google Scholar 

  47. Rankin A, Bunting BP, Poínhos R, van der Lans IA, Fischer AR, Kuznesof S, et al. Food choice motives, attitude towards and intention to adopt personalised nutrition. Public Health Nutr. 2018;14:2606–16.

    Google Scholar 

  48. Raats MM, Shepherd R, Sparks P. Including moral dimensions of choice within the structure of the theory of planned behavior. J Appl Soc Psychol. 1995;25:484–94.

    Google Scholar 

  49. Sparks P, Shepherd R. The role of moral judgments within expectancy-value-based attitude-behavior models. Ethics Behav. 2002;12:299–321.

    Google Scholar 

  50. Sparks P, Shepherd R, Frewer LJ. Assessing and structuring attitudes toward the use of gene technology in food production: the role of perceived ethical obligation. Basic Appl Soc Psychol. 1995;16:267–85.

    Google Scholar 

  51. Dean M, Raats MM, Shepherd R. Moral concerns and consumer choice of fresh and processed organic foods. J Appl Soc Psychol. 2008;38:2088–107.

    Google Scholar 

  52. Gaskell G, Allum N, Bauer M, Durant J, Allansdottir A, Bonfadelli H, et al. Biotechnology and the European public. Nat Biotechnol 2000;18:935.

    CAS  PubMed  Google Scholar 

  53. Amin L, Jahi JM, Md. AR, Osman NM, Mahadi NM. Uncovering factors influencing Malaysian public attitude towards modern biotechnology. Asia-Pac J Mol Biol Biotechnol. 2006;14:33–9.

    Google Scholar 

  54. Bentler PM, Chou CP. Practical issues in structural modeling. Socio Methods Res. 1987;16:78–117.

    Google Scholar 

  55. Kline RB. Principles and practice of structural equation modeling: New York: Guilford Press Google Scholar; 2011.

  56. Bandalos DL. Relative performance of categorical diagonally weighted least squares and robust maximum likelihood estimation. Struct Equ Model. 2014;21:102–16.

    Google Scholar 

  57. Kelley J. Public perceptions of genetic engineering: Australia, 1994. Canberra: Department of Industry, Science and Technology; 1995.

  58. Kirk SFL, Greenwood D, Cade JE, Pearman AD. Public perception of a range of potential food risks in the United Kingdom. Appetite. 2002;38:189–97.

    PubMed  Google Scholar 

  59. Rohrmann B. Risk perception of different societal groups: Australian findings and crossnational comparisons. Aust J Psychol. 1994;46:150–63.

    Google Scholar 

  60. Hair JF, Black WC, Babin BJ, Anderson RE. Multivariate Data Analysis. 7th ed. New York, NY, USA: Pearson; 2009.

  61. Heale R, Twycross A. Validity and reliability in quantitative studies. Evid-Based Nurs. 2015;18:66–67.

    PubMed  Google Scholar 

  62. Akter S, D’Ambra J, Ray P. Trustworthiness in mHealth information services: an assessment of a hierarchical model with mediating and moderating effects using partial least squares (PLS). J Am Soc Inf Sci Technol. 2011;62:100–16.

    Google Scholar 

  63. Memon AH, Rahman IA. Analysis of cost overrun factors for small scale construction projects in Malaysia using PLS-SEM method. Mod Appl Sci. 2013;7:78.

    Google Scholar 

  64. Tenenhaus M, Vinzi VE, Chatelin YM, Lauro C. PLS path modeling. Comput Stat Data Anal. 2005;48:159–205.

    Google Scholar 

  65. Chin WW. How to write up and report PLS analyses. In: Esposito Vinzi, V., Chin, W.W., Henseler, J., Wang, H., Eds, Handbook of Partial Least Squares: Concepts, Methods and Applications. Heidelberg, Dordrecht, London, New York: Springer; 2010. pp. 655-690. https://doi.org/10.1007/978-3-540-32827-8_29.

  66. Hu LT, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct Equ Model. 1999;6:1–55.

    Google Scholar 

  67. Haga SB, O’Daniel JM, Tindall GM, Lipkus IR, Agans R. Survey of US public attitudes toward pharmacogenetic testing. Pharmacogenomics J. 2012;12:197.

    CAS  PubMed  Google Scholar 

  68. Allum N, Sibley E, Sturgis P, Stoneman P. Religious beliefs, knowledge about science and attitudes towards medical genetics. Public Underst Sci. 2014;23:833–49.

    PubMed  Google Scholar 

  69. Mustapa MAC, Amin L, Mahadi Z, Razman MR. Malaysian stakeholders’ intention to adopt genetic testing. Acad Strateg Manag J. 2019;18:1–5.

    Google Scholar 

  70. Haga SB, Barry WT, Mills R, Ginsburg GS, Svetkey L, Sullivan J, et al. Public knowledge of and attitudes toward genetics and genetic testing. Genet Test Mol Biomark. 2013;17:327–35.

    Google Scholar 

  71. Gaskell G, Allansdottir A, Allum N, Castro P, Esmer Y, Fischler C, et al. The 2010 Eurobarometer on the life sciences. Nat Biotechnol. 2011;29:113.

    CAS  PubMed  Google Scholar 

  72. Vallée Marcotte B, Cormier H, Garneau V, Robitaille J, Desroches S, Vohl MC. Nutrigenetic testing for personalized nutrition: an evaluation of public perceptions, attitudes, and concerns in a Population of French Canadians. Lifestyle Genom. 2019;11:155–62.

    Google Scholar 

  73. Fallaize R, Macready AL, Butler LT, Ellis JA, Lovegrove JA. An insight into the public acceptance of nutrigenomic-based personalised nutrition. Nutr Res Rev. 2013;26:39–48.

    CAS  PubMed  Google Scholar 

  74. Rogausch A, Prause D, Schallengerb A, Brockmöller J, Himmel W. Patients’ and physicians’ perspectives on pharmacogenetic testing. Pharmacogenomics. 2006;7:49–59.

    PubMed  Google Scholar 

  75. Siegrist M. The influence of trust and perceptions of risks and benefits on the acceptance of gene technology. Risk Anal. 2000;20:195–204.

    CAS  PubMed  Google Scholar 

  76. Frewer LJ, Scholderer J, Bredahl L. Communicating about the risks and benefits of genetically modified foods: the mediating role of trust. Risk Anal. 2003;23:1117–33.

    PubMed  Google Scholar 

  77. Gutteling J, Haussen L, Van Der Veer N, Seydel E. Trust in governance and the acceptance of genetically modified food in the Netherlands. Public Underst Sci. 2006;15:103–12.

    Google Scholar 

  78. Hossain F, Onyango B. Product attributes and consumer acceptance of nutritionally enhanced genetically modified foods. Int J Consum Stud. 2004;28:255–67.

    Google Scholar 

  79. Tanaka Y. Major psychological factors affecting acceptance of gene-recombination technology. Risk Anal. 2004;24:1575–83.

    PubMed  Google Scholar 

  80. Ho SS, Brossard D, Scheufele DA. Effects of value predispositions, mass media use, and knowledge on public attitudes toward embryonic stem cell research. Int J Public Opin Res. 2008;20:171–92.

    Google Scholar 

  81. Chatters LM. Religion and health: public health research and practice. Annu Rev Public Health. 2000;21:335–67.

    CAS  PubMed  Google Scholar 

  82. Wallace JM, Forman TA. Religion’s role in promoting health and reducing risk among American youth. Heal Educ Behav. 1998;25:721–41.

    Google Scholar 

  83. Alves RR, da N, Alves H, da N, Barboza RRD, Souto W, de MS. Influência da religiosidade na saúde. Cienc e Saude Coletiva. 2010;15:2105–11.

    Google Scholar 

  84. Department of Statistics Malaysia. Current Population Estimates, Malaysia, 2018–2019; 2020. https://www.dosm.gov.my/v1/index.php.

  85. Jamal R. Precision medicine: is Malaysia ready? Asia-Pacific. J Mol Med. 2017;7:1–4.

    CAS  Google Scholar 

  86. RinggitPlus. Best health insurance for cancer in Malaysia; 2020. https://ringgitplus.com/en/health-insurance.

Download references

Acknowledgements

The authors would like to thank all the respondents who took part in this study. The data collection was funded by Universiti Kebangsaan Malaysia under the project STEM-2014-005, while data analysis, writing and publication of the paper were funded by Universiti Kebangsaan Malaysia under the project DCP-2017-005/2.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Latifah Amin.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This study has been approved by the Medical Research and Ethics Committee (MREC), Ministry of Health Malaysia (MOH), reference number (5)KKM/NIHSEC/P17-1382.

Informed consent

Written informed consent was given before the respondents answered the questionnaires and this was recorded by the enumerators.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mustapa, M.A.C., Amin, L. & Mahadi, Z. Determinants of stakeholders’ intention to adopt pharmacogenomic. Pharmacogenomics J 20, 801–812 (2020). https://doi.org/10.1038/s41397-020-0167-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41397-020-0167-0

This article is cited by

Search

Quick links