Perceived risk and anxiety in online transaction intention
DOI:
https://doi.org/10.22478/ufpb.2238-104X.2024v14n1.66412Keywords:
Online transaction, perceive risk, use intention, online transaction intention, anxiety, consumer behaviorAbstract
Purpose: This study aims to develop a model to explain how perceived risk and anxiety affect the intention to make transactions online. Methods: We applied an online survey consisting of 285 valid respondents. The data were analyzed with structural equation modeling using Smart-PLS 3.2.8 with the application of PLS-PM model through Confirmatory Factor Analysis. Findings: Results demonstrate that social, physical, and performance risks were not statistically significant to explain the intention to conduct transactions online, meaning only that time risk, anxiety and psychological risk are statistically significant influences. Theoretical contributions: Although the literature demonstrates that technological anxiety can catalyze perceived risks, this research found that it did not strongly affect the analyzed consumer sample. Thus, respondents positively feel safer when doing financial online transactions. This research advances discussions on consumers performing an online transaction facing emotional, physical, and financial risks. Practical implication: This study shows that consumer behavior has been less affected by emotional factors, such as anxiety and risk perception, arising from the use of digital technologies to carry out online transactions. A relevant aspect to be explored by managers and decision makers is to further explore the convenience factor in their actions, which will further reduce the effects of risk perception and anxiety in carrying out online transactions. Because if they know how the consumers behave during an online transaction, they can improve the actions to favor a successful transaction.
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Aboelmaged, M., & Gebba, T. R. (2013). Mobile banking adoption: An examination of technology acceptance model and theory of planned behavior. International Journal of Business Research and Development, 2(1), 35-50. https://doi.org/10.24102/ijbrd.v2i1.263
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1080/08870446.2011.613995
Alcántara-Pilar, J. M., Blanco-Encomienda, F. J., Armenski, T., & Del Barrio-García, S. (2018). The antecedent role of online satisfaction, perceived risk online, and perceived website usability on the affect towards travel destinations. Journal of Destination Marketing & Management, 9, 20-35. https://doi.org/10.1016/j.jdmm.2017.09.005
Alloulbi, A., Öz, T., & Alzubi, A. (2022). The use of artificial intelligence for smart decision-making in smart cities: A moderated mediated model of technology anxiety and internal threats of IoT. Mathematical Problems in Engineering, 1–12. https://doi.org/10.1155/2022/6707431
Amirtha, R., Sivakumar, V. J., & Yujong Hwang. (2021). Influence of perceived risk dimensions on e-shopping behavioural intention among women: A family life cycle stage perspective. Journal of Theoretical & Applied Electronic Commerce Research, 16(3), 320–355. https://doi.org/10.3390/jtaer16030022
AlSoufi, A., & Ali, H. (2014). Customers perception of mbanking adoption in Kingdom of Bahrain: An empirical assessment of an extended tam model. International Journal of Managing Information Technology, 6(1), 1-12.
https://doi.org/10.5121/ijmit.2014.6401
Bagozzi, R. P., & Warshaw, P. R. (1990). Trying to consume. Journal of Consumer Research, 17(2), 127-140. https://doi.org/10.1086/208543
Banerjee, S., & Vidyasagar, T. J. (2021). Scale development for analyzing impact of various risk dimensions on online shopping intensions amongst Indian youth. International Management Review, 17, 29–39.
Bauer, R. A. (1960). Consumer behavior as risk taking. In R. S. Hancock (Ed.), Dynamic marketing for a changing world. Chicago: American Marketing Association, 389-398.
Belkhamza, Z., & Wafa, S. K. W. S. A. (2006). Evaluation of the effect of perceived system risk on the intention to use e-commerce: An empirical study on tourism organization. Proceedings of Le 3ième Ecole Informatique de Printemps–EIP, 6, 189-198.
Bensaou, M., & Venkatraman, N. (1996). Inter-organizational relationships and information technology: A conceptual synthesis and a research framework. European Journal of Information Systems, 5(2), 84-91. https://doi.org/10.1057/ejis.1996.15
Brown, S. A., Fuller, R., & Vician, C. (2004). Who’s afraid of the virtual world? The role of anxiety in computer-mediated communication use and satisfaction. Journal of the Association for Information Systems, 5(2), 81–109. https://doi.org/10.17705/1jais.00046
Capgemini. (2022). World Payments Report 2022. Capgemini. https://www.capgemini.com/insights/research-library/world-payments-report/
Carvache-Franco, O., Loaiza-Torres, J., Soto-Montenegro, C., Carvache-Franco, M., & Carvache-Franco, W. (2022). The risks perceived by the consumer in the acceptance of electronic commerce. A study of Bolivia. PLoS ONE, 17(11), 1–15. https://doi.org/10.1371/journal.pone.0276853
Celik, H. (2011). Influence of social norms, perceived playfulness and online shopping anxiety on customers' adoption of online retail shopping: An empirical study in the Turkish context. International Journal of Retail & Distribution Management, 39(6), 390-413. https://doi.org/10.1108/09590551111137967
Celik, H. (2016). Customer online shopping anxiety within the Unified Theory of Acceptance and Use Technology (UTAUT) framework. Asia Pacific Journal of Marketing and Logistics, 28(2), 278-307. https://doi.org/10.1108/APJML-05-2015-0077
Chatelin, Y. M., Vinzi, V. E., & Tenenhaus, M. (2002). State-of-art on PLS Path Modeling Through the Available Software. Groupe HEC.
Chen, R., & He, F. (2003). Examination of brand knowledge, perceived risk and consumers' intention to adopt an online retailer. Total Quality Management & Business Excellence, 14(6), 677-693. https://doi.org/10.1080/1478336032000053825
Cherry, J., & Fraedrich, J. (2002). Perceived risk, moral philosophy and marketing ethics: mediating influences on sales managers' ethical decision-making. Journal of Business Research, 55(12), 951-962. https://doi.org/10.1016/S0148-2963(00)00215-0
Chin, W. W., Henseler, J., & Wang, H. (2016). Handbook of Partial Least Squares: Concepts, Methods and Applications (V. E. Vinzi, org.). S.l.: Springer.
Chiu, C. M., & Wang, E. T. (2008). Understanding Web-based learning continuance intention: The role of subjective task value. Information & Management, 45(3), 194-201. https://doi.org/10.1016/j.im.2008.02.003
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2 edition). Hillsdale, N.J: Routledge.
Compeau, D., Higgins, C. A., & Huff, S. (1999). Social cognitive theory and individual reactions to computing technology: A longitudinal study. MIS Quarterly, 145-158. https://doi.org/10.2307/249749
Cox, D. F., & Rich, S. V. (1964). Perceived risk and consumer decision making: The case of telephone shopping. Journal of Marketing Research, 1, 32-9. https://doi.org/10.1177/002224376400100405
Cox, D. F. (1967), Risk handling in consumer behavior: An intensive study of two cases, In: Cox, P. F. (Ed.), Risk taking and information handling in consumer behavior, graduate school of business administration, Harvard University, Boston, 34-81.
Creswell, J. W. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.
Cunningham, S. M. (1967). The major dimensions of perceived risk. In Cox, D. F. (Ed.) Risk taking and information handling in consumer behavior. Cambridge, Mass: Harvard University Press, 82-108.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-341. https://doi.org/10.2307/249008
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003. https://doi.org/10.1287/mnsc.35.8.982
Delloite. (2022). Pesquisa FEBRABAN de Tecnologia Bancária 2022 (Volume 3: Transações bancárias, 3, p. 24). Febraban.
DeVellis, R. F. (2016). Scale development: Theory and applications. Sage publications.
Dimitriadis, S., & Kyrezis, N. (2010). Linking trust to use intention for technology‐enabled bank channels: The role of trusting intentions. Psychology & Marketing, 27(8), 799-820. https://doi.org/10.1002/mar.20358
Dowling, G. R., & Staelin, R. (1994). A model of perceived risk and intended risk-handling activity. Journal of Consumer Research, 21(1), 119-134. https://doi.org/10.1086/209386
Eastin, M. S. (2002). Diffusion of e-commerce: An analysis of the adoption of four e-commerce activities. Telematics and Informatics, 19(3), 251-267. https://doi.org/10.1016/S0736-5853(01)00005-3
Ebrahimi, P., Khajeheian, D., Soleimani, M., Gholampour, A., & Fekete-Farkas, M. (2022). User engagement in social network platforms: What key strategic factors determine online consumer purchase behaviour? Economic Research-Ekonomska Istrazivanja, 1–32. https://doi.org/10.1080/1331677x.2022.2106264
Engel, J. F., Blackwell, R. D., & Miniard, P. W. (1990). Customer behavior. Hinsdale, IL: Dryden.
Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451-474. https://doi.org/10.1016/S1071-5819(03)00111-3
Fishbein, M., & Ajzen, I. (1975), Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
Forsythe, S. M., & Shi, B. (2003). Consumer patronage and risk perceptions in internet shopping. Journal of Business Research, 56(11), 867-875. https://doi.org/10.1016/S0148-2963(01)00273-9
Foucault, B. E., & Scheufele, D. A. (2002). Web vs campus store? Why students buy textbooks online. Journal of Consumer Marketing, 19(5), 409-423. https://doi.org/10.1108/07363760210437632
Fowler, F. J. (2013). Survey research methods. Sage publications.
Frik, A., & Mittone, L. (2019). Factors influencing the perception of website privacy trustworthiness and users’ purchasing intentions: The behavioral economics perspective. Journal of Theoretical and Applied Electronic Commerce Research, 14(3), 89-125. https://doi.org/10.4067/S0718-18762019000300107
Garson, G. D. (2016). Partial Least Squares: Regression and structural equation models. Statistical Associates Publishers, Asheboro.
Hair, J., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (2 edition). Los Angeles: SAGE Publications, Inc.
Hasan, B., & Ahmed, M. U. (2010). A path analysis of the impact of application-specific perceptions of computer self-efficacy and anxiety on technology acceptance. Journal of Organizational and End User Computing, 22(3), 82-95. https://doi.org/10.4018/joeuc.2010070105
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Henseler, J., Ringle, C.M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In: New challenges to international marketing (pp. 277-319). Emerald Group Publishing Limited.
Ho, S. S., & Ng, V. T. (1994). Customers′ risk perceptions of electronic payment systems. International Journal of Bank Marketing, 12(8), 26-38. https://doi.org/10.1108/02652329410069029
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling. A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
Hubert, M., Blut, M., Brock, C., Zhang, R. W., Koch, V., & Riedl, R. (2018). The influence of acceptance and adoption drivers on smart home usage. European Journal of Marketing. 53(6), 1073-1098. https://doi.org/10.1108/EJM-12-2016-0794
Igbaria, M., & Iivari, J. (1995). The effects of self-efficacy on computer usage. Omega, 23(6), 587-605. https://doi.org/10.1016/0305-0483(95)00035-6
Jacoby, J., & Kaplan L.B. (1972). The components of perceived risk. Association for Consumer Research Special Volumes, 382-393.
Jarvenpaa, S. L., Tractinsky, N., & Saarinen, L. (1999). Consumer trust in an internet store: A cross-cultural validation. Journal of Computer-Mediated Communication, 5(2), JCMC526. https://doi.org/10.1111/j.1083-6101.1999.tb00337.x
Kaplan, L. B., Syzbillo, G. J., & Jacoby, J. (1974). Components of perceived risk in product purchase: A cross-validation. Journal of Applied Psychology, 59(3), 287–291. https://psycnet.apa.org/doi/10.1037/h0036657
Khoa, B. T., & Huynh, T. T. (2022). How do customer anxiety levels impact relationship marketing in electronic commerce? Cogent Business & Management, 9(1), 1–19. https://doi.org/10.1080/23311975.2022.2136928
Kim, A. J., & Ko, E. (2012). Do social media marketing activities enhance customer equity? An empirical study of luxury fashion brand. Journal of Business Research, 65(10), 1480-1486. https://doi.org/10.1016/j.jbusres.2011.10.014
Klopping, I. M., & McKinney, E. (2004). Extending the technology acceptance model and the task-technology fit model to consumer e-commerce. Information Technology, Learning & Performance Journal, 22(1).
Koksal, M. H. (2016). The intentions of Lebanese consumers to adopt mobile banking. International Journal of Bank Marketing, 34(3), 327-346. https://doi.org/10.1108/IJBM-03-2015-0025
Kovacs, M. H., & Farias, S. A. (2004). Dimensões de riscos percebido nas compras pela internet. RAE-eletrônica, 3(2), 1-18. https://doi.org/10.1590/S1676-56482004000200013
Kumar, A., Lee, H. J., & Kim, Y. K. (2009). Indian consumers' purchase intention toward a United States versus local brand. Journal of Business Research, 62(5), 521-527. https://doi.org/10.1016/j.jbusres.2008.06.018
Lim, H., & Dubinsky, A. J. (2005). The theory of planned behavior in e‐commerce: Making a case for interdependencies between salient beliefs. Psychology & Marketing, 22(10), 833-855. https://doi.org/10.1002/mar.20086
Littler, D., & Melanthiou, D. (2006). Consumer perceptions of risk and uncertainty and the implications for behaviour towards innovative retail services: The case of internet banking. Journal of Retailing and Consumer Services, 13(6), 431-443. https://doi.org/10.1016/j.jretconser.2006.02.006
Loewenstein, G. F., Weber, E. U., Hsee, C. K., & Welch, N. (2001). Risk as feelings. Psychological Bulletin, 127(2), 267. https://psycnet.apa.org/doi/10.1037/0033-2909.127.2.267
Malhotra, N. K. (2007). Marketing research an applied orientation. 5Th Ed. Prentice-Hall of India Pvt. Limited.
Mitchell, V. W. (1999). Consumer perceived risk: Conceptualizations and models. European Journal of Marketing, 33 (1/2), 163–195. https://doi.org/10.1108/03090569910249229
Mitchell, V. W., & Greatorex, M. (1993). Risk perception and reduction in the purchase of consumer services. Service Industries Journal, 13(4), 179-200. https://doi.org/10.1080/02642069300000068
Noble, S. M., Haytko, D. L., & Phillips, J. (2009). What drives college-age generation Y consumers? Journal of Business Research, 62(6), 617-628. https://doi.org/10.1016/j.jbusres.2008.01.020
Nuijten, A. L. P., Keil, M., & Zwiers, B. (2023). Internal auditors’ perceptions of information technology-related risks: A comparison between general auditors and information technology auditors. Journal of Information Systems, 37(1), 67–83. https://doi.org/10.2308/ISYS-2020-040
Pande, J. (2019). Cashless transaction–mobile transaction. Available at SSRN 3309289.
Park, J., Ahn, J., Thavisay, T., & Ren, T. (2019). Examining the role of anxiety and social influence in multi-benefits of mobile payment service. Journal of Retailing and Consumer Services, 47, 140-149. https://doi.org/10.1016/j.jretconser.2018.11.015
Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101-134. https://doi.org/10.1080/10864415.2003.11044275
Pavlou, P. A., & Fygenson, M. (2006). Understanding and predicting electronic commerce adoption: An extension of the theory of planned behavior. MIS Quarterly, 115-143. https://doi.org/10.2307/25148720
Pestana, M. H., & Gageiro, J. N. (2013). Análise de dados para as ciências sociais: A Complementariedade do SPSS - 5 ED. Lisboa: ND-EDICOES SILABO.
Peter, J. P., & Tarpey, L. X. (1975). A comparative analysis of three consumer decision strategies. Journal of Consumer Research, 2(1), 29–37. https://doi.org/10.1086/208613
Peter, J. P., & Ryan, M. J. (1976). An investigation of perceived risk at the brand level. Journal of Marketing Research, 13(2), 184-188. https://doi.org/10.1177/002224377601300210
Powell, A. L. (2013). Computer anxiety: Comparison of research from the 1990s and 2000s. Computers in Human Behavior, 29(6), 2337-2381. https://doi.org/10.1016/j.chb.2013.05.012
Quintal, V., Phau, I., Sims, D., & Cheah, I. (2016). Factors influencing generation Y’s purchase intentions of prototypical versus me-too brands. Journal of Retailing and Consumer Services, 30, 175-183. https://doi.org/10.1016/j.jretconser.2016.01.019
Ramos, F. L., Ferreira, J. B., Freitas, A. S. D., & Rodrigues, J. W. (2018). The effect of trust in the intention to use m-banking. BBR. Brazilian Business Review, 15(2), 175-191. https://doi.org/10.15728/bbr.2018.15.2.5
Ring, P. S., & Van De Ven, A. H. (1994). Developmental process of cooperative interorganizational relationships. Academy of Management Review, 19(1), 90-118. https://doi.org/10.5465/amr.1994.9410122009
Ringle, C., Wende, S., & Becker, J. M. (2015). Product | SmartPLS. Retrieved on18 de abril de 2018, from: https://www.smartpls.com/
Ringle, C. M., Da Silva, D., & Bido, D. D. S. (2014). Modelagem de equações estruturais com utilização do SmartPLS. Revista Brasileira de Marketing, 13(2), 56-73. https://doi.org/10.5585/remark.v13i2.2717
Roselius, T. (1971). Consumer ranking of risk reduction methods. Journal of Marketing, 35, 56-61. https://doi.org/10.1177/002224297103500110
Rundmo, T., & Nordfjærn, T. (2017). Does risk perception really exist? Safety science, 93, 230-240. https://doi.org/10.1016/j.ssci.2016.12.014
Russell, G., & Bradley, G. (1997). Teachers' computer anxiety: Implications for professional development. Education and Information Technologies, 2(1), 17-30. https://doi.org/10.1023/A:1018680322904
Saadé, R. G., & Kira, D. (2006). The emotional state of technology acceptance. Issues in Informing Science & Information Technology, 3.
Secchi, J. D., Silva, W. V., Corso, J. M. D., & Tortato, U. (2012). Relações de gênero no comportamento de compra pela Internet: Estudo das dimensões do risco percebido. Revista Sociais e Humanas. 25(2), 253–272. https://periodicos.ufsm.br/sociaisehumanas/article/view/2819
Selltiz, C., Wrightsman, L. S., & Cook, S. W. (2007). Métodos de pesquisa nas relações sociais. Delineamentos De Pesquisa - Volume 1 (Edição: 1, Vol. 1). São Paulo: EPU.
Škerháková, V., Ali Taha, V., Tirpák, D., & Kráľ, Š. (2022). Perception of corporate reputation in the era of digitization: Case study of online shopping behavior on young consumers. Sustainability, 14(21), 14302. https://doi.org/10.3390/su142114302
Slovic, P. (1992). Perception of risk: Reflections on the psychometric paradigm. New York: Praeger
Slovic, P., & Peters, E. (2006). Risk perception and affect. Current Directions in Psychological Science, 15(6), 322-325. https://doi.org/10.1111/j.1467-8721.2006.00461.x
Starr, C. (1969). Social benefit versus technological risk. Science, 1232-1238. https://doi.org/10.1126/science.165.3899.1232
Stone, R. N., & Grønhaug, K. (1993). Perceived risk: Further considerations for the marketing discipline. European Journal of Marketing, 27(3), 39-50. https://doi.org/10.1108/03090569310026637
Sweeney, J. C., Soutar, G. N., & Johnson, L. W. (1999). The role of perceived risk in the quality-value relationship: A study in a retail environment. Journal of Retailing, 75(1), 77-105. https://doi.org/10.1016/S0022-4359(99)80005-0
Tenenhaus, M., Vinzi, V. E., Chatelin, Y. M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159–205. https://doi.org/10.1016/j.csda.2004.03.005
Theodorou, A., Hatzithomas, L., Fotiadis, T., Diamantidis, A., & Gasteratos, A. (2023). The impact of the COVID-19 pandemic on online consumer behavior: Applying the theory of planned behavior. Sustainability, 15(3), 2545. https://doi.org/10.3390/su15032545
Triandis, H. C. (1979). Values, attitudes, and interpersonal behavior. In: Nebraska symposium on motivation. University of Nebraska Press.
Tsai, Y. C., & Yeh, J. C. (2010). Perceived risk of information security and privacy in online shopping: A study of environmentally sustainable products. African Journal of Business Management, 4(18), 4057-4066.
Venkatesh, V., & Davis, F. D. (2000). Theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
Vijayasarathy, L. R. (2004). Predicting consumer intentions to use on-line shopping: The case for an augmented technology acceptance model. Information & Management, 41(6), 747-762. https://doi.org/10.1016/j.im.2003.08.011
Walsh, G., Schaarschmidt, M., & Ivens, S. (2017). Effects of customer-based corporate reputation on perceived risk and relational outcomes: Empirical evidence from gender moderation in fashion retailing. Journal of Product & Brand Management, 26(3), 227-238. https://doi.org/10.1108/JPBM-07-2016-1267
Wilson, M. L., Huggins-Manley, A. C., Ritzhaupt, A. D., & Ruggles, K. (2023). Development of the Abbreviated Technology Anxiety Scale (ATAS). Behavior Research Methods, 55(1), 185–199. https://doi.org/10.3758/s13428-022-01820-9
Wolter, J. S., Bacile, T. J., & Xu, P. (2023). How online incivility affects consumer engagement behavior on brands’ social media. Journal of Service Research, 26(1), 103–119. https://doi.org/10.1177/10946705221096192
Wu, K., Vassileva, J., Noorian, Z., & Zhao, Y. (2015). How do you feel when you see a list of prices? The interplay among price dispersion, perceived risk and initial trust in Chinese C2C market. Journal of Retailing and Consumer Services, 25, 36-46. https://doi.org/10.1016/j.jretconser.2015.03.007
Yang, Q., Pang, C., Liu, L., Yen, D. C., & Tarn, J. M. (2015). Exploring consumer perceived risk and trust for online payments: An empirical study in China’s younger generation. Computers in Human Behavior, 50, 9-24. https://doi.org/10.1016/j.chb.2015.03.058
Yoo, B., Donthu, N., & Lee, S. (2000). An examination of selected marketing mix elements and brand equity. Journal of the Academy of Marketing Science, 28(2), 195-211. https://doi.org/10.1177/0092070300282002
Zhou, M., Chen, G. H., Ferreira, P., & Smith, M. D. (2021). Consumer behavior in the online classroom: Using video analytics and machine learning to understand the consumption of video courseware. Journal of Marketing Research, 58(6), 1079–1100. https://doi.org/10.1177/00222437211042013
Zikmund, W. G., & Scott, J. E. (1974). A multivariate analysis of perceived risk self-confidence and information sources. ACR North American Advances, 1, 406-.416.