Determinants of Intention to Use DevOps in Cambodia’s Technology Industry

Main Article Content

Soksophay LIM
Somsit Duang-Ek-Anong

Abstract

This research investigates the key determinants that impact the intention of developers to use DevOps practice in technology industry, mainly software development, within Phnom Penh, Cambodia. The study was carried out using a quantitative research method to survey 472 software developers, tech entrepreneurs, DevOps practitioners, software project team members, and IT leaders familiar with DevOps practice. The respondents came from software development, technology startup, telecom, internet service providers, financial service institution, technology consulting, and system integrators. The survey employed non-probability sampling method – judgmental, snowball, and convenience sampling. Online Google form was used in the survey from the period January to June 2021. Also, confirmatory factor analysis and structure equation model were used to validate and identify the relationship and the impact of various factors on the intention to use DevOps. Organizational usefulness, personal awareness, and perceived compatibility have significant direct impacts on the intention to use DevOps software development methodology by developers and practitioners. Also, subjective norm and perceived behavioral control internal have the indirect impact on the Intention through organizational usefulness. Moreover, perceived number of users impacts significantly on the perceived availability of complementary services; both indirectly impact the intention to use DevOps through perceived compatibility. Whereas, perceived cost and perceived pisk are not found to have significant impact on intention to use DevOps by the developers.

Downloads

Download data is not yet available.

Article Details

How to Cite
LIM, S., & Duang-Ek-Anong, S. . (2021). Determinants of Intention to Use DevOps in Cambodia’s Technology Industry. AU-GSB E-JOURNAL, 14(2), 27-39. https://doi.org/10.14456/augsbejr.2021.12
Section
Articles

References

Accenture Technology. (2015). DevOps: Innovative engineering practices for continuous software delivery. Accenture Technology.

Agarwal, R. (2000). Individual Acceptance of Information Technologies. In: Framing the Domains of it Management: Projecting the Future. 85–104.

Agarwal, Ritu, & Prasad, J. (1997). The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies. Decision Sciences, 28(3), 557–582. https://doi.org/10.1111/j.1540-5915.1997.tb01322.x

Ainur, A. K., Sayang, M. D., Jannoo, Z., & Yap, B. W. (2017). Sample size and non-normality effects on goodness of fit measures in structural equation models. Pertanika Journal of Science and Technology, 25(2), 575–586. ISSN: 0128-7680.

Ajzen, I. (1988). Attitudes, Personality, and Behavior. Milton-Keynes, England: Open. University Press & Chicago, IL: Dorsey Press.

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-t

Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behaviour (Vol. 278). Prentice-hall Englewood Cliffs, NJ.

Amin, H., Hamid, M. R. A., Lada, S., & Anis, Z. (2008). The adoption of mobile banking in Malaysia: The case of Bank Islam Malaysia Berhad (BIMB). International Journal of Business and Society, 9(2), 43.

Armitage, C. J., Conner, M., Loach, J., & Willetts, D. (1999). Different perceptions of control: Applying an extended theory of planned behavior to legal and illegal drug use. Basic and Applied Social Psychology, 21(4), 301–316. https://doi.org/10.1207/S15324834BASP2104_4

Atlassian. (2020). DevOps: Breaking development and operation barriers. Atlassian.Com. Retrieved December 24, 2019, from https://www.atlassian.com/devops

Baumgartner, H., & Homburg, C. (1996). Applications of structural equation modeling in marketing and consumer research: A review. International Journal of Research in Marketing, 13(2), 139–161. https://doi.org/10.1016/0167-8116(95)00038-0

Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246. https://doi.org/10.1037/0033-2909.107.2.238

Beschorner, N., Neumann, J., Martin, M. E. S., & Larson, B. (2018). Benefiting from the Digital Economy. World Bank. https://doi.org/10.1596/30926

Blunch, N. J. (2017). Introduction to Structural Equation Modeling using IBM SPSS Statistics and AMOS. In Introduction to Structural Equation Modeling using IBM SPSS Statistics and AMOS. SAGE Publications, Ltd. https://doi.org/10.4135/9781526402257

Browne, K. (2005). Snowball sampling: Using social networks to research non-heterosexual women. International Journal of Social Research Methodology: Theory and Practice, 8(1), 47–60. https://doi.org/10.1080/1364557032000081663

Brynjolfsson, E., & Kemerer, C. F. (1996). Network Externalities in Microcomputer Software: An Econometric Analysis of the Spreadsheet Market. Management Science, 42(12), 1627–1647. https://doi.org/10.1287/mnsc.42.12.1627

Byrne, B. M. (2013). Structural equation modeling with AMOS: Basic concepts, applications, and programming, second edition. In Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming, Second Edition. https://doi.org/10.4324/9780203805534

Chen, C. (2013). Perceived risk, usage frequency of mobile banking services. Managing Service Quality: An International Journal, 23(5), 410–436. https://doi.org/10.1108/msq-10-2012-0137

Chen, L. da. (2008). A model of consumer acceptance of mobile payment. International Journal of Mobile Communications, 6(1), 32. https://doi.org/10.1504/ijmc.2008.015997

Chitungo, S. K., & Munongo, S. (2013). Determinants of Farmers’ Decision To Access Credit: the Case of Zimbabwe. Russian Journal of Agricultural and Socio-Economic Sciences, 17(5), 7–12. https://doi.org/10.18551/rjoas.2013-05.02

Clark-Carter, D. (2018). Quantitative psychological research: The complete student’s companion. Routledge.

Comrey, A. L., & Lee, H. B. (2013). A First Course in Factor Analysis. A First Course in Factor Analysis. https://doi.org/10.4324/9781315827506

Cooper, D. R., Schindler, P. S., & Sun, J. (2006). Business Research Methods (11th ed.). Mcgraw-hill New York.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3), 319–339. 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

du Plessis, M. (2007). The role of knowledge management in innovation. Journal of Knowledge Management, 11(4), 20–29. https://doi.org/10.1108/13673270710762684

Ewe, S. Y., Yap, S. F., & Lee, C. K. C. (2015). Network externalities and the perception of innovation characteristics: Mobile banking. Marketing Intelligence and Planning, 33(4), 592–611. https://doi.org/10.1108/MIP-01-2014-0006

Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39. https://doi.org/10.2307/3151312

Forsgren, N., & Humble, J. (2015). The Role of Continuous Delivery in it and Organizational Performance. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2681909

Forsgren, N., Humble, J., & Kim, G. (2018). Accelerate: State of DevOps 2018 Strategies for a New Economy. DevOps Research and Assessment [DORA]. https://cloudplatformonline.com/rs/248-TPC-286/images/DORA-State of DevOps.pdf

Forsgren, N., Smith, D., Humble, J., & Frazelle, J. (2019). 2019 Accelerate State of DevOps Report.

Gray, D. E. (2019). Doing research in the business world. SAGE Publications Limited.

Hair, J. F., & Black, W. C. (2009). Multivariate Data Analysis: Global Edition. Prentice Hall.

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial Least Squares Structural Equation Modeling: Rigorous Applications, Better Results and Higher Acceptance. Long Range Planning, 46(1–2), 1–12. https://doi.org/10.1016/j.lrp.2013.01.001

Hardgrave, B. C., & Johnson, R. A. (2003). Toward an information system development acceptance model: The case of object-oriented systems development. IEEE Transactions on Engineering Management, 50(3), 322–336. https://doi.org/10.1109/TEM.2003.817293

Harrison, D. A., Mykytyn, P. P., & Riemenschneider, C. K. (1997). Executive Decisions About Adoption of Information Technology in Small Business: Theory and Empirical Tests. Information Systems Research, 8(2), 171–195. https://doi.org/10.1287/isre.8.2.171

Hollenbeck, J. R., & Klein, H. J. (1987). Goal commitment and the goal-setting process: Problems, prospects, and proposals for future research. Journal of Applied Psychology, 72(2), 212–220. https://doi.org/10.1037/0021-9010.72.2.212

Hootsuite Social Report. (2019). Digital Cambodia report 2019. Hootsuite Social Report. Retrieved February 01, 2020, from https://www.slideshare.net/DataReportal/digital-2019-cambodia-january-2019-v01

Howard, J. & Moore, W. (1982). Changes in consumer Behavior Over the Product Life Cycle, in Tushman and Moore,. Readings in the Management of Innovation, 128.

Humble, J., & Molesky, J. (2011). Why enterprises must adopt devops to enable continuous delivery. Cutter IT Journal, 24(8), 6–12.

Islam, M. A., Khan, M. A., Ramayah, T., & Hossain, M. M. (2011). The Adoption of Mobile Commerce Service among Employed Mobile Phone Users in Bangladesh: Self-efficacy as a Moderator. International Business Research, 4(2). https://doi.org/10.5539/ibr.v4n2p80

Katz, M. L., & Shapiro, C. (1985). Network externalities, competition, and compatibility. The American Economic Review, 75(3), 424–440.

Katz, M. L., & Shapiro, C. (1986). Technology Adoption in the Presence of Network Externalities. Journal of Political Economy, 94(4), 822–841. https://doi.org/10.1086/261409

Kauffman, R. J., McAndrews, J., & Wang, Y. M. (2000). Opening the “Black Box” of Network Externalities in Network Adoption. Information Systems Research, 11(1), 61–82. https://doi.org/10.1287/isre.11.1.61.11783

Kim, G. (2012). The three ways: The principles underpinning DevOps. IT Revolution Press Blog. Retrieved February 01, 2020, from https://itrevolution.com/the-three-ways-principles-underpinning-devops

Kline, R. B. (2015). Principles and practice of structural equation modeling. Guilford publications.

Leibenstein, H. (1950). Bandwagon, snob, and veblen effects in the theory of consumers’ demand. Quarterly Journal of Economics, 64(2), 183–207. https://doi.org/10.2307/1882692

Liebowitz, S. J., & Margolis, S. E. (1994). Network Externality: An Uncommon Tragedy. Journal of Economic Perspectives, 8(2), 133–150. https://doi.org/10.1257/jep.8.2.133

Lin, C. P., & Bhattacherjee, A. (2008). Elucidating individual intention to use interactive information technologies: The role of network externalities. International Journal of Electronic Commerce, 13(1), 85–108. https://doi.org/10.2753/JEC1086-4415130103

Machogu, A. M., & Okiko, L. (2012). The perception of bank employees towards cost of adoption, risk of innovation, and staff Training’s influence on the adoption of information and communication technology in the Rwandan commercial banks. Journal of Internet Banking and Commerce, 17(2), 1–15.

Mahatanankoon, P., & Vila-Ruiz, J. (2008). Why won’t consumers adopt m-commerce? an exploratory study. Journal of Internet Commerce, 6(4), 113–128. https://doi.org/10.1080/15332860802086367

Masombuka, T., & Mnkandla, E. (2018). A DevOps collaboration culture acceptance model. Proceedings of the Annual Conference of the South African Institute of Computer Scientists and Information Technologists, 279–285. https://doi.org/10.1145/3278681.3278714

Mathieson, K. (1991). Predicting User Intentions: Comparing the Technology Acceptance Model with the Theory of Planned Behavior. Information Systems Research, 2(3), 173–191. https://doi.org/10.1287/isre.2.3.173

Molla, A., & Licker, P. S. (2005). Perceived e-readiness factors in e-commerce adoption: An empirical investigation in a developing country. International Journal of Electronic Commerce, 10(1), 83–110. https://doi.org/10.1080/10864415.2005.11043963

Moore, G. C., & Benbasat, I. (1991). Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation. Information Systems Research, 2(3), 192–222. https://doi.org/10.1287/isre.2.3.192

Naicker, V., & Van Der Merwe, D. B. (2018). Managers’ perception of mobile technology adoption in the Life Insurance industry. Information Technology and People, 31(2), 507–526. https://doi.org/10.1108/ITP-09-2016-0212

Nunnally, J. C. (1967). Psychometric theory. McGraw-Hill New York.

Pikkarainen, T., Pikkarainen, K., Karjaluoto, H., & Pahnila, S. (2004). Consumer acceptance of online banking: An extension of the technology acceptance model. In Internet Research (Vol. 14, Issue 3, pp. 224–235). https://doi.org/10.1108/10662240410542652

Rahman, M. M., & Sloan, T. (2017). User adoption of mobile commerce in Bangladesh: Integrating perceived risk, perceived cost and personal awareness with TAM. The International Technology Management Review, 6(3), 103. https://doi.org/10.2991/itmr.2017.6.3.4

Riemenschneider, C. K., Hardgrave, B. C., & Davis, F. D. (2002). Explaining software developer acceptance of methodologies: A comparison of five theoretical models. IEEE Transactions on Software Engineering, 28(12), 1135–1145. https://doi.org/10.1109/TSE.2002.1158287

Rogers, D. L. (2014). The network is your customer: five strategies to thrive in a digital age. Yale University Press.

Rogers, E. M. (2003). Diffusion of innovations, 5th edn Tampa. FL: Free Press.[Google Scholar].

Royal Government of Cambodia. (2018). Fourth rectangular strategy phase 4. Retrieved April 15, 2020, from http://cnv.org.kh/wp-content/uploads/2012/10/Rectangular-Strategy-Phase-IV-of-the-Royal-Government-of-Cambodia-of-the-Sixth-Legislature-of-the-National-Assembly-2018-2023.pdf

Samadi, M., & Yaghoob-Nejadi, A. (2009). A survey of the effect of consumers’ perceived risk on purchase intention in e-shopping. Business Intelligence Journal, 2(2), 261–275.

Sathye, S., Prasad, B., Sharma, D., Sharma, P., & Sathye, M. (2018). Factors influencing the intention to use of mobile value-added services by women-owned microenterprises in Fiji. The Electronic Journal of Information Systems in Developing Countries, 84(2), e12016. https://doi.org/10.1002/isd2.12016

Sharma, G. P., Verma, R. C., & Pathare, P. (2005). Mathematical modeling of infrared radiation thin layer drying of onion slices. Journal of Food Engineering, 71(3), 282–286.

Song, M., Parry, M. E., & Kawakami, T. (2009). Incorporating Network Externalities into the Technology Acceptance Model. Journal of Product Innovation Management, 26(3), 291–307. https://doi.org/10.1111/j.1540-5885.2009.00659.x

Sripalawat, J., Thongmak, M., & Ngramyarn, A. (2011). M-banking in metropolitan Bangkok and a comparison with other countries. Journal of Computer Information Systems, 51(3), 67–76.

Startup Kingdom. (2019). Cambodia’s Vibrant TechStartup Ecosystem in 2018 (Issue January).

Suoranta, M. (2003). Adoption of mobile banking in Finland (Issue 28). Jyväskylän yliopisto.

Taylor, S., & Todd, P. A. (1995). Understanding information technology usage. In Information Systems Research (Vol. 6, Issue 2, pp. 144–176). https://doi.org/10.1287/isre.6.2.144

Tornatzky, L. G., & Klein, K. J. (1982). Innovation Characteristics and Innovation Adoption-Implementation: a Meta-Analysis of Findings. IEEE Transactions on Engineering Management, EM-29(1), 28–45. https://doi.org/10.1109/TEM.1982.6447463

Venkatesh, V., & Davis, F. D. (2000). A 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. In MIS Quarterly: Management Information Systems (Vol. 27, Issue 3). https://doi.org/10.2307/30036540

Verdegem, P., & Verleye, G. (2009). User-centered E-Government in practice: A comprehensive model for measuring user satisfaction. Government Information Quarterly, 26(3), 487–497. https://doi.org/10.1016/j.giq.2009.03.005

Wei, T. T., Marthandan, G., Chong, A. Y.-L., Ooi, K.-B., & Arumugam, S. (2009). What drives Malaysian m-commerce adoption? An empirical analysis. Industrial Management & Data Systems, 109(3), 370–388. https://doi.org/10.1108/02635570910939399

World Bank. (2019). Exploring the Opportunities for Women-owned SMEs in Cambodia. Exploring the Opportunities for Women-Owned SMEs in Cambodia. https://doi.org/10.1596/32753

World Bank. (2020). Cambodia Country overview. World Bank Website. https://www.worldbank.org/en/country/cambodia/overview

Wu, J.-H., & Wang, S.-C. (2005). What drives mobile commerce? Information & Management, 42(5), 719–729. https://doi.org/10.1016/j.im.2004.07.001

Wu, J. H., & Wang, Y. M. (2006). Measuring KMS success: A respecification of the DeLone and McLean’s model. Information and Management, 43(6), 728–739. https://doi.org/10.1016/j.im.2006.05.002

Zhou, T. (2015). The effect of network externality on mobile social network site continuance. Program, 49(3), 289–304. https://doi.org/10.1108/PROG-10-2014-0078

Most read articles by the same author(s)