Chuyển đổi số trong bối cảnh cách mạng công nghiệp 4.0 - Kỷ yếu hội thảo khoa học quốc tế: Phần 2

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Tiếp nội dung phần 1, cuốn Kỷ yếu hội thảo khoa học quốc tế Chuyển đổi số trong bối cảnh cách mạng công nghiệp 4.0: Phần 2 gồm các nội dung chính như sau: Chuyển đổi số trong lĩnh vực kinh tế, kinh doanh và quản lý; Các vấn đề lý luận, thực tiễn và bài học kinh nghiệm về chuyển đổi số trong bối cảnh cách mạng công nghiệp 4.0. Mời các bạn cùng tham khảo!

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Nội dung Text: Chuyển đổi số trong bối cảnh cách mạng công nghiệp 4.0 - Kỷ yếu hội thảo khoa học quốc tế: Phần 2

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IMPACTS OF DIGITAL TRANSFORMATION ON JOB PERFORMANCE AND INTERMEDIARY ROLES OF HUMAN RESOURCE DEVELOPMENT: A CASE STUDY OF VIETNAM’S COMMERCIAL BANKS Assoc.Prof. Nguyen Thi Bich Loan, Assoc.Prof. Nguyen Thi Minh Nhan Thuongmai University Abstract: Digital transformation has become an inevitable trend of the time and Vietnam’s commercial banks are among Top 10 sectors with digital transformation speed. This research aims to investigate the impacts of digital transformation on job performance via the intermediary roles of human resource development. The research used SEM model to test hypotheses based on the data collected from 225 banking staff in Vietnam. The research findings confirm the intermediary roles of human resource development in the relations between digital transformation and job performance, also indicate that digital transformation has stronger impacts on job performance than on human resource development. The research also provides some proposals to help commercial banks accelerate the digital transformation process so as to develop human resources and raise job performance in the digital environment. Keywords: Digital transformation, Job performance, Human resource development. ẢNH HƯỞNG CHUYỂN ĐỔI SỐ ĐẾN HIỆU SUẤT CÔNG VIỆC VÀ VAI TRÒ TRUNG GIAN CỦA PHÁT TRIỂN NGUỒN NHÂN LỰC: NGHIÊN CỨU TRƯỜNG HỢP CÁC NGÂN HÀNG THƯƠNG MẠI VIỆT NAM Tóm tắt: Chuyển đổi số trở thành xu hướng tất yếu của thời đại và ngân hàng thương mại Việt Nam là một trong số những địa chỉ hiện có tốc độ chuyển đổi số nằm trong Top 10. Mục đích của nghiên cứu là khám phá ảnh hưởng của chuyển đổi số đến hiệu suất công việc thông qua vai trò trung gian của phát triển nguồn nhân lực. Bài báo sử dụng mô hình cấu trúc (SEM) để kiểm định giả thuyết nghiên cứu dựa trên dữ liệu thu thập được từ 225 nhân viên ngân hàng thương mại Việt Nam. Kết quả nghiên cứu xác nhận vai trò trung gian của phát triển nguồn nhân lực trong mối quan hệ giữa chuyển đổi số và hiệu suất công việc, đồng thời cũng cho thấy chuyển đổi số tác động đến hiệu suất công việc mạnh hơn so với tác động đến phát triển nguồn nhân lực. Nghiên cứu còn đưa ra một số khuyến nghị để các ngân hàng thương mại thúc đẩy quá trình chuyển đổi số, qua đó phát triển nguồn nhân lực số và cải thiện hiệu suất công việc trong môi trường số. Từ khóa: Chuyển đổi số; Hiệu suất công việc; Phát triển nguồn nhân lực. 1. Introduction Digital transformation has become a widely-used term in recent time. Digital transformation is a global trend and becomes vital to countries, organizations and enterprises (Knut H. Rolland and Ole Hanseth, 2021). Thanks to digital transformation, the 495
world is witnessing substantial changes and rapid development in the digital competence of human resources, labor productivity, user experience as well as the advent of many new business models. The wave of digital transformation in the banking system is sweeping across the world, Vietnam included. In 2020, the ICT index (indicating the developments in information and communication technology) of the banking sector was 0.5112, ranking 7th. Although digital transformation has been cared of, there exist considerable disparities in the investments of banks in the digital transformation process. For example, the ICT index of BIDV and Agribank were 0.7762 and 0.3844 respectively (MOST, 2021). But regardless of these differences, the impacts of digital transformation on human resource development and job performance in commercial banks are undeniable. Therefore, this research aims to identify the relations between digital transformation and human resource development and job performance. 2. Literature review In the global scale, numerous studies have presented definitions, elements and foundations of digital transformation and its benefits, notably including James Gleick (2006), The Information: A History, a Theory, a Flood; Tanguy Catlin et al. (2017), A Roadmap for a Digital Transformation, McKinsey; Stanford Technology Ventures Program (2005), Geoffrey Moore - Core and Context; Thomas M. Siebel (2019), Digital Transformation. PACE Institute of Management... In his research on the benefits of digital transformation, Betchoo Nirmal Kumar (2016) built up and tested a model to evaluate the positive impacts of digital transformation on human resource development and job performance for different positions which constitute the elements of human resource management. In Vietnam, inheriting from the model of Kumar (2016), Nguyễn Hoàng Nam (2022) conducted a study on 200 staff, executives and managers in human resource sector. His findings indicated that digital transformation has positive impacts on human resource management; the specific impact levels of digital transformation on elements of human resource management including job performance, talent management, human resource management via standardized β are 0.808; 0.599; 0.504 respectively. Also, human resource development also has considerable impact on people’s jobs (Goad, 2002). Therefore, a study on the impacts of digital transformation on job performance with human resource development playing the intermediary role is of great scientific significance. Banking is a sector with rapid digital transformation speed in Vietnam. Digital transformation is considered an important solution for banks to improve user experience as well as supply new services. Therefore, banking staff must become fast digital transformation human resources with adequate qualifications to meet the demand (including knowledge, skills and attitudes to make data-based decisions) to enhance job performance. However, empirical research on the relations between digital transformation and job performance in Vietnam’s commercial banks remains modest. Therefore, it is necessary to have more practical research to help banking leaders understand the importance of digital transformation in human resource development and job performance improvement to gain sustainable competitive advantages. 496
3. Theoretical grounds 3.1. Concepts (i) Digital Transformation The idea of digital transformation was first introduced by Daniel Bell in 1973 in the book titled The Coming of Post-industrial Society, exploring the history of developing the structures of human socio-economic correlations. The changes in these structures have affected the order of the industrial revolution, creating changes which are named “information era”. After that, the world has witnessed the convergence of a set of new technologies including Cloud Computing, Big Data, Artificial Intelligence, Internet of Things. This convergence has created digital transformation (Nguyễn Thị Minh Nhàn, 2022). So far, there have been different definitions of digital transformation but in general, they can be categorized into two main approaches. The first approach is from technical perspective: Digital transformation refers to changes and transformation created on the basis of digital technology; (Nwankpa and Roumani, 2016); is transformation in organization promoted by new technological solutions and trends (Heilig et al., 2017). More specifically, digital transformation is the expansion of modern information technology such as data analysis, mobile computing, social networking or smart embedded devices and the innovation of traditional technology (Chanias, 2017). Therefore, digital transformation is also described as changes created by information technology as a means to automate tasks (Legner et al., 2017) or a process to develop an entity by remarkably changing its features via the combination of information, computers, communication and connection technology (Vial, 2019). The second approach is benefits/consequences of digital transformation: Westerman, G. et al. (2011); Karagiannaki et al. (2017) stated that digital transformation can be understood as using technology to improve efficiency or approaches of enterprises. Digital transformation involves using new digital technology to facilitate business innovations such as raising customer experience, arranging production operations appropriately or creating new business models (Fitzgerald, M. et al., 2013); Reis J. et al., 2018). Taking this approach, Solis et al. (2014) saw digital transformation as restructuring or investing in new technology or business models to draw customers more effectively and enhance their experience when using products and services. In the meantime, Morakanyane et al. (2017) proposed that digital transformation is an evolutionary process which makes use of digital technology to enable business models, operational process and customer experience to create higher values. In this study, we adopt the view that digital transformation is an evolutionary process involving changing perceptions, establishing digital culture, applying digital technology to create radical and comprehensive changes in management and operation modes and create new values. This concept is supported by the approach to digital transformation from organizations. Accordingly, the vitality for an organization and the way to success for an enterprise in the information era are to be based on digital technology in their cultural activities and business models, which stem from the 497
perceptions and commitments to digital transformation of leaders with specific and clear plans (Thomas M. Siebel, 2019). (ii) Human Resource Development Human resource development is a function of human resource management in organizations. Human resource development is a complicated and controversial concept due to its being inter-sectoral and multi-sectoral features (McGuire, 2011). Human resource development has some interactions with other functions of human resource management such as training, organizational development, career development (Bierema and Cseh, 2014; Wang et al., 2017). Besides, human resource development also involves new aspects such as intellectual management, manpower capital, social capital and learning organization (McGoldrick et al., 2002, cited by McGuire, 2011). Upon analyzing 32 definitions of human resource development, Wang et al. (2017) stated that defining this concept according to its functions can hardly provide adequate cover, especially when new functions appear. By generalizing common features from various definitions and emphasizing the nature of human resource development, he provided the following definition: Human resource development is a mechanism of establishing values, beliefs and providing skills for the organization’s members via training activities so as to realize the organization’s goals. The highlight of this definition is, besides mentioning the nature of human resource development, it can reflect the roles of human resource development in the relations with the parent system - the organization. However, it seems more appropriate to use the term “competence” than “skills” in the definition, because “skills” focus more on the abilities to accomplish an action of humans (Cottrell, 2013) while “competence” has a broader scale. “Competence” is the combination of knowledge, skills, attitudes and qualities of a person related to the tasks that he is doing. (iii) Job Performance Job performance is regarded as a broad topic among the research on economics, psychology and management science thanks to its important significance to the development of each organization in particular and the society in general (Ng & Feldman, 2009). Job performance involves laborers fulfilling their set targets (Murphy,1989). The research of Viswesvaran & Ones (2000) defined job performance as the behavior and outcomes gained by the staff which contribute to the organization’s goals. Job performance is the combination of staff behavior which can be measured, controlled and evaluated. Job performance of each laborer plays an important role and determines the overall performance of the organization. On this basis, we adopt the following definition: Job performance reflects the outcomes of staff accomplishing their tasks in the aspects of attitudes, behavior and achievements that contribute to the organization’s goals. 3.2. Research model and hypotheses Stemming from theoretical and practical gaps that have been identified, this research aims to answer the following questions (see Figure 1): 498
- How does digital transformation affect human resource development and job performance in commercial banks? - Does digital transformation human resource development play intermediary roles in the relations between digital transformation and job performance? - What should Vietnam’s commercial banks do when implementing digital transformation to raise job performance of human resources? Human resource development H2 H3 Job Digital H1 performance transformation Figure 1: Proposed research model (1) Digital transformation helps optimize staff performance. Bekkhus (2016) identified that digital transformation is using digital technology to improve staff performance. Applying smart technological solutions in digital transformation helps to raise labor productivity by automating processes, reducing manual work, cutting costs, etc. Besides, digital transformation also involves digitalizing sales and communication channels, providing new modes of interactions with customers as well as digitalizing some services of the enterprises so as to replace or strengthen physical services. Staff in enterprises have more time to raise their professional skills and qualification and implement tasks which bring higher added values. Digital transformation raises customer experience. Digital transformation helps to provide high-quality experience, meet customer demand and desires with good, fast and accurate services. Technology is reshaping the efficiency of human resources (Oxford Economics, 2012). Digital transformation optimizes staff performance, service quality and customer experience. According to research conducted in 2017 by Microsoft in Asia-Pacific, digital transformation may raise labor productivity in 2020 by 21%, 85% jobs in the region may change in the following three years. Digital transformation helps enterprises automate low- value jobs, thereby saving salary costs. Staff have more time to raise their skills and qualification and implement higher-value tasks. It also increases satisfaction for customers and staff. Digital transformation enables staff to access information everywhere, every time; helps them to implement tasks in flexible space and time. Wolf (2015) believed that job performance management is being transformed by digital technology. Therefore, the first research hypothesis is proposed as follows: 499
H1 - Digital transformation has positive impacts on job performance (2) Digital transformation not only affects the activities of the entire organizations and enterprises but also exerts considerable impacts on human resources. Bondarouk and Ruël (2009) emphasized the important role of digitalization in human resource management. Digital transformation affects the changes in staff’s perceptions, distinctive skills in different functions in the future. Digital transformation also creates opportunities for managers to automate the performance management procedures and changes. Digital technology facilitates flexible integration with focus placed on developing and maintain new skills of staff, so it plays a decisive role in the success of businesses (Henry, 2013). Digital transformation also involves using digital technology to promote and strengthen communication, collaboration and connection - not just between staff and the organization but also between staff (Hunt, 2014). Based on these arguments, the following research hypothesis is proposed: H2 - Digital transformation has positive impacts on human resource development (3) The Theory of Social Exchange, introduced in the late 20 th century, focused on evaluating human’s personal benefits in social relationships. Accordingly, laborers in organizations tend to take actions that bring them the highest benefits. Therefore, they may seek to raise their skills to increase their job performance, thereby increasing their personal benefits (Turner, 2001). The Expectancy Theory was developed on the view that behavior and working motivations of humans are not only determined by reality but also by their perceived expectations in the future. Laborers will try to work if they know that it will bring them better results or valuable rewards. As such, staff will try to equip themselves with sufficient knowledge, skills and attitudes to adapt to the working environment and raise their own job performance. As analyzed before, staff competence results from the process of human resource development. Therefore, the third hypothesis is proposed as follows: H3 - Human resource development has positive impacts on job performance 4. Research methods 4.1. Research sample The research was conducted with a sample including staff working in Vietnam’s commercial banks (BIDV; Tien Phong Bank; Military Bank; Vietbank; Bac A Bank; Maritime Bank; VP Bank; ACB; VietCapital Bank; Sacom Bank; DongA Bank; Construction Bank) chosen by convenient sampling method. The research team collected 278 questionnaires, of which 225 were valid and therefore used for analysis. Question allocation ranges between 15 and 25 questions per bank. The research used 15 observation variables to measure 3 factors. According to the standard of Hair (2006), the minimum sample size must be 5 times the number of observation variables. In this research, 15*5 = 75 questionnaires. Therefore, with the total number of 225 valid questionnaires, the research met the standards on sampling size for EFA analysis and multi-regression model testing. 500
4.2. Observation variables In addition to demographic questions, the questionnaire was designed with questions linked to observation variables which are measured by Likert 5 scale (1: Totally disagree; 2: do not agree; 3: Neutral; 4: Agree; 5: Totally agree). Observation variables used in this research were generalized and summarized from previous studies, then modified and supplemented on the basis of expert interviews (see Table 1). The research team invited 10 experts for interviews; they all were people with profound knowledge of the research topic, including 03 university lecturers, 02 scientists working in research institutes and 04 leaders of digital transformation and human resource management in Vietnam’s commercial banks. The interview results helped the team to modify observation variables in the following ways: (i) clarifying the meanings of observation variables after being translated; (ii) supplementing 02 new observation variables including DIG 5 - I apply digital transformation in work (which establishes that the surveyed person has already participated in digital transformation in his/her work) and HRD 3 - Digital transformation helps me become more willing to learn and create (which adds observation variables of the development of attitudes in digital transformation environment); (iii) separating the observation variable “Digital transformation helps me to increase responses and adaptability in work” into 2 variables of PER 4 - Digital transformation helps me to increase work responses and PER 5 - Digital transformation helps me to raise adaptability in work (because responses and adaptability are different). Table 1: Measurements of elements in the research model Code Research variables and Observation variables Origin DIG Digital transformation DIG1 Company’s leaders know of digital transformation DIG2 Company’s leaders are committed to digital transformation 1Office (2020) translated from Company’s leaders invest in digital transformation Harald Linné and DIG3 platforms and infrastructure Christian Frank DIG4 Company’s leaders develop digital transformation culture Results of expert DIG5 I apply digital transformation in work interviews HRD Human resource development Digital transformation helps me to develop knowledge of HRD1 technology Kumar (2016) Digital transformation helps me to develop skills of work HRD2 perception Digital transformation helps me to become more willing to Results of expert HRD3 work and create interviews Digital transformation helps me to develop career HRD4 Kumar (2016) continuously 501
HRD5 Digital transformation helps me to gain career promotion PER Job performance PER1 Digital transformation helps me to raise labor productivity Digital transformation helps me to improve customer PER2 services Kumar (2016) PER3 Digital transformation helps me to increase work values Digital transformation helps me to increase work PER4 responses Results of expert Digital transformation helps me to raise adaptability in interviews PER5 work 5. Research findings 5.1. Cronbach's Alpha testing Analysis results reveal that Cronbach's Alpha (Cα) is > 0.6, which indicates high reliability. The lowest corrected item-total correlation is > 0.3 and Cronbach's Alpha if item deleted is smaller than Cronbach's Alpha coefficient of the total variable indicate that all observation variables could be used for EFA analysis. Table 2: Reliability of research variables Variable “Digital transformation”: Cα = 0.884; Number of observation variables: 5 Scale Value if Variance of Scale if Item-total Cronbach's Alpha if Item Deleted Item Deleted correlation Item Deleted DIG1 11.59 15.002 0.736 0.856 DIG2 11.23 15.482 0.662 0.873 DIG3 11.23 15.158 0.718 0.861 DIG4 11.37 14.439 0.767 0.849 DIG5 11.19 14.599 0.726 0.859 Variable “Human resource development”: Cα = 0.861; Number of observation variables: 5 HRD1 14.31 10.412 0.781 0.805 HRD2 14.54 11.437 0.584 0.857 HRD3 14.40 10.635 0.703 0.826 HRD4 14.32 11.442 0.654 0.839 HRD5 14.37 11.430 0.685 0.832 Variable “Job performance": Cα = 0.843; Number of observation variables = 5 PER1 14.60 9.955 0.730 0.788 PER2 14.53 10.286 0.677 0.803 PER3 14.11 10.867 0.604 0.822 PER4 14.30 10.237 0.660 0.807 PER5 14.26 10.569 0.574 0.831 Source: collected by the research team from SPSS 502
5.2. Exploratory factor analysis (EFA) EFA analysis for factor groups reveals that KMO = 0.831 > 0.5, sig. of Bartlett's = 0.000 < 0.05, satisfying the condition. Data used was suitable with EFA analysis, sig. < 0.05, so it is possible to conclude that observation variables are correlated with each other. These indicators satisfy the conditions for EFA model. Table 3: KMO and Bartlett’s Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.831 Approx. Chi-Square 1746.578 Bartlett's Test of Df 105 Sphericity Sig. 0.000 Source: Collected by research team from SPSS software The three main factors have the total explanation rate of 65.641% > 50%, indicating that the total explanation capacity of the model is 65.641% of the practical value, among which 34.515% of data variations can be explained by the first main factor; 19.711% of data variations can be explained by the second main factor; 11.414% of data variations can be explained by the third main factor. Besides, the breakpoint used with Eigenvalues is 11.414 > 1, satisfying the condition (see Table 4). Table 4: Total Variance Explained Eigenvalues Sum of Square uploaded Factor Total % variance % Total % variance % accumulated accumulated 1 5.177 34.515 34.515 5.177 34.515 34.515 2 2.957 19.711 54.226 2.957 19.711 54.226 3 1.712 11.414 65.641 1.712 11.414 65.641 Source: Collected by research team from SPSS software Pattern Matrixa Component Matrix analysis shows that factors loading of observation variables are > 0.5, satisfying the conditions. Regression testing shows the following factors: DIG including DIG4, DIG1, DIG5, DIG3, DIG2; HRD including HRD3, HRD1, HRD5, HRD4, HRD2; PER including PER 1, PER4, PER2, PER5, PER3 (see Table 5). Table 5: Rotated Matrix Factors 1 2 3 DIG4 0.857 DIG1 0.851 DIG5 0.811 DIG3 0.805 503
DIG2 0.769 HRD3 0.848 HRD1 0.826 HRD5 0.752 HRD4 0.749 HRD2 0.707 PER1 0.795 PER4 0.773 PER2 0.751 PER5 0.747 PER3 0.702 Source: Collected by research team from SPSS software 5.3. Confirmatory factor analysis To test the suitability of the measurement model with the collected data (market data), the research team used confirmatory factor analysis (CFA) method via AMOS software version 24. The first CFA analysis indicates that CFI, RMSEA satisfy the conditions. However, TLI= 0.894; GFI=0.882 do not satisfy the conditions of being higher than 0.9, PCLOSE= 0.000 not higher than or equal 0.01. Therefore, the researchers connected e as suggested in Modification indices of Covariance table, including e12 -e15, e11- e15 The second CFA analysis of the measurement model (see Figure 2) for Chi square/df = 1.894, TLI, CFI, GFI are higher than 0.9 (Bentler & Bonett, 1980), RMSEA = 0.063< 0.08 (Steiger, 1998), PCLOSE= 0.012 >0.01 (Hu & Bentler, 1999), so it is possible to infer that the model is considered suitable with the market data and enables that observation variables are uni-dimensional. Figure 2: Results of CFA analysis (standardized) Source: Collected by research team from AMOS software 504
Convergent validity: All standardized weights are > 0.5, indicating that the measurements of all scales have convergent validity. Discriminant validity: Correlation coefficients between researched concepts in the model are positive and < 1 and different from 1 (see Table 6), P-value is small and < 0.05, so the correlation coefficients of each concept pair is different from 1 with sig. of 95%. Therefore, all researched concepts in the model have discriminant validity. Table 6: Correlation coefficients Estimate DIG4
Figure 3: Corrected SEM analysis results Source: Collected by research team from AMOS software The SEM analysis results for regression weights for factors DIG, HRD, PER show that all factors have statistical significance of 5% because P-value < 0.1. Table 7: Regression Weights Unstandardized Standardized Standard Critical Significance regression regression deviation value C.R. level P coefficients coefficients S.E. HRD
collected sample. The results of testing the difference between estimate sample and bootstrap estimate is very small, critical value is smaller than 2 (Table 2), indicating that in reality, the estimate sample can be extended to the general population. It is possible to conclude that the estimate model is robust and reliable. Table 8: Results of estimate coefficients via bootstrap (n= 1000) Variable relations SE SE-SE Mean Bias SE-Bias HRD
(DIG2; DIG3; DIG4, DIG5) and only 1 (DIG1: Company’s leaders know of digital transformation has mean value at “agree” level, but all have very high standard deviation > 1.12 (see Table 9). Moreover, ICT index 2020 of the banking sector was 0.5112 but there are differences between banks. For example, ICT index of BIDB was 0.7762 but that of Social Policy Bank was the lowest with just 0.1065 (MOST, 2021). This inevitably affects human resource development and job performance of staff. Table 9: Staff’s opinion on digital transformation in commercial banks Standard Code Digital transformation Mean deviation DIG1 Company’s leaders know of digital transformation 3.44 1.125 Company’s leaders are committed to digital DIG2 3.08 1.135 transformation Company’s leaders invest in digital DIG3 3.08 1.121 transformation platforms and infrastructure Company’s leaders develop digital transformation DIG4 3.22 1.177 culture DIG5 I apply digital transformation in work 3.04 1.198 Mean 3.172 Source: Collected by research team from SPSS software 6.2. Management implications Based on the research findings, the research team seeks answers to question 3: What should Vietnam’s commercial banks do when implementing digital transformation to raise job performance of human resources? via some proposals to boost digital transformation as follows: (i) Raising awareness and commitments to digital transformation: To make changes, the entire leader team of the banks, from managing director, members of management board to each functional division, must have strong commitments. The changes must be implemented systematically and comprehensively. Implementing a digital transformation program means building, carrying out and utilizing dozens, even hundreds of AI, IoT applications in every operational aspect of the banks, from human resource management, customer relationships to financing, product designs and value chain operations. Therefore, the leaders need to acquire fundamental and overall knowledge of technology and set up specific digital transformation goals that are appropriate with business models and product features. Digital transformation is an evolutionary process that requires changes in perceptions as well as determination and consistence from leaders. Changes in the perception of leaders may trigger the changes in the perception of staff. The right perception facilitate the development of digital transformation competencies via training programs on modern technology. This helps to fully exploit the potential of technological development, as reflected in the human resource development to raise labor productivity and reduce job loss that results from the automation process. 508
(ii) Selecting and investing in appropriate digital infrastructure and technological platforms: identifying and arranging the orders of priority for the selection of appropriate digital platform. This must be implemented systematically (from surveying staff and leaders on which division and procedure should be automated; deciding on the orders of priority; brainstorming ideas; arranging the orders of priority according to their impacts on the banks, etc.). The prerequisites for sustainable digital transformation are selecting core technological platform. For example, for digital transformation in human resource management system, legacy systems should be replaced by integrated cloud computing and firm digital infrastructure, digital technology enables staff to access information everywhere, every time, helping them to do tasks in flexible space and time, restricting manual work and automating jobs such as calculating wages, sending payroll to staff, declaring personal income taxes, saving personnel profiles, etc.; at the same time enables leaders to keep track of personnel fluctuations on their smartphones via diagrams, thereby deciding on the best personnel services. Previously, in Vietnam, up to 60 - 70% of CEOs made decisions based on their intuition without statistical reports; but now thanks to technological solutions, they can have adequate data to make decisions to raise productivity, lead the enterprises on the right way and create competitive advantages (Phạm Hải Văn, 2022). (iii) Developing digital transformation culture: Digital transformation means transforming the system, so for the process to take place conveniently and successfully, all stakeholders should have good understanding and be engaged in the process. Enterprises that have gained success in digital transformation all view that changing corporate culture is more difficult than changing the technology. For all staff in the banks to understand and participate in digital transformation, it is necessary to: identify digital transformation as a core strategy; establish task forces in digital transformation; identify specific and clear roadmaps that are based on practical analysis as well as the consistence and determination in the implementing process; promote internal communication, spread information to staff in advance to lay psychological foundations for staff in the digital transformation process. REFERENCES 1. Bentler, M., & Bonett, G. (1980). Significance Tests and Goodness of Fit in the Analysis of Covariance Structures. Psychological Bulletin, 88. 2. Bierema, L. L. and Cseh, M. (2014), ‘A Critical, Feminist Turn in HRD. A Humanistic Ethos’, in Chalofsky, N. E., Rocco, T. S., and Morris, M. L. (eds) Handbook of Human Resource Development. New Jersey: John Wiley & Sons, Inc., pp. 125-143. 3. Chanias, S. (2017), Mastering Digital Transformation: the Path of a Financial Services Provider towards a Digital Transformation Strategy. In: European Conference of Information Systems, Guimaraes, Portugal, pp. 16-31. 4. Cottrell, S. (2013), The Study Skills Handbook, 4 edn, Palgrave MacMillan Ltd. 5. Fitzgerald, M. (2014), Inside Renault’s Digital Factory. MIT Sloan Manage. Rev. 55 (3), pp. 1-4. 6. Goad, T. W. (2002), Information Literacy and Workplace Performance, Westport, Conn: Quorum Books. 509
7. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E. & Tatham, R. L. (2006), Multivariate data analysis 6th Edition, New Jersey: Pearson Education. 8. Heilig, L., Schwarze, S., Voss, S. (2017), An Analysis of Digital Transformation in the History and Future of Modern Ports. In: Hawaii International Conference on System Sciences, Waikoloa Beach, HI, pp. 1341-1350. 9. Hu, L. T., & Bentler, P. M. (1999). Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria versus New Alternatives. Structural Equation Modeling, 6. 10. Hunt, C. (2014), Transforming Talent Management: The Impact of Social and Digital Tech, The Denovati group. 11. Karagiannaki, A., Vergados, G., Fouskas, K. (2017), The Impact of Digital Transformation in the Financial Services Industry: Insights from an Open Innovation Initiative in Fintech in Greece. In: Mediterranean Conference of Information Systems, Genoa, Italy. 12. Kumar, B. N. (2016), Digital Transformation and its Impact on Human Resource Management: A Case Analysis of Two Unrelated Businesses in the Mauritian Public Service, International Journal of Novel Research in Interdisciplinary Studies, Vol.3, No.5, pp.1-10, DOI: 10.1109/ EmergiTech.2016.7737328. 13. Knut-H. Rolland, Ole Hanseth (2021). Managing Path Dependency in Digital Transformation Processes: A Longitudinal Case study of an Enterprise Document Management Platform. Procedia Computer Science, 181:765-774, https://doi.org/10.1016/j.procs.2021.01.229.11. 14. Legner, C., Eymann, T., Hess, T., Matt, C., Böhmann, T., Drews, P., Mädche, A., Urbach, N., Ahlemann, F. (2017), Digitalization: Opportunity and Challenge for the Business and Information Systems Engineering Community. Bus. Inform. Syst. Eng. 59 (4), pp. 301-308. 15. Mc Guire, D. (2011), ‘Foundations of Human Resource Development’, in McGuire, D. and Jørgensen, K. M. (eds) Human Resource Development: Theory and Practice. London: SAGE Publications Ltd., pp. 1-11 16. Mc Henry, T. (2013), Microsoft HRD: The World of Work Is Changing, Microsoft Corporation. 17. Morakanyane, R., Grace, A.A., O’Reilly, P. (2017), Conceptualizing Digital Transformation in Business Organizations: A Systematic Review of Literature. In: Bled eConference, Bled, Slovenia, pp. 427-444 18. Murphy, K. R. (1989), Dimensions of Job Performance. In R. F. Dillon & J. W. Pellegrino (Eds.), Testing: Theoretical and Applied Perspectives, pp. 218-247. 19. MOST (2021), Reports on ICT Vietnam 2020, Information and Communication Publishing House. 20. Nguyen Hoang Nam (2022), Evaluating Impacts of Digital Transformation on Human Resource Management in Vietnam, Proceedings of National Conference on “Digital Transformation and Human Resource Management”, National University Hanoi Publishing House, pp.734-754. 510
21. Nguyen Thi Minh Nhan (2022), Procedures of Digital transformation in Human resource Management in Vietnam’s Enterprises, Proceedings of National Conference on “Digital Transformation and Human Resource Management”, National University Hanoi Publishing House, pp.14-29. 22. Ng, Thomas WH, and Daniel C. Feldman. "How Broadly Does Education Contribute to Job Performance?." Personnel psychology 62.1 (2009), pp. 89-134. 23. Nwankpa, J.K., Roumani, Y. (2016), IT Capability and Digital Transformation: A Firm Performance Perspective. In: International Conference of Information Systems, Dublin, Ireland. 24. Oxford Economics (2012), The Digital Transformation of People Management, www.sap.com. 25. Reis J., Amorim M., Melão N. and Matos P. (2018) Digital Transformation: A Literature Review and Guidelines for Future Research. In: Rocha Á., Adeli H., Reis L.P., Costanzo S. (eds), Trends and Advances in Information Systems and Technologies. WorldCIST’18 2018. Advances in Intelligent Systems and Computing, 745. Springer, Cham. 26. Solis, B., Lieb, R., Szymanski, J. (2014). The 2014 State of Digital Transformation. Altimeter Group. 27. Stanford Technology Ventures Program (2005), Geoffrey Moore - Core and Context. 28. Steiger's (1998) Multiple Sample RMSEA Adjustment to Other Noncentrality Parameter-Based Statistics, Structural Equation Modeling A Multidisciplinary Journal 11(3). 29. Tanguy Catlin et al. (2017), A Roadmap for a Digital Transformation, McKinsey. 30. Thomas M. Siebel, 2019), Digital Transformation. PACE Institute of Management. 31. Turner Sandra G. (2001), “Resilience and Social Work Practice: Three Case Studies”, SAGE Journals Vol. 82, Issue 5, pp. 441-448. 32. Vial G. (2019). Understanding Digital Transformation: A Review and a Research Agenda.” Journal of Strategic Information Systems, 28, pp.118-144 33. Viswesvaran, C., & Ones, D. S. (2000), Perspectives on Models of Job Performance, International Journal of Selection and Assessment, 8(4), pp. 216-226. 34. Wang, G. G., Werner, J.M., Sun, J.Y., Gilley, A., and Gilley, J.W. (2017), ‘Means vs ends: Theorizing a Definition of Human Resource Development’, Personnel Review, 46(6), pp. 1165-1181. 35. Westerman, G., Calméjane, C., Bonnet, D., Ferraris, P. & McAfee, A. (2011). Digital Transformation: A Road-Map for Billion-Dollar Organizations. MIT Sloan Management, MIT Center for Digital Business và Capgemini Consulting. 36. Wolf, W. (2015), Collaborative Talks Series: Why Performance Matters in the Age of Digital, collaborative.com. 37. 1Office translate (2020) from Harald Linné and Christian Frank, Human Resource Management in Digital Transformation Era 511
GIẢI PHÁP THÚC ĐẨY ỨNG DỤNG CÔNG NGHỆ SỐ TRONG HOẠT ĐỘNG NGÂN HÀNG TẠI VIỆT NAM PGS,TS. Bùi Văn Trịnh - Trường Đại học Cửu Long TS. Phạm Minh Trí - Trường Đại học Trà Vinh Tóm tắt: Sự phát triển của công nghệ số đã và đang có những tác động tích cực đến hoạt động của ngân hàng, tạo nên sự chuyển đổi mạnh mẽ trong hệ thống quản lý, cung cấp các sản phẩm, dịch vụ tiên tiến nhằm gia tăng hiệu quả hoạt động và trải nghiệm của khách hàng. Ứng dụng công nghệ số vào hoạt động ngân hàng được xem là giai đoạn tiếp theo sau giai đoạn số hóa của quá trình chuyển đổi số. Nghiên cứu thực hiện với mục đích thảo luận một số công nghệ được áp dụng vào hoạt động ngân hàng như: trí tuệ nhân tạo, học máy, dữ liệu lớn, điện toán đám mây, chuỗi khối... đồng thời phân tích, đánh giá việc ứng dụng công nghệ số vào lĩnh vực ngân hàng. Trên cơ sở đó, nhóm tác giả đề xuất một số giải pháp để thúc đẩy việc ứng dụng công nghệ số vào hoạt động ngân hàng. Từ khóa: Công nghệ số, sản phẩm số, Công ty công nghệ tài chính. SOLUTIONS TO PROMOTE THE APPLICATION OF DIGITAL TECHNOLOGY IN BANKING ACTIVITIES IN VIETNAM Abstract: The development of digital technology has had a positive impact on the bank's operations, creating a powerful transformation in the management system and providing advanced products and services to increase performance and customer experiences. Applying digital technology to banking activities is considered the next stage after the digitization phase of the digital transformation process. The study's aim discusses some technologies applied to banking activities such as artificial intelligence, machine learning, big data, cloud computing, blockchain, etc. Next is the analysis and evaluation of the application of digital technology to the banking sector. On that basis, the authors propose some solutions to promote the application of digital technology in banking activities. Keywords: Digital technology, digital product, Fintech. 1. Giới thiệu Trong các năm qua, công nghệ số đang dần làm thay đổi cơ bản các hình thức cung ứng dịch vụ ngân hàng truyền thống. Các ngân hàng thương mại (NHTM) trong nước đã triển khai ứng dụng nhiều công nghệ tiên tiến vào hoạt động ngân hàng như: Trí tuệ nhân tạo (AI)/ học máy (ML), điện toán đám mây (Clouding Computing), dữ liệu lớn (Big data), vạn vật kết nối (IoT)... để đánh giá, phân loại khách hàng và quyết định giải ngân hay giúp đơn giản hóa quy trình, thủ tục và rút ngắn thời gian giải ngân, cho vay. Song song với việc ứng dụng công nghệ số, các NHTM còn hợp tác với các Công ty công nghệ tài chính 512