Determining the priority levels of criteria for evaluating the digital transformation level of businesses in vietnam nguyen thi anh tuyet

International Journal of Management & Entrepreneurship Research International Journal of Management & Entrepreneurship Research International Journal of Management & Entrepreneurship Research Vol No (2023) Published: 2023-06-01 Articles • STRATEGIC MANAGEMENT AND PERFORMANCE OF MANUFACTURING FIRMS IN DELTA STATE Joy SAKPAIDE, Vincent I O ODIRI, Eferayejene Joseph SAKPAIDE 326-340 o • PDF MANAGING WORKFORCE DIVERSITY AND THE QUEST FOR ETHICAL LEADERSHIP IYAMABHOR Martins, OGUNDARE, Justice Taiwo, Roland Orie Akpubi, OGBOR John O 341-359 o • PDF THE EFFECT OF SOCIAL MEDIA ON CUSTOMER RELATIONSHIP MANAGEMENT; A CASE OF AIRLINE INDUSTRY CUSTOMERS Dr Premkumar Arul, Dr Muhammad Tahir 360-372 o • PDF SUSTAINING ORGANIZATIONAL PROFITABILITY THROUGH ENHANCED QUALITY CONTROL PRACTICES IN MANUFACTURING BUSINESSES KIFORDU A Anthony, ARUBAYI O Damaro, MOGBOLU Ngozi 373-385 o • PDF PERFORMANCE APPRAISAL SYSTEM AND TEACHER EFFECTIVENESS IN PUBLIC BASIC SCHOOLS IN GHANA Lydia Osarfo Achaa 386-404 o • PDF DEVELOPING LECTURERS IN HIGHER EDUCATION INSTITUTIONS IN VIETNAM Ha Thi Ngoc 405-409 o • PDF DETERMINING THE PRIORITY LEVELS OF CRITERIA FOR EVALUATING THE DIGITAL TRANSFORMATION LEVEL OF BUSINESSES IN VIETNAM Luu Huu Van, Nguyen Thi Anh Tuyet 410-417 o • PDF CONFLICT MANAGEMENT AND TEACHERS' JOB PERFORMANCE IN SENIOR SECONDARY SCHOOLS IN KOGI STATE, NIGERIA Lukeman Abdul, Samuel Muhammed Enefu, Ph.D, Edime Yunusa 418-442 o PDF International Journal of Management & Entrepreneurship Research, Volume 5, Issue 6, June 2023 OPEN ACCESS International Journal of Management & Entrepreneurship Research P-ISSN: 2664-3588, E-ISSN: 2664-3596 Volume 5, Issue 6, P.No.410-417, June 2023 DOI:10.51594/ijmer.v5i6.500 Fair East Publishers Journal Homepage: www.fepbl.com/index.php/ijmer DETERMINING THE PRIORITY LEVELS OF CRITERIA FOR EVALUATING THE DIGITAL TRANSFORMATION LEVEL OF BUSINESSES IN VIETNAM Luu Huu Van1 & Nguyen Thi Anh Tuyet2 Academy of Policy and Development, Ministry of Planning and Investment, Nam An Khanh Urban Area, Hanoi 100000, Vietnam East Asia University of Technology, Trinh Van Bo Road, Hanoi 100000, Vietnam _ Corresponding Author: Nguyen Thi Anh Tuyet Corresponding Author Email: tuandat151107@gmail.com Article Received: 03-05-23 Accepted: 29-05-23 Published: 13-06-23 Licensing Details: Author retains the right of this article The article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 License (http://www.creativecommons.org/licences/by-nc/4.0/), which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the Journal open access page _ ABSTRACT The objective of this research is to determine the priority levels of criteria for evaluating the digital transformation level of businesses in Vietnam In this study, the Analytic Hierarchy Process (AHP) method using fuzzy numbers is employed Data for the research is collected through interviews with a group of experts and managers Six criteria are used to assess the digital transformation level of businesses in Vietnam, including infrastructure and digital technology, data and information assets, strategy, digital transformation of corporate culture, digital experience for customers, and operations The analysis results indicate that the infrastructure and digital technology criterion plays the most important role in assessing the digital transformation level of businesses in Vietnam Keywords: AHP, Digital Transformation Level, Fuzzy Numbers _ INTRODUCTION Digital transformation plays a crucial role in impacting the entire organization and bringing about changes in various aspects of the business such as work processes, infrastructure, Van & Tuyet, P.No 410-417 Page 410 International Journal of Management & Entrepreneurship Research, Volume 5, Issue 6, June 2023 business models, and human resource management (Gilchrist, 2016; Schallmo et al., 2017; Hausberg et al., 2019) Digital transformation contributes to increased productivity and wealth, encompassing both digitization and digitalization (Autio, 2017; Rose and Chilvers, 2018; Vial, 2019; Kelly Rijswijk et al., 2021) Digitization and intelligent automation will contribute to the overall increase in global domestic product in the coming years (Pricewaterhouse Coopers, 2019) Digital transformation can be applied to all aspects of the agricultural system and reflects the change in managing overall resources towards high optimization, individualized, intelligent, and predictive management, in real-time, super-connected, and data-driven (Trendov et al., 2019) Some studies indicate that digital transformation is a solution to the challenges that agriculture faces as part of the transition to Agriculture 4.0 and contributes to transforming the agricultural system (World Bank, 2019; Trendov et al., 2019; Klerkx and Rose, 2020; Herrero et al., 2021; Klerkx and Begemann, 2020) Kelly Rijswijk et al (2021) have pointed out that digital technology is often seen as an opportunity to create a sustainable future for agriculture Therefore, digital transformation in agriculture is considered a global policy priority (Trendov et al., 2019; World Bank, 2017, 2019; Kelly Rijswijk et al., 2021) However, currently, the majority of small and medium-sized enterprises (SMEs) in developing countries in general and Vietnam in particular still lack awareness of the importance of digital transformation These businesses are not truly prepared for digital transformation, thus they are unable to build and transition to digital business models and take advantage of the opportunities brought about by digitization (Lokuge et al., 2019) Vietnam, with its starting point as a relatively backward economy, faces limitations in harnessing the advantages of the agricultural sector Small and medium-sized enterprises still have certain limitations Therefore, keeping up with the trends of the times is essential during the period of integration Vietnam is an agricultural country, with a significant portion of its land being coastal areas, thus the development of the advantages of the agricultural sector holds great potential In recent years, most basic infrastructure and digital technology groups in agriculture have been deployed or started to be tested in Vietnam and other developing countries Some models and standards have been used to analyze and assess the level of digital transformation in small and medium-sized enterprises, as indicated in recent studies Baumüller (2015) highlighted the application of GIS technology and remote sensing to develop software for early detection and warning of forest fires from satellite images, software for monitoring and early detection of forest loss and degradation Additionally, the M-Farm application in Kenya led to farmers changing their farming models, resulting in higher market prices Jerome Buvat et al (2017) pointed out that the cultural factor within businesses also plays an important role in digital transformation Businesses need to change their mission, vision, and core values to engage employees in the process of change and cultural transformation within the organization Juan Antonio et al (2020) revealed that in the field of livestock farming, the Internet of Things (IoT) technology, including basic communication infrastructure, is used to connect smart objects ranging from sensors, vehicles, mobile devices to remote data collection based on intelligent analysis, user communication, and agricultural revolution Carlos et al (2021) analyzed digital transformation in the olive industry in the short and medium term using various methods such as SWOT, PESTLE, and the Analytic Hierarchy Process (AHP) Van & Tuyet, P.No 410-417 Page 411 International Journal of Management & Entrepreneurship Research, Volume 5, Issue 6, June 2023 Criteria such as technology, technical level, and socio-economic and political factors were used in the research to analyze digital transformation in the agricultural sector in the Andalusia region The criterion of digital customer experience has also been highlighted by several authors in the context of digital transformation Ameen et al (2021) developed a theoretical model integrating trust and sacrifice, which are considered intermediate factors for the impacts of the remaining four factors, including individual, convenience, service quality, and AI support Huang et al (2021) identified four main customer experiences with service robots, including experiences of decision-making hesitation, perceptual experiences, emotional experiences, and affection experiences Molinillo et al (2022) conducted a survey of 545 users of retail applications to study the retail application experience of customers in relation to customer loyalty These technologies will have their own position and impact in the agricultural value chain and can provide the agriculture sector with tools and information for more informed decision-making, improved productivity, and effective management Strategic standards are also one of the important criteria that affect the digital transformation of businesses Fernandez-Vidal et al (2021) have pointed out that most companies choose small transformation strategies to achieve larger transformation goals The research provides conceptual guidelines for selecting appropriate strategic tools Chanias et al (2019) analyzed digital transformation strategic planning in digital organizations from the perspective of a financial service provider The article highlights the model's results, which indicate that digital transformation strategy is a highly dynamic process involving iterative learning and doing Orhan et al (2022) identified the driving force behind business strategies, and digital transformation has enabled significant changes in the passenger journey of airlines In the context of the Covid-19 pandemic, digital infrastructure standards are also one of the important criteria that impact digital transformation in businesses worldwide Most businesses have had to operate and work remotely, transitioning to digital operations Employees and business leaders have had to enhance their technological capabilities or apply technologies in their business operations (Breier et al., 2020; Paolo and Lapo, 2022) The application of digital infrastructure contributes to cost reduction, helps limit and increase the value of products, and improves product quality, bringing new breakthroughs for businesses Nowadays, businesses are adopting data modeling, which brings benefits such as cost reduction, time-saving in data analysis and evaluation, and helps identify business principles and processes Therefore, according to the author, data and company assets are also important criteria with significant impact on digital transformation in small and medium-sized enterprises Furthermore, operational standards are also an important criterion that influences the digital transformation process in businesses Businesses with sound and proper operational practices will bring effectiveness and stability to the company Table presents groups of criteria for measuring the level of digital transformation in businesses Table Criteria for Measuring the Level of Digital Transformation in Businesses No Criteria References Infrastructure and Digital Technology (C1) Data and Information Assets (C2) Baumüller (2015), Jerome Buvat et al (2017), Chanias et al (2019), Fernandez-Vidal et al (2019), Juan Atonio et al (2020), Breier et al (2020), Carugati et al (2020), Ameen et al (2021), Huang et al (2021), Carlos et al (2021), Molinillo et al (2022), Orhan et al (2022), Paolo and Lapo (2022) Van & Tuyet, P.No 410-417 Page 412 International Journal of Management & Entrepreneurship Research, Volume 5, Issue 6, June 2023 Strategy (C3) Digital Transformation of Organizational Culture (C4) Digital Customer Experience (C5) Operations (C6) Fuzzy AHP In this study, the fuzzy analytic hierarchy process (FAHP) proposed by Chang (1996) was used to determine the priority level of the digital transformation measurements for businesses The steps of Chang's (1996) method are presented as follows: Step 1: Determine the value of the fuzzy composite number The value of the fuzzy composite number for the i-th criterion is calculated using the Equation:  n m  Si   M    M gji  j 1  i 1 j 1  m 1 j gi (1) where:   m m  m  n m n m n m n m j j M  l , m , u   j  j  j ,  M gi   i 1  j 1 lij ,  i 1  j 1 mij ,  i 1  j 1 uij  gi j 1 j 1  j 1 j 1  i 1 j 1 i = 1, 2, …., n; j = 1,2,…, m Step 2: Comparison of two fuzzy numbers The degree of possibility of the comparison between two fuzzy numbers is determined as follows: m V ( S1  S2 )  sup  min( M1 ( x), M (y))  (2)  1 if m1  m2   if l2  u1 Theo đó, V ( S1  S )  0  l2  u1  others l  u  m  m  2 1 (3) yx Step 3: Calculate the degree of possibility of the occurrence of the relationship that a fuzzy number is better than the remaining fuzzy numbers V (S  S1, S2 , Sn )  V ( S  S1 ) (S  S2 ) ( S  S n )   V ( S  Si ), i  1, , n (4) Step 4: Calculate the vector W' W '  (d '( A1 ), d '( A2 ), , d '( An ))T (5) assuming that: d '( Ai )  minV(Si  St ) and i= 1,2,…, n; t = 1, 2, ….n and i ≠ t Normalized weights: Van & Tuyet, P.No 410-417 Page 413 International Journal of Management & Entrepreneurship Research, Volume 5, Issue 6, June 2023 W  (d ( A1 ), d ( A2 ), , d(A n ))T Wi  Wi (6) n W i 1 i The Application of the Analytic Hierarchy Process in Determining the Priority Level of Digital Transformation Measurement Criteria in Businesses in Vietnam Table presents the linguistic variables and corresponding fuzzy numbers used in the study The research's input data was collected through in-depth interviews with experts Table Linguistic Variables and Fuzzy Numbers Levels Fuzzy number (1,1,1) (2,3,4) (4,5,6) (6,7,8) (9,9,9) Inverse fuzzy number (1,1,1) (1/4,1/3,1/2) (1/6,1/5,1/4) (1/8,1/7,1/6) (1/9,1/9,1/9) Definition Equally important Somewhat more important More important Much more important Extremely more important a Determine the comparative relationship between pairs of criteria Table presents the average comparison values between the criteria for measuring the level of digital transformation of businesses based on expert interviews and Table Table Average Comparison Values between Criteria Criteria C1 C2 C3 C4 C5 C6 C1 (1, 1, 1) (0.13,0.14,0.1 7) (0.11,0.11,0.1 1) (0.17,0.20,0.2 5) (0.25,0.33,0.5 0) (0.25,0.33,0.5 0) (6,7,8) (9,9,9) (1,1,1) (2,3,4) (0.25,0.33,0.50 ) (1,1,1) (4,5,6) (0.25,0.33,0.50 ) (0.17,0.20,0.25 ) (2,3,4) (4,5,6) (1,1,1) (2,3,4) (0.17,0.20,0.25 ) (0.13,0.14,0.17 ) (0.25,0.33,0.50 ) (4,5,6) (6,7,8) (2,3,4) (1,1,1) (2,3,4) (0.17,0.20,0.2 5) (0.13,0.14,0.1 7) (0.25,0.33,0.5 0) (1,1,1) (4,5,6) (6,7,8) (2,3,4) (1,1,1) C2 C3 C4 C5 C6 (1,1,1) b Calculate the aggregated value for criteria Table presents the aggregated values of the criteria for measuring the level of digital transformation of businesses using Equation Table Aggregated Values of the Criteria TT Criteria Aggregated Values (0.256,0.353,0.487) C1 (0.040,0.061,0.094) C2 (0.019,0.024,0.033) C3 (0.082,0.124,0.187) C4 (0.152,0.218,0.312) C5 (0.152,0.218,0.312) C6 c Comparison between two aggregated values Using equations and 3, the comparison results between the aggregated values are presented in Table Van & Tuyet, P.No 410-417 Page 414 International Journal of Management & Entrepreneurship Research, Volume 5, Issue 6, June 2023 Table Comparison Results between Criteria Comparison V(S1>=S2) V(S1>=S3) V(S1>=S4) V(S1>=S5) Value 1 1 Comparison V(S2>=S1) V(S2>=S3) V(S2>=S4) V(S2>=S5) Value 0.16 Comparison V(S3>=S1) V(S3>=S2) V(S3>=S4) V(S3>=S5) Value 0 0 Comparison V(S4>=S1) V(S4>=S2) V(S4>=S3) V(S4>=S5) Value 1 0.27 Comparison V(S5>=S1) V(S5>=S2) V(S5>=S3) V(S5>=S4) Value 0.29 1 Table Occurrence Level of One Criterion being More Important than the Remaining Criteria The occurrence level of the relationship Values d(C1) d(C2) d(C3) d(C4) 0.267 d(C5) 0.293 d(C6) 0.293 d Determining the importance level of the criteria Table presents the results of the importance level of the criteria measuring the digital transformation of businesses using equation (6) Table Importance Level of the Criteria Importance level W(C1) W(C2) W(C3) W(C4) W(C5) W(C6) Value 0.539 0 0.144 0.158 0.158 The analysis results from Table reveal the priority level of the weights: the criterion Infrastructure and Digital Technology (C1) has the highest impact on digital transformation in the agricultural sector, with an impact level of W(C1) = 0.539 The two criteria Customer Digital Experience (C5) and Operations (C6) have the same impact level of W(C5) = W(C6) = 0.158, making them the second most influential factors/criteria in the digital transformation of the agricultural sector The fourth most impactful criterion on digital transformation in the agricultural sector is Digital Transformation of Organizational Culture (C4), with an impact level of W(C4) = 0.144 Lastly, the two criteria Data and Information Assets (C2) and Strategy (C3) have no impact on digital transformation in the agricultural sector, with impact levels of W(C2) = W(C3) = CONCLUSION AND RECOMMENDATIONS Based on the overview and research findings, digital transformation is an inevitable trend for businesses To achieve successful digital transformation, businesses need to focus on the criteria analyzed in Table However, particular attention should be given to the "Infrastructure and Digital Technology" factor as it has the highest impact level Subsequently, businesses should gradually lower the priority for the remaining factors The second-priority criteria are "Customer Digital Experience" and "Operations." Specifically, the criterion "Customer Digital Experience" involves applying digital tools and platforms to business operations to support customers in choosing products and services The next priority criterion is "Digital Transformation of Organizational Culture." Lastly, the two criteria that Van & Tuyet, P.No 410-417 Page 415 International Journal of Management & Entrepreneurship Research, Volume 5, Issue 6, June 2023 have no impact on digital transformation in the agricultural sector are "Data and Information Assets" and "Strategy," both with an impact level of This is not reasonable as impact levels should be greater than This limitation highlights the need for future improvement of the Analytic Hierarchy Process (AHP) method or the application of more suitable methods to determine the priority level of criteria for assessing the level of digital transformation in businesses References Ameen, N., Tarhini, A., Reppel, A., & Anand, A (2021) Customer experiences in the age of artificial intelligence Computers in Human Behavior, 114, 106548 Autio, E (2017) Digitalisation, ecosystems, entrepreneurship and policy Baumüller, H (2015) Assessing the role of mobile phones in offering price information and market linkages: the case of m-farm in Kenya, EJISDC, 68(6), 1-16 Breier, M., Kallmuenzer, A., Clauss, T., Gast, J., Kraus, S., & Tiberius, V (2021) The role of business model innovation in the hotel industry during the COVID-19 crisis International Journal of Hotel Management, 92, 102723 Chanias, S., Michael, D.M., & Hess, T (2019) Digital transformation strategy making in pre-digital organizations: The case of a financial services provider The Journal of Strategic Information Systems, 28(1), 17-33 Gilchrist, A., (2016) Industry 4.0: The Industrial Internet of Things Apress, 2016 ISBN 148422048X, 9781484220481 Hausberg, J.P., Liere-Nethler, K., Packmohr, S., Pakura, S., & Vogelsang, K (2019) Research streams on digital transformation from a holistic business perspective: a systematic literature review and citation network analysis Journal of Business Economics, 89, 931–963 https://doi.org/10.1007/s11573-019-00956-z Herrero, M., Thornton, P.K., Mason-D’Croz, D., Palmer, J., Bodirsky, B.L., Pradhan, P., Barrett, C.B., Benton, T.G., Hall, A., Pikaar, I., Bogard, J.R., Bonnett, G.D., Bryan, B.A., Campbell, B.M., Christensen, S., Clark, M., Fanzo, J., Godde, C.M., Jarvis, A., Loboguerrero, A.M., Mathys, A., McIntyre, C.L., Naylor, R.L., Nelson, R., Obersteiner, M., Parodi, A., Popp, A., Ricketts, K., Smith, P., Valin, H., Vermeulen, S J., Vervoort, J., van Wijk, M., van Zanten, H.H.E., West, P.C., Wood, S.A., & Rockstrom, J., (2021) Articulating the effect of food systems innovation on the Sustainable Development Goals The Lancet Planetary Health, 5, e50–e62 Huang, D., Chen, Q., Huang, J., Kong, S., & LI, Z (2021) Customer-robot interactions: Understanding customer experience with service robots International Journal of Hospitality Management, 99, 103078 Jerome Buvat, cộng (2017) The discipline of innovation Capgemini Digital Transformation Institute Fernandez-Vidal, J., Gasco, G.J., & Llopis, J (2021) Digitalization and corporate transformation: The case of European oil & gas firms Technological Forecasting And Social Change, 174, 121293 Klerkx, L., & Begemann, S (2020) Supporting food systems transformation: the what, why, who, where and how of mission-oriented agricultural innovation systems Agricultural System, 184, 102901 https://doi.org/10.1016/j.agsy.2020.102901 Van & Tuyet, P.No 410-417 Page 416 International Journal of Management & Entrepreneurship Research, Volume 5, Issue 6, June 2023 Klerkx, L., & Rose, D (2020) Dealing with the game-changing technologies of Agriculture 4.0: how we manage diversity and responsibility in food system transition pathways? Global Food Security 24, 100347 Lokuge, S., Sedera, D., Grover, V., & Dongming, X (2019) Organisational readiness for digital innovation: development and empirical calibration of a construct Information and Management, 5600(3), 445-461 Doi: 10.1016/j.im.2018.09.001 Molinillo, S., Aguilar-Illescas, R., Anaya-Sanchez, R., & Carvajal-Trujillo, E (2022) The customer retail app experience: Implications for customer loyalty Journal of Retailing and Consumer Services, 65, 102842 Orhan, K., Shamshiev, M., Ezhov, M., Plaksin, A., Kurbanova, A., Ünsal, G., Gusarev, M., Golitsyna, M., Aksoy, S., Mısırlı, M., et al (2022) AI-based automatic segmentation of craniomaxillofacial anatomy from CBCT scans for automatic detection of pharyngeal airway evaluations in OSA patients Scientific Reports, 12, 118 Paolo, R., & Lapo, M (2022) Is COVID-19 enough? Which underestimated conditions characterise the adoption of complex information infrastructures in small and mediumsized enterprises Journal of Business Research, 144, 1249-1255 Pricewaterhousecoopers (2019) Worldwide Real Estate Investment Trust (REIT) Regimes Rose, D.C., Chilvers, J (2018) Agriculture 4.0: broadening responsible innovation in an era of smart farming Frontiers in Sustainable Food Systems, 2(87) https://doi.org/10.3389/fsufs.2018.00087 Schallmo, D., Williams, C.A., & Boardman, L (2017) Digital Transformation Of Business Models — Best Practice, Enablers, And Roadmap International Journal of Innovation Management 21(8) (December 2017) 1740014 (17 pages) DOI: 10.1142/S136391961740014X Trendov, N.M., Varas, S., & Zeng, M., (2019) Digtal Technologies in Agriculture and Rural Areas - Status Report Rome Vial, G., (2019) Understanding digital transformation: A review and a research agenda The Journal of Strategic Information Systems, 28, 118–144 World Bank (2017) ICT in Agriculture: Connecting Smallholders to Knowledge, World Bank (2019) Future of Food harnessing digital technologies to improvefood system outcomes Washington, DC Van & Tuyet, P.No 410-417 Page 417