Research Article
BibTex RIS Cite

Investigating the Mobile Learning Readiness Level of Managers in the Digital Transformation Process of Companies : An Empirical Study

Year 2023, , 252 - 265, 31.03.2023

Özlem Efiloğlu Kurt

https://doi.org/10.26466/opusjsr.1160243

Abstract

Companies are undergoing a process of change in all organizational processes along with digital transformation processes. Mobile devices are increasingly entering people’s daily lives as different smart devices and educational processes in the form of mobile learning. These developments in the field of technology are also effective in companies involved in the digital transformation process. These trends in developed countries are also becoming widespread in developing countries. In this study 109 managers working in a well-known company in the restaurant sector in Turkey to mobile learning processes and the factors affecting their readiness for mobile learning were examined. A partial least squares (PLS) path modeling approach is employed to examine relationships using SmartPLS 3. As a result of the analyses, facilitating conditions and social influence variables were found to have a positive effect on the behavioral intention during the acceptance process of managers’ mobile learning. In addition, it was found that among the control variables, there was a statistically significant difference only for time spent on the Internet with a smartphone. These results are generally consistent with the findings in the literature. This situation simultaneously draws attention to the future potential of mobile learning in terms of companies in our country in the context of digital transformation. With the implementation of this study in different sectors, the awareness of this issue in our country can be increased.

Keywords

Digital Transformation Mobile Learning Personal Innovativeness Behavioral Intention.

References

  • Abar, B., & Loken, E. (2010). Self-regulated learning and self-directed study in a pre-college sample. Learning and individual differences, 20(1), 25-29.
  • Abu-Al-Aish, A., & Love, S. (2013). Factors influencing students’ acceptance of m-learning: An investigation in higher education. International Review of Research in Open and Distributed Learning, 14(5), 82-107.
  • Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information systems research, 9(2), 204-215.
  • Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
  • Ajzen, I., & Fishbein, M. (1969). The prediction of behavioral intentions in a choice situation. Journal of experimental social psychology, 5(4), 400-416.
  • Al-Adwan, A. S., Al-Madadha, A., & Zvirzdinaite, Z. (2018). Modeling students’ readiness to adopt mobile learning in higher education: An empirical study. International Review of Research in Open and Distributed Learning, 19(1), 221-241.
  • Al-Adwan, A., Al-Adwan, A., & Smedley, J. (2013). Exploring students acceptance of e-learning using Technology Acceptance Model in Jordanian universities. International Journal of Education and Development using ICT, 9(2), 4-18.
  • Alamri, M. M. (2021). Using blended project-based learning for students’ behavioral intention to use and academic achievement in higher education. Education Sciences, 11(5), 207.
  • Ali, F., Rasoolimanesh, S. M., Sarstedt, M., Ringle, C. M., & Ryu, K. (2018). An assessment of the use of partial least squares structural equation modeling (PLS-SEM) in hospitality research. International Journal of Contemporary Hospitality Management, 30(1), 514-538.
  • Alkiş, N., & Coşkunçay, D. F. (2021). Mobil öğrenmenin kabulü: Sistematik literatür incelemesi. Erzincan Üniversitesi Eğitim Fakültesi Dergisi, 20(2), 571-589.
  • Almasri, A. K. M. (2014). The influence on mobile learning based on technology acceptance model (Tam), mobile readiness (Mr) and perceived interaction (Pi) for higher education students. International Journal of Technical Re-search and Applications, 2(1), 05-11.
  • Al-Rahmi, A. M., Al-Rahmi, W. M., Alturki, U., Aldraiweesh, A., Almutairy, S., & Al-Adwan, A. S. (2022). Ac-ceptance of mobile technologies and M-learning by university students: An empirical investigation in higher education. Education and Information Technologies, 27(6), 1-22.
  • Aytar, Ü. O. (2019). Endüstri 4.0 ve bu paradigmanın örgüt yönetimi üzerindeki olası etkileri. İş, Güc: Endüstri Iliskileri ve İnsan Kaynakları Dergisi, 21(2), 75-90.
  • Balkaya, S., & Akkucuk, U. (2021). Adoption and use of learning management systems in education: The role of playfulness and self-management. Sustainability, 13(3), 1127.
  • Bassiouni, D. H., Hackley, C., & Meshreki, H. (2019). The integration of video games in family-life dynamics: An adapted technology acceptance model of family intention to consume video games. Information Technology & People, 32(6), 1376-1396.
  • Basuki, R., Tarigan, Z., Siagian, H., Limanta, L., Setiawan, D., & Mochtar, J. (2022). The effects of perceived ease of use, usefulness, enjoyment and intention to use online platforms on behavioral intention in online movie watching during the pandemic era. International Journal of Data and Network Science, 6(1), 253-262.
  • Batalla-Busquets, J. M., & Martínez-Argüelles, M. J. (2014). Determining factors in online training in companies. The International Journal of Management Education, 12(2), 68-79.
  • Boyle, R. J., & Ruppel, C. P. (2006). The effects of personal innovativeness, perceived risk, and computer self-efficacy on online purchasing intent. Journal of International Technology and Information Management, 15(2), 5.
  • Briz-Ponce, L., Pereira, A., Carvalho, L., Juanes-Méndez, J. A., & García-Peñalvo, F. J. (2016). Learning with mobile technologies–Students’ behavior. Computers in human behavior, 72, 612-620.
  • Burton-Jones, A., & Straub Jr, D. W. (2006). Reconceptualizing system usage: An approach and empirical test. Information systems research, 17(3), 228-246.
  • Cao, J., Shang, Y., Mok, Q., & Lai, I. K. W. (2019, March). The impact of personal innovativeness on the intention to use cloud classroom: an empirical study in China. In International conference on technology in education (p.179-188). Springer.
  • Chao, C. M. (2019). Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in psychology, 10, 1652.
  • Chickowski, E. (2019, April 8). Putting HR at the heart of digital transformation. https://digirupt.io/putting-hr-at-the-heart-of-digital-transformation/ (Erişim Tarihi: 30.03.2022).
  • Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace 1. Journal of applied social psychology, 22(14), 1111-1132.
  • Deloitte (2017). Rewriting the rules for the digital age. https://www2.deloitte.com/content/dam/Deloitte/global/Documents/About-Deloitte/central-europe/ce-global-human-capital-trends.pdf (Erişim Tarihi: 30.03.2022).
  • Fitrianie, S., Horsch, C., Beun, R. J., Griffioen-Both, F., & Brinkman, W. P. (2021). Factors affecting user’s behavioral intention and use of a mobile-phone-delivered cognitive behavioral therapy for insomnia: A small-scale UTAUT analysis. Journal of Medical Systems, 45(12), 1-18.
  • Galić, S., Lušić, Z., & Stanivuk, T. (2020). E-learning in maritime affairs. Journal of Naval Architecture and Marine Engineering, 17(1), 38-50.
  • Garcia-Arroyo, J., & Osca, A. (2021). Big data contributions to human resource management: A systematic review. The International Journal of Human Resource Management, 32(20), 4337-4362. https://doi.org/10.1080/09585192.2019.1674357
  • Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152. https://doi.org/10.2753/MTP1069-6679190202
  • Hamidi, H., & Chavoshi, A. (2018). Analysis of the essential factors for the adoption of mobile learning in higher education: A case study of students of the University of Technology. Telematics and Informatics, 35(4), 1053-1070.
  • Henseler, J. (2012). PLS-MGA: A non-parametric approach to partial least squares-based multi-group analysis. In Challenges at the interface of data analysis, computer science, and optimization (p.495-501). Springer, Berlin, Hei-delberg.
  • Henseler, J., Ringle, C.M. and Sinkovics, R.R. (2009). The use of partial least squares path modeling in international marketing. Sinkovics, R.R. and Ghauri, P.N. (Ed.) New Challenges to International Marketing (Advances in Interna-tional Marketing, Vol. 20), Emerald Group Publishing Limited, Bingley, pp. 277-319.
  • Ho, J. C., Wu, C. G., Lee, C. S., & Pham, T. T. T. (2020). Factors affecting the behavioral intention to adopt mobile banking: An international comparison. Technology in Society, 63, 101360.
  • 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.
  • Ismail, M. H., Khater, M., & Zaki, M. (2017). Digital business transformation and strategy: What do we know so far. Cambridge Service Alliance, 10(1), 1-35.
  • Khrais, L. T. & Alghamdi, A. M. (2021). Investigating of mobile learning technology acceptance in companies. Ilkogretim Online, 20(5), 233-244.
  • Klein, M. (2020). İşletmelerde dijital dönüşüm ve etmenleri. Journal of Business in The Digital Age, 3(1), 24-35. Kraus, S., Durst, S., Ferreira, J. J., Veiga, P., Kailer, N., & Weinmann, A. (2022). Digital transformation in business and management research: An overview of the current status quo. International Journal of Information Management, 63, 102466.
  • Kuo, Y. F., & Yen, S. N. (2009). Towards an understanding of the behavioral intention to use 3G mobile value-added services. Computers in Human Behavior, 25(1), 103-110.
  • Kwong, K., & Wong, K. (2013). Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS. Marketing Bulletin, 24(1), 1-32.
  • Lowenthal, J. N. (2010). Using mobile learning: Determinates impacting behavioral intention. The Amer. Jrnl. of Distance Education, 24(4), 195-206.
  • Lu, J., Yao, J. E., & Yu, C. S. (2005). Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. The Journal of Strategic Information Systems, 14(3), 245-268.
  • Lucas Jr, H., Agarwal, R., Clemons, E. K., El Sawy, O. A., & Weber, B. (2013). Impactful research on transformational information technology: An opportunity to inform new audiences. Mis Quarterly, 37(2), 371-382.
  • Mahat, J., Ayub, A. F. M., & Luan, S. (2012). An assessment of students’ mobile self-efficacy, readiness and personal innovativeness towards mobile learning in higher education in Malaysia. Procedia-Social and Behavioral Scienc-es, 64, 284-290.
  • Matt, C., Hess, T., & Benlian, A. (2015). Digital transformation strategies. Business & information systems engineer-ing, 57(5), 339-343.
  • McGuire, R. (2020, October 19). HR: The Data-Powered Engine at the Center of Transformation. https://www.capgemini.com/2020/10/intelligent-hr/ (Erişim Tarihi: 30.03.2022).
  • Moos, D. C. (2010). Nonlinear technology: Changing the conception of extrinsic motivation?. Computers & Educa-tion, 55(4), 1640-1650.
  • Mun, Y. Y., Jackson, J. D., Park, J. S., & Probst, J. C. (2006). Understanding information technology acceptance by individual professionals: Toward an integrative view. Information & management, 43(3), 350-363.
  • Mussa, I. H. (2020). Mobile learning adoption in the Middle East: Limitations, challenges and features of the mobile devices. Int. J. Contemp. Manag. Inf. Technol, 1(1), 30-36.
  • Noor, A. M., Mahmood, N. H. N., & Zakaria, W. N. W. (2021). The impact of mobile learning application through intention to use on employees skill usage. International Journal of Academic Research in Business and Social Scienc-es, 11(11), 342 – 361.
  • Özguner, Z. (2021). Evaluation of critical success factors playing roles in the digital transformation process. Journal of Economics and Business Issues, 1(1), 39-49.
  • Park, S. Y., Nam, M. W., & Cha, S. B. (2012). University students' behavioral intention to use mobile learning: Evaluating the technology acceptance model. British journal of educational technology, 43(4), 592-605.
  • Parviainen, P., Tihinen, M., Kääriäinen, J., & Teppola, S. (2017). Tackling the digitalization challenge: how to benefit from digitalization in practice. International journal of information systems and project management, 5(1), 63-77.
  • Pillai, R., & Sivathanu, B. (2018). An empirical study on the adoption of M-learning apps among IT/ITeS employ-ees. Interactive Technology and Smart Education, 15(3), 182-204.
  • Poór, J., Sasvári, P., Szalay, Z., Pető, I., Gyurián, N., Suhajda, C. J., & Zsigri, F. (2020). The implementation and management of e-learning in companies: The state of e-learning in Hungary based on empirical re-search. Journal of Engineering Management and Competitiveness (JEMC), 10(1), 3-14.
  • Purcarea, A. A., Popescu, M., & Gheorghe, S. (2018). Research on Modern Methods of Adopting and Implementing E-Learning within Companies. In Proceedings of the International Association for Development of the Information Socie-ty (IADIS) International Conference on e-Learning (p.177-181).
  • Püschel, J., Mazzon, J. A., & Hernandez, J. M. C. (2010). Mobile banking: proposition of an integrated adoption intention framework. International Journal of bank marketing, 28(5), 389-409.
  • Reinartz, W., Haenlein, M., & Henseler, J. (2009). An empirical comparison of the efficacy of covariance-based and variance-based SEM. International Journal of research in Marketing, 26(4), 332-344.
  • Ringle, C. M., Wende, S., & Becker, J. M. (2015). SmartPLS 3. Boenningstedt. SmartPLS GmbH. Sağlam, M. (2021). İşletmelerde geleceğin vizyonu olarak dijital dönüşümün gerçekleştirilmesi ve dijital dönüşüm ölçeğinin Türkçe uyarlaması. İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, 20(40), 395-420.
  • Sen, S. (2020). Digital HR strategy: Achieving sustainable transformation in the digital age. Kogan Page Publishers.
  • Shapiro, T. (2017). Key determinants of M-learning adoption for optimal professional development in the workplace in South Africa. (Doctoral dissertation). University of the Witwatersrand, Faculty of Humanities, School of Education.
  • Shorfuzzaman, M., & Alhussein, M. (2016). Modeling learners’ readiness to adopt mobile learning: A perspective from a GCC higher education institution. Mobile information systems. https://doi.org/10.1155/2016/6982824
  • Simarmata, M. T., & Hia, I. J. (2020). The role of personal innovativeness on behavioral intention of Information Technology. Journal of Economics and Business, 1(2), 18-29.
  • Sung, H. N., Jeong, D. Y., Jeong, Y. S., & Shin, J. I. (2015). The effects of self-efficacy and social influence on behavioral intention in mobile learning service. Advanced Science and Technology Letters, 103, 27-30.
  • Thatcher, J. B., & Perrewe, P. L. (2002). An empirical examination of individual traits as antecedents to computer anxiety and computer self-efficacy. MIS Quarterly, 26(4), 381-396.
  • To, A. T., & Trinh, T. H. M. (2021). Understanding behavioral intention to use mobile wallets in Vietnam: Extending the tam model with trust and enjoyment. Cogent Business & Management, 8(1), 1891661.
  • Tortora, D., Chierici, R., Briamonte, M. F., & Tiscini, R. (2021). ‘I digitize so I exist’. Searching for critical capabilities affecting firms’ digital innovation. Journal of Business Research, 129, 193-204.
  • Um, N. (2021). Learners' attitude toward e-learning: The effects of perceived system quality and e-learning usefulness, self-management of learning, and self-efficacy. International Journal of Contents, 17(2), 41-47.
  • Velananda, Y. L., & Wanninayake, W. M. C. B. (2020). Readiness to adopt M-Learning solutions as the training platform among corporate companies in Sri Lanka. In Proceedings of the International Conference on Business & In-formation (ICBI). University of Kelaniya. (p.788-803).
  • Venkatesh, V. (2022). Adoption and use of AI tools: A research agenda grounded in UTAUT. Annals of Operations Research, 308(1), 641-652.
  • Venkatesh, V., Brown, S. A., Maruping, L. M., & Bala, H. (2008). Predicting different conceptualizations of system use: The competing roles of behavioral intention, facilitating conditions, and behavioral expectation. MIS Quarter-ly, 32(3), 483-502.
  • 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.
  • Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889-901.
  • Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The journal of strategic information systems, 28(2), 118-144.
  • Wang, Y. S., Wu, M. C., & Wang, H. Y. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British journal of educational technology, 40(1), 92-118.
  • Wu, X., Tam, C. M., & Fang, S. (2020, August). Users’ behavioral intention toward M-learning in tourism english education: A case study of macao. In International Conference on Technology in Education (p.308-322). Springer.
  • Yeh, C. H., Wang, Y. S., Wang, Y. M., & Liao, T. J. (2021). Drivers of mobile learning app usage: An integrated perspective of personality, readiness, and motivation. Interactive Learning Environments. https://doi.org/10.1080/10494820.2021.1937658
  • Zhonggen, Y., & Xiaozhi, Y. (2019). An extended technology acceptance model of a mobile learning technolo-gy. Computer Applications in Engineering Education, 27(3), 721-732.
  • Zou, X., & Zhang, X. (2013). Effect of different score reports of Web-based formative test on students' self-regulated learning. Computers & Education, 66, 54-63.

Year 2023, , 252 - 265, 31.03.2023

Özlem Efiloğlu Kurt

https://doi.org/10.26466/opusjsr.1160243

Abstract

İşletmeler, dijital dönüşüm süreçleriyle birlikte tüm organizasyonel süreçlerinde bir değişim sü-recinden geçmektedir. Mobil cihazlar, giderek artan oranda insanların günlük hayatlarına farklı akıllı cihazlar olarak ve eğitim süreçlerine de mobil öğrenme şeklinde girmektedir. Teknoloji alanın-daki bu gelişmeler, dijital dönüşüm sürecinde yer alan işletmelerde de etkili olmaktadır. Gelişmiş ülkelerdeki bu eğilimler, gelişmekte olan ülkelerde de yaygınlaşmaktadır. Bu çalışmada Türkiye’de restoran sektöründe yer alan tanınmış bir işletmede çalışan 109 yöneticinin mobil öğrenme süreçleri ve bu yöneticilerin mobil öğrenmeye hazırbulunuşluklarını etkileyen faktörler incelenmiştir. SmartPLS 3 kullanılarak ilişkileri incelemek için kısmi en küçük kareler (PLS) yöntemi kullanılmıştır. Analizler sonucunda yöneticilerin mobil öğrenme kabulü sürecinde, kolaylaştırıcı şartlar ve sosyal etki değişkenlerinin davranışsal niyet değişkenini pozitif ve anlamlı bir şekilde etkilediği görülmüştür. Ayrıca kontrol değişkenleri arasında sadece internette akıllı telefon ile internette geçirilen süre için istatistiksel olarak anlamlı bir fark olduğu belirlenmiştir. Bu sonuçlar literatürdeki sonuçlarla genel olarak uyumludur. Bu durum aynı zamanda, mobil öğrenmenin dijital dönüşüm çerçevesinde, ülkemizdeki işletmeler açısından gelecekteki potansiyeline dikkat çekmektedir. Bu çalışmanın farklı sektörlerde yapılması ile ülkemizde bu konudaki farkındalık artırılabilir.

Keywords

Dijital Dönüşüm Mobil Öğrenme Kişisel Yenilikçilik Davranışsal Niyet.

References

  • Abar, B., & Loken, E. (2010). Self-regulated learning and self-directed study in a pre-college sample. Learning and individual differences, 20(1), 25-29.
  • Abu-Al-Aish, A., & Love, S. (2013). Factors influencing students’ acceptance of m-learning: An investigation in higher education. International Review of Research in Open and Distributed Learning, 14(5), 82-107.
  • Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information systems research, 9(2), 204-215.
  • Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
  • Ajzen, I., & Fishbein, M. (1969). The prediction of behavioral intentions in a choice situation. Journal of experimental social psychology, 5(4), 400-416.
  • Al-Adwan, A. S., Al-Madadha, A., & Zvirzdinaite, Z. (2018). Modeling students’ readiness to adopt mobile learning in higher education: An empirical study. International Review of Research in Open and Distributed Learning, 19(1), 221-241.
  • Al-Adwan, A., Al-Adwan, A., & Smedley, J. (2013). Exploring students acceptance of e-learning using Technology Acceptance Model in Jordanian universities. International Journal of Education and Development using ICT, 9(2), 4-18.
  • Alamri, M. M. (2021). Using blended project-based learning for students’ behavioral intention to use and academic achievement in higher education. Education Sciences, 11(5), 207.
  • Ali, F., Rasoolimanesh, S. M., Sarstedt, M., Ringle, C. M., & Ryu, K. (2018). An assessment of the use of partial least squares structural equation modeling (PLS-SEM) in hospitality research. International Journal of Contemporary Hospitality Management, 30(1), 514-538.
  • Alkiş, N., & Coşkunçay, D. F. (2021). Mobil öğrenmenin kabulü: Sistematik literatür incelemesi. Erzincan Üniversitesi Eğitim Fakültesi Dergisi, 20(2), 571-589.
  • Almasri, A. K. M. (2014). The influence on mobile learning based on technology acceptance model (Tam), mobile readiness (Mr) and perceived interaction (Pi) for higher education students. International Journal of Technical Re-search and Applications, 2(1), 05-11.
  • Al-Rahmi, A. M., Al-Rahmi, W. M., Alturki, U., Aldraiweesh, A., Almutairy, S., & Al-Adwan, A. S. (2022). Ac-ceptance of mobile technologies and M-learning by university students: An empirical investigation in higher education. Education and Information Technologies, 27(6), 1-22.
  • Aytar, Ü. O. (2019). Endüstri 4.0 ve bu paradigmanın örgüt yönetimi üzerindeki olası etkileri. İş, Güc: Endüstri Iliskileri ve İnsan Kaynakları Dergisi, 21(2), 75-90.
  • Balkaya, S., & Akkucuk, U. (2021). Adoption and use of learning management systems in education: The role of playfulness and self-management. Sustainability, 13(3), 1127.
  • Bassiouni, D. H., Hackley, C., & Meshreki, H. (2019). The integration of video games in family-life dynamics: An adapted technology acceptance model of family intention to consume video games. Information Technology & People, 32(6), 1376-1396.
  • Basuki, R., Tarigan, Z., Siagian, H., Limanta, L., Setiawan, D., & Mochtar, J. (2022). The effects of perceived ease of use, usefulness, enjoyment and intention to use online platforms on behavioral intention in online movie watching during the pandemic era. International Journal of Data and Network Science, 6(1), 253-262.
  • Batalla-Busquets, J. M., & Martínez-Argüelles, M. J. (2014). Determining factors in online training in companies. The International Journal of Management Education, 12(2), 68-79.
  • Boyle, R. J., & Ruppel, C. P. (2006). The effects of personal innovativeness, perceived risk, and computer self-efficacy on online purchasing intent. Journal of International Technology and Information Management, 15(2), 5.
  • Briz-Ponce, L., Pereira, A., Carvalho, L., Juanes-Méndez, J. A., & García-Peñalvo, F. J. (2016). Learning with mobile technologies–Students’ behavior. Computers in human behavior, 72, 612-620.
  • Burton-Jones, A., & Straub Jr, D. W. (2006). Reconceptualizing system usage: An approach and empirical test. Information systems research, 17(3), 228-246.
  • Cao, J., Shang, Y., Mok, Q., & Lai, I. K. W. (2019, March). The impact of personal innovativeness on the intention to use cloud classroom: an empirical study in China. In International conference on technology in education (p.179-188). Springer.
  • Chao, C. M. (2019). Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in psychology, 10, 1652.
  • Chickowski, E. (2019, April 8). Putting HR at the heart of digital transformation. https://digirupt.io/putting-hr-at-the-heart-of-digital-transformation/ (Erişim Tarihi: 30.03.2022).
  • Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace 1. Journal of applied social psychology, 22(14), 1111-1132.
  • Deloitte (2017). Rewriting the rules for the digital age. https://www2.deloitte.com/content/dam/Deloitte/global/Documents/About-Deloitte/central-europe/ce-global-human-capital-trends.pdf (Erişim Tarihi: 30.03.2022).
  • Fitrianie, S., Horsch, C., Beun, R. J., Griffioen-Both, F., & Brinkman, W. P. (2021). Factors affecting user’s behavioral intention and use of a mobile-phone-delivered cognitive behavioral therapy for insomnia: A small-scale UTAUT analysis. Journal of Medical Systems, 45(12), 1-18.
  • Galić, S., Lušić, Z., & Stanivuk, T. (2020). E-learning in maritime affairs. Journal of Naval Architecture and Marine Engineering, 17(1), 38-50.
  • Garcia-Arroyo, J., & Osca, A. (2021). Big data contributions to human resource management: A systematic review. The International Journal of Human Resource Management, 32(20), 4337-4362. https://doi.org/10.1080/09585192.2019.1674357
  • Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152. https://doi.org/10.2753/MTP1069-6679190202
  • Hamidi, H., & Chavoshi, A. (2018). Analysis of the essential factors for the adoption of mobile learning in higher education: A case study of students of the University of Technology. Telematics and Informatics, 35(4), 1053-1070.
  • Henseler, J. (2012). PLS-MGA: A non-parametric approach to partial least squares-based multi-group analysis. In Challenges at the interface of data analysis, computer science, and optimization (p.495-501). Springer, Berlin, Hei-delberg.
  • Henseler, J., Ringle, C.M. and Sinkovics, R.R. (2009). The use of partial least squares path modeling in international marketing. Sinkovics, R.R. and Ghauri, P.N. (Ed.) New Challenges to International Marketing (Advances in Interna-tional Marketing, Vol. 20), Emerald Group Publishing Limited, Bingley, pp. 277-319.
  • Ho, J. C., Wu, C. G., Lee, C. S., & Pham, T. T. T. (2020). Factors affecting the behavioral intention to adopt mobile banking: An international comparison. Technology in Society, 63, 101360.
  • 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.
  • Ismail, M. H., Khater, M., & Zaki, M. (2017). Digital business transformation and strategy: What do we know so far. Cambridge Service Alliance, 10(1), 1-35.
  • Khrais, L. T. & Alghamdi, A. M. (2021). Investigating of mobile learning technology acceptance in companies. Ilkogretim Online, 20(5), 233-244.
  • Klein, M. (2020). İşletmelerde dijital dönüşüm ve etmenleri. Journal of Business in The Digital Age, 3(1), 24-35. Kraus, S., Durst, S., Ferreira, J. J., Veiga, P., Kailer, N., & Weinmann, A. (2022). Digital transformation in business and management research: An overview of the current status quo. International Journal of Information Management, 63, 102466.
  • Kuo, Y. F., & Yen, S. N. (2009). Towards an understanding of the behavioral intention to use 3G mobile value-added services. Computers in Human Behavior, 25(1), 103-110.
  • Kwong, K., & Wong, K. (2013). Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS. Marketing Bulletin, 24(1), 1-32.
  • Lowenthal, J. N. (2010). Using mobile learning: Determinates impacting behavioral intention. The Amer. Jrnl. of Distance Education, 24(4), 195-206.
  • Lu, J., Yao, J. E., & Yu, C. S. (2005). Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. The Journal of Strategic Information Systems, 14(3), 245-268.
  • Lucas Jr, H., Agarwal, R., Clemons, E. K., El Sawy, O. A., & Weber, B. (2013). Impactful research on transformational information technology: An opportunity to inform new audiences. Mis Quarterly, 37(2), 371-382.
  • Mahat, J., Ayub, A. F. M., & Luan, S. (2012). An assessment of students’ mobile self-efficacy, readiness and personal innovativeness towards mobile learning in higher education in Malaysia. Procedia-Social and Behavioral Scienc-es, 64, 284-290.
  • Matt, C., Hess, T., & Benlian, A. (2015). Digital transformation strategies. Business & information systems engineer-ing, 57(5), 339-343.
  • McGuire, R. (2020, October 19). HR: The Data-Powered Engine at the Center of Transformation. https://www.capgemini.com/2020/10/intelligent-hr/ (Erişim Tarihi: 30.03.2022).
  • Moos, D. C. (2010). Nonlinear technology: Changing the conception of extrinsic motivation?. Computers & Educa-tion, 55(4), 1640-1650.
  • Mun, Y. Y., Jackson, J. D., Park, J. S., & Probst, J. C. (2006). Understanding information technology acceptance by individual professionals: Toward an integrative view. Information & management, 43(3), 350-363.
  • Mussa, I. H. (2020). Mobile learning adoption in the Middle East: Limitations, challenges and features of the mobile devices. Int. J. Contemp. Manag. Inf. Technol, 1(1), 30-36.
  • Noor, A. M., Mahmood, N. H. N., & Zakaria, W. N. W. (2021). The impact of mobile learning application through intention to use on employees skill usage. International Journal of Academic Research in Business and Social Scienc-es, 11(11), 342 – 361.
  • Özguner, Z. (2021). Evaluation of critical success factors playing roles in the digital transformation process. Journal of Economics and Business Issues, 1(1), 39-49.
  • Park, S. Y., Nam, M. W., & Cha, S. B. (2012). University students' behavioral intention to use mobile learning: Evaluating the technology acceptance model. British journal of educational technology, 43(4), 592-605.
  • Parviainen, P., Tihinen, M., Kääriäinen, J., & Teppola, S. (2017). Tackling the digitalization challenge: how to benefit from digitalization in practice. International journal of information systems and project management, 5(1), 63-77.
  • Pillai, R., & Sivathanu, B. (2018). An empirical study on the adoption of M-learning apps among IT/ITeS employ-ees. Interactive Technology and Smart Education, 15(3), 182-204.
  • Poór, J., Sasvári, P., Szalay, Z., Pető, I., Gyurián, N., Suhajda, C. J., & Zsigri, F. (2020). The implementation and management of e-learning in companies: The state of e-learning in Hungary based on empirical re-search. Journal of Engineering Management and Competitiveness (JEMC), 10(1), 3-14.
  • Purcarea, A. A., Popescu, M., & Gheorghe, S. (2018). Research on Modern Methods of Adopting and Implementing E-Learning within Companies. In Proceedings of the International Association for Development of the Information Socie-ty (IADIS) International Conference on e-Learning (p.177-181).
  • Püschel, J., Mazzon, J. A., & Hernandez, J. M. C. (2010). Mobile banking: proposition of an integrated adoption intention framework. International Journal of bank marketing, 28(5), 389-409.
  • Reinartz, W., Haenlein, M., & Henseler, J. (2009). An empirical comparison of the efficacy of covariance-based and variance-based SEM. International Journal of research in Marketing, 26(4), 332-344.
  • Ringle, C. M., Wende, S., & Becker, J. M. (2015). SmartPLS 3. Boenningstedt. SmartPLS GmbH. Sağlam, M. (2021). İşletmelerde geleceğin vizyonu olarak dijital dönüşümün gerçekleştirilmesi ve dijital dönüşüm ölçeğinin Türkçe uyarlaması. İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, 20(40), 395-420.
  • Sen, S. (2020). Digital HR strategy: Achieving sustainable transformation in the digital age. Kogan Page Publishers.
  • Shapiro, T. (2017). Key determinants of M-learning adoption for optimal professional development in the workplace in South Africa. (Doctoral dissertation). University of the Witwatersrand, Faculty of Humanities, School of Education.
  • Shorfuzzaman, M., & Alhussein, M. (2016). Modeling learners’ readiness to adopt mobile learning: A perspective from a GCC higher education institution. Mobile information systems. https://doi.org/10.1155/2016/6982824
  • Simarmata, M. T., & Hia, I. J. (2020). The role of personal innovativeness on behavioral intention of Information Technology. Journal of Economics and Business, 1(2), 18-29.
  • Sung, H. N., Jeong, D. Y., Jeong, Y. S., & Shin, J. I. (2015). The effects of self-efficacy and social influence on behavioral intention in mobile learning service. Advanced Science and Technology Letters, 103, 27-30.
  • Thatcher, J. B., & Perrewe, P. L. (2002). An empirical examination of individual traits as antecedents to computer anxiety and computer self-efficacy. MIS Quarterly, 26(4), 381-396.
  • To, A. T., & Trinh, T. H. M. (2021). Understanding behavioral intention to use mobile wallets in Vietnam: Extending the tam model with trust and enjoyment. Cogent Business & Management, 8(1), 1891661.
  • Tortora, D., Chierici, R., Briamonte, M. F., & Tiscini, R. (2021). ‘I digitize so I exist’. Searching for critical capabilities affecting firms’ digital innovation. Journal of Business Research, 129, 193-204.
  • Um, N. (2021). Learners' attitude toward e-learning: The effects of perceived system quality and e-learning usefulness, self-management of learning, and self-efficacy. International Journal of Contents, 17(2), 41-47.
  • Velananda, Y. L., & Wanninayake, W. M. C. B. (2020). Readiness to adopt M-Learning solutions as the training platform among corporate companies in Sri Lanka. In Proceedings of the International Conference on Business & In-formation (ICBI). University of Kelaniya. (p.788-803).
  • Venkatesh, V. (2022). Adoption and use of AI tools: A research agenda grounded in UTAUT. Annals of Operations Research, 308(1), 641-652.
  • Venkatesh, V., Brown, S. A., Maruping, L. M., & Bala, H. (2008). Predicting different conceptualizations of system use: The competing roles of behavioral intention, facilitating conditions, and behavioral expectation. MIS Quarter-ly, 32(3), 483-502.
  • 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.
  • Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889-901.
  • Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The journal of strategic information systems, 28(2), 118-144.
  • Wang, Y. S., Wu, M. C., & Wang, H. Y. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British journal of educational technology, 40(1), 92-118.
  • Wu, X., Tam, C. M., & Fang, S. (2020, August). Users’ behavioral intention toward M-learning in tourism english education: A case study of macao. In International Conference on Technology in Education (p.308-322). Springer.
  • Yeh, C. H., Wang, Y. S., Wang, Y. M., & Liao, T. J. (2021). Drivers of mobile learning app usage: An integrated perspective of personality, readiness, and motivation. Interactive Learning Environments. https://doi.org/10.1080/10494820.2021.1937658
  • Zhonggen, Y., & Xiaozhi, Y. (2019). An extended technology acceptance model of a mobile learning technolo-gy. Computer Applications in Engineering Education, 27(3), 721-732.
  • Zou, X., & Zhang, X. (2013). Effect of different score reports of Web-based formative test on students' self-regulated learning. Computers & Education, 66, 54-63.
There are 78 citations in total.

Details

Primary Language English
Journal Section Research Articles
Authors

Özlem Efiloğlu Kurt

Publication Date March 31, 2023
Published in Issue Year 2023

Cite

APA Efiloğlu Kurt, Ö. (2023). Investigating the Mobile Learning Readiness Level of Managers in the Digital Transformation Process of Companies : An Empirical Study. OPUS Journal of Society Research, 20(52), 252-265. https://doi.org/10.26466/opusjsr.1160243