Role of Big data Capabilities and Adoption in Innovation: Moderating Influence of Employee Engagement
Abstract
Aim: South Korea’s commitment to fostering a digitally well-informed society and its early embrace of mobile technologies has positioned the country at the forefront of the global mobile tech arena. South Korea has emerged as a global powerhouse in the technology sector, with its mobile tech industry playing a pivotal role in the country’s economic landscape. Korean mobile tech companies have garnered international recognition for their innovation, cutting-edge technologies, and influential contributions to the global tech ecosystem. This descriptive and quantitative study investigates the intricate relationships among key variables, namely big data adoption, big data analytics capabilities, employee engagement, digital innovation, and innovative behavior within the framework of Korean mobile tech companies.
Methodology: TUtilizing a cross-sectional design and convenience sampling, data was collected from 213 managers working in this industry. The research draws on the Resource-Based View (RBV) and Dynamic Capability Theory to provide a comprehensive understanding of the interplay between these factors. The study employs Smart PLS (Partial Least Squares) for analysis, leveraging its suitability for complex structural equation modeling.
Findings: Through this methodology, the research aims to uncover the nuanced connections between big data dynamics, employee engagement, and innovation within the unique context of Korean mobile tech companies. Smart PLS facilitates the simultaneous examination of multiple relationships and allows for the exploration of complex interactions, aligning with the multifaceted nature of the study’s objectives.
Implications/Novel Contribution: The findings are anticipated to contribute valuable insights to both academia and industry by offering a robust examination of the mechanisms shaping innovation in Korean mobile tech companies. As organizations navigate the evolving landscape of technology and data-driven decision-making, the outcomes of this study aim to inform strategic decision-making and enhance the understanding of the factors influencing digital innovation and innovative behavior in this specific sector
References
Al-Khatib, A. W. (2022). Intellectual capital and innovation performance: The moderating role of big data analytics: evidence from the banking sector in Jordan. EuroMed Journal of Business, 17(3), 391-423.
Arias-Pérez, J., Coronado-Medina, A., & Perdomo-Charry, G. (2022). Big data analytics capability as a mediator in the impact of open innovation on firm performance. Journal of Strategy and Management, 15(1), 1-15.
Awan, U., Bhatti, S. H., Shamim, S., Khan, Z., Akhtar, P., & Balta, M. E. (2022). The role of big data analytics in manufacturing agility and performance: Moderation-mediation analysis of organizational creativity and of the involvement of customers as data analysts. British Journal of Management, 33(3), 1200-1220.
Awan, U., Shamim, S., Khan, Z., Zia, N. U., Shariq, S. M., & Khan, M. N. (2021). Big data analytics capability and decision-making: The role of data-driven insight on circular economy performance. Technological Forecasting and Social Change, 168, 120766.
Barreiro, C. A., & Treglown, L. (2020). What makes an engaged employee? A facet-level approach to trait emotional intelligence as a predictor of employee engagement. Personality and Individual Differences, 159, 109892.
Bleady, A., Ali, A. H., & Ibrahim, S. B. (2018). Dynamic capabilities theory: Pinning down a shifting concept. Academy of Accounting and Financial Studies Journal, 22(2), 1-16.
Ciampi, F., Demi, S., Magrini, A., Marzi, G., & Papa, A. (2021). Exploring the impact of big data analytics capabilities on business model innovation: The mediating role of entrepreneurial orientation. Journal of Business Research, 123, 1-13.
Cosimato, S., & Vona, R. (2021). Digital innovation for the sustainability of reshoring strategies: A literature review. Sustainability, 13(14), 7601.
Di Vaio, A., Palladino, R., Pezzi, A., & Kalisz, D. E. (2021). The role of digital innovation in knowledge management systems: A systematic literature review. Journal of Business Research, 123, 220-231.
Freeman, R. E., Dmytriyev, S. D., & Phillips, R. A. (2021). Stakeholder theory and the resource-based view of the firm. Journal of Management, 47(7), 1757-1770.
Hair, J., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems, 117(3), 442-458.
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24.
Hair Jr, J. F., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101-110.
Heslina, H., & Syahruni, A. (2021). The influence of information technology, human resources competency and employee engagement on performance of employees. Golden Ratio of Human Resource Management, 1(1), 01-12.
Ibrahim, A. A. A., Abu Bakar, A. R., & Ahmad, S. Z. (2024). Exploring the relationship between big data analytics capability and organization’s strategic intent: mediating role of environmental scanning. Journal of Internet Commerce, 23(1), 26-49.
Jaouadi, M. H. O. (2022). Investigating the influence of big data analytics capabilities and human resource factors in achieving supply chain innovativeness. Computers & Industrial Engineering, 168, 108055.
Jin, D. Y. (2018). Evolution of Korea’s mobile technologies: A historical approach. Mobile Media & Communication, 6(1), 71-87. Kapoor, M., & Aggarwal, V. (2020). Tracing the economics behind dynamic capabilities theory. International Journal of Innovation Science, 12(2), 187-201.
Kör, B., Wakkee, I., & van der Sijde, P. (2021). How to promote managers’ innovative behavior at work: Individual factors and perceptions. Technovation, 99, 102127.
Kwon, K., & Kim, T. (2020). An integrative literature review of employee engagement and innovative behavior: Revisiting the JD-R model. Human Resource Management Review, 30(2), 100704.
Lee, W., Yoon, J., Altmann, J., & Lee, J.-D. (2021). Model for identifying firm’s product innovation dynamics: Applied to the case of the Korean mobile phone industry. Technology Analysis & Strategic Management, 33(4), 335-348.
Liu, Y., Dong, J., Mei, L., & Shen, R. (2023). Digital innovation and performance of manufacturing firms: An affordance perspective. Technovation, 119, 102458.
Lu, X., Yu, H., & Shan, B. (2022). Relationship between employee mental health and job performance: Mediation role of innovative behavior and work engagement. International Journal of Environmental Research and Public Health, 19(11), 6599.
Mahmood, Q.-U.-A., Ahmed, R., & Philbin, S. P. (2023). The moderating effect of big data analytics on green human resource management and organizational performance. International Journal of Management Science and Engineering Management, 18(3), 177-189.
Maroufkhani, P., Tseng, M.-L., Iranmanesh, M., Ismail, W. K. W., & Khalid, H. (2020). Big data analytics adoption: Determinants and performances among small to medium-sized enterprises. International Journal of Information Management, 54, 102190.
Mendling, J., Pentland, B. T., & Recker, J. (2020). Building a complementary agenda for business process management and digital innovation (Vol. 29) (No. 3). Taylor & Francis.
Park, J.-H., & Kim, Y. B. (2021). Factors activating big data adoption by Korean firms. Journal of Computer Information Systems, 61(3), 285-293.
Shamim, S., Zeng, J., Khan, Z., & Zia, N. U. (2020). Big data analytics capability and decision making performance in emerging market firms: The role of contractual and relational governance mechanisms. Technological Forecasting and Social Change, 161, 120315.
Shibin, K., Dubey, R., Gunasekaran, A., Hazen, B., Roubaud, D., Gupta, S., & Foropon, C. (2020). Examining sustainable supply chain management of SMEs using resource based view and institutional theory. Annals of Operations Research, 290, 301-326.
Su, X., Zeng, W., Zheng, M., Jiang, X., Lin, W., & Xu, A. (2022). Big data analytics capabilities and organizational performance: The mediating effect of dual innovations. European Journal of Innovation Management, 25(4), 1142-1160.
Sung, W., & Kim, C. (2021). A study on the effect of change management on organizational Innovation: Focusing on the mediating effect of members’ innovative behavior. Sustainability, 13(4), 2079.
Taher, M. (2011). Resource-based view theory. In Information systems theory: Explaining and predicting our digital society. Springer.
Wang, C., Xu, J., Zhang, T. C., & Li, Q. M. (2020). Effects of professional identity on turnover intention in China’s hotel employees: The mediating role of employee engagement and job satisfaction. Journal of Hospitality and Tourism Management, 45, 10-22.
Wheeler, B. C. (2002). NEBIC: A dynamic capabilities theory for assessing net-enablement. Information Systems Research, 13(2), 125-146.
Zhe, B. O. M., & Hamid, N. A. (2021). The impact of digital technology, digital capability and digital innovation on small business performance. Research in Management of Technology and Business, 2(1), 499-509.
Zheng, L. J., Zhang, J. Z., Wang, H., & Hong, J. F. (2022). Exploring the impact of big data analytics capabilities on the dual nature of innovative activities in MSMEs: A data-agility-innovation perspective. Annals of Operations Research, 11, 1-29.
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