Organizational Ambidexterity as an Outcome of Quality Dimensions and Triple Helix: The Role of Technology Readiness and User Satisfaction
Abstract
:1. Introduction
2. Literature Review and Hypothesis
2.1. Triple Helix
2.2. Quality Dimensions
2.3. Technology Readiness
2.4. User Satisfaction
2.5. Organizational Ambidexterity
- 1)
- A compelling strategic intent that intellectually justifies the importance of both exploration and exploitation.
- 2)
- The articulation of a shared vision and values that create a common identity for all exploitation and exploration entities.
- 3)
- A senior team explicitly responsible for the unit’s exploration and exploitation strategy; there is a reward system for shared destiny, and the strategy is relentlessly communicated.
- 4)
- Separate but aligned organizational architectures (business model, structure, incentives, metrics, and culture) for exploration and exploitation units and focused integration at the senior and tactical levels to properly leverage organizational benefits.
- 5)
- The ability of senior leadership to tolerate and resolve tensions arising from different alignments.
3. Hypothesis Development
4. Research Method
- n = number of samples
- N = total population
- e = % tolerable accuracy tolerance
5. Data Analysis
5.1. Outer Model and Validation
5.2. Result of Inner Model and Testing of Hypotheses
5.3. Mediation Effects Testing
6. Discussion
7. Conclusions, Limitations and Future Work
7.1. Theoretical Implications
7.2. Managerial Implications
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Triple Helix; Source: [12,13,14,15,17,23] | |
---|---|
UNR1 | The university plays an essential part in the organization’s growth. |
UNR2 | The university delivers solutions to the organization in the form of new knowledge. |
UNR3 | Education experts are concerned with the growth of the organization. |
UNR4 | The university gives the organization active assistance. |
UNR5 | With assistance from the university, the organization’s quality has improved. |
INR1 | The industry delivers trained labor for the accomplishment of organizational objectives. |
INR2 | Industry assists a business in the processing or marketing of its goods or services. |
INR3 | Because of the industry’s involvement, the organization’s services or products are of higher quality. |
INR4 | Industry has a crucial influence in an organization’s finance. |
INR5 | I believe that a strong organization is one that has relations to multiple industries. |
GOR1 | The government must establish a relationship with all organizations. |
GOR2 | A reputable organization complies with government regulations. |
GOR3 | The government must evaluate an organization’s latest advances. |
GOR4 | A government that communicates actively with organizations inspires my trust in innovation development. |
GOR5 | I anticipate that the government will oversee the growth of each government organization. |
Quality Dimensions; source: [18,20,21,22] | |
SYQ1 | I always evaluate a business’s worth based on the system they employ. |
SYQ2 | I anticipate system procedures that are straightforward. |
SYQ3 | I expect the system to utilize cutting-edge technology. |
SYQ4 | I value systems with an intuitive interface. |
SYQ5 | The development of a system within an organization is critical to me. |
KMQ1 | The company’s knowledge and information must be accurate. |
KMQ2 | I prioritize businesses that can effectively communicate information. |
KMQ3 | I expect the company’s information to be truthful and trustworthy. |
KMQ4 | A business that can process the information it has is a good company. |
KMQ5 | Information and knowledge are crucial to a company. |
SEQ1 | The primary focus of an organization should be on the quality of its service. |
SEQ2 | I give preference to companies who maintain the quality of their services. |
SEQ3 | I believe a successful company ensures the quality of its offerings. |
SEQ4 | I want the company to deliver the greatest customer service possible constantly. |
SEQ5 | The service quality of the company must be able to improve over time. |
SYQ1 | I always evaluate a business’s worth based on the system they employ. |
Technology Readiness; Source: [23,24,25] | |
OPT1 | My trust grows when an organization can guarantee ambidexterity. |
OPT2 | I have more confidence in a company that ensures its business continuity. |
OPT3 | My optimism grows when the company always puts my satisfaction first. |
INV1 | I prefer companies that can continue to grow and innovate. |
INV2 | For me, a good company can keep up with the times. |
INV3 | I use the services of the organization to improve my knowledge. |
DIC1 | I am concerned that my privacy is no longer protected in the advancing world. |
DIC2 | I have doubts about organizations that never adopt the latest innovations. |
DIC3 | Organizations that fail to innovate well are putting their members at a disadvantage. |
INS1 | Organizational developments are too fast for me to keep up with them. |
INS2 | Innovations complicate procedures. |
INS3 | A lot of people are better educated than I am. |
User Satisfaction; Source: [26,28,29,30,31] | |
SAT1 | When the organization uses the most recent innovations, I am satisfied. |
SAT2 | I am content if the organization prioritizes its members. |
SAT3 | If the organization can continue to expand, I will always be a member or customer. |
SAT4 | I favor an organization that can determine the requirements of its members or clients. |
SAT5 | I am content if I can utilize the organization’s most recent innovations. |
Organizational Ambidexterity; Source: [34,36,37,38] | |
ORA1 | Organizations must be capable of further expansion. |
ORA2 | Organizations must always prioritize their members. |
ORA3 | Organizations should focus on the most recent innovations. |
ORA4 | A well-balanced development is an indication of a well-established organization. |
ORA5 | The development of an organization is more valuable than its profit. |
DIC | GOR | INR | INS | INV | KMQ | OPT | ORA | SAT | SEQ | SYQ | UNR | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
DIC1 | 0.845 | 0.846 | 0.856 | 0.735 | 0.523 | 0.553 | 0.643 | 0.844 | 0.800 | 0.754 | 0.835 | 0.857 |
DIC2 | 0.886 | 0.705 | 0.766 | 0.815 | 0.574 | 0.615 | 0.727 | 0.704 | 0.712 | 0.615 | 0.695 | 0.766 |
DIC3 | 0.865 | 0.685 | 0.655 | 0.844 | 0.880 | 0.858 | 0.769 | 0.687 | 0.644 | 0.635 | 0.677 | 0.656 |
GOR1 | 0.821 | 0.823 | 0.825 | 0.682 | 0.478 | 0.513 | 0.597 | 0.815 | 0.749 | 0.697 | 0.811 | 0.827 |
GOR2 | 0.698 | 0.853 | 0.829 | 0.671 | 0.538 | 0.545 | 0.590 | 0.862 | 0.870 | 0.858 | 0.860 | 0.830 |
GOR3 | 0.704 | 0.873 | 0.843 | 0.685 | 0.503 | 0.532 | 0.613 | 0.878 | 0.865 | 0.838 | 0.879 | 0.841 |
GOR4 | 0.678 | 0.828 | 0.763 | 0.623 | 0.474 | 0.477 | 0.553 | 0.835 | 0.775 | 0.826 | 0.836 | 0.763 |
GOR5 | 0.542 | 0.658 | 0.507 | 0.563 | 0.567 | 0.594 | 0.573 | 0.625 | 0.523 | 0.643 | 0.631 | 0.495 |
INR1 | 0.765 | 0.792 | 0.816 | 0.654 | 0.449 | 0.480 | 0.565 | 0.782 | 0.722 | 0.674 | 0.784 | 0.792 |
INR2 | 0.648 | 0.807 | 0.794 | 0.627 | 0.488 | 0.494 | 0.543 | 0.814 | 0.823 | 0.819 | 0.812 | 0.780 |
INR3 | 0.669 | 0.814 | 0.833 | 0.673 | 0.466 | 0.489 | 0.582 | 0.822 | 0.848 | 0.829 | 0.816 | 0.811 |
INR4 | 0.726 | 0.776 | 0.862 | 0.679 | 0.516 | 0.549 | 0.621 | 0.781 | 0.844 | 0.735 | 0.775 | 0.858 |
INR5 | 0.751 | 0.675 | 0.802 | 0.726 | 0.542 | 0.552 | 0.740 | 0.677 | 0.740 | 0.624 | 0.674 | 0.788 |
INS1 | 0.659 | 0.551 | 0.594 | 0.798 | 0.489 | 0.526 | 0.613 | 0.552 | 0.580 | 0.517 | 0.542 | 0.580 |
INS2 | 0.761 | 0.737 | 0.791 | 0.812 | 0.523 | 0.556 | 0.677 | 0.734 | 0.752 | 0.695 | 0.728 | 0.778 |
INS3 | 0.828 | 0.658 | 0.625 | 0.834 | 0.852 | 0.816 | 0.714 | 0.660 | 0.616 | 0.607 | 0.649 | 0.625 |
INV1 | 0.865 | 0.686 | 0.655 | 0.844 | 0.882 | 0.860 | 0.769 | 0.688 | 0.645 | 0.636 | 0.678 | 0.656 |
INV2 | 0.547 | 0.468 | 0.458 | 0.565 | 0.840 | 0.765 | 0.588 | 0.455 | 0.440 | 0.428 | 0.456 | 0.453 |
INV3 | 0.561 | 0.459 | 0.426 | 0.584 | 0.890 | 0.872 | 0.717 | 0.452 | 0.419 | 0.427 | 0.448 | 0.424 |
KMQ1 | 0.856 | 0.680 | 0.647 | 0.832 | 0.863 | 0.851 | 0.755 | 0.679 | 0.631 | 0.625 | 0.671 | 0.647 |
KMQ2 | 0.549 | 0.467 | 0.460 | 0.559 | 0.831 | 0.757 | 0.577 | 0.456 | 0.441 | 0.427 | 0.456 | 0.454 |
KMQ3 | 0.543 | 0.444 | 0.410 | 0.560 | 0.866 | 0.855 | 0.695 | 0.436 | 0.401 | 0.409 | 0.432 | 0.408 |
KMQ4 | 0.605 | 0.480 | 0.491 | 0.642 | 0.720 | 0.844 | 0.860 | 0.476 | 0.489 | 0.440 | 0.467 | 0.494 |
KMQ5 | 0.684 | 0.617 | 0.561 | 0.649 | 0.725 | 0.857 | 0.858 | 0.608 | 0.557 | 0.552 | 0.606 | 0.574 |
OPT1 | 0.655 | 0.527 | 0.535 | 0.693 | 0.773 | 0.882 | 0.905 | 0.523 | 0.535 | 0.493 | 0.515 | 0.539 |
OPT2 | 0.694 | 0.610 | 0.562 | 0.669 | 0.738 | 0.868 | 0.884 | 0.603 | 0.553 | 0.546 | 0.598 | 0.571 |
OPT3 | 0.780 | 0.724 | 0.830 | 0.752 | 0.542 | 0.562 | 0.774 | 0.726 | 0.786 | 0.671 | 0.721 | 0.828 |
ORA1 | 0.838 | 0.828 | 0.833 | 0.702 | 0.497 | 0.533 | 0.618 | 0.823 | 0.757 | 0.705 | 0.816 | 0.834 |
ORA2 | 0.711 | 0.859 | 0.833 | 0.683 | 0.543 | 0.550 | 0.595 | 0.875 | 0.881 | 0.868 | 0.865 | 0.832 |
ORA3 | 0.709 | 0.875 | 0.852 | 0.689 | 0.498 | 0.529 | 0.615 | 0.885 | 0.869 | 0.842 | 0.880 | 0.850 |
ORA4 | 0.694 | 0.841 | 0.772 | 0.635 | 0.484 | 0.488 | 0.562 | 0.855 | 0.788 | 0.837 | 0.849 | 0.777 |
ORA5 | 0.551 | 0.669 | 0.523 | 0.572 | 0.583 | 0.607 | 0.589 | 0.644 | 0.542 | 0.656 | 0.644 | 0.512 |
SAT1 | 0.708 | 0.860 | 0.831 | 0.681 | 0.538 | 0.544 | 0.593 | 0.876 | 0.880 | 0.870 | 0.866 | 0.830 |
SAT2 | 0.522 | 0.710 | 0.671 | 0.521 | 0.366 | 0.383 | 0.448 | 0.706 | 0.789 | 0.808 | 0.703 | 0.659 |
SAT3 | 0.692 | 0.841 | 0.845 | 0.685 | 0.486 | 0.511 | 0.599 | 0.850 | 0.875 | 0.849 | 0.845 | 0.840 |
SAT4 | 0.762 | 0.800 | 0.875 | 0.702 | 0.530 | 0.571 | 0.648 | 0.806 | 0.868 | 0.755 | 0.800 | 0.878 |
SAT5 | 0.780 | 0.722 | 0.831 | 0.752 | 0.545 | 0.566 | 0.775 | 0.724 | 0.785 | 0.668 | 0.719 | 0.828 |
SEQ1 | 0.670 | 0.819 | 0.752 | 0.614 | 0.470 | 0.472 | 0.543 | 0.824 | 0.759 | 0.819 | 0.826 | 0.754 |
SEQ2 | 0.547 | 0.659 | 0.517 | 0.565 | 0.557 | 0.581 | 0.567 | 0.626 | 0.532 | 0.654 | 0.632 | 0.506 |
SEQ3 | 0.666 | 0.821 | 0.792 | 0.642 | 0.492 | 0.502 | 0.556 | 0.838 | 0.839 | 0.847 | 0.832 | 0.794 |
SEQ4 | 0.516 | 0.684 | 0.641 | 0.508 | 0.363 | 0.378 | 0.436 | 0.677 | 0.744 | 0.790 | 0.676 | 0.629 |
SEQ5 | 0.608 | 0.759 | 0.770 | 0.601 | 0.429 | 0.441 | 0.521 | 0.772 | 0.799 | 0.808 | 0.764 | 0.774 |
SYQ1 | 0.816 | 0.817 | 0.819 | 0.680 | 0.475 | 0.509 | 0.593 | 0.808 | 0.742 | 0.691 | 0.808 | 0.824 |
SYQ2 | 0.682 | 0.831 | 0.807 | 0.652 | 0.519 | 0.528 | 0.579 | 0.840 | 0.849 | 0.837 | 0.847 | 0.818 |
SYQ3 | 0.690 | 0.861 | 0.828 | 0.668 | 0.493 | 0.520 | 0.599 | 0.865 | 0.853 | 0.828 | 0.871 | 0.828 |
SYQ4 | 0.646 | 0.809 | 0.747 | 0.602 | 0.469 | 0.466 | 0.534 | 0.817 | 0.755 | 0.813 | 0.824 | 0.742 |
SYQ5 | 0.524 | 0.647 | 0.497 | 0.539 | 0.545 | 0.572 | 0.555 | 0.613 | 0.511 | 0.632 | 0.624 | 0.484 |
UNR1 | 0.792 | 0.791 | 0.796 | 0.662 | 0.470 | 0.502 | 0.581 | 0.783 | 0.718 | 0.668 | 0.785 | 0.814 |
UNR2 | 0.626 | 0.768 | 0.750 | 0.596 | 0.462 | 0.475 | 0.535 | 0.775 | 0.786 | 0.778 | 0.783 | 0.784 |
UNR3 | 0.653 | 0.808 | 0.813 | 0.645 | 0.469 | 0.485 | 0.561 | 0.819 | 0.843 | 0.842 | 0.812 | 0.817 |
UNR4 | 0.741 | 0.794 | 0.863 | 0.694 | 0.525 | 0.562 | 0.637 | 0.800 | 0.857 | 0.750 | 0.793 | 0.876 |
UNR5 | 0.755 | 0.701 | 0.817 | 0.727 | 0.535 | 0.555 | 0.758 | 0.703 | 0.766 | 0.648 | 0.701 | 0.825 |
References
- Gibson, C.B.; Birkinshaw, J. The antecedents, consequences, and mediating role of organizational ambidexterity. Acad. Manag. J. 2004, 47, 209–226. [Google Scholar] [CrossRef]
- Raisch, S.; Birkinshaw, J. Organizational ambidexterity: Antecedents, outcomes, and moderators. J. Manag. 2008, 34, 375–409. [Google Scholar] [CrossRef] [Green Version]
- O’Reilly, C.A.; Tushman, M.L. Organizational Ambidexterity: Past, Present, and Future. Acad. Manag. Perspect. 2013, 27, 324–338. [Google Scholar] [CrossRef] [Green Version]
- Junni, P.; Sarala, R.M.; Taras, V.A.S.; Tarba, S.Y. Organizational ambidexterity and performance: A meta-analysis. Acad. Manag. Perspect. 2013, 27, 299–312. [Google Scholar] [CrossRef]
- Raisch, S.; Birkinshaw, J.; Probst, G.; Tushman, M.L. Organizational ambidexterity: Balancing exploitation and exploration for sustained performance. Organ. Sci. 2009, 20, 685–695. [Google Scholar] [CrossRef] [Green Version]
- Paireekreng, W.; Osathanukroh, J.; Supasak, C. A Study of Influence Factors for Advertising on Messaging Applications Towards Mobile Buyer ’s Decision Making. IJIIS Int. J. Inform. Inf. Syst. 2019, 2, 82–90. [Google Scholar]
- Astuti, T.; Puspita, B. Analysis of Customer Transaction Data Associations Based on The Apriori Algorithm. IJIIS Int. J. Inform. Inf. Syst. 2020, 3, 23–28. [Google Scholar] [CrossRef]
- Madu, C.N.; Madu, A.A. Dimensions of e-quality. Int. J. Qual. Reliab. Manag. 2002, 19, 246–258. [Google Scholar] [CrossRef]
- Jen, L.; Lin, Y. A Brief Overview of the Accuracy of Classification Algorithms for Data Prediction in Machine Learning Applications. J. Appl. Data Sci. 2021, 2, 84–92. [Google Scholar] [CrossRef]
- Lehtinen, U.; Lehtinen, J.R. Two approaches to service quality dimensions. Serv. Ind. J. 1991, 11, 287–303. [Google Scholar] [CrossRef]
- Sebastianelli, R.; Tamimi, N. How product quality dimensions relate to defining quality. Int. J. Qual. Reliab. Manag. 2002, 19, 442–453. [Google Scholar] [CrossRef]
- Leydesdorff, L.; Etzkowitz, H. Triple Helix of innovation: Introduction. Sci. Public Policy 1998, 25, 358–364. [Google Scholar] [CrossRef]
- Etzkowitz, H.; Zhou, C. Triple Helix twins: Innovation and sustainability. Sci. Public Policy 2006, 33, 77–83. [Google Scholar] [CrossRef]
- Leydesdorff, L.; Zawdie, G. The triple helix perspective of innovation systems. Technol. Anal. Strateg. Manag. 2010, 22, 789–804. [Google Scholar] [CrossRef] [Green Version]
- Aini, Q.; Hammad, J.A.; Taher, T.; Ikhlayel, M. Classification of Tweets Causing Deadlocks in Jakarta Streets with the Help of Algorithm C4.5. J. Appl. Data Sci. 2021, 2, 143–156. [Google Scholar] [CrossRef]
- Tomura, N.; Uehara, S.; Kaneta, K.; Hara, R.; Sasaki, R.; Tsuchida, M.; Shibuya, A.; Yamashita, M. Construction of the E-Government Case Study of Japan and Estonia. Int. J. Appl. Inf. Manag. 2021, 1, 145–151. [Google Scholar] [CrossRef]
- Saputro, P.H.; Nanang, H. Exploratory Data Analysis & Booking Cancelation Prediction on Hotel Booking Demands Datasets. J. Appl. Data Sci. 2021, 2, 40–56. [Google Scholar]
- Praetorius, A.-K.; Klieme, E.; Herbert, B.; Pinger, P. Generic dimensions of teaching quality: The German framework of three basic dimensions. ZDM 2018, 50, 407–426. [Google Scholar] [CrossRef]
- Sharma, S.K.; Sharma, M. Examining the role of trust and quality dimensions in the actual usage of mobile banking services: An empirical investigation. Int. J. Inf. Manag. 2019, 44, 65–75. [Google Scholar] [CrossRef]
- Pakurár, M.; Haddad, H.; Nagy, J.; Popp, J.; Oláh, J. The service quality dimensions that affect customer satisfaction in the Jordanian banking sector. Sustainability 2019, 11, 1113. [Google Scholar] [CrossRef] [Green Version]
- Ramya, N.; Kowsalya, A.; Dharanipriya, K. Service quality and its dimensions. EPRA Int. J. Res. Dev. 2019, 4, 38–41. [Google Scholar]
- Alfy, S.E.; Abukari, A. Revisiting perceived service quality in higher education: Uncovering service quality dimensions for postgraduate students. J. Mark. High. Educ. 2020, 30, 1–25. [Google Scholar] [CrossRef]
- Jafari-Sadeghi, V.; Garcia-Perez, A.; Candelo, E.; Couturier, J. Exploring the impact of digital transformation on technology entrepreneurship and technological market expansion: The role of technology readiness, exploration and exploitation. J. Bus. Res. 2021, 124, 100–111. [Google Scholar] [CrossRef]
- Mukhtar, H.; Andi, W.; Nuryanto, N.; Hafidz, F.A.; Mohammad, R.A. Implementation of Knowledge Management in Different Industries. IJIIS Int. J. Inform. Inf. Syst. 2021, 4, 103–111. [Google Scholar]
- Trang, N.H. Limitations of Big Data Partitions Technology. J. Appl. Data Sci. 2020, 1, 11–19. [Google Scholar] [CrossRef]
- Kwon, M.; Remøy, H.; van den Dobbelsteen, A. User-focused office renovation: A review into user satisfaction and the potential for improvement. Prop. Manag. 2019, 37, 470–489. [Google Scholar] [CrossRef]
- Adiandari, A.M. Financial Performance Innovation Since Digital Technology Entered Indonesian MSMEs. Int. J. Appl. Inf. Manag. 2022, 2, 50–58. [Google Scholar]
- Thelen, G. Leadership in a Global World Management Training Requirement Using the Example of the Asian Studies Program at University of Applied Sciences (HTWG) Konstanz. Int. J. Appl. Inf. Manag. 2021, 1, 125–135. [Google Scholar] [CrossRef]
- Alessa, T.; Abdi, S.; Hawley, S.M.; de Witte, L. Mobile apps to support the self-management of hypertension: Systematic review of effectiveness, usability, and user satisfaction. JMIR mHealth uHealth 2018, 6, e10723. [Google Scholar] [CrossRef] [Green Version]
- Chen, T.; Peng, L.; Yin, X.; Rong, J.; Yang, J.; Cong, G. Analysis of user satisfaction with online education platforms in China during the COVID-19 pandemic. Healthcare 2020, 8, 200. [Google Scholar] [CrossRef]
- Hariguna, T.; Sukmana, H.T.; Kim, J.I. Survey Opinion using Sentiment Analysis. J. Appl. Data Sci. 2020, 1, 35–40. [Google Scholar] [CrossRef]
- Hitoshi, H.; Kamei, S.; Ohashi, M. The Effectiveness of The Body of Knowledge Process in The Startup Analysis of Efficiency by Applying Startup Management Body of Knowledge (SUBOK) Guide. Int. J. Appl. Inf. Manag. 2021, 1, 70–80. [Google Scholar] [CrossRef]
- Chen, L.; Yang, Y.; Wang, N.; Yang, K.; Yuan, Q. How serendipity improves user satisfaction with recommendations? A large-scale user evaluation. In Proceedings of the The World Wide Web Conference 2019, San Francisco, CA, USA, 13–17 May 2019; pp. 240–250. [Google Scholar]
- Tarba, S.Y.; Jansen, J.J.P.; Mom, T.J.M.; Raisch, S.; Lawton, T.C. A microfoundational perspective of organizational ambidexterity: Critical review and research directions. Long Range Plann. 2020, 53, 102048. [Google Scholar] [CrossRef]
- Pertusa-Ortega, E.M.; Molina-Azorín, J.F.; Tarí, J.J.; Pereira-Moliner, J.; López-Gamero, M.D. The microfoundations of organizational ambidexterity: A systematic review of individual ambidexterity through a multilevel framework. BRQ Bus. Res. Q. 2021, 24, 355–371. [Google Scholar] [CrossRef]
- Park, Y.; Pavlou, P.A.; Saraf, N. Configurations for achieving organizational ambidexterity with digitization. Inf. Syst. Res. 2020, 31, 1376–1397. [Google Scholar] [CrossRef]
- Mom, T.J.M.; Chang, Y.-Y.; Cholakova, M.; Jansen, J.J.P. A multilevel integrated framework of firm HR practices, individual ambidexterity, and organizational ambidexterity. J. Manag. 2019, 45, 3009–3034. [Google Scholar] [CrossRef] [Green Version]
- Clauss, T.; Kraus, S.; Kallinger, F.L.; Bican, P.M.; Brem, A.; Kailer, N. Organizational ambidexterity and competitive advantage: The role of strategic agility in the exploration-exploitation paradox. J. Innov. Knowl. 2021, 6, 203–213. [Google Scholar] [CrossRef]
- Bui, T.-D.; Tsai, F.M.; Tseng, M.-L.; Tan, R.R.; Yu, K.D.S.; Lim, M.K. Sustainable supply chain management towards disruption and organizational ambidexterity: A data driven analysis. Sustain. Prod. Consum. 2021, 26, 373–410. [Google Scholar] [CrossRef]
- Parmenter, T.R. An analysis of the dimensions of quality of life for people with physical disabilities. In Quality of Life for Handicapped People; Routledge: Oxfordshire, UK, 2021; pp. 7–36. [Google Scholar]
- Biswas, A.; Jaiswal, D.; Kant, R. Augmenting bank service quality dimensions: Moderation of perceived trust and perceived risk. Int. J. Product. Perform. Manag. 2021. [Google Scholar] [CrossRef]
- Kumar, L.; Hossain, N.U.I.; Fazio, S.A.; Awasthi, A.; Jaradat, R.; Babski-Reeves, K. A data driven decision model for assessing the enablers of quality dimensions: Context of industry 4.0. CIRP J. Manuf. Sci. Technol. 2021, 35, 896–910. [Google Scholar] [CrossRef]
- Damerji, H.; Salimi, A. Mediating effect of use perceptions on technology readiness and adoption of artificial intelligence in accounting. Account. Educ. 2021, 30, 107–130. [Google Scholar] [CrossRef]
- Chau, K.Y.; Law, K.M.Y.; Tang, Y.M. Impact of self-directed learning and educational technology readiness on synchronous E-learning. J. Organ. End User Comput. 2021, 33, 1–20. [Google Scholar] [CrossRef]
- Martínez-Plumed, F.; Gómez, E.; Hernández-Orallo, J. Futures of artificial intelligence through technology readiness levels. Telemat. Inform. 2021, 58, 101525. [Google Scholar] [CrossRef]
- Kaun, C.; Jhanjhi, N.Z.; Goh, W.W.; Sukumaran, S. Implementation of decision tree algorithm to classify knowledge quality in a knowledge intensive system. MATEC Web Conf. 2021, 335, 4002. [Google Scholar] [CrossRef]
- Flavián, C.; Pérez-Rueda, A.; Belanche, D.; Casaló, L.V. Intention to use analytical artificial intelligence (AI) in services–the effect of technology readiness and awareness. J. Serv. Manag. 2021, 33, 293–320. [Google Scholar] [CrossRef]
- Oppong, E.; Hinson, R.E.; Adeola, O.; Muritala, O.; Kosiba, J.P. The effect of mobile health service quality on user satisfaction and continual usage. Total Qual. Manag. Bus. Excel. 2021, 32, 177–198. [Google Scholar] [CrossRef]
- Riyanto, R. Modelling Customers Lifetime Value For Non-Contractual Business. IJIIS Int. J. Inform. Inf. Syst. 2021, 4, 55–62. [Google Scholar] [CrossRef]
- Prabowo, N.A. Social Network Analysis for User Interaction Analysis on Social Media Regarding E-Commerce Business. IJIIS Int. J. Inform. Inf. Syst. 2021, 4, 95–102. [Google Scholar] [CrossRef]
- Twum, K.K.; Adams, M.; Budu, S.; Budu, R.A.A. Achieving university libraries user loyalty through user satisfaction: The role of service quality. J. Mark. High. Educ. 2022, 32, 54–72. [Google Scholar] [CrossRef]
- Hair, J.; Hollingsworth, C.L.; Randolph, A.B.; Chong, A.Y.L. An updated and expanded assessment of PLS-SEM in information systems research. Ind. Manag. Data Syst. 2017, 117, 442–458. [Google Scholar] [CrossRef]
- Hair, J.F., Jr.; Matthews, L.M.; Matthews, R.L.; Sarstedt, M. PLS-SEM or CB-SEM: Updated guidelines on which method to use. Int. J. Multivar. Data Anal. 2017, 1, 107–123. [Google Scholar] [CrossRef]
- Hair, J.F.; Risher, J.J.; Sarstedt, M.; Ringle, C.M. When to use and how to report the results of PLS-SEM. Eur. Bus. Rev. 2019, 31, 2–24. [Google Scholar] [CrossRef]
Construct | Definition | Source |
---|---|---|
Triple Helix | The triple helix is described as a concept of collaboration between government, university, and industry in which the government is a policy maker, the university is a research development center, and industry is a provider of services to the community must achieve common goals. | [12,13,14,15,17] |
Quality Dimension | Quality dimensions are the requirements for a product’s value to match customer expectations, whereas product quality dimensions include systems, information or knowledge, services, goods, as well as appropriateness or correctness. | [18,20,21,22] |
Technology Readiness | Refers to a combination of technology-related beliefs that collectively determine a customer’s, employee’s, or executive’s tendencies to adopt new technology to achieve their objectives, both at work and during leisure time. | [23,25] |
User Satisfaction | Refers to how comfortable the user is with the system and how well they like it or how innovative the system is while they are using it and consuming content. | [26,27,28,29] |
Organizational Ambidexterity | The degree to which a business or an organization can balance the introduction of new technologies with the preservation of existing ones and the maintenance of earnings. | [34,35,37,38] |
Characteristics | Items | Frequencies | Percentages |
---|---|---|---|
Gender | Male | 278 | 67.64% |
Female | 133 | 32.36% | |
Age | 21–30 | 50 | 12.16% |
31–40 | 188 | 45.74% | |
>41 | 173 | 42.09% | |
Educational Level | Senior High School | 65 | 15.81% |
Associate Degree | 73 | 17.76% | |
Bachelor | 105 | 25.54% | |
Postgraduate | 168 | 40.87% |
Name of Construct | VIF |
---|---|
GOR → TER | 2.264 |
UNR → TER | 2.450 |
INR → TER | 2.575 |
SYQ → TER | 1.000 |
SYQ → SAT | 2.889 |
KMQ → TER | 1.834 |
KMQ → SAT | 1.947 |
SEQ → TER | 1.676 |
SEQ → SAT | 1.304 |
TER → ORA | 2.706 |
TER → SAT | 2.128 |
SAT → ORA | 2.706 |
Measurement Items | Loading Factors | Cronbach’s Alpha | Composite Reliability | Average Variance Extracted (AVE) |
---|---|---|---|---|
DIC1 | 0.845 | 0.833 | 0.899 | 0.749 |
DIC2 | 0.886 | |||
DIC3 | 0.865 | |||
INS1 | 0.798 | 0.749 | 0.856 | 0.664 |
INS2 | 0.812 | |||
INS3 | 0.834 | |||
INV1 | 0.882 | 0.842 | 0.904 | 0.758 |
INV2 | 0.840 | |||
INV3 | 0.890 | |||
OPT1 | 0.905 | 0.815 | 0.891 | 0.733 |
OPT2 | 0.884 | |||
OPT3 | 0.774 | |||
GOR1 | 0.823 | 0.867 | 0.905 | 0.658 |
GOR2 | 0.853 | |||
GOR3 | 0.873 | |||
GOR4 | 0.828 | |||
GOR5 | 0.658 | |||
INR1 | 0.816 | 0.879 | 0.912 | 0.675 |
INR2 | 0.794 | |||
INR3 | 0.833 | |||
INR4 | 0.862 | |||
INR5 | 0.802 | |||
KMQ1 | 0.851 | 0.890 | 0.919 | 0.695 |
KMQ2 | 0.757 | |||
KMQ3 | 0.855 | |||
KMQ4 | 0.844 | |||
KMQ5 | 0.857 | |||
UNR1 | 0.814 | 0.881 | 0.913 | 0.679 |
UNR2 | 0.784 | |||
UNR3 | 0.817 | |||
UNR4 | 0.876 | |||
UNR5 | 0.825 | |||
ORA1 | 0.823 | 0.876 | 0.911 | 0.675 |
ORA2 | 0.875 | |||
ORA3 | 0.885 | |||
ORA4 | 0.855 | |||
ORA5 | 0.644 | |||
SAT1 | 0.880 | 0.895 | 0.923 | 0.706 |
SAT2 | 0.789 | |||
SAT3 | 0.875 | |||
SAT4 | 0.868 | |||
SAT5 | 0.785 | |||
SEQ1 | 0.819 | 0.844 | 0.890 | 0.619 |
SEQ2 | 0.654 | |||
SEQ3 | 0.847 | |||
SEQ4 | 0.790 | |||
SEQ5 | 0.808 | |||
SYQ1 | 0.808 | 0.855 | 0.898 | 0.640 |
SYQ2 | 0.847 | |||
SYQ3 | 0.871 | |||
SYQ4 | 0.824 | |||
SYQ5 | 0.624 |
DIC | GOR | INR | INS | INV | KMQ | OPT | ORA | SAT | SEQ | SYQ | TER | UNR | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
DIC | 0.865 | ||||||||||||
GOR | 0.755 | 0.811 | |||||||||||
INR | 0.769 | 0.737 | 0.822 | ||||||||||
INS | 0.726 | 0.799 | 0.621 | 0.815 | |||||||||
INV | 0.775 | 0.631 | 0.601 | 0.779 | 0.871 | ||||||||
KMQ | 0.791 | 0.656 | 0.626 | 0.79 | 0.658 | 0.834 | |||||||
OPT | 0.728 | 0.723 | 0.748 | 0.823 | 0.702 | 0.605 | 0.856 | ||||||
ORA | 0.755 | 0.795 | 0.74 | 0.799 | 0.625 | 0.648 | 0.72 | 0.821 | |||||
SAT | 0.725 | 0.739 | 0.766 | 0.796 | 0.588 | 0.614 | 0.728 | 0.646 | 0.84 | ||||
SEQ | 0.768 | 0.655 | 0.791 | 0.746 | 0.583 | 0.598 | 0.665 | 0.656 | 0.642 | 0.787 | |||
SYQ | 0.744 | 0.696 | 0.637 | 0.787 | 0.619 | 0.642 | 0.713 | 0.693 | 0.64 | 0.654 | 0.800 | ||
TER | 0.751 | 0.613 | 0.623 | 0.648 | 0.697 | 0.721 | 0.628 | 0.710 | 0.694 | 0.746 | 0.601 | 0.790 | |
UNR | 0.770 | 0.735 | 0.682 | 0.81 | 0.600 | 0.629 | 0.752 | 0.739 | 0.662 | 0.688 | 0.737 | 0.621 | 0.824 |
Hypothesis | Path Coefficient | T statistics | p Values | Results | |
---|---|---|---|---|---|
H1 | GOR→TER | 0.421 | 2.431 | 0.015 | Accepted |
H2 | UNR→TER | 0.168 | 2.299 | 0.022 | Accepted |
H3 | INR→TER | 0.297 | 3.853 | 0.000 | Accepted |
H4 | SYQ→TER | −0.372 | 2.219 | 0.027 | Accepted |
H5 | SYQ→SAT | 0.202 | 2.532 | 0.012 | Accepted |
H6 | KMQ→TER | 0.657 | 33.263 | 0.000 | Accepted |
H7 | KMQ→SAT | −0.316 | 6.593 | 0.000 | Accepted |
H8 | SEQ→TER | −0.109 | 2.349 | 0.019 | Accepted |
H9 | SEQ→SAT | 0.564 | 8.710 | 0.000 | Accepted |
H10 | TER→ORA | 0.160 | 5.783 | 0.000 | Accepted |
H11 | TER→SAT | 0.503 | 7.424 | 0.000 | Accepted |
H12 | SAT→ORA | 0.820 | 31.260 | 0.000 | Accepted |
Construct | Construct Relationship | t Value of Path Coefficient | Sobel Test | p-Value |
---|---|---|---|---|
GOR→TER→ORA | GOR→TER | 2.431 | 2.241 | 0.025 |
TER→ORA | 5.783 | |||
GOR→TER→SAT | GOR→TER | 2.431 | 2.310 | 0.020 |
TER→SAT | 7.424 | |||
UNR→TER→ORA | UNR→TER | 2.299 | 2.136 | 0.032 |
TER→ORA | 5.783 | |||
UNR→TER→SAT | UNR→TER | 2.299 | 2.196 | 0.028 |
TER→SAT | 7.424 | |||
INR→TER→ORA | INR→TER | 3.853 | 3.206 | 0.001 |
TER→ORA | 5.783 | |||
INR→TER→SAT | INR→TER | 3.853 | 3.419 | 0.000 |
TER→SAT | 7.424 | |||
SYQ→TER→ORA | SYQ→TER | 2.219 | 2.071 | 0.038 |
TER→ORA | 5.783 | |||
SYQ→TER→SAT | SYQ→TER | 2.219 | 2.126 | 0.033 |
TER→SAT | 7.424 | |||
SYQ→SAT→ORA | SYQ→SAT | 2.532 | 2.523 | 0.011 |
SAT→ORA | 31.260 | |||
KMQ→TER→ORA | KMQ→TER | 33.263 | 5.697 | 0.000 |
TER→ORA | 5.783 | |||
KMQ→TER→SAT | KMQ→TER | 33.263 | 7.245 | 0.000 |
TER→SAT | 7.424 | |||
KMQ→SAT→ORA | KMQ→SAT | 6.593 | 6.451 | 0.000 |
SAT→ORA | 31.260 | |||
SEQ→TER→ORA | SEQ→TER | 2.349 | 2.176 | 0.029 |
TER→ORA | 5.783 | |||
SEQ→TER→SAT | SEQ→TER | 2.349 | 2.239 | 0.025 |
TER→SAT | 7.424 | |||
SEQ→SAT→ORA | SEQ→SAT | 8.710 | 8.390 | 0.000 |
SAT→ORA | 31.260 | |||
TER→SAT→ORA | TER→SAT | 7.424 | 7.223 | 0.000 |
SAT→ORA | 31.260 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Prasetio, A.B.; Aboobaider, B.b.M.; Ahmad, A.b. Organizational Ambidexterity as an Outcome of Quality Dimensions and Triple Helix: The Role of Technology Readiness and User Satisfaction. Sustainability 2022, 14, 14237. https://doi.org/10.3390/su142114237
Prasetio AB, Aboobaider BbM, Ahmad Ab. Organizational Ambidexterity as an Outcome of Quality Dimensions and Triple Helix: The Role of Technology Readiness and User Satisfaction. Sustainability. 2022; 14(21):14237. https://doi.org/10.3390/su142114237
Chicago/Turabian StylePrasetio, Agung Budi, Burhanuddin bin Mohd Aboobaider, and Asmala bin Ahmad. 2022. "Organizational Ambidexterity as an Outcome of Quality Dimensions and Triple Helix: The Role of Technology Readiness and User Satisfaction" Sustainability 14, no. 21: 14237. https://doi.org/10.3390/su142114237