1. Home
  2. Vol 3, No 3 (2022)
  3. Ruangkanjanases

Assessing Supply Chain Management Ambidexterity, Integration of Knowledge Management Use and User Satisfaction

Athapol Ruangkanjanases, Taqwa Hariguna, Willy Abdillah

Abstract


This study aims to address the knowledge gap in supply chain management between large corporations and small and medium enterprises by investigating the ambidexterity of supply chain management in small and medium enterprises. The study also focuses on the integration of knowledge management, user satisfaction, and supply chain management ambidexterity as its main novelty. A quantitative empirical technique was used, utilizing online data collection evaluated through partial least squares analysis with a sample of 372 reliable data points. This study presents five hypotheses, and the results of the Smart PLS 4 analysis indicate that all four theories have positive and significant influences. In addition, the results can be used for research in supply chain management and knowledge management, as well as for making plans to improve the quality of managed organizations.

 

Doi: 10.28991/HEF-2022-03-03-08

Full Text: PDF


Keywords


Supply Chain Management Dimension; Knowledge Management Use; User Satisfaction; Supply Chain Management Ambidexterity; Small Medium Enterprises.

References


Friedman, R. S., & Prusak, L. (2008). On heuristics, narrative and knowledge management. Technovation, 28(12), 812–817. doi:10.1016/j.technovation.2008.07.002.

Okunoye, A., & Karsten, H. (2002). Where the global needs the local: Variation in enablers in the knowledge management process. Journal of Global Information Technology Management, 5(3), 12–31. doi:10.1080/1097198X.2002.10856329.

DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9–30. doi:10.1080/07421222.2003.11045748.

Lin, W. T., & Shao, B. B. M. (2000). The relationship between user participation and system success: a simultaneous contingency approach. Information & Management, 37(6), 283–295. doi:10.1016/s0378-7206(99)00055-5.

Lee, J., & Lee, J. N. (2009). Understanding the product information inference process in electronic word-of-mouth: An objectivity-subjectivity dichotomy perspective. Information and Management, 46(5), 302–311. doi:10.1016/j.im.2009.05.004.

Wang, Y. S., Wang, H. Y., & Shee, D. Y. (2007). Measuring e-learning systems success in an organizational context: Scale development and validation. Computers in Human Behavior, 23(4), 1792–1808. doi:10.1016/j.chb.2005.10.006.

Muylle, S., Moenaert, R., & Despontin, M. (2004). The conceptualization and empirical validation of web site user satisfaction. Information & Management, 41(5), 543–560. doi:10.1016/s0378-7206(03)00089-2.

Kulkarni, U. R., Ravindran, S., & Freeze, R. (2006). A knowledge management success model: Theoretical development and empirical validation. Journal of Management Information Systems, 23(3), 309–347. doi:10.2753/MIS0742-1222230311.

Markus, M. L. (2001). Toward a theory of knowledge reuse: Types of knowledge reuse situations and factors in reuse success. Journal of Management Information Systems, 18(1), 57–93. doi:10.1080/07421222.2001.11045671.

Jennex, M., & Olfman, L. (2005). Assessing Knowledge Management Success. International Journal of Knowledge Management (IJKM), 1(2), 33–49. doi:10.4018/jkm.2005040104.

Tseng, Y. M. (2007). The Impacts of Relationship Marketing Tactics on Relationship Quality in Service Industry. The Business Review, Cambridge, 7(2), 310–314.

Handzic, M. (2005). Knowledge management: Through the technology glass. World Scientific Publishing, New Jersey, United States. doi:10.1142/5639.

Denning, S. (2006). Effective storytelling: Strategic business narrative techniques. Strategy and Leadership, 34(1), 42–48. doi:10.1108/10878570610637885.

Chan, I., & Chao, C.-K. (2008). Knowledge management in small and medium-sized enterprises. Communications of the ACM, 51(4), 83–88. doi:10.1145/1330311.1330328.

Ketchen, D. J., Rebarick, W., Hult, G. T. M., & Meyer, D. (2008). Best value supply chains: A key competitive weapon for the 21st century. Business Horizons, 51(3), 235–243. doi:10.1016/j.bushor.2008.01.012.

Chow, W. S., Madu, C. N., Kuei, C. H., Lu, M. H., Lin, C., & Tseng, H. (2008). Supply chain management in the US and Taiwan: An empirical study. Omega, 36(5), 665–679. doi:10.1016/j.omega.2006.01.001.

Aini, Q. (2021). Classification of Tweets Causing Deadlocks in Jakarta Streets with the Help of Algorithm C4.5. Journal of Applied Data Sciences, 2(4), 143–156. doi:10.47738/jads.v2i4.43.

Davenport, T., & Prusak, L. (1998). Working knowledge: how organizations manage what they know. (1998). Choice Reviews Online, 35(09), 35-5167-35–5167. doi:10.5860/choice.35-5167.

Adiandari, A. M. (2022). Financial Performance Innovation Since Digital Technology Entered Indonesian MSMEs. International Journal for Applied Information Management, 2(1), 50–58.

Trang, N. (2020). Data mining for Education Sector, a proposed concept. Journal of Applied Data Sciences, 1(1), 11–19. doi:10.47738/jads.v1i1.7.

Wong, C. W. Y., Wong, C. Y., & Boon-Itt, S. (2013). The combined effects of internal and external supply chain integration on product innovation. International Journal of Production Economics, 146(2), 566–574. doi:10.1016/j.ijpe.2013.08.004.

Kristal, M. M., Huang, X., & Roth, A. V. (2010). The effect of an ambidextrous supply chain strategy on combinative competitive capabilities and business performance. Journal of Operations Management, 28(5), 415–429. doi:10.1016/j.jom.2009.12.002.

March, J. G. (2021). Exploration and exploitation in organizational learning. Studi Organizzativi, 2, 71–87. doi:10.3280/so2008-002006.

Kortmann, S. (2015). The Mediating Role of Strategic Orientations on the Relationship between Ambidexterity-Oriented Decisions and Innovative Ambidexterity. Journal of Product Innovation Management, 32(5), 666–684. doi:10.1111/jpim.12151.

Tomura, N. (2021). Construction of the E-Government Case Study of Japan and Estonia. International Journal for Applied Information Management, 1(3), 145–151. doi:10.47738/ijaim.v1i3.16.

Alfazzi, F. (2022). A Knowledge Behavioral and Intelligence Management in Fostering Entrepreneurship for Modern Industries. International Journal for Applied Information Management, 2(4), 95–105. doi:10.47738/ijaim.v2i4.42.

Durst, S., & Runar Edvardsson, I. (2012). Knowledge management in SMEs: a literature review. Journal of Knowledge Management, 16(6), 879–903. doi:10.1108/13673271211276173.

Dotsika, F., & Patrick, K. (2013). Collaborative KM for SMEs: a framework evaluation study. Information Technology & People, 26(4), 368–382. doi:10.1108/itp-11-2012-0142.

Astuti, T., & Puspita, B. (2020). Analysis of Customer Transaction Data Associations Based on the Apriori Algorithm. International Journal of Informatics and Information Systems, 3(1), 23–28. doi:10.47738/ijiis.v3i1.4.

Thelen, G. (2021). Leadership in a Global World Management Training Requirement Using the Example of the Asian Studies Program at University of Applied Sciences (HTWG) Konstanz. International Journal for Applied Information Management, 1(3), 125–135. doi:10.47738/ijaim.v1i3.14.

Partanen, J., Kohtamäki, M., Patel, P. C., & Parida, V. (2020). Supply chain ambidexterity and manufacturing SME performance: The moderating roles of network capability and strategic information flow. International Journal of Production Economics, 221, 1-12. doi:10.1016/j.ijpe.2019.08.005.

Hariguna, T. (2020). Survey Opinion using Sentiment Analysis. Journal of Applied Data Sciences, 1(1), 35–40. doi:10.47738/jads.v1i1.10.

Burgess, S., Sellitto, C., & Karanasios, S. (2009). Effective web presence solutions for small businesses: Strategies for successful implementation: Strategies for successful implementation. IGI Global. doi:10.4018/978-1-60566-224-4.

Rai, A., Lang, S. S., & Welker, R. B. (2002). Assessing the Validity of IS Success Models: An Empirical Test and Theoretical Analysis. Information Systems Research, 13(1), 50–69. doi:10.1287/isre.13.1.50.96.

Meehan, J., & Muir, L. (2008). SCM in Merseyside SMEs: Benefits and barriers. TQM Journal, 20(3), 223–232. doi:10.1108/17542730810867245.

Lenny Koh, S.C., Demirbag, M., Bayraktar, E., Tatoglu, E., & Zaim, S. (2007). The impact of supply chain management practices on performance of SMEs. Industrial Management & Data Systems, 107(1), 103–124. doi:10.1108/02635570710719089.

Zimmerman, J. B., Anastas, P. T., Erythropel, H. C., & Leitner, W. (2020). Designing for a green chemistry future. Science, 367(6476), 397-400. doi:10.1126/science.aay3060.

Park, D., & Krishnan, H. A. (2001). Supplier selection practices among small firms in the United States: Testing three models. Journal of Small Business Management, 39(3), 259–271. doi:10.1111/0447-2778.00023.

Hitoshi, H. (2021). The Effectiveness of the Body of Knowledge Process in the Startup Analysis of Efficiency by Applying Startup Management Body of Knowledge (SUBOK) Guide. International Journal for Applied Information Management, 1(2), 28–49. doi:10.47738/ijaim.v1i2.11.

Hanafi, M. (2021). Implementation of Knowledge Management in Different Industries. International Journal of Informatics and Information Systems, 4(2), 103–111. doi:10.47738/ijiis.v4i2.107.

Paireekreng, W., Osathanukroh, J., & Supasak, C. (2019). A Study of Influence Factors for Advertising on Messaging Applications towards Mobile Buyer's Decision Making. International Journal of Informatics and Information Systems, 2(2), 82-90.

Edvardsson, I. R., & Kristjan Oskarsson, G. (2011). Knowledge management and value creation in service firms. Measuring Business Excellence, 15(4), 7–15. doi:10.1108/13683041111184062.

Prabowo, N. (2021). Social Network Analysis for User Interaction Analysis on Social Media Regarding E-Commerce Business. IJIIS: International Journal of Informatics and Information Systems, 4(2), 95–102. doi:10.47738/ijiis.v4i2.106.

Riyanto, R. (2021). Modelling Customers Lifetime Value for Non-Contractual Business. IJIIS: International Journal of Informatics and Information Systems, 4(1), 55–62. doi:10.47738/ijiis.v4i1.77.

Skok, W., & Kalmanovitch, C. (2005). Evaluating the role and effectiveness of an intranet in facilitating knowledge management: A case study at Surrey County Council. Information and Management, 42(5), 731–744. doi:10.1016/j.im.2004.04.008.

Garrity, E. J., & Sanders, G. L. (Eds.). (1998). Information systems success measurement. IGI Publishing, Hershey, United States. doi: 10.4018/978-1-878289-03-2.

Rosliadewi, L. (2020). Analysis of Transaction Data for Modeling the Pattern of Goods Purchase Supporting Goods Location. Journal of Applied Data Sciences, 1(2), 65–75. doi:10.47738/jads.v1i2.54.

Nanang, H. (2021). Exploratory Data Analysis & Booking Cancelation Prediction on Hotel Booking Demands Datasets. Journal of Applied Data Sciences, 2(1). doi:10.47738/jads.v2i1.20.

Wixom, B. H., & Todd, P. A. (2005). A Theoretical Integration of User Satisfaction and Technology Acceptance. Information Systems Research, 16(1), 85–102. doi:10.1287/isre.1050.0042.

Iivari, J. (2005). An empirical test of the DeLone-McLean model of information system success. ACM SIGMIS Database: The Database for Advances in Information Systems, 36(2), 8–27. doi:10.1145/1066149.1066152.

Seddon, P., & Kiew, M. Y. (1996). A partial test and development of DeLone and McLean's model of IS success. Australasian Journal of Information Systems, 4(1). doi:10.3127/ajis.v4i1.379.

Tzortzaki, A. M., & Mihiotis, A. (2014). A Review of Knowledge Management Theory and Future Directions. Knowledge and Process Management, 21(1), 29–41. doi:10.1002/kpm.1429.

Straub, D., Limayem, M., & Karahanna-Evaristo, E. (1995). Measuring System Usage: Implications for IS Theory Testing. Management Science, 41(8), 1328–1342. doi:10.1287/mnsc.41.8.1328.

Seddon, P. B. (1997). A Respecification and Extension of the DeLone and McLean Model of IS Success. Information Systems Research, 8(3), 240–253. doi.:10.1287/isre.8.3.240.

Levinthal, D. A., & March, J. G. (1993). The myopia of learning. Strategic Management Journal, 14(S2), 95–112. doi:10.1002/smj.4250141009.

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. doi:10.1108/EBR-11-2018-0203.

Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295-336.

Benaroch, Lichtenstein, & Robinson. (2006). Real Options in Information Technology Risk Management: An Empirical Validation of Risk-Option Relationships. MIS Quarterly, 30(4), 827. doi:10.2307/25148756.

Henseler, J., Fassott, G. (2010). Testing Moderating Effects in PLS Path Models: An Illustration of Available Procedures. Handbook of Partial Least Squares. Springer Handbooks of Computational Statistics. Springer, Berlin, Germany. doi:10.1007/978-3-540-32827-8_31.

Chin, W. W., & Newsted, P. R. (1999). Structural equation modeling analysis with small samples using partial least squares. Statistical strategies for small sample research, 1(1), 307-341, SAGE, Thousand Oaks, United States.

hin, W.W. (2010). How to Write Up and Report PLS Analyses. Handbook of Partial Least Squares. Springer Handbooks of Computational Statistics. Springer, Berlin, Germany. doi:10.1007/978-3-540-32827-8_29.

Anderson, J. C., & Gerbing, D. W. (1988). Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach. Psychological Bulletin, 103(3), 411–423. doi:10.1037/0033-2909.103.3.411.

Nunnally, J. C. (1978). Psychometric Theory (2nd Ed.) McGrawe Hill, New York, United States.

Rivard, S., & Huff, S. L. (1988). Factors of success for end-user computing. Communications of the ACM, 31(5), 552–561. doi:10.1145/42411.42418.

Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39. doi:10.2307/3151312.

Fornell, C., & Bookstein, F. L. (1982). Two Structural Equation Models: LISREL and PLS Applied to Consumer Exit-Voice Theory. Journal of Marketing Research, 19(4), 440. doi:10.2307/3151718.

DeLone, W. H., & McLean, E. R. (1992). Information Systems Success: The Quest for the Dependent Variable. Information Systems Research, 3(1), 60–95. doi:10.1287/isre.3.1.60.

Anantatmula, V., & Kanungo, S. (2006). Structuring the underlying relations among the knowledge management outcomes. Journal of Knowledge Management, 10(4), 25–42. doi:10.1108/13673270610679345.

Mukhtar, A., Romli, A., Noor, N. M., Abdullateef, M., & Al-Bashiri, H. (2021, August). Inventory Visibility Scenario to Reduce Safety Stock in Supply Chain Network Using Blockchain Hyperledger Composer. International Conference on Software Engineering & Computer Systems and 4th International Conference on Computational Science and Information Management (ICSECS-ICOCSIM), IEEE, 535-540. doi:10.1109/ICSECS52883.2021.00104.

Fernandes, A. C., Vilhena, E., Oliveira, R., Sampaio, P., & Carvalho, M. S. (2022). Supply chain quality management impact on organization performance: results from an international survey. International Journal of Quality & Reliability Management, 39(2), 630-646. doi:10.1108/IJQRM-05-2020-0159.

Watts, M., Murphy, E., Keogh, B., Downes, C., Doyle, L., & Higgins, A. (2021). Deciding to discontinue prescribed psychotropic medication: A qualitative study of service users’ experiences. International Journal of Mental Health Nursing, 30, 1395-1406. doi:10.1111/inm.12894.

Gupta, H., Kumar, S., Kusi-Sarpong, S., Jabbour, C. J. C., & Agyemang, M. (2021). Enablers to supply chain performance on the basis of digitization technologies. Industrial Management & Data Systems, 121(9), 1915-1938. doi:10.1108/IMDS-07-2020-0421.

Kumar, A., Singh, R. K., & Modgil, S. (2020). Exploring the relationship between ICT, SCM practices and organizational performance in agri-food supply chain. Benchmarking: An International Journal, 27(3), 1003-1041. doi:10.1108/BIJ-11-2019-0500.

Vates, U. K., Sharma, B. P., Kanu, N. J., Gupta, E., & Singh, G. K. (2022). Modeling and optimization of IoT factors to enhance agile manufacturing strategy-based production system using SCM and RSM. Smart Science, 10(2), 158-173. doi:10.1080/23080477.2021.2017543.


Full Text: PDF

DOI: 10.28991/HEF-2022-03-03-08

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Athapol Ruangkanjanases, Taqwa Hariguna, Willy Abdillah