Advancing Frugal and Supply Chain Innovation Through Analytics Adoption

DOI: https://doi.org/10.65967/cpbo.v2i3.75

Authors

  • Zainab Nur'aini Faculty of Economics and Business, Universitas Muhammadiyah Purwokerto, Purwokerto, Indonesia
  • Aulia Octaviani Faculty of Economics and Business, Universitas Muhammadiyah Purwokerto, Purwokerto, Indonesia

Institutional Pressures, Supply Chain Analytics, Supply Chain Innovation, Frugal Innovation, Manufacturing Industry

Abstract

This study aims to explore how institutional pressures influence the adoption of supply chain analytics (SCA) and to assess its effects on supply chain (SC) innovation and frugal innovation. In addition, the research investigates the relationship between SC innovation and frugal innovation. Data were gathered from manufacturing firms operating in Jordan and analyzed using Partial Least Squares (PLS) path analysis. The results reveal that coercive pressure does not have a significant impact on SCA adoption, whereas normative and mimetic pressures positively and significantly encourage its implementation. The findings further indicate that SCA adoption contributes significantly to both SC innovation and frugal innovation. Moreover, SC innovation is found to positively influence frugal innovation. These results enrich the existing literature by providing deeper insights into the interconnections among institutional pressures, SCA adoption, SC innovation, and frugal innovation. The study highlights that recognizing the influence of institutional pressures on SCA adoption can help organizations strengthen innovation capabilities, enhance competitiveness, and achieve sustainable growth in an increasingly dynamic supply chain environment.

References

Aamer, A., Yani, L. E., & Priyatna, I. A. (2020). Data Analytics In Supply Chain Management: Review Of Machine Learning Applications In Demand Forecasting. Operations And Supply Chain Management: An International Journal, 14(1), 1–13. Https://Doi.Org/10.31387/OSCM0440281

Abou Kamar, M. (2021). Transforming Hotel Supply Chain Using Intelligent Decision Support Systems: Prospects And Challenges. Journal Of Association Of Arab Universities For Tourism And Hospitality, 20(2), 216–246. Https://Doi.Org/10.21608/JAAUTH.2021.63136.1136

Abourokbah, S. H., Mashat, R. M., & Salam, M. A. (2023). Role Of Absorptive Capacity, Digital Capability, Agility, And Resilience In Supply Chain Innovation Performance. Sustainability, 15(4), 3636. Https://Doi.Org/10.3390/Su15043636

Abuzaid, A., Alateeq, M., Baqleh, L., Madadha, S., & Haraisa, Y. (2023). The Moderating Effect Of Strategic Momentum On The Relationship Between Big Data Analytics Capabilities And Lean Supply Chain Practices. Uncertain Supply Chain Management, 11(3), 1085–1098. Https://Doi.Org/10.5267/J.Uscm.2023.4.013

Ahmed, M. U., Shafiq, A., & Mahmoodi, F. (2022). The Role Of Supply Chain Analytics Capability And Adaptation In Unlocking Value From Supply Chain Relationships. Production Planning & Control, 33(8), 774–789. Https://Doi.Org/10.1080/09537287.2020.1836416

Alsmadi, A., Al-Gasaymeh, A., Alrawashdeh, N., & Alhwamdeh, L. (2022). Financial Supply Chain Management: A Bibliometric Analysis For 2006–2022. Uncertain Supply Chain Management, 10(3), 645–656. Https://Doi.Org/10.5267/J.Uscm.2022.5.010

Alsmairat, M. A. (2023). Big Data Analytics Capabilities, Supply Chain Innovation, Customer Readiness, And Digital Supply Chain Performance: The Mediating Role Of Supply Chain Resilience. International Journal Of Advanced Operations Management, 15(1), 82–97. Https://Doi.Org/10.1504/IJAOM.2023.129525

Asmussen, C. B., & Møller, C. (2020). Enabling Supply Chain Analytics For Enterprise Information Systems: A Topic Modelling Literature Review And Future Research Agenda. Enterprise Information Systems, 14(5), 563–610. Https://Doi.Org/10.1080/17517575.2020.1734240

Bag, S., & Rahman, M. S. (2024). Navigating Circular Economy: Unleashing The Potential Of Political And Supply Chain Analytics Skills Among Top Supply Chain Executives For Environmental Orientation, Regenerative Supply Chain Practices, And Supply Chain Viability. Business Strategy And The Environment, 33(2), 504–528. Https://Doi.Org/10.1002/Bse.3507

Bag, S., Pretorius, J. H. C., Gupta, S., & Dwivedi, Y. K. (2021). Role Of Institutional Pressures And Resources In The Adoption Of Big Data Analytics Powered Artificial Intelligence, Sustainable Manufacturing Practices And Circular Economy Capabilities. Technological Forecasting And Social Change, 163, 120420. Https://Doi.Org/10.1016/J.Techfore.2020.120420

Bai, S., & Jia, X. (2023). The Impact Of The Cost-Sharing Contract On Capital-Constrained Agricultural Supply Chains. SAGE Open, 13(1), 1–14. Https://Doi.Org/10.1177/21582440231156157

Bank Of America Institute. (2022). Addressing Scarcity In A Transforming World. Https://Business.Bofa.Com/Content/Dam/Flagship/Bank-Of-America-Institute/Esg/Addressing-Scarcity-In-A-Transforming-World-April-2022.Pdf

Barbosa, M. W., Vicente, A. D. L. C., Ladeira, M. B., & Oliveira, M. P. V. D. (2018). Managing Supply Chain Resources With Big Data Analytics: A Systematic Review. International Journal Of Logistics Research And Applications, 21(3), 177–200. Https://Doi.Org/10.1080/13675567.2017.1369501

Bhatti, S. H., Hussain, W. M. H. W., Khan, J., Sultan, S., & Ferraris, A. (2024). Exploring Data-Driven Innovation: What’s Missing In The Relationship Between Big Data Analytics Capabilities And Supply Chain Innovation? Annals Of Operations Research, 333(2), 799–824. Https://Doi.Org/10.1007/S10479-022-04772-7

Büyüközkan, G., & Güler, M. (2021). A Combined Hesitant Fuzzy MCDM Approach For Supply Chain Analytics Tool Evaluation. Applied Soft Computing, 112, 107812. Https://Doi.Org/10.1016/J.Asoc.2021.107812

Chen, D. Q., Preston, D. S., & Swink, M. (2015). How The Use Of Big Data Analytics Affects Value Creation In Supply Chain Management. Journal Of Management Information Systems, 32(4), 4–39. Https://Doi.Org/10.1080/07421222.2015.1138364

Daneshvar Kakhki, M., Rea, A., & Deiranlou, M. (2023). Data Analytics Dynamic Capabilities For Triple-A Supply Chains. Industrial Management & Data Systems, 123(2), 534–555. Https://Doi.Org/10.1108/IMDS-03-2022-0167

Daradkeh, M. (2023). Navigating Value Co-Destruction In Open Innovation Communities: An Empirical Study Of Expectancy Disconfirmation And Psychological Contracts In Business Analytics Communities. Behavioral Sciences, 13(4), 334. Https://Doi.Org/10.3390/Bs13040334

Dimaggio, P. J., & Powell, W. W. (1983). The Iron Cage Revisited: Institutional Isomorphism And Collective Rationality In Organizational Fields. American Sociological Review, 48(2), 147–160. Https://Doi.Org/10.2307/2095101

Dubey, R., Gunasekaran, A., Childe, S. J., Blome, C., & Papadopoulos, T. (2019). Big Data And Predictive Analytics And Manufacturing Performance: Integrating Institutional Theory, Resource-Based View And Big Data Culture. British Journal Of Management, 30(2), 341–361. Https://Doi.Org/10.1111/1467-8551.12355

Dubey, R., Bryde, D. J., Dwivedi, Y. K., Graham, G., & Foropon, C. (2022). Impact Of Artificial Intelligence-Driven Big Data Analytics Culture On Agility And Resilience In Humanitarian Supply Chains: A Practice-Based View. International Journal Of Production Economics, 250, 108618. Https://Doi.Org/10.1016/J.Ijpe.2022.108618

Fantazy, K., & Tipu, S. A. A. (2024). Linking Big Data Analytics Capability And Sustainable Supply Chain Performance: Mediating Role Of Knowledge Development. Management Research Review, 47(4), 512–536. Https://Doi.Org/10.1108/MRR-01-2023-0018

Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models With Unobservable Variables And Measurement Error. Journal Of Marketing Research, 18(1), 39–50. Https://Doi.Org/10.2307/3151312

Fosso Wamba, S., & Akter, S. (2019). Understanding Supply Chain Analytics Capabilities And Agility For Data-Rich Environments. International Journal Of Operations & Production Management, 39(6/7/8), 887–912. Https://Doi.Org/10.1108/IJOPM-01-2019-0025

Gloet, M., & Samson, D. (2022). Knowledge And Innovation Management To Support Supply Chain Innovation And Sustainability Practices. Information Systems Management, 39(1), 3–18. Https://Doi.Org/10.1080/10580530.2020.1818898

Gupta, S., Modgil, S., Gunasekaran, A., & Bag, S. (2020). Dynamic Capabilities And Institutional Theories For Industry 4.0 And Digital Supply Chains. Supply Chain Forum: An International Journal, 21(3), 139–157. Https://Doi.Org/10.1080/16258312.2020.1757369

Hair, J. F., Jr., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial Least Squares Structural Equation Modeling (PLS-SEM): An Emerging Tool In Business Research. European Business Review, 26(2), 106–121. Https://Doi.Org/10.1108/EBR-10-2013-0128

Hämäläinen, E., & Inkinen, T. (2019). Industrial Applications Of Big Data In Disruptive Innovations Supporting Environmental Reporting. Journal Of Industrial Information Integration, 16, 100105. Https://Doi.Org/10.1016/J.Jii.2019.100105

Han, Y., & Xie, L. (2023). Platform Network Ties And Enterprise Innovation Performance: The Role Of Network Bricolage And Platform Empowerment. Journal Of Innovation & Knowledge, 8(4), 100416. Https://Doi.Org/10.1016/J.Jik.2023.100416

Hasan, R., Kamal, M. M., Daowd, A., Eldabi, T., Koliousis, I., & Papadopoulos, T. (2024). Critical Analysis Of The Impact Of Big Data Analytics On Supply Chain Operations. Production Planning & Control, 35(1), 46–70. Https://Doi.Org/10.1080/09537287.2022.2047237

Herden, T. T. (2020). Explaining The Competitive Advantage Generated From Analytics With The Knowledge-Based View: The Example Of Logistics And Supply Chain Management. Business Research, 13(1), 163–214. Https://Doi.Org/10.1007/S40685-019-00104-X

Hoehle, H., Aloysius, J. A., Chan, F., & Venkatesh, V. (2018). Customers’ Tolerance For Validation In Omnichannel Retail Stores: Enabling Logistics And Supply Chain Analytics. The International Journal Of Logistics Management, 29(2), 704–722. Https://Doi.Org/10.1108/IJLM-08-2017-0219

Hopkins, J. L. (2021). An Investigation Into Emerging Industry 4.0 Technologies As Drivers Of Supply Chain Innovation In Australia. Computers In Industry, 125, 103323. Https://Doi.Org/10.1016/J.Compind.2020.103323

Hu, X., & Zhang, L. (2023). Research On The Integration Level Measurement And Optimization Path Of Industrial Chain, Innovation Chain And Service Chain. Journal Of Innovation & Knowledge, 8(3), 100368. Https://Doi.Org/10.1016/J.Jik.2023.100368

Iftikhar, A., Ali, I., Arslan, A., & Tarba, S. (2024). Digital Innovation, Data Analytics, And Supply Chain Resiliency: A Bibliometric-Based Systematic Literature Review. Annals Of Operations Research, 333(2), 825–848. Https://Doi.Org/10.1007/S10479-022-04765-6

Jardim, L., Pranto, S., Ruivo, P., & Oliveira, T. (2021). What Are The Main Drivers Of Blockchain Adoption Within Supply Chain? An Exploratory Research. Procedia Computer Science, 181, 495–502. Https://Doi.Org/10.1016/J.Procs.2021.01.195

Jeske, D., & Calvard, T. (2020). Big Data: Lessons For Employers And Employees. Employee Relations: The International Journal, 42(1), 248–261. Https://Doi.Org/10.1108/ER-06-2018-0159

Kalaitzi, D., & Tsolakis, N. (2022). Supply Chain Analytics Adoption: Determinants And Impacts On Organisational Performance And Competitive Advantage. International Journal Of Production Economics, 248, Article 108466. Https://Doi.Org/10.1016/J.Ijpe.2022.108466

Kamboj, S., & Rana, S. (2023). Big Data-Driven Supply Chain And Performance: A Resource-Based View. The TQM Journal, 35(1), 5–23. Https://Doi.Org/10.1108/TQM-02-2021-0036

Karaman Kabadurmus, F. N. (2020). Antecedents To Supply Chain Innovation. The International Journal Of Logistics Management, 31(1), 145–171. Https://Doi.Org/10.1108/IJLM-04-2019-0096

Kauppi, K., & Luzzini, D. (2021). Measuring Institutional Pressures In A Supply Chain Context: Scale Development And Testing. Supply Chain Management: An International Journal, 27(7), 79–107. Https://Doi.Org/10.1108/SCM-04-2021-0169

Khan, S. A. R., Piprani, A. Z., & Yu, Z. (2023). Supply Chain Analytics And Post-Pandemic Performance: Mediating Role Of Triple-A Supply Chain Strategies. International Journal Of Emerging Markets, 18(6), 1330–1354. Https://Doi.Org/10.1108/IJOEM-11-2021-1744

Khanal, P. B., Aubert, B. A., Bernard, J. G., Narasimhamurthy, R., & De, R. (2022). Frugal Innovation And Digital Effectuation For Development: The Case Of Lucia. Information Technology For Development, 28(1), 81–110. Https://Doi.Org/10.1080/02681102.2021.1920874

Kitsis, A. M., & Chen, I. J. (2021). Do Stakeholder Pressures Influence Green Supply Chain Practices? Exploring The Mediating Role Of Top Management Commitment. Journal Of Cleaner Production, 316, Article 128258. Https://Doi.Org/10.1016/J.Jclepro.2021.128258

Kock, N. (2015). Common Method Bias In PLS-SEM: A Full Collinearity Assessment Approach. International Journal Of E-Collaboration, 11(4), 1–10. Https://Doi.Org/10.4018/IJEC.2015100101

Kun, M. (2022). Linkages Between Knowledge Management Process And Corporate Sustainable Performance Of Chinese Smes: Mediating Role Of Frugal Innovation. Frontiers In Psychology, 13, Article 850820. Https://Doi.Org/10.3389/Fpsyg.2022.850820

Lai, K. H., Feng, Y., & Zhu, Q. (2023). Digital Transformation For Green Supply Chain Innovation In Manufacturing Operations. Transportation Research Part E: Logistics And Transportation Review, 175, Article 103145. Https://Doi.Org/10.1016/J.Tre.2023.103145

Lee, D. (2019). Implementation Of Collaborative Activities For Sustainable Supply Chain Innovation: An Analysis Of The Firm Size Effect. Sustainability, 11(11), Article 3026. Https://Doi.Org/10.3390/Su11113026

Lee, V. H., Foo, P. Y., Cham, T. H., Hew, T. S., Tan, G. W. H., & Ooi, K. B. (2024). Big Data Analytics Capability In Building Supply Chain Resilience: The Moderating Effect Of Innovation-Focused Complementary Assets. Industrial Management & Data Systems, 124(3), 1203–1233. Https://Doi.Org/10.1108/IMDS-07-2022-0411

Liu, Y., Fang, W., Feng, T., & Gao, N. (2022). Bolstering Green Supply Chain Integration Via Big Data Analytics Capability: The Moderating Role Of Data-Driven Decision Culture. Industrial Management & Data Systems, 122(11), 2558–2582. Https://Doi.Org/10.1108/IMDS-11-2021-0696

Lopez-Morales, B., Gutierrez, L., Llorens-Montes, F. J., & Rojo-Gallego-Burín, A. (2023). Enhancing Supply Chain Competences Through Supply Chain Digital Embeddedness: An Institutional View. Journal Of Business & Industrial Marketing, 38(3), 533–552. Https://Doi.Org/10.1108/JBIM-07-2021-0354

Mageto, J. (2021). Big Data Analytics In Sustainable Supply Chain Management: A Focus On Manufacturing Supply Chains. Sustainability, 13(13), Article 7101. Https://Doi.Org/10.3390/Su13137101

Naeini, A. B., Abaee, A., & Zamani, M. (2019). Designing A Business Intelligence Conceptual Model Of Supply Chain Management In Sales-Based Smes. International Journal Of Logistics Systems And Management, 34(2), 154–171. Https://Doi.Org/10.1504/IJLSM.2019.102213

Nassani, A. A., Sinisi, C., Paunescu, L., Yousaf, Z., Haffar, M., & Kabbani, A. (2022). Nexus Of Innovation Network, Digital Innovation And Frugal Innovation Towards Innovation Performance: Investigation Of Energy Firms. Sustainability, 14(7), Article 4330. Https://Doi.Org/10.3390/Su14074330

Ogbuke, N. J., Yusuf, Y. Y., Dharma, K., & Mercangoz, B. A. (2022). Big Data Supply Chain Analytics: Ethical, Privacy And Security Challenges Posed To Business, Industries And Society. Production Planning & Control, 33(2–3), 123–137. Https://Doi.Org/10.1080/09537287.2020.1810764

Ogunrinde, A. (2022). The Effectiveness Of Soft Skills In Generating Dynamic Capabilities In ICT Companies. ESIC Market, 53(3), 1–27. Https://Doi.Org/10.7200/Esicm.53.286

Pedroza-Gutiérrez, C., & Hernández, J. M. (2020). Social Networks And Supply Chain Management In Fish Trade. SAGE Open, 10(2), 1–18. Https://Doi.Org/10.1177/2158244020931815

Qu, X., Qin, X., & Wang, X. (2023). Construction Of Frugal Innovation Path In The Context Of Digital Transformation: A Study Based On NCA And QCA. Sustainability, 15(3), Article 2158. Https://Doi.Org/10.3390/Su15032158

Shafiq, A., Ahmed, M. U., & Mahmoodi, F. (2020). Impact Of Supply Chain Analytics And Customer Pressure For Ethical Conduct On Socially Responsible Practices And Performance: An Exploratory Study. International Journal Of Production Economics, 225, Article 107571. Https://Doi.Org/10.1016/J.Ijpe.2019.107571

Shahzad, F., Du, J., Khan, I., & Wang, J. (2022). Decoupling Institutional Pressure On Green Supply Chain Management Efforts To Boost Organizational Performance: Moderating Impact Of Big Data Analytics Capabilities. Frontiers In Environmental Science, 10, Article 911392. Https://Doi.Org/10.3389/Fenvs.2022.911392

Shamout, M. D. (2019). Does Supply Chain Analytics Enhance Supply Chain Innovation And Robustness Capability? Organizacija, 52(2), 95–106. Https://Doi.Org/10.2478/Orga-2019-0007

Shamout, M. D. (2023). A Configural Model Of Analytics Capabilities, Ambidexterity, Co-Opetition, And Firm Performance In The Supply Chain Context. Business Strategy And Development, 6(2), 128–139. Https://Doi.Org/10.1002/Bsd2.228

Shibin, K. T., Dubey, R., Gunasekaran, A., Luo, Z., Papadopoulos, T., & Roubaud, D. (2018). Frugal Innovation For Supply Chain Sustainability In Smes: Multi-Method Research Design. Production Planning & Control, 29(11), 908–927. Https://Doi.Org/10.1080/09537287.2018.1493139

Singh, N., Lai, K. H., & Zhang, J. Z. (2024). Intellectual Core In Supply Chain Analytics: Bibliometric Analysis And Research Agenda. International Journal Of Information Technology & Decision Making, 23(2), 539–567. Https://Doi.Org/10.1142/S0219622023300021

Tagscherer, F., & Carbon, C. C. (2023). Leadership For Successful Digitalization: A Literature Review On Companies’ Internal And External Aspects Of Digitalization. Sustainable Technology And Entrepreneurship, 2(2), Article 100039. Https://Doi.Org/10.1016/J.Stae.2023.100039

Takahashi, A. R. W., & Sander, J. A. (2017). Combining Institutional Theory With Resource-Based Theory To Understand Processes Of Organizational Knowing And Dynamic Capabilities. European Journal Of Management Issues, 25(1), 43–48. Https://Doi.Org/10.15421/191707

Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic Capabilities And Strategic Management. Strategic Management Journal, 18(7), 509–533. Https://Doi.Org/10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z

Villena, V. H., & Dhanorkar, S. (2020). How Institutional Pressures And Managerial Incentives Elicit Carbon Transparency In Global Supply Chains. Journal Of Operations Management, 66(6), 697–734. Https://Doi.Org/10.1002/Joom.1088

Weyrauch, T., & Herstatt, C. (2017). What Is Frugal Innovation? Three Defining Criteria. Journal Of Frugal Innovation, 2(1), 1–17. Https://Doi.Org/10.1186/S40669-016-0005-Y

Wong, D. T. W., & Ngai, E. W. T. (2019). Critical Review Of Supply Chain Innovation Research (1999–2016). Industrial Marketing Management, 82, 158–187. Https://Doi.Org/10.1016/J.Indmarman.2019.01.017

Yaseen, S. G., El Qirem, I. A., & Dajani, D. (2022). Islamic Mobile Banking Smart Services Adoption And Use In Jordan. ISRA International Journal Of Islamic Finance, 14(3), 349–362. Https://Doi.Org/10.1108/IJIF-04-2021-0065

Yousaf, Z., Panait, M., Tanveer, U., Cretu, A., Hrebenciuc, A., & Zahid, S. M. (2022). Value Creation Through Frugal Innovation, Innovation Capability And Knowledge Sharing In A Circular Economy. Sustainability, 14(14), Article 8504. Https://Doi.Org/10.3390/Su14148504

Zameer, H., Wang, Y., Yasmeen, H., & Mubarak, S. (2022). Green Innovation As A Mediator In The Impact Of Business Analytics And Environmental Orientation On Green Competitive Advantage. Management Decision, 60(2), 488–507. Https://Doi.Org/10.1108/MD-01-2020-0065

Zhang, X. (2018). Frugal Innovation And The Digital Divide: Developing An Extended Model Of The Diffusion Of Innovations. International Journal Of Innovation Studies, 2(2), 53–64. Https://Doi.Org/10.1016/J.Ijis.2018.06.001

Zhu, S., Song, J., Hazen, B. T., Lee, K., & Cegielski, C. (2018). How Supply Chain Analytics Enables Operational Supply Chain Transparency: An Organizational Information Processing Theory Perspective. International Journal Of Physical Distribution & Logistics Management, 48(1), 47–68. Https://Doi.Org/10.1108/IJPDLM-11-2017-0341

Downloads

Published

2026-06-22

How to Cite

Advancing Frugal and Supply Chain Innovation Through Analytics Adoption . (2026). Current Perspective on Business Operations, 2(3), 292-305. https://doi.org/10.65967/cpbo.v2i3.75

How to Cite

Advancing Frugal and Supply Chain Innovation Through Analytics Adoption . (2026). Current Perspective on Business Operations, 2(3), 292-305. https://doi.org/10.65967/cpbo.v2i3.75

Similar Articles

11-20 of 23

You may also start an advanced similarity search for this article.