How to cite this paper
Nuseir, M & ElRefae, G. (2021). The effect of supply chain connectivity and task technology fit on efficiency: Exploring mediating role of big data analytic.Uncertain Supply Chain Management, 9(4), 1017-1026.
Refrences
Abdullah, F., Ward, R., & Ahmed, E. (2016). We are investigating the influence of TAM's most commonly used external variables on students' Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios. Computers in human behavior, 63, 75-90.
Al-Shiakhli, S. (2019). Big Data Analytics: A Literature Review Perspective.
Ali, J., Azeem, M., Marri, M. Y. K., & Khurram, S. (2021). University Social Responsibility and Self Efficacy as Antecedents of Intention to use E-Learning: Examining Mediating Role of Student Satisfaction. Psychology and Education Journal, 58(2), 4219-4230.
Alsadi, A. K., Alaskar, T. H., & Mezghani, K. (2021). Adoption of big data analytics in supply chain management: combining organizational factors with supply chain connectivity. International Journal of Information Systems and Supply Chain Management (IJISSCM), 14(2), 88-107.
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.
Bauerová, R., & Klepek, M. (2018). Technology acceptance as a determinant of online grocery shopping adoption. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 66(3), 737-746.
Bienhaus, F., & Haddud, A. (2018). Procurement 4.0: factors influencing the digitization of procurement and supply chains. Business Process Management Journal, 24(4), 965-984.
Brandon‐Jones, E., Squire, B., Autry, C. W., & Petersen, K. J. (2014). A contingent resource‐based perspective of supply chain resilience and robustness. Journal of Supply Chain Management, 50(3), 55-73.
Cheng, J.-H., & Lu, K.-L. (2018). The Impact of Big Data Analytics Use on Supply Chain Performance---Efficiency and Adaptability as Mediators. Computer Science.
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295-336.
D'Ambra, J., Wilson, C. S., & Akter, S. (2013). Application of the task‐technology fit model to structure and evaluate the adoption of E‐books by Academics. Journal of the American Society for information science and technology, 64(1), 48-64.
Dang, Y. M., Zhang, Y. G., Brown, S. A., & Chen, H. (2020). Examining the impacts of mental workload and task-technology fit on user acceptance of the social media search system. Information Systems Frontiers, 22(3), 697-718.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
Dubey, R., Altay, N., Gunasekaran, A., Blome, C., Papadopoulos, T., & Childe, S. J. (2018). Supply chain agility, adaptability, and alignment: empirical evidence from the Indian auto components industry. International Journal of Operations & Production Management, 38(1), 129-148.
Elgendy, N., & Elragal, A. (2014). Big data analytics: a literature review paper. Paper presented at the Industrial conference on data mining.
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics: Sage Publications Sage CA: Los Angeles, CA.
Gangwar, H. (2020a). Big Data Adoption: A Comparative Study of the Indian Manufacturing and Services Sectors Optimizing Data and New Methods for Efficient Knowledge Discovery and Information Resources Management: Emerging Research and Opportunities (pp. 138-171): IGI Global.
Gangwar, H. (2020b). Big Data Analytics Usage and Business Performance: Integrating the Technology Acceptance Model (TAM) and Task Technology Fit (TTF) model. Electronic Journal of Information Systems Evaluation, 23(1), pp45‑64-pp45‑64.
García, S., Ramírez-Gallego, S., Luengo, J., Benítez, J. M., & Herrera, F. (2016). Big data preprocessing: methods and prospects. Big Data Analytics, 1(1), 1-22.
Hair, J. F., Celsi, M., Ortinau, D. J., & Bush, R. P. (2010). Essentials of marketing research (Vol. 2): McGraw-Hill/Irwin New York, NY.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2012). Partial least squares: the better approach to structural equation modeling? Long Range Planning, 45(5-6), 312-319.
Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information systems, 47, 98-115.
Howard, M. C., & Rose, J. C. (2019). Refining and extending task–technology fit theory: Creation of two task–technology fit scales and empirical construct clarification. Information & Management, 56(6), 103134.
Jabeen, S., & Ali, J. (2022). Impact of Servant Leadership on the Development of Change-Oriented Citizenship Behavior: Multi-Mediation Analysis of Change Readiness and Psychological Empowerment Key Factors and Use Cases of Servant Leadership Driving Organizational Performance (pp. 110-129): IGI Global.
Kamble, S., Gunasekaran, A., & Arha, H. (2019). Understanding the Blockchain technology adoption in supply chains-Indian context. International Journal of Production Research, 57(7), 2009-2033.
Koseleva, N., & Ropaite, G. (2017). Big data in building energy efficiency: understanding big data and main challenges. Procedia Engineering, 172, 544-549.
Ma, Y. J., Gam, H. J., & Banning, J. (2017). Perceived ease of use and usefulness of sustainability labels on apparel products: application of the technology acceptance model. Fashion and Textiles, 4(1), 1-20.
Manfred, K. (2018). Trading Costs in Africa: Does International Supply Chain Connectivity Matter? Journal of Economic Development, 43(2).
Marangunić, N., & Granić, A. (2015). Technology acceptance model: a literature review from 1986 to 2013. Universal access in the information society, 14(1), 81-95.
Matopoulos, A., Barros, A. C., & van der Vorst, J. J. (2015). Resource-efficient supply chains: a research framework, literature review and research agenda. Supply Chain Management: An International Journal, 20i(2).
Moatti, V., Ren, C. R., Anand, J., & Dussauge, P. (2015). Disentangling the performance effects of efficiency and bargaining power in horizontal growth strategies: An empirical investigation in the global retail industry. Strategic Management Journal, 36(5), 745-757.
Özköse, H., Arı, E. S., & Gencer, C. (2015). Yesterday, today, and tomorrow of big data. Procedia-Social and Behavioral Sciences, 195, 1042-1050.
Patalinghug, E. E. (2015). Supply chain connectivity: Enhancing participation in the global supply chain.
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.
Samuel, N., Onasanya, S., & Olumorin, C. (2018). Nigerian university lecturers perceived usefulness, ease of use, and adequacy of use of mobile technologies by Nigerian university lecturers. International Journal of Education and Development using ICT, 14(3).
Sarstedt, M., Ringle, C. M., Henseler, J., & Hair, J. F. (2014). On the emancipation of PLS-SEM: A commentary on Rigdon (2012). Long Range Planning, 47(3), 154-160.
Seyedghorban, Z., Tahernejad, H., Meriton, R., & Graham, G. (2020). Supply chain digitalization: past, present, and future. Production Planning & Control, 31(2-3), 96-114.
Shabbir, M. Q., & Gardezi, S. B. W. (2020). Application of big data analytics and organizational performance: the mediating role of knowledge management practices. Journal of Big Data, 7(1), 1-17.
Shahbaz, M., Gao, C., Zhai, L., Shahzad, F., & Hu, Y. (2019). Investigating the adoption of big data analytics in healthcare: the moderating role of resistance to change. Journal of Big Data, 6(1), 1-20.
Sivarajah, U., Irani, Z., Gupta, S., & Mahroof, K. (2020). Role of big data and social media analytics for business to business sustainability: A participatory web context. Industrial Marketing Management, 86, 163-179.
Srimarut, T., & Mekhum, W. (2020). From Supply Chain Connectivity (SCC) to Supply Chain Agility (SCA), Adaptability and Alignment: Mediating Role of Big Data Analytics Capability. International Journal of Supply Chain Management, 9(1), 183-189.
Sugandini, D., Purwoko, P., Pambudi, A., Resmi, S., Reniati, R., Muafi, M., & Adhyka Kusumawati, R. (2018). The role of uncertainty, perceived ease of use, and perceived usefulness towards the technology adoption. International Journal of Civil Engineering and Technology (IJCIET), 9(4), 660-669.
Vanani, I. R., & Majidian, S. (2019). Literature Review on Big Data Analytics Methods. Social Media and Machine Learning.
Vanduhe, V. Z., Nat, M., & Hasan, H. F. (2020). Continuance intentions to use gamification for training in higher education: Integrating the technology acceptance model (TAM), Social motivation, and task technology fit (TTF). IEEE Access, 8, 21473-21484.
Wang, G., Gunasekaran, A., Ngai, E. W., & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, 98-110.
Wang, L., Yang, M., Pathan, Z. H., Salam, S., Shahzad, K., & Zeng, J. (2018). Analysis of influencing factors of big data adoption in Chinese enterprises using DANP technique. Sustainability, 10(11), 3956.
Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221-232.
Al-Shiakhli, S. (2019). Big Data Analytics: A Literature Review Perspective.
Ali, J., Azeem, M., Marri, M. Y. K., & Khurram, S. (2021). University Social Responsibility and Self Efficacy as Antecedents of Intention to use E-Learning: Examining Mediating Role of Student Satisfaction. Psychology and Education Journal, 58(2), 4219-4230.
Alsadi, A. K., Alaskar, T. H., & Mezghani, K. (2021). Adoption of big data analytics in supply chain management: combining organizational factors with supply chain connectivity. International Journal of Information Systems and Supply Chain Management (IJISSCM), 14(2), 88-107.
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.
Bauerová, R., & Klepek, M. (2018). Technology acceptance as a determinant of online grocery shopping adoption. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 66(3), 737-746.
Bienhaus, F., & Haddud, A. (2018). Procurement 4.0: factors influencing the digitization of procurement and supply chains. Business Process Management Journal, 24(4), 965-984.
Brandon‐Jones, E., Squire, B., Autry, C. W., & Petersen, K. J. (2014). A contingent resource‐based perspective of supply chain resilience and robustness. Journal of Supply Chain Management, 50(3), 55-73.
Cheng, J.-H., & Lu, K.-L. (2018). The Impact of Big Data Analytics Use on Supply Chain Performance---Efficiency and Adaptability as Mediators. Computer Science.
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295-336.
D'Ambra, J., Wilson, C. S., & Akter, S. (2013). Application of the task‐technology fit model to structure and evaluate the adoption of E‐books by Academics. Journal of the American Society for information science and technology, 64(1), 48-64.
Dang, Y. M., Zhang, Y. G., Brown, S. A., & Chen, H. (2020). Examining the impacts of mental workload and task-technology fit on user acceptance of the social media search system. Information Systems Frontiers, 22(3), 697-718.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
Dubey, R., Altay, N., Gunasekaran, A., Blome, C., Papadopoulos, T., & Childe, S. J. (2018). Supply chain agility, adaptability, and alignment: empirical evidence from the Indian auto components industry. International Journal of Operations & Production Management, 38(1), 129-148.
Elgendy, N., & Elragal, A. (2014). Big data analytics: a literature review paper. Paper presented at the Industrial conference on data mining.
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics: Sage Publications Sage CA: Los Angeles, CA.
Gangwar, H. (2020a). Big Data Adoption: A Comparative Study of the Indian Manufacturing and Services Sectors Optimizing Data and New Methods for Efficient Knowledge Discovery and Information Resources Management: Emerging Research and Opportunities (pp. 138-171): IGI Global.
Gangwar, H. (2020b). Big Data Analytics Usage and Business Performance: Integrating the Technology Acceptance Model (TAM) and Task Technology Fit (TTF) model. Electronic Journal of Information Systems Evaluation, 23(1), pp45‑64-pp45‑64.
García, S., Ramírez-Gallego, S., Luengo, J., Benítez, J. M., & Herrera, F. (2016). Big data preprocessing: methods and prospects. Big Data Analytics, 1(1), 1-22.
Hair, J. F., Celsi, M., Ortinau, D. J., & Bush, R. P. (2010). Essentials of marketing research (Vol. 2): McGraw-Hill/Irwin New York, NY.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2012). Partial least squares: the better approach to structural equation modeling? Long Range Planning, 45(5-6), 312-319.
Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information systems, 47, 98-115.
Howard, M. C., & Rose, J. C. (2019). Refining and extending task–technology fit theory: Creation of two task–technology fit scales and empirical construct clarification. Information & Management, 56(6), 103134.
Jabeen, S., & Ali, J. (2022). Impact of Servant Leadership on the Development of Change-Oriented Citizenship Behavior: Multi-Mediation Analysis of Change Readiness and Psychological Empowerment Key Factors and Use Cases of Servant Leadership Driving Organizational Performance (pp. 110-129): IGI Global.
Kamble, S., Gunasekaran, A., & Arha, H. (2019). Understanding the Blockchain technology adoption in supply chains-Indian context. International Journal of Production Research, 57(7), 2009-2033.
Koseleva, N., & Ropaite, G. (2017). Big data in building energy efficiency: understanding big data and main challenges. Procedia Engineering, 172, 544-549.
Ma, Y. J., Gam, H. J., & Banning, J. (2017). Perceived ease of use and usefulness of sustainability labels on apparel products: application of the technology acceptance model. Fashion and Textiles, 4(1), 1-20.
Manfred, K. (2018). Trading Costs in Africa: Does International Supply Chain Connectivity Matter? Journal of Economic Development, 43(2).
Marangunić, N., & Granić, A. (2015). Technology acceptance model: a literature review from 1986 to 2013. Universal access in the information society, 14(1), 81-95.
Matopoulos, A., Barros, A. C., & van der Vorst, J. J. (2015). Resource-efficient supply chains: a research framework, literature review and research agenda. Supply Chain Management: An International Journal, 20i(2).
Moatti, V., Ren, C. R., Anand, J., & Dussauge, P. (2015). Disentangling the performance effects of efficiency and bargaining power in horizontal growth strategies: An empirical investigation in the global retail industry. Strategic Management Journal, 36(5), 745-757.
Özköse, H., Arı, E. S., & Gencer, C. (2015). Yesterday, today, and tomorrow of big data. Procedia-Social and Behavioral Sciences, 195, 1042-1050.
Patalinghug, E. E. (2015). Supply chain connectivity: Enhancing participation in the global supply chain.
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.
Samuel, N., Onasanya, S., & Olumorin, C. (2018). Nigerian university lecturers perceived usefulness, ease of use, and adequacy of use of mobile technologies by Nigerian university lecturers. International Journal of Education and Development using ICT, 14(3).
Sarstedt, M., Ringle, C. M., Henseler, J., & Hair, J. F. (2014). On the emancipation of PLS-SEM: A commentary on Rigdon (2012). Long Range Planning, 47(3), 154-160.
Seyedghorban, Z., Tahernejad, H., Meriton, R., & Graham, G. (2020). Supply chain digitalization: past, present, and future. Production Planning & Control, 31(2-3), 96-114.
Shabbir, M. Q., & Gardezi, S. B. W. (2020). Application of big data analytics and organizational performance: the mediating role of knowledge management practices. Journal of Big Data, 7(1), 1-17.
Shahbaz, M., Gao, C., Zhai, L., Shahzad, F., & Hu, Y. (2019). Investigating the adoption of big data analytics in healthcare: the moderating role of resistance to change. Journal of Big Data, 6(1), 1-20.
Sivarajah, U., Irani, Z., Gupta, S., & Mahroof, K. (2020). Role of big data and social media analytics for business to business sustainability: A participatory web context. Industrial Marketing Management, 86, 163-179.
Srimarut, T., & Mekhum, W. (2020). From Supply Chain Connectivity (SCC) to Supply Chain Agility (SCA), Adaptability and Alignment: Mediating Role of Big Data Analytics Capability. International Journal of Supply Chain Management, 9(1), 183-189.
Sugandini, D., Purwoko, P., Pambudi, A., Resmi, S., Reniati, R., Muafi, M., & Adhyka Kusumawati, R. (2018). The role of uncertainty, perceived ease of use, and perceived usefulness towards the technology adoption. International Journal of Civil Engineering and Technology (IJCIET), 9(4), 660-669.
Vanani, I. R., & Majidian, S. (2019). Literature Review on Big Data Analytics Methods. Social Media and Machine Learning.
Vanduhe, V. Z., Nat, M., & Hasan, H. F. (2020). Continuance intentions to use gamification for training in higher education: Integrating the technology acceptance model (TAM), Social motivation, and task technology fit (TTF). IEEE Access, 8, 21473-21484.
Wang, G., Gunasekaran, A., Ngai, E. W., & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, 98-110.
Wang, L., Yang, M., Pathan, Z. H., Salam, S., Shahzad, K., & Zeng, J. (2018). Analysis of influencing factors of big data adoption in Chinese enterprises using DANP technique. Sustainability, 10(11), 3956.
Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221-232.