Better understanding of technology effects in adoption of predictive supply chain business analytics among SMEs: fresh insights from developing countries

Abstract

PurposeThis study contributes to the supply chain management (SCM) literature differently. It offers insightful information about the use and adoption of technologies for small and medium-sized enterprises (SMEs) in developing countries. Some challenges regarding the predictive supply chain business analytics (SCBA) tools and their prediction remain unexplored and require addressing and examination in developing economies. Therefore, this study examines the substantial roles of relative advantage (RA) and compatibility (Comp.) in using technology in predictive SCBA adoption among SMEs in developing countries.Design/methodology/approachThis paper performed a quantitative survey-based study to analyze the substantial role of RA and Comp. with the aim of using predictive SCBA adoption. To this end, the author conducted an online survey through which 262 SMEs from developing countries (i.e. Jordan, Kingdom of Saudi Arabia (KSA), the United Arab Emirates (UAE), Egypt, Turkey and Qatar) only responded by email.FindingsThe partial least squares structural equation modeling (PLS-SEM) clearly shows a significant relationship between RA and predictive SCBA adoption. Still, Comp. does not significantly affect the use of predictive SCBA adoption.Originality/valueSuch findings of this study can provide insightful implications for stakeholders and policymakers regarding the importance of using predictive SCBA adoption in SMEs in developing countries.

Fostering comparative advantage: the roles of data-driven competitive sustainability, green product innovation and green process innovation through moderated-mediation model

“…Ramadan et al. , 2020; AL-Shboul, 2023a) due to the big data has huge focusing now by many scholars. It is considered a vital factor in the digitalization era particularly industry 5.0 for various fields of knowledge in general and business and logistics sciences in particular (Bag et al.…”

“…Thus, the significant role of RB&CDACS in creating CA has been emphasized by many previous studies (e.g. Ramadan et al, 2020;AL-Shboul, 2023a) due to the big data has huge focusing now by many scholars. It is considered a vital factor in the digitalization era particularly industry 5.0 for various fields of knowledge in general and business and logistics sciences in particular (Bag et al, 2022;Ciampi et al, 2021;Dahiya et al, 2022).…”

“…Therefore, big data analyzes the need, in their work, for various technological techniques and specialized and distinguished human competencies to deal with the huge amount of data, which enhances the achievement of new insights that were not previously known, which leads to great benefits on both levels SCs and the firm in general (Cao et al, 2022). Studies in the literature have emphasized the positive association between RB&CDACs and the lower cost, quality and time of products and services produced by MFs that adopt RB&CDACs in their operations and processes for achieving CA (Ramadan et al, 2020;Ali et al, 2020;Begum et al, 2022;Bag et al, 2022;Al-Khatib, 2022;Behl et al, 2022;AL-Shboul, 2023a). Dahiya et al (2022) emphasized that RB&CDACs provide more efficient approaches to enhance production operations and service delivery to be more efficient than traditional methods to improve its CA.…”

“…, 2021), organizational culture and climate (Ivaldi et al. , 2022; AL-Shboul, 2023a), human resources management (Sun et al. , 2018).…”

“…Therefore, innovation adoption responses for MFs operations are vital to creating green actions that conserve the natural environment from the manufacturing and disposal of non-green products and production processes. Despite the significant role of RB&CDAC in achieving green innovation and enhancing the MFs' capabilities for creating CA; we would like to point out that there are many confounding variables in the literature that could affect the mentioned variables and their associations as mentioned in the proposed model such as readiness of the infrastructure (AL-Shboul, 2017), third-party logistics service providers (AL-Shboul, 2023b), and intentions for investment to update technologies as big and cloud data analytics and its necessary applications (El-Kassar and Singh, 2019;AL-Khatib, 2022), top management commitment and support (Dahiya et al, 2022), Fourth Industry Revolution (Ali et al, 2020), training and adopting talented employees (Bag et al, 2020), adopting green technologies (Belhadi et al, 2021), organizational culture and climate (Ivaldi et al, 2022;AL-Shboul, 2023a), human resources management (Sun et al, 2018). Thus, due to the limitation of the study such as time, cost and effort, we focused only on the mentioned variables to be examined.…”