Data-driven innovations (DDI) have significantly impacted firms’ operations thanks to the massive exploitation of huge data. However, to leverage big data and achieve supply chain innovation, a variety of complementary resources are necessary. In this study, we hypothesise that supply chain innovation (SCI) is dependent on firms’ big data analytics capabilities (BAC). Furthermore, we propose that this relation is mediated by two crucial capabilities of agility and adaptability that enable firms to efficiently meet the challenges of supply chain ambidexterity. Finally, we also test the moderating role of technology uncertainty in our research model. We collected data from 386 manufacturing firms in Pakistan and tested our model using structural equation modelling. The results confirmed our initial hypotheses that agility and adaptability both mediated our baseline relationship of BAC and big data innovation in supply chains. We further found support for the moderating role of technology uncertainty. Furthermore, technology uncertainty moderates the relationship between BAC and SCI. This study extends the current literature on digital analytics capabilities and innovation along the supply chain. Practically, our research suggests that investment in big data can result in affirmative consequences, if firms cultivate capabilities to encounter supply chain ambidexterity through agility and adaptability. Accordingly, we suggest that managers belonging to manufacturing firms need to build up these internal capabilities and to monitor and assess technology uncertainty in the environment.

The aim of this study is to examine the factors affecting customer satisfaction in online shopping. The conceptual model for this study was developed based on the previous research in the online shopping context. In this research, ten hypotheses on factors affecting customer satisfaction in online shopping are outlined with empirical data from 337 respondents. Data was collected through a Google form. The regression results show that customer service, information quality, response time, transaction capability, delivery, merchandise attributes, security/privacy, convenient payment method, and price have significant positive influence on customer satisfaction in online shopping. From a managerial viewpoint, this study results provide support for investment decisions for customer satisfaction from online retailers in Malaysia.

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Drawing on dynamic capabilities and the resource-based view, we propose a conceptual model that encompasses big data analytics capabilities (BDAC), digital platform capabilities and network capabilities, supply chain innovation, and firm performance. We use the structural equation modeling to empirically validate this model on the time-lagged data of 221 micro, small, and medium enterprises (MSMEs) in the manufacturing sectors. The empirical results of our data analysis showed that BDAC significantly improved platform and networking capabilities. BDAC also improved supply chain innovation and thus financial performance. Our data indicated that networking capabilities mediated the relationships of both (a) BDAC-supply chain innovation and (b) BDAC-financial performance. Meanwhile, digital platforms mediated only the BDAC-supply chain innovation relationship. The outcomes of sequential mediation confirmed the role of both digital platform and network capabilities and supply chain innovation in the BDAC-firm performance link. Our results provide theoretical implications to operations management and offer practical insights for managers working in manufacturing MSMEs.

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