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The objective of this research is to evaluate and rank the factors influencing omnichannel (OC) logistics, while also investigating the significant impact of big data analytics in improving these drivers of OC logistics.

Using exploratory sequential mixed method design, an in-person interview survey was conducted to identify and stratifies the enablers of OC retailing. These interviews were supplemented with a case study in an apparel firm to prioritise the enablers of OC logistics. Further, a survey was conducted to understand the role of big data analytics in improving drivers of OC logistics as well as the role of Individual capability and organisational capability in big data usage for omnichannel retailing.

Findings represent that information management is the most important driver followed by inventory management and network design for improving OC logistics. Further, significant relationship between big data analytics and drivers of omnichannel logistics has been reported.

This study identifies and classifies the drivers of OC retailing relating to their level of criticality in OC logistics which will assists practitioners to prioritise their tasks for the successful development of OC logistics. The study will also help practitioners to use BDA for developing the drivers of OC.

The study substantiates and adds to the BDA literature by emphasising the positive role of BDA in development of OC driver and highlighting the significant role of drivers of BDA in its usage.

The study aims to identify the potential drivers of big data analytics (BDA) practices in the supply chain and develop a sustainability evaluation model to evaluate drivers of big data for sustainability development.

The mixed-method approach was applied to assess sustainability dimensions and calculate the score using two phases. In Phase I, the BDA drivers in the e-commerce industry were finalised using the partial least square based structural equation modelling (PLS-SEM) method. In Phase II, a case study in the Indian fashion e-commerce industry was carried out to evaluate sustainability dimensions with respect to drivers of BDA and the sustainability score was calculated using the fuzzy analytical hierarchical process (AHP) method.

The index for economic sustainability (0.220), social sustainability (0.142) and environmental sustainability (0.182) were derived. The higher index value of economic sustainability compared to social sustainability and environmental sustainability signified those drivers of big data bring social and environmental uncertainty along with economic sustainability.

The study will help practitioners promote BDA use for developing environmental/social/economic sustainability in supply chains. Policymakers must ensure whether the integration of BDA practices brings down cost and brings strategic value for ensuring big data success. The study will help managers decide a constant trade-off between the requirement for social, environmental and economic performance.

The study corroborates and adds to the BDA literature by emphasising the positive role of BDA in sustainability development in the supply chain area and highlighting the significant role of different drivers of BDA in sustainability development.

The purpose of this paper is to review the literature on information technology adoption in organizations to understand the need of integrated models for technology adoption. It further makes an attempt to identify key parameters to integrate technology acceptance model (TAM) and technology-organization-environment (TOE) framework for firm level technology adoption. This integration is intended to improve predictive power of resulting model.

The research papers are accessed from the popular databases from 2000 to 2012. The selected papers have addressed technology adoption in context of recent technologies such as e-commerce, ERP, RFID, EDI and knowledge management, etc. The paper attempts to review the studies based on TAM model and TOE framework to identify relevant set of variables for the adoption of these technologies in organizations.

TAM and its extended versions have high capability to explain the technology adoption while the significance of TOE framework is similarly recognized in explaining technology adoption. This review presents a holistic picture of a set of variables which can be used in the adoption of similar technologies in future. Further, the study has advocated the integration of TAM model and TOE framework to improve their explanatory power in technology adoption. The identified set of variables of TAM model and TOE framework can be used to integrate the two. Guidelines for integrating the two are also explained.

This study provides a platform for studying adoption of similar technologies using integration of TAM and TOE.

The researchers and managers can use the set of variables identified for adoption of similar technologies in organizations.

The review presents a set of variables which can be used to study adoption of similar technologies in future.

The purpose of this paper is to integrate TAM model and TOE framework for cloud computing adoption at organizational level.

A conceptual framework was developed using technological and organizational variables of TOE framework as external variables of TAM model while environmental variables were proposed to have direct impact on cloud computing adoption. A questionnaire was used to collect the data from 280 companies in IT, manufacturing and finance sectors in India. The data were analyzed using exploratory and confirmatory factor analyses. Further, structural equation modeling was used to test the proposed model.

The study identified relative advantage, compatibility, complexity, organizational readiness, top management commitment, and training and education as important variables for affecting cloud computing adoption using perceived ease of use (PEOU) and perceived usefulness (PU) as mediating variables. Also, competitive pressure and trading partner support were found directly affecting cloud computing adoption intentions. The model explained 62 percent of cloud computing adoption.

The model can be used as a guideline to ensure a positive outcome of the cloud computing adoption in organizations. It also provides relevant recommendations to achieve conducive implementation environment for cloud computing adoption.

This study integrates two of the information technology adoption models to improve predictive power of resulting model.

This study aims to examine the current state of literature on structural equation modeling (SEM) studies in “cloud computing” domain with respect to study domains of research studies, theories and frameworks they use and SEM models they design.

Systematic literature review (SLR) protocol is followed. In total, 96 cloud computing studies from 2009 to June 2018 that used SEM obtained from four databases are selected, and relevant data are extracted to answer the research questions.

A trend of increasing SEM usage over years in cloud studies is observed, where technology adoption studies are found to be more common than the use studies. Articles appear under four main domains, namely, business, personal use, education and health care. Technology acceptance model (TAM) is found to be the most commonly used theory. Adoption, intention to use and actual usage are the most common selections for dependent variables in SEM models, whereas security and privacy concerns, costs, ease of use, risks and usefulness are the most common selections for causal factors.

Previous cloud computing SLR studies did not focus on statistical analysis method used in primary studies. This review will display the current state of SEM studies in cloud domain for all future academics and practical professionals.