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The purpose of this paper is to provide an insight about factors affecting Big Data Analytics (BDA) utilization and adoption in Indian firms. Research studies have so far focused on BDA adoption in developed economies. This study examines the factors that influence BDA usage and adoption in the context of emerging economies.

This study proposed a theoretical model of factors influencing BDA utilization and adoption. Two independent research streams – first, the top managers’ perceived strategic value (PSV) in BDA and second, the factors that influence the adoption of BDA theoretically – have been integrated with the technology-organization-environment (TOE) framework. In the BDA context, there was a theoretical necessity to identify the driver and barriers of BDA from the TOE framework on PSV and adoption of BDA. A qualitative exploratory study using face-to-face semi-structured interviews was carried out to collect data from 22 different enterprises and service providers in India. India was selected as the context as it is one of the fastest growing large economies of the world with huge potential of BDA to improve the business landscape.

The results showed that the major reason behind BDA non-adoption is that the organizations did not realize the strategic value (SV) of BDA, and they were not ready to make the changes because of technological, organizational and environmental difficulties. The findings corroborate previous results about significant factors affecting IT adoption and implementation and provide new and interesting insights. The main factors identified as playing a significant role in organizations’ adoption of BDA were SV of BDA, complexity, compatibility, IT assets, top management support, organization data environment, perceived costs, external pressure and industry type.

The main limitation related to this study is the difficulty in generalizing the findings to a larger population of enterprises. To overcome this, a statistical survey has been planned to be conducted in the future.

The BDA adoption model in this study will have both managerial implications for practitioners in India, as well as those in other developing countries, and academic implications for researchers who are interested in BDA adoption in developing counties, in terms of formulating better strategies for BDA adoption. For managers, using the research model of this study could assist in increasing their understanding of why some organizations choose to adopt BDA, while similar ones facing similar conditions do not. Also, the understanding of the strategic utilization of BDA in different business processes may improve the adoption of BDA in organizations.

This paper contributes in exploring and enhancing the understanding of the factors affecting the utilization and adoption of BDA in organizations from an Indian perspective. This study is an attempt to develop and explore a BDA adoption model by the fusion of PSV and TOE framework. The effect of the three contexts of this framework (technological, organizational and environmental) on the strategic utilization of BDA has been studied for the first time.

Though Blockchain has been studied in numerous contexts, the understanding of the impacts of Blockchain in achieving competitive advantages remains unexplored. Many industries, organizations and firms are still in a “wait and see” mode. This study aims at examining the effects of the technological, organizational and environmental factors drawn from the TOE framework in generating competitive advantage.

A dual-staged deep learning structural equation modeling artificial neural network analysis was conducted on 211 samples of small and medium enterprises. Four neural network models were engaged to rank the normalized importance of each of the predictor variables.

The research model can expound 57.99 and 47.33% of the variance in Blockchain adoption and competitive advantage correspondingly. The study successfully identified nonlinear relationships. The theoretical and managerial contributions are useful to scholars and practitioners such as industrial players, investors, chief executive officers (CEOs), managers, decision-makers and other stakeholders that intend to use Blockchain technology.

Unlike the existing technological–organizational–environmental (TOE) framework that uses a linear model and theoretically assumes that all relationships are linear, this has been the first study, which has successfully validated that there exist nonlinear relationships in the TOE framework. Further, very little has been theorized on the impacts of Blockchain adoption on competitive advantage, especially in the context of SMEs. Therefore, this study is the first one to provide the necessary theoretical foundation that may further extend the current knowledge of Blockchain technology adoption and its impacts.

The study challenges the assumption of independence among Technological, Organizational and Environmental (TOE) factors and investigates the influence of TOE factors on Big Data Analytics (BDA) adoption among Small and Medium Enterprises (SMEs). Top management support was proposed as a mediator between technological and organizational factors and BDA adoption. Furthermore, the moderating effect of environmental factors on the association between relative advantage, compatibility, competitiveness, organizational readiness and BDA adoption was evaluated.

Data were collected from 171 SME manufacturing firms and analyzed using the partial least squares technique.

The findings confirmed the interrelationships among the TOE factors. The effects of compatibility, competitiveness and organizational readiness on BDA adoption were mediated by top management support. Furthermore, environmental factors moderate the influences of compatibility and organizational readiness on top management support.

The findings contribute to the TOE model by challenging the assumption of independence among TOE factors, and future studies should use this model with more caution and consider the potential relationships between TOE factors.

This paper aims to explore the challenges and opportunities of blockchain technology adoption from the lens of the technological–organisational–environmental (TOE) framework for operational excellence in the UK automotive industry context.

The research methodology of this study follows a systematic review approach, which analyses existing academic published research papers in the top 35 academic journals. There was no specific timeframe established for this study and shortlisting the articles through a set of used keywords. A sample of 71 articles was shortlisted and analysed to provide a discussion on technological and management challenges and opportunities of blockchain adoption from the lens of the TOE framework for operational excellence.

The findings of this study present significant theoretical and managerial implications and deep understanding for firms seeking to understand the challenges and opportunities of blockchain adoption for their operational excellence.

The systematic literature approach was considered for the present study to explore existing academic papers on technological and management challenges and opportunities from the lens of TOE framework for operational excellence, whereas a more specified method meta-analysis can be considered for future research. The study has been explored in the UK automotive industry context, which has been considered as the limitation of generalisation across countries and industries.

This paper represents the most comprehensive literature study related to the technological and management challenges and opportunities of blockchain from the TOE framework angle for operational excellence.

Big data analytics (BDA) is recognized as a turning point for firms to improve their performance. Although small- and medium-sized enterprises (SMEs) are crucial for every economy, they are lagging far behind in the usage of BDA. This study aims to provide a single and unified model for the adoption of BDA among SMEs with the integration of the technology–organization–environment (TOE) model and resource-based view.

A survey of 112 manufacturing SMEs in Iran was conducted, and the data were analysed using structural equation modelling to test the model of this study.

The results offer evidence of a BDA mediation effect in the relationship between technological, organizational and environmental contexts, and SMEs performance. The findings also demonstrated that technological and organizational elements are the more significant determinants of BDA adoption in the context of SMEs. In addition, the result of this study confirmed that BDA adoption could enhance the financial and market performance of SMEs.

Providing a single unified framework of BDA adoption for SMEs enables them to appreciate the importance of most influential elements (technology, organization and environment) in the adoption of BDA. Also, this study may encourage SMEs to be more willing to use BDA in their businesses.

Although there are studies on BDA adoption and firm performance among large companies, there is a lack of empirical research on SMEs, in particular, based on the TOE model. SMEs differ from large companies in terms of the availability of resources and size. Therefore, this study aimed to initiate a conceptual framework of BDA adoption for SMEs to assist them to be able to take advantage of the adoption of such technology.

The purpose of this study is to develop a model to understand the relationships among technology, organizational and environmental (TOE) contexts, intention to adopt cloud computing (IACC) and actual usage of cloud computing (AUCC) in small and medium enterprises (SMEs) in Malaysia as a developing country. More specifically, this paper seeks to explore the mediation effect of IACC on the relationship between TOE context and AUCC.

A positivist research approach was selected for this study. Drawing largely upon the TOE framework, this study uses survey data from 209 Malaysian SMEs. Structural equation modelling (SEM) based on partial least squares (PLS) was used to assess the structural relations of the research model.

The results of the structural model show that data security, technology readiness, top management support, competitive pressure and innovativeness are the most significant factors in predicting the adoption of cloud computing in Malaysian SMEs. Further, the results indicate that intention to adopt cloud computing can play a mediating role between TOE factors and the actual usage of cloud computing.

The focus upon Malaysian SMEs may diminish the generalizability of the findings. This study provides profound insight into the management and foundation of cloud computing, different types of cloud services and deployment models that could facilitate the management of enterprise strategic resources and contribute to the performance improvement. This study also provides another important implication for practitioners regarding the absolute necessity of value drivers’ identification within enterprise and understand the causal relationships, which are vital in driving those values.

This study provides several practical guidance for practitioners in deploying cloud services which are most suitable option for their specific technology requirement in their enterprise to enjoy the full benefits of their intangible assets. Another significant implication of this study lies in the fact that it may require a different emphasis on nature and adoption design when there is a higher level of stress on technology-related and cloud computing resources.

This study contributes to the extant literature by developing an integrative model to identify how a wide set of contextual factors can determine the intention to adopt cloud computing and, in turn, influence the actual usage of cloud computing in SMEs in Malaysia as a developing country.

The research explores the shift to Quality 4.0, examining the move towards a data-focussed transformation within organizational frameworks. This transition is characterized by incorporating Industry 4.0 technological innovations into existing quality management frameworks, signifying a significant evolution in quality control systems. Despite the evident advantages, the practical deployment in the Indian manufacturing sector encounters various obstacles. This research is dedicated to a thorough examination of these impediments. It is structured around a set of pivotal research questions: First, it seeks to identify the key barriers that impede the adoption of Quality 4.0. Second, it aims to elucidate these barriers' interrelations and mutual dependencies. Thirdly, the research prioritizes these barriers in terms of their significance to the adoption process. Finally, it contemplates the ramifications of these priorities for the strategic advancement of manufacturing practices and the development of informed policies. By answering these questions, the research provides a detailed understanding of the challenges faced. It offers actionable insights for practitioners and policymakers implementing Quality 4.0 in the Indian manufacturing sector.

Employing Interpretive Structural Modelling and Matrix Impact of Cross Multiplication Applied to Classification, the authors probe the interdependencies amongst fourteen identified barriers inhibiting Quality 4.0 adoption. These barriers were categorized according to their driving power and dependence, providing a richer understanding of the dynamic obstacles within the Technology–Organization–Environment (TOE) framework.

The study results highlight the lack of Quality 4.0 standards and Big Data Analytics (BDA) tools as fundamental obstacles to integrating Quality 4.0 within the Indian manufacturing sector. Additionally, the study results contravene dominant academic narratives, suggesting that the cumulative impact of organizational barriers is marginal, contrary to theoretical postulations emphasizing their central significance in Quality 4.0 assimilation.

This research provides concrete strategies, such as developing a collaborative platform for sharing best practices in Quality 4.0 standards, which fosters a synergistic relationship between organizations and policymakers, for instance, by creating a joint task force, comprised of industry leaders and regulatory bodies, dedicated to formulating and disseminating comprehensive guidelines for Quality 4.0 adoption. This initiative could lead to establishing industry-wide standards, benefiting from the pooled expertise of diverse stakeholders. Additionally, the study underscores the necessity for robust, standardized Big Data Analytics tools specifically designed to meet the Quality 4.0 criteria, which can be developed through public-private partnerships. These tools would facilitate the seamless integration of Quality 4.0 processes, demonstrating a direct route for overcoming the barriers of inadequate standards.

This research delineates specific obstacles to Quality 4.0 adoption by applying the TOE framework, detailing how these barriers interact with and influence each other, particularly highlighting the previously overlooked environmental factors. The analysis reveals a critical interdependence between “lack of standards for Quality 4.0” and “lack of standardized BDA tools and solutions,” providing nuanced insights into their conjoined effect on stalling progress in this field. Moreover, the study contributes to the theoretical body of knowledge by mapping out these novel impediments, offering a more comprehensive understanding of the challenges faced in adopting Quality 4.0.

This study aims to identify the impact of mobile payment on firm performance by developing a model based on the technology, organization and environment framework (TOE framework) including relative advantage, complexity, compatibility, innovativeness, mobile payment knowledge, critical mass, competitive pressure and external support.

The data were collected from the retail and service firms in Bangkok, Thailand (n = 387). Multiple regression analysis was applied to test the proposed model and carried out in SPSS version 25.

The results indicated that the TOE factors, including relative advantage, innovativeness, mobile payment knowledge, critical mass, competitive pressures and external supports, can predict firm performance. While innovativeness is the strongest predictor of positive firm performance, on the other hand, critical mass is found to be negatively significant on firm performance.

This research suggests that firms that accept mobile payment can identify the positive impact on firm performance and it is important for payment service providers and the government to work closely with firms.

As some merchants still refuse to implement mobile payment services in their business, this current study seeks to understand the impact of mobile payment. However, not many studies are reported its impact in Southeast Asia. This study is probably the first in Thailand to examine the impact of mobile payment on firm performance in the retail and service firms.

The purpose of this paper is to refine and expand technology adoption theory for a healthcare logistics setting by combining the technology–organization–environment framework with a business process management (BPM) perspective. The paper identifies and ranks factors impacting the decision to implement instances of technologies in healthcare logistics processes.

A multiple case study is carried out at five Danish hospitals to investigate the bed logistics process. A combined technology adoption and BPM lens is applied to gain an understanding of the reasoning behind technology adoption.

A set of 17 factors impacting the adoption of technologies within healthcare logistics was identified. The impact factors perceived as most important to the adoption of technologies in healthcare logistics processes relate to quality, employee work conditions and employee engagement.

This paper seeks to understand how managers can use knowledge about impact factors to improve processes through technology adoption. The findings of this study provide insights about the factors impacting the adoption of technologies in healthcare logistics processes. Differences in perceived importance of factors enable ranking of impact factors, and prioritization of changes to be implemented. The study is limited to five hospitals, but is expected to be representative of public hospitals in developed countries and applicable to similar processes.

The study contributes to the empirical research within the field of BPM and technology adoption in healthcare. Furthermore, the findings of this study enable managers to make an informed decision about technology adoption within a healthcare logistics setting.

Switching to public cloud enterprise resource planning (ERP) systems not only provides financial and functional benefits to organizations, but also results in sunk costs of incumbent systems and uncertainty costs of cloud systems. The purpose of this study is to investigate the enablers and inhibitors concerning switching to cloud ERP systems at the organizational level.

Data were collected from 212 top managers and owners of the enterprises in Taiwan, and 10 hypotheses were examined using structural equation modeling.

Technological (system quality), organizational (financial advantage), and environmental contexts (industry pressure) are found to be the antecedents of switching benefits. Perceived risk of cloud ERP systems and satisfaction with and breadth of use of incumbent ERP systems are found to be the predictors of switching costs. Switching benefits positively affect switching intention, but switching costs negatively affect switching intention.

This study develops a theoretical model grounded in a set of theoretical foundations, including two-factor theory, technology-organization-environment (TOE) framework, information systems (IS) success model, and expectation confirmation theory (ECT). Two-factor theory is used to characterize switching benefits and costs that affect switching intention. Technological factors come from IS success model, and the factors affecting benefits are organized based on TOE framework. Sunk costs of incumbent ERP systems are developed based on ECT.

Different from previous studies on cloud computing adoption, this study provides insights into switching intention to cloud computing. The study also proposes an integrated model grounded in multiple perspectives to explain organizations' decisions to switch to cloud ERP systems. These findings help cloud service providers better understand how to promote cloud ERP adoption from technical, organizational, and environmental perspectives.