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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.

This paper aims at identifying the key factors influencing the adoption intention of data analytics for predictive maintenance (PdM) from the lens of the Technology–Organization–Environment (TOE) framework in the Singapore Process Industries context. The research model aids practitioners and researchers in developing a holistic maintenance strategy for large-scale asset-heavy process industries.

The TOE framework has been used in this study to consider a wide set of TOE factors and develop a research model with the support of literature. A survey is undertaken and the structural equation modelling (SEM) technique is adopted to test the hypotheses of the proposed model.

This research highlights the significant roles of digital infrastructure readiness, security and privacy, top management support, organizational competence, partnership with external consultants and government support in influencing adoption intention of data analytics for PdM. Perceived challenges related to organizational restructuring and process automation are not found significant in influencing the adoption intention.

This paper reports valuable insights on adoption intention of data analytics for PdM with relevant implications for the various stakeholders such as the leaders and senior managers of process manufacturing industry companies, government agencies, technology consultants and service providers.

This research uniquely validates the model for the adoption of data analytics for PdM in the process industries using the TOE framework. It reveals the significant technology, organizational and environmental factors influencing the adoption intention and highlights the relevant insights and implications for stakeholders.

This study aims to investigate the factors influencing the adoption intention of artificial intelligence (AI) by micro, small and medium enterprises (MSMEs) in Jordan.

The study adopts the technology–organization–environment (TOE) model. It examines the moderating effects of innovation culture, employee digital skill level and market competition on the relationships between the independent and dependent variables. A survey was utilized to collect data from 537 MSME owners or managers in Jordan and employed partial least squares structural equation modeling to test the hypotheses.

The results of the study support seven out of eight hypotheses. Business innovativeness, management support, perceived benefits and technological infrastructure have positive and significant effects on AI adoption intention, while perceived costs have no significant effect. However, the innovation culture, employee digital skill level and market competition were found to moderate the relationships between some of the independent variables and dependent variables.

The study provides valuable insights and recommendations for MSME owners, managers, employees, policymakers, educators and researchers interested in promoting and facilitating AI adoption by MSMEs in Jordan.

The current attempt extends the TOE framework by adding significant constructs representing the three contexts. Moreover, it is one of the few studies that analyzed the factors influencing the adoption intention of AI by MSMEs in Jordan, which are significant to the Jordanian economy and represent 99.5% of enterprises.

This paper aims to examine the determinant factors that influence the adoption of mobile banking by small and medium enterprises (SMEs) in Palestine. The aim of this paper has supported with the objectives to identify the role of mobile banking practices in enhancing sustainable growth and development of Palestine SMEs; determining the factors that influencing the SMEs as a service and products providers’ intention to adopt mobile banking and proposing a conceptual model for adoption mobile banking development by SMEs sectors in Palestine

Hypotheses were developed guided by the technology organisation-environment (TOE) model. The primary data was collected from 408 SMEs in Palestine using questionnaires and 8 interviews.

The empirical results are based on partial least squares analysis and statistical package for the social sciences. The findings presented in this paper reveal that SMEs factors, enabling environment and business model are the main determinant factors affecting the intention to adopt mobile banking by the SMEs sector in Palestine.

The main contribution of this study is the concise identification of the obstacles and facilitators to SMEs’ mobile banking, especially in developing countries. A suggestion for further study was made. The findings could be useful to policymakers, government institutions and agencies especially in Palestine and other development partners in designing and directing their policy intervention.

This study contributes significantly to the theoretical understanding of mobile banking through the development of a mobile banking framework for the SMEs’ sector in Palestine. A major contribution of this study is to the existing knowledge and literature in mobile banking by developing a TOE framework for providers (supply-side) factors in general and in particular in Palestine to fill the gaps in past studies.

The purpose of this study is to analyse the factors influencing intention to adopt 3D design digital technologies (3DDTs) by Colombian firms.

A conceptual framework was developed using technology-organization-environment (TOE) and technology acceptance model (TAM) theoretical frameworks. From a sample of 138 firms, a structural equation model was analysed with partial least squares (SEM-PLS).

The study identified that perceived usefulness in the technological dimension; technological competence and top management support in the organizational dimension; and competitive pressures in the environmental dimension, are variables affecting intention to adopt 3DDT. The effects of the mediating variables with respect to intention to adopt the technology are also analysed, such as perceived usefulness on the effect of ease of use; top management support on the effect of technological competence and financial readiness; and competitive pressures on the effect of stakeholder pressure. The model explained 71.1% of the 3DDT intention to adopt.

The model can be used as a guideline to ensure a positive outcome of the 3DDT adoption in organizations. The results could be useful to understand a technological adoption process for digital transformation.

The proposed model integrates some contributions from the TAM and TOE theories and identifies some novel mediating effects that improve its predictive and explanatory power. Furthermore, this is a pioneering study in empirical research on 3DDT in the context of a developing country, specifically in Colombia. The findings from this study provide a foundation for other studies, as well as constructive insights for digital transformation, due to its infancy in an emerging economy.

The purpose of this study is to examine the factors that influence Malaysian Small and Medium Enterprises (SMEs) to adopt mobile commerce (m-commerce) by integrating the constructs of Technology Acceptance Model 3 (TAM 3), Universal Theory of Acceptance and Use of Technology 2 and Technology-Organization-Environment model. Though numerous m-commerce adoption (MCA) studies have been conducted, lesser attention is paid to how hedonic motivation (HM) can influence organizational users such as SMEs. This study bridges the gap by integrating the three models to provide a new lens to guide SMEs.

To examine the factors that influence the adoption of m-commerce, the researchers collected data from SMEs in Malaysia using an online survey. The sample size of the participants was determined through the available list provided by SME Corp Malaysia. The researchers also used Krejcie and Morgan’s sample size and G * Power techniques to determine that the sample size was appropriate. The data collected were analyzed using partial least square-structural equation modeling.

The findings of this study reveal that technological factors (computer self-efficacy [CSE], result demonstrability [RD] and computer anxiety [CA]) positively and significantly influence MCA. Likewise, the organizational/environmental factors (m-commerce knowledge, pressure from trading partners and pressure from competitors) positively and significantly influence MCA. The moderating influence of HM was also achieved on the relationship between CSE and RD. However, the proposed hedonic moderating relationship between CA and the adoption of m-commerce is not significant.

This study integrates three models to explain the adoption of m-commerce among SMEs in Malaysia and tested the moderating influence of HM. The results obtained better explain the decision by the SMEs to use m-commerce.

The study critically considered how m-commerce can be adopted by SMEs in Malaysia, which previous studies have largely ignored. Considering this, the study, therefore, advances a new relationship by integrating Technology-organization-environment model with TAM 3 and the moderating influence of HM to explain MCA among SMEs. This paper is one of the few research studies to test the moderating influence of HM in this regard.

This study aims to investigate the factors influencing the adoption of data analytics in performance management. By examining the role of organizational and environmental contexts, this study contributes to the existing literature by proposing a novel and detailed technology-organization-environment (TOE) model for the complex interplay between firm characteristics and the adoption of data analytics. The results offer valuable insights and practical implications for organizations seeking to leverage data analytics for effective performance management.

The research draws upon a data set encompassing over 21,869 companies operating across all European Union member states. A multilevel logistic regression model was developed to evaluate the influence of organizational and environmental factors on the likelihood of adopting performance analytics in organizations.

The findings indicate that the lack of awareness of the benefits of data analytics and its practical application to address specific business challenges is a significant barrier to its adoption. Organizational contexts, such as variable-pay systems, employee training, hierarchical structures and frequency of monetary rewards, also influence the adoption of data analytics.

The study informs managers about the strategic role of data analytics capabilities in performance management for improved business intelligence and driving data culture.

The study helps managers understand the strategic role of data analytics capabilities in performance management, leading to improved business intelligence and fostering a data-driven culture in five key areas: structural alignment, strategic decision-making, resource allocation, performance improvement and change management.

The study advances the TOE theory, making it a more detailed and complete framework, particularly applicable to the adoption of performance analytics. It identifies the main factors of adoption that play a crucial role in this process.

The purpose of this study was to develop and empirically examine a model of cloud-based human resource information systems (HRIS) adoption by small businesses based on the technology–organization–environment model (Tornatzky & Fleischer, 1990).

This study utilized a survey of 41 small- to medium-sized enterprises in the northeastern United States to examine what HR functions were being supported by cloud-based HRIS and the relationship between three technology factors, three organizational factors, and three environmental factors, and their relationship with the adoption of cloud-based HRIS.

Findings indicated that small businesses are most likely to implement cloud-based HRIS to support day-to-day HR operations. In addition, the findings indicated that top management support (positive), vendor support (positive), and anticipated growth (negative) were each related to organizational adoption of cloud-based HRIS.

The study illustrates how the adoption of a cloud-based HRIS is motivated by different factors than those underlying the adoption of other types of information systems. Executives and small business owners will need to adapt their strategy when considering cloud-based HRIS compared to other types of systems.

Given that small- to medium-sized organizations are the backbone of most global economies, findings from this study can help support society by helping these businesses better understand how to best consider the factors that will support the implementation of cloud-based HRIS.

This chapter represents one of the first to empirically validate a model of the factors affecting adoption of cloud-based HRIS by small businesses.

A lack of reliable and effective communication tools poses major barriers impeding the performance of construction projects consisting of diverse participants. Although some construction project teams (CPTs) begin to apply social media (SM) as an available approach for project management the entire mechanism of SM adoption in this specific context remains understudied. Therefore, this study aims to adopt a CPT's lens to investigate the critical antecedents and associated effects underlying SM adoption in the construction industry.

Based on the technology–organization–environment (TOE) theory, a conceptual model was proposed and tested by empirical data collected from 159 CPTs in China. Structural equation modeling technique was employed for data analysis.

The results demonstrate that all the five extracted TOE-based antecedents including two technological factors (i.e. compatibility and expected cost), one organizational factor (i.e. top management support) and two environmental factors (i.e. project partner collaboration and project fit) are crucial to the adoption of SM in CPTs. Besides, SM acceptance is found to mediate the relationships between organizational and environmental factors and SM use. Moreover, SM use significantly predicts the communication effectiveness of CPTs.

A questionnaire study based on cross-sectional data from China may only unveil the logic of SM adoption in the context of Chinese construction industry within a shorter time interval. It is recommended that future research could develop longitudinal studies among various construction practitioners in different countries to further specify and generalize the current findings.

This paper provides a comprehensive understanding of SM adoption in the construction industry by exploring the preadoption antecedents and postadoption effects from the perspective of project teams. The empirical findings advance the current web-based project management literature and afford new insights for construction practitioners into better managing SM application to reap its full capabilities in projects.

This study aims to provide a series of drivers that prompt the blockchain technology (BT) adoption decisions in circular supply chain finance (SCF) and also assesses their degrees of influence and interrelationships, which leads to the construction of a theoretical model depicting the influence mechanism of BT adoption decisions in circular SCF.

This study mainly uses the technology-organization-environment (TOE) framework, which focuses on the aspects based on the nature of innovation, intra-organizational characteristics and extra environmental consideration, to identify the drivers of blockchain adoption in circular SCF context, while the significance and causality of the drivers are explained using interpreting structural models (ISMs) and the decision-making trial and evaluation laboratory (DEMATEL) method.

The findings of this study indicate that government policy and technological comparative advantage are the underlying reasons for BT adoption decisions, management commitment and financial expectations are the critical drivers of BT adoption decisions while other factors are the receivers of the mechanism.

This study provides theoretical references and empirical insights that influence the technology adoption decisions of both BT and circular SCF by practitioners.

The theoretical research contributes significantly to current research and knowledge in both BT and circular SCF fields, especially by extending the existing TOE model by combining relevant enablers from technological, organizational and external environmental aspects with the financial performance objectives of circular SCF services, which refer to the optimization of the financial resources flows and financing efficiency.