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The T-O-E framework enjoys robust scholarly accolade but it rarely espouses clearly task and individual factors. Although task and individual contexts had been separately addressed by task-technology-fit (TTF) and unified theory of acceptance and use of technology (UTAUT), respectively, the purpose of this paper is to complement and/or extend the T-O-E’s insights by integrating TTF and UTAUT frameworks, and developing and empirically testing a 12-factor framework that spans five contexts.

Survey data were proportionally collected from six groups of small service enterprises with strong operations in Port Harcourt, Nigeria and the mode of sampling was purposive and snow-ball while analysis involved logistic likelihood regression.

The relationship between adoption and the factors within the contexts of technology, organization, environment and task were statistically supported though some had negative coefficients. For individual context, social factor had a statistically significant negative coefficient but hedonistic drive was not statistically supported.

The study is limited by its scope of coverage; therefore, extended data are needed to apply the findings to other sectors/industries and to factor in the implementation and post-adoption phases and business-to-business adoption in order to forge a more integrated and holistic adoption framework.

The findings encourage vendors and policy makers to place more premiums on organizational and task factors than on technological, environmental and individual factors and to craft informed marketing programs that would appeal to actual and potential adopters and cause them to progress in the loyalty ladder.

This paper contributes to the growing research on technology adoption; it uses factors within the T-O-E, TTF and UTAUT frameworks to explain adoption of technologies and to establish the underlying relationships amongst T-O-E factors through integrating other useful frameworks.

This paper aims to propose and test a ten-factor framework of four contexts from technology-organization-environment (T-O-E) theory and unified theory of acceptance and use of technology (UTAUT) to provide insight(s) that complements and extends extant inquiries on technology adoption.

Survey data were collected from small service enterprises with strong operations in Port Harcourt, Nigeria, and the mode of sampling was purposive and snow ball, whereas analysis involved structural equation modeling.

The results show that factors in the technological, organizational and environmental contexts have direct statistically significant relationship with adoption; thus, adoption is more driven by T-O-E factors than by individual factors. For individual context, social factor equally was statistically supported, whereas hedonistic drive was not.

The study is limited by its scope of data collection and phases; therefore, extended data are needed to apply the findings to other sectors/industries/countries and to factor in the implementation and post-adoption phases and business to business (B2B) adoption to forge a more holistic framework.

Implicit is that the findings encourage vendors and policy makers to recognize the strength of interpersonal and group relationships in addition to T-O-E contexts in developing investment decisions.

The paper contributes to the growing research on innovation adoption by using factors within the T-O-E and UTAUT frameworks to explain SMEs’ adoption of technologies.

This paper aims to consolidate the state of research on information behaviour. Regardless of the scholarly surge on information behaviour using numerous models and extended ones, gaps still exist on the development of more pragmatic frameworks that explain and/or predict the information behaviour across different fragmented environmental situations. Therefore, this paper extends technology-organization-environment (T-O-E) framework and synthesizes different research positions into a multi-dimensional framework that assists small and medium enterprises (SMEs) to understand their information behaviour and to make informed decisions on the adoption of emerging information communication technology (EICT).

This study deployed an inductive approach; data were generated from unstructured and semi-structured interviews with 20 participants drawn purposively from Luton directories.

This study developed a more integrated framework underpinned by T-O-E framework, which helped to identify how EICT adoption is critically shaped by perceived information need, information sources selection and the factors determining information source selection.

Qualitative research is normally subjective, inductive, interpretive and limited on the sample used. However, because of the limited number of interviews used, as well as industry-specific nature of the study, the generalization of the framework and the findings will be difficult, hence, the need to test the framework on a broader population and cross-context testing.

The T-O-E framework is extended to Expectancy (T-O-E-E) to provide a more robust insight into information behaviour of SMEs from a multi-dimensional perspective. It also offers a more analytical framework for exploring critical issues identified and demonstrates the capacity to provide a reliable explanation to the findings. Further, it serves as a tool for assessing the benefits or challenges of SMEs information behaviour especially on the EICT adoption decision.

The importance of humans to the successful delivery of construction projects has led to the emergence of research attention on construction workforce management. As such, this chapter uncovers emotional intelligence (EI) and the external environment as critical aspects of workforce management practices that have not gained substantial attention in past workforce management studies. While some theories and models (existing outside the construction domain) have considered the external environment, none of these models is specific to the construction industry. Furthermore, EI has received less attention within existing workforce management models. Through a review of related studies and theories, this chapter noted that the EI of construction workers and their senior management is crucial to the performance of these workers and the ultimate performance of their organisations. In the same vein, since construction organisations do not operate in silos, the external environment significantly influences the operations of organisations in the construction industry. The environment exact pressures that can influence workforce management practices and technological innovations construction organisations adopt.

This book aimed to conceptualise a construction workforce management model suitable for effectively managing workers in construction organisations. To this end, this chapter presents the conceptualised model, which consists of seven workforce management practices with their respective measurement variables. Drawing from existing theories, models, and practices, the chapter concludes that a construction organisation that will attain its strategic objectives in the current fourth industrial revolution era must be willing to promote effective recruitment and selection, compensation and benefits, performance management and appraisal, employee involvement and empowerment, training and development, as well as improving workers emotional intelligence and handling external environment pressure. These practices can promote proactiveness, participation, and improved skills and can lead to effective commitment, better quality, and flexibility within the organisation.

The workforce management model conceptualised for the effective management of the construction workforce was subjected to expert scrutiny to determine the suitability and applicability of the identified practices and their attributed variables to the construction industry. In achieving this, a Delphi approach was adopted using experts from construction organisations in South Africa. These experts comprised workforce management personnel and construction professionals in senior management positions. The data were analysed using appropriate statistical tools such as interquartile deviation, Kendell’s coefficient of concordance, and chi square to determine consensus among these experts. After a two-round Delphi, the seven constructs proposed in the conceptualised workforce management model were adjudged to be important and worthy of adoption by construction organisations seeking to improve workforce management in the current fourth industrial revolution era.

Human resource managers are adopting AI technology for conducting various tasks of human resource management, starting from manpower planning till employee exit. AI technology is prominently used for talent acquisition in organizations. This research investigates the adoption of AI technology for talent acquisition.

This study employs Technology-Organization-Environment (TOE) and Task-Technology-Fit (TTF) framework and proposes a model to explore the adoption of AI technology for talent acquisition. The survey was conducted among the 562 human resource managers and talent acquisition managers with a structured questionnaire. The analysis of data was completed using PLS-SEM.

This research reveals that cost-effectiveness, relative advantage, top management support, HR readiness, competitive pressure and support from AI vendors positively affect AI technology adoption for talent acquisition. Security and privacy issues negatively influence the adoption of AI technology. It is found that task and technology characteristics influence the task technology fit of AI technology for talent acquisition. Adoption and task technology fit of AI technology influence the actual usage of AI technology for talent acquisition. It is revealed that stickiness to traditional talent acquisition methods negatively moderates the association between adoption and actual usage of AI technology for talent acquisition. The proposed model was empirically validated and revealed the predictors of adoption and actual usage of AI technology for talent acquisition.

This paper provides the predictors of the adoption of AI technology for talent acquisition, which is emerging extensively in the human resource domain. It provides vital insights to the human resource managers to benchmark AI technology required for talent acquisition. Marketers can develop their marketing plan considering the factors of adoption. It would help designers to understand the factors of adoption and design the AI technology algorithms and applications for talent acquisition. It contributes to advance the literature of technology adoption by interweaving it with the human resource domain literature on talent acquisition.

This research uniquely validates the model for the adoption of AI technology for talent acquisition using the TOE and TTF framework. It reveals the factors influencing the adoption and actual usage of AI technology for talent acquisition.

This paper presents the findings from the assessment of the determinants of cloud computing (CC) deployment by construction organisations. Using the technology-organisation-environment (TOE) framework, the study strives to improve construction organisations' project delivery and digital transformation by adopting beneficial technologies like CC.

This study adopted a post-positivism philosophical stance using a deductive approach with a questionnaire administered to construction organisations in South Africa. The data gathered were analysed using descriptive and inferential statistics. Also, the fusion of structural equation modelling (SEM) and machine learning (ML) regression models helped to gain a robust understanding of the key determinants of using CC.

The study found that the use of CC by construction organisations in South Africa is still slow. SEM indicated that this slow usage is influenced by six technology and environmental factors, namely (1) cost-effectiveness, (2) availability, (3) compatibility, (4) client demand, (5) competitors' pressure and (6) trust in cloud service providers. ML models developed affirmed that these variables have high predictive power. However, sensitivity analysis revealed that the availability of CC and CC's ancillary technologies and the pressure from competitors are the most important predictors of CC usage in construction organisations.

The paper offers a theoretical backdrop for future works on CC in construction, particularly in developing countries where such a study has not been explored.

This paper aims to analyse the unified theory of acceptance and use of technology (UTAUT) and UTAUT2 constructs used in research on information and communication technology (ICT) adoption and use among micro, small and medium enterprises (MSMEs) in non-organisation for economic co-operation and development (OECD) countries. It also investigates the areas of ICT adoption along the value chain in studies using these constructs.

Systematic literature review (SLR) was conducted, where 910 studies were retrieved manually in five academic databases. Forty-eight studies were finalised after four filtration levels.

Majority of the studies were published within the past six years, and 85.42% were studies in the form of journal papers. UTAUT constructs more researched compared to UTAUT2 constructs. More than half of the studies investigated ICT application in value chain boundaries, while 16 studies were organisation-wide studies.

With developments in MSMEs’ technology, the UTAUT2 model must be expanded to internal company operations including finance and infrastructure maintenance. To boost competitiveness and productivity, non-OECD authorities should focus on the cost and user-centric characteristics of MSMEs’ technology adoption.

Although SLRs on UTAUT and UTAUT2 constructs have been attempted previously, this study contributes to the body of knowledge by focusing analysis on the application of those constructs on MSMEs in non-OECD countries and also by situating ICT adoption along the value chain of enterprises.

Despite smart construction technology's great potential to improve the productivity of the architectural, engineering and construction (AEC) industry, the implementation of smart construction technology has failed to achieve the expected benefits due to the negative usage behaviors of construction enterprise employees. This study aims to identify the determinants and their configuration effects on the smart construction technology usage behavior (SCTUB) based on the Technology-Organization-Environment (TOE) framework. This study then verifies the practical paths to improve the employee's SCTUB from the configuration perspective.

A mixed-method approach involving survey and qualitative comparative analysis (QCA) is conducted in this study. Based on the detailed literature review and semi-structured interview, this study identifies the factors and proposes the TOE framework to determine the configuration conditions affecting employee's SCTUB and verify practical paths to promote this user behavior.

The TOE framework's technical, organizational and environmental elements are interdependent. The emergence of a high SCTUB is not determined by a single determinant but by configuration conditions. Four equifinal conditions (e.g. organization-technology type, technology-organization type, environment type and organization-technology balanced type) are verified to promote construction enterprise employee's SCTUB.

The four verified configuration conditions could guide construction enterprises to formulate complementary strategies for promoting the construction enterprises' employees to implement smart construction technology and achieve the enterprise's digital transformation.

The inter-dependence of the three-dimension factors, namely technical, organizational and environmental elements are explored to enrich the literature on the TOE framework. Meanwhile, the configuration effects of these factors on usage behavior are identified, expanding the literature on the information technology acceptance model.