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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.

The purpose of this paper is to attempts to provide further insight into IS adoption by investigating how 12 factors within the technology-organization-environment framework explain small- and medium-sized enterprises’ (SMEs) adoption of enterprise resource planning (ERP) software.

The approach for data collection was questionnaire survey involving executives of SMEs drawn from six fast service enterprises with strong operations in Port Harcourt. The mode of sampling was purposive and snow ball and analysis involves logistic regression test; the likelihood ratios, Hosmer and Lemeshow’s goodness of fit, and Nagelkerke’s R² provided the necessary lenses.

The 12 hypothesized relationships were supported with each factor differing in its statistical coefficient and some bearing negative values. ICT infrastructures, technical know-how, perceived compatibility, perceived values, security, and firm’s size were found statistically significant adoption determinants. Although, scope of business operations, trading partners’ readiness, demographic composition, subjective norms, external supports, and competitive pressures were equally critical but their negative coefficients suggest they pose less of an obstacle to adopters than to non-adopters. Thus, adoption of ERP by SMEs is more driven by technological factors than by organizational and environmental factors.

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 and to factor in the implementation and post-adoption phases in order to forge a more integrated and holistic adoption framework.

The model may be used by IS vendors to make investment decisions, to meet customers’ needs, and to craft informed marketing programs that would appeal to actual and potential adopters and cause them to progress in the customer loyalty ladder.

The paper contributes to the growing research on IS innovations’ adoption by using factors within the T-O-E framework to explains SMEs’ adoption of ERP.

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 propose a framework that integrated the technology acceptance model (TAM), theory of planned behaviour (TPB) and technology-organization-environment (TOE) and extended the constructs to enrich literature and capture some peculiarities of small and medium-scale enterprises (SMEs). Individually, the frameworks of extant TAM, TPB and TOE are insightful to the understanding of e-commerce adoption but a bit parochial in their constructs and so, can rarely provide clear lenses to deal with SMEs.

The adoption of e-commerce depends largely on the users’ conscious assessment of the influencing constructs as proposed, among others, in theories of reasoned action, TAM, TPB and TOE. This paper reviewed, synthesized and extended the constructs of these models in an integrated framework. The proposed integrated framework led to 18 propositions to promote and facilitate future research, and to guide explanation and prediction of e-commerce adoption in an organized system.

The introduced constructs in the integrated framework (e.g. company mission, individual difference factors, perceived trust and perceived service quality) introduce socio-technical systems and improve the theoretical base of adoption.

Neither the adoption drivers nor the constructs in the theoretical framework are mutually exclusive and exhaustive; rather, they are complementary and could incorporate other factors. Although the theoretical implications of the findings of this paper extend the scope of adoption drivers, the proposed framework needs to be tested empirically.

The integrated and extended theoretical framework links three adoption drivers and attempts to improve existing knowledge on e-commerce adoption and to provide bases for more informed decision(s).

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.

The fuel retailing landscape in India is undergoing a structural shift with the reforms undertaken by the government that would help the private and foreign firms to enter this market. India is poised to become the world's largest growth market for energy by the mid-2020s. IoT has become an integral digital technology for the fuel retailers in the retail oil outlet (ROO) ecosystem. The purpose of this paper is to develop an Internet of Things (IoT) business model for the Indian oil marketing companies (OMCs') ROOs.

Using literature review along with a survey among 660 respondents led to 402 valid observations, and the variables that contributed to IoT adoption at the OMCs' ROOs were identified. Using the BMC tool (Osterwalder and Pigneur, 2009), the relative importance of the variables within each building block was established. The means of all the variables were measured against the average of all the variables, and significant differences were searched for in each block. The notable differences of the means along with significant high and low means were highlighted.

This paper depicts empirical research that led to a framework of an IoT business model for Indian public sector ROOs. It also represents the usefulness of the Technology-Organization-Environment framework at the OMCs ROOs and extends the literature by incorporating “data security” to the existent framework of technology, organization and environment within the IoT ecosystem.

The outcome of the research should be analysed in the Indian context as all the respondents were from India. The study was conducted for the ROOs of Indian Oil Corporation Limited, Hindustan Petroleum Corporation Limited and Bharat Petroleum Corporation Limited and excluded their downstream operations. The dispensers in the OMCs vary along with their marketing strategies in the retail segments. The IoT business model can be customized for a particular OMC, which is scope of further research.

The study has practical implications for those global fuel retailers embarking on the IoT adoption drive at their ROOs about the need to install “data security” measures in the connected IoT environment. The study provides insights on how the OMCs can stay ahead of competition in the Indian market vis-à-vis the private sector fuel retailers by embracing the IoT business model.

The new emerging technological business models provide competitive edge to those organizations adopting them (Barney, 1991; Clemons and Row, 1991; Feeny and Ives, 1990). The study will enable the OMCs to implement the IoT business model at their ROOs for enhancing their revenue streams and profitability and lowering of operating costs. The study provides insights on how the OMCs can stay ahead of competition in the Indian market vis-à-vis the private sector fuel retailers by embracing the IoT business model.

The contribution of the paper is that it is among the first to map the variables that contribute to IoT adoption at the OMCs' ROOs, within the building blocks of the BMC tool (Osterwalder and Pigneur, 2009), according to their importance. To retain their dominance and have a first-mover competitive advantage, this study enables the OMCs to adopt the IoT business model and transform their ROOs into Internet-connected intelligent fuel outlets.

The purpose of this study is to explore and examine the determinants of artificial intelligence (AI) adoption by human resource management (HRM). Further, the impact of AI adoption by HR department on their effectiveness has also been tested.

A model explaining the antecedents of AI adoption by HRM is proposed in this study. The proposed model is based on task–organization–environment and task–technology fit models. A two-step partial least square-based structural equational modelling (PLS-SEM) has been used for testing the model. Data was collected from 210 HRM employees (only senior level or specialized HR positions), working in IT firms located in Delhi-NCR region.

Literature review shows that among others, organizational preparedness, perceived benefits and technology readiness determine AI adoption which in turn can make HR system more effective. Results of PLS-SEM support all hypothesized relationships and validate the proposed model.

Considering paucity of research on antecedents of AI adoption by human resource department, this study adds significantly to the body of knowledge. Additionally, based on the findings of statistical analysis, certain AI-related recommendations are given to HRM.

The purpose of this study is to examine the factors that affect the adoption of electronic invoices and in turn the impact of these factors on the tax compliance process efficiency of companies.

A questionnaire survey was distributed to 276 users who adopted electronic invoicing. Partial least squares regression was used to analyze the collected data.

This study found that perceived benefits and trust in e-government had a positive influence on the adoption of electronic invoices. At the same time, the adoption of electronic invoice was found to have a positive impact on the overall efficiency of the tax compliance process. Moreover, the factors affecting adoption of electronic invoices can have a mediating effect on that adoption and tax compliance process efficiency.

This study only explored these influencing factors on companies that have adopted electronic invoicing. Future research should distinguish between adopters and non-adopters.

The results of this study can guide tax authorities and other electronic invoice suppliers when promoting the adoption of electronic invoicing.

This research can provide guidance to tax authorities to improve their own electronic invoice system by creating a workforce that have the skills to strengthen citizen’s trust in the electronic invoice system.

This study contributes to the electronic adoption literature by examining those factors that impact tax compliance processes efficiency.

Enterprise resource planning (ERP) consultants have the expertise required to understand the specific contextual needs of an ERP client, implement tailored business processes that meet those needs, and ensure that no potential benefit offered by the ERP remains unexplored by the client. However, conflicts between ERP clients and consultants are a significant source of non-benefit realisation, making managing client–consultant agency crucial to ERP post-implementation benefits realisation. This paper aims to elucidate how managing client–consultant agency affects the benefits derived from ERP systems.

This paper uses microfinance institutions in 15 sub-Saharan African countries to explore different paths through which managing client–consultant agency leads to benefit realisation in ERP projects. It uses partial least squares structural equation modelling to analyse data from 127 managers and explains the results using insights from agency theory and the information system (IS) success model.

This paper reveals three routes through which contractual agreements and conflict resolution strategies lead to benefits realisation in ERP projects.

This is the first study that attempts to provide quantitative evidence of how managing the complex relationship between ERP project stakeholders affects ERP project success. It also contributes a novel theoretical model for ERP benefits realisation to complement existing research on ERP agency issues, critical success factors, and benefits realisation.

Building information modelling (BIM) and augmented reality (AR) are unique technologies in the digitalized construction industry. In spite of the numerous benefits of BIM-AR, its adoption has been at a relatively slow pace. The purpose of this study is to investigate how the factors within technology–organization–environment (TOE) framework influence the adoption of BIM-AR in the context of construction companies in a developing country.

By using a mainly deductive quantitative design, survey data were collected from senior management of built environment companies in Ghana using questionnaires. The study adopted a mixture of both purposive and snowball sampling approaches. Partial least squares structural equation modelling was used to analyse how the factors within the TOE framework explain BIM-AR adoption in Ghana.

Findings from the study show that the top three factors within the TOE framework that facilitate the adoption of BIM-AR include ICT infrastructure within construction firms; the size of the construction firm, which may influence the financial capacity to accommodate BIM-AR; and competitive pressure. The inhibitors of BIM-AR at the company level included external support and trading partners’ readiness.

Implicit is that the significant factors will be useful to policymakers and companies in developing programs that appeal to non-adopters to aid in mitigating their challenges and further enhance BIM-AR adoption.

The value of this paper has been the use of the theoretical framework TOE to explain the adoption factors of BIM-AR in the Ghanaian construction industry. The originality of the paper is further anchored in consideration of BIM-AR, which is quite nascent in emerging countries.