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The paper aims to characterize anomaly detection in hyperspectral imagery.

This paper develops an adaptive causal anomaly detector (ACAD) to investigate several issues encountered in hyperspectral image analysis which have not been addressed in the past. It also designs extensive synthetic image‐based computer simulations and real image experiments to substantiate the work proposed in this paper.

This paper developed an ACAD and custom‐designed computer simulations and real image experiments to successfully address several issues in characterizing anomalies for detection, which are – first, how large size for a target to be considered as an anomaly? Second, how an anomaly responds to its proximity? Third, how sensitive for an anomaly to noise? Finally, how different anomalies to be detected? Additionally, it also demonstrated that the proposed ACAD can be implemented in real time processing and implementation.

This paper is the first work on investigation of several issues related to anomaly detection in hyperspectral imagery via extensive synthetic image‐based computer simulations and real image experiments. In addition, it also develops a new developed an ACAD to address these issues and substantiate its performance.

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.