Abstract

Purpose– This paper aims to explore whether internal context – decision-makers’ perception of characteristics of the information content – might predict the variation in adoption rates of different types of content, and whether innovation adoption theory might represent important factors of this decision-making process Corporate management decides what types of environmental information content to disclose/adopt.Design/methodology/approach– Actual adoption rates of 13 information content categories are computed using content analysis of annual reports for 62 listed companies. Each content category is seen as an innovation the company decides to adopt or not. Interviews with management in several companies illustrate the decision process of disclosure, and help predict adoption rates. Predicted and actual adoption rates are compared.Findings– Adoption rates vary considerably among the 13 types of content. The absolute level of adoption rates is affected by company size and environmental risk. However, those content categories that have either relatively high or low adoption rates are consistent among the subsamples, regardless of these corporate characteristics. This consistent variation in adoption rates seems to be predicted well by innovation adoption theory and its focus on the five attributes of the information itself: compatibility, trialability, complexity, observability and relative advantage.Research limitations/implications– The theoretical framework allows for different or changing internal and general contexts, and should be applicable to other settings, even though the particular predictions for adoption rates in this paper may not be applied as such.Originality/value– The level of analysis is changed from company level, which dominates previous research, to information content (individual content categories). Perceived attributes of the information content itself and innovation adoption theory are used for the first time to explain reason for the reporting practice, and are considered fruitful tools to predict consistent variations in adoption rates among different types of content. This approach provides new insight into the driving forces of supply of disclosure.

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