Capital Connectivity in Integrated Reports: Datasets from International Companies

The panel dataset provides information on the degree of capital connectivity disclosed in 840 integrated reports from 80 Asia-Australian, 78 European, and 10 American firms over a five-year period from 2018 to 2022. The sample includes firms from eleven different sectors, as defined by the Global Industry Classification Standards (GICS). The dataset focuses on international firms that voluntarily adopt integrated reporting, with these reports available in the official IR database. Data extraction is performed using a robotic software, which systematically codes the quantity and sequence of specific capitals in the reports. The software uses a list of keywords related to the six capitals outlined in the IR Framework: Financial, Manufactured, Intellectual, Human, Social & Relationship, and Natural. A self-calculated connectivity score is then generated to assess firms’ capital connectivity, by counting the average number of different types of capital coded within a specific context in each integrated report. This score ranges from a minimum of one (1) and a maximum of six (6), reflecting the average number of different types of capital codes. Additional scoring is calculated based on the percentage of capital disclosed directly, followed by another capital. The perfect score of 100% indicates that a different capital follows every other capital disclosure, while the lowest possible score of zero means none. Please refer to the excel file for detailed scoring measurement. Experts from industries and academicians have validated the instrument used to measure capital connectivity. To assess the reliability of the connectivity score, ten (10) integrated reports were randomly selected and manually coded, and the score was calculated based on the manual coding. Weighted Cohen’s Kappa inter-rater reliability test was used to compare Connectivity Score (CS) 1 and 2 using manual and automated coding. A coefficient of .99 (CS1) and .94 (CS2) indicates almost perfect agreement between manual and automated coding. In addition, a Pearson correlation is used to compare the manual and automated coding for Connectivity Score 3. A coefficient of .90 (CS3) shows that the manual score is statistically related with automated score at a .01 level of significance. This suggests that the scores calculated using robotic software is reliable. The dataset is a valuable indicator of how committed IR adopters are to achieving connectivity, reflecting the integrative thinking involved i