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Abstract

Studies on the use of information vary significantly regarding the specificity of information examined. Some studies analyze the use of information in (functional) fields such as marketing, management accounting, or public policy.605 They typically do not specify the types of information used for particular purposes such as decision-making or influencing subordinates. In contrast, other studies on the use of information focus directly on specific types of information arguing that they are especially relevant to the respective research context. For instance, they focus on the use of cost accounting, performance measurement, or budgetary information.606

Keywords

Factor Loading Exploratory Factor Analysis Cash Flow Statement Average Factor Loading Measurement Model Result 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 610.
    The WSR ranks the differences between a single data set of a sample and compares the sum of positive, negative, and equal ranks against a critical value. More specifically, it ranks the absolute differences between two variables and splits the ranks into three groups (negative, positive, and ties). While negative and positive ranks include those cases for which the value of the second variable exceeds or is below the value of the first variable, ties contain cases for which the two variables are equivalent. The pairs are ordered in relation to the absolute values of their differences, and after that, the sum of the ranks of the positive values is compared with the sum of the ranks of the negative values. If the two variables show no particular pattern in their relative behaviors, the positive and negative values should be distributed even through the ranks and consequently the rank-sums should be approximately equal. Distortions between the rank-sums indicate that a variable is significantly different from the other. For a detailed discussion of the WSR cf. Sachs (2006), pp. 411–414; Bortz (2005), pp. 153f. and McClave/Benson/Sincich (2005), pp. 1087f.Google Scholar
  2. 611.
    Cf. Sachs (2006), p. 411.Google Scholar
  3. 613.
    Cf. Chin (1998b), p. 316.Google Scholar
  4. 614.
    The path coefficients’ significance was determined by the bootstrapping resampling procedure. As recommended for PLS analysis, 500 resamples were run, simulating a number of 51 cases. Cf. Efron/ Gong (1983), p. 38.Google Scholar
  5. 615.
    “Standardized paths should be at least 0.20 and ideally above 0.30 in order to be considered meaningful.” Chin (1998a), p. xiii; further cf. Lohmöller (1989), pp. 60f.Google Scholar
  6. 617.
    Cf. chapter D4.3.3 for a detailed description of the analysis of mediating effects; further cf. Sobel (1982), pp. 292–299.Google Scholar
  7. 620.
    Cf. Shrout/ Bolger (2002), p. 434.Google Scholar
  8. 625.
    Cf. Baron/ Kenny (1986), p. 1174.Google Scholar

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