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An important problem facing managers is how to enhance the credibility, or believability, of their earnings forecasts. In this paper, we experimentally test whether a characteristic of a management earnings forecast-namely, whether it is disaggregated-can affect its credibility. We also test whether disaggregation moderates the relation between managerial incentives and forecast credibility. Disaggregated forecasts include an earnings forecast as well as forecasts of other key line items comprising that earnings forecast. Our results indicate that disaggregated forecasts are judged to be more credible than aggregated ones and that disaggregation works to counteract the effect of high incentives.We also develop and test an original model that explains how disaggregation positively impacts three factors that, in turn, influence forecast credibility: perceived precision of management's beliefs, perceived clarity of the forecast, and perceived financial reporting quality. We show that forecast disaggregation works to remedy incentive problems only via its effect on perceived financial reporting quality. Overall, our study adds to our understanding of how managers can credibly communicate their expectations about the future to market participants.

In this paper, we propose a new scheme for Built-In Test (BIT) that uses Multiple-polynomial Linear Feedback Shift Registers (MP-LFSR's). The same MP-LFSR that generates random patterns to cover easy to test faults is loaded with seeds to generate deterministic vectors for difficult to test faults. The seeds are obtained by solving systems of linear equations involving the seed variables for the positions where the test cubes have specified values. We demonstrate that MP-LFSR's produce sequences with significantly reduced probability of linear dependence compared to single polynomial LFSR's. We present a general method to determine the probability of encoding as a function of the number of specified bits in the test cube, the length of the LFSR and the number of polynomials. Theoretical analysis and experiments show that the probability of encoding a test cube with s specified bits in an s-stage LFSR with 16 polynomials is 1–10^{-6}. We then present the new BIT scheme that allows for an efficient encoding of the entire test set. Here the seeds are grouped according to the polynomial they use and an implicit polynomial identification reduces the number of extra bits per seed to one bit. The paper also shows methods of processing the entire test set consisting of test cubes with varied number of specified bits. Experimental results show the tradeoffs between test data storage and test application time while maintaining complete fault coverage.Index Terms—Built-In Test, hardware test pattern generators, input test data compression and decompression, multiple-polynomial LFSR, reseeding, scan design

SYNOPSIS: In this paper, we provide a framework in which to view management earnings forecasts. Specifically, we categorize earnings forecasts as having three components—antecedents, characteristics, and consequences—that roughly correspond to the timeline associated with an earnings forecast. By evaluating management earnings forecast research within the context of this framework, we render three conclusions. First, forecast characteristics appear to be the least understood component of earnings forecasts—both in terms of theory and empirical research—even though it is the component over which managers have the most control. Second, much of the prior research focuses on how one forecast antecedent or characteristic influences forecast consequences and does not study potential interactions among the three components. Third, much of the prior research ignores the iterative nature of management earnings forecasts—that is, forecast consequences of the current period influence antecedents and chosen characteristics in subsequent periods. Implications for researchers, educators, managers, investors, and regulators are provided.