By Marc Epstein
"Advances in administration Accounting" ("AMA") publishes well-developed articles on numerous present themes in administration accounting which are correct to researchers in either perform and academe. As one of many top-rated administration accounting examine journals, "AMA" is definitely poised to satisfy the wishes of administration accounting students.
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In particular, nonﬁnancial performance measures with forward-looking properties are considered desirable because these performance measures should lead to a more efﬁcient allocation of resources for managers, investors, and creditors as their decisions are based on the expectations of future rewards. Several accounting researchers have examined the predictive ability and value relevance of nonﬁnancial performance measures such as customer satisfaction on ﬁrms’ future performance (Dresner & Xu, 1995; Ittner & Larcker, 1998; Behn & Riley, 1999; Lambert, 1999; Riley, Pearson, & Trompeter, 2003).
16 AL BENTO AND LOURDES FERREIRA WHITE The other variables were measured on a ﬁve-point Likert scale to show the extent to which each item was applicable to the respondent’s business unit (see Fig. 1 for details): design purposes: 12 items dealing with purposes for the SPMS (1 ¼ not used, 5 ¼ extensively used); information quality and effectiveness: one item each (1 ¼ poor, 5 ¼ excellent); degree of BSC adoption: eight items pertaining to dimensions of the business on which the SPMS had a cause-and-effect relationship (1 ¼ no effect, 5 ¼ signiﬁcant effect); impact on HRs and business results: 6 items listing HR practices, and 10 items listing aspects of the business (1 ¼ no effect, 5 ¼ signiﬁcant effect).
6. 7. 8. Correlation Matrix of SPMS Variables. 001. 3. Data Analysis We performed path analysis to determine the relationships among the variables described in Fig. 1. We tested whether SPMS characteristics affected SPMS outcomes and which SPMS characteristics and outcomes had a direct or indirect impact on performance (on HR practices and on business results). This technique helped us to investigate not only the relationships within each set of variables but also to compare the relative magnitude of the relationships among sets of variables.