Thirty-eight Angus-cross beef cows were used to evaluate differences in DMI, residual feed intake (RFI), and endocrine markers on the basis of cow size and RFI ranking during 2 stages of production. Cows housed in individual pens (2.2 × 9.1 m) were fed, over a 70-d feeding period, 30% Bermuda grass hay and 70% ryegrass baleage diet during lactation (LACT) and a 100% ryegrass hay diet during postweaning (NOLACT). Individual daily feed intake, BW, and BCS were recorded, and hip height was used to determine frame score (FS). Feed intake was used to calculate RFI for each cow, and cow was the experimental unit. Blood samples were obtained on d 0 and 70 and were analyzed for glucose, insulin, leptin, triiodothyronine (T3), and thyroxine (T4). Cows were assigned to a light (LIT) or heavy (HEV) BW groups on the basis of mean BW at the beginning of the LACT period. On the basis of RFI values for each feeding period, cows were placed into a negative (NEG; RFI < 0.00) or positive (POS; RFI > 0.00) RFI group and into a low (LOW; ≤0.2 SD mean RFI), medium (MED; within ±0.19 SD), or high (HI; ≥0.2 SD mean RFI) RFI group. During LACT, DMI was 4.8% greater (P = 0.03) and FS was greater (P < 0.01; 6.4 and 5.5 ± 0.16) for the HEV compared with LIT BW cows. No RFI by day interaction or RFI group main effect occurred for endocrine markers during LACT; however, a negative relationship (P = 0.04) existed between BW group and combined T3 data, and a positive relationship (P = 0.04) existed between RFI and combined insulin data. For both LACT and NOLACT, RFI was similar (P > 0.05) among BW groups; however, DMI was 6.5% and 8.9% greater (P < 0.01) for POS compared with NEG RFI in the LACT and NOLACT periods. In LACT, DMI was greater (P < 0.01) for HI and MED RFI compared with LOW RFI, and in NOLACT, DMI was greater (P < 0.01) for the HI compared with MED and LOW RFI cows and MED compared with LOW RFI cows. During NOLACT, DMI was 8.9% greater (P < 0.01) for the HEV (12.4 ± 0.22 kg) compared with LIT (11.3 ± 0.19 kg) BW cows. Change in BCS was greater (P ≤ 0.03) in higher RFI cows in both RFI groups only in the NOLACT period. Differences in T3 and T4 on d 0 and 70 were 25% and 15% greater (P ≤ 0.04) for the LIT BW group compared with the HEV BW group. A negative correlation existed (P ≤ 0.04) between BW group and T3 and T4, as well as leptin and RFI (P = 0.03). Although cow BW was independent of RFI and T3 and T4 levels tended to be greater in lighter BW cows, DMI was consistently greater for cows with heavier BW and higher RFIvalues.

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ChatGPT, a language-learning model chatbot, has garnered considerable attention for its ability to respond to users’ questions. Using data from 14 countries and 186 institutions, we compare ChatGPT and student performance for 28,085 questions from accounting assessments and textbook test banks. As of January 2023, ChatGPT provides correct answers for 56.5 percent of questions and partially correct answers for an additional 9.4 percent of questions. When considering point values for questions, students significantly outperform ChatGPT with a 76.7 percent average on assessments compared to 47.5 percent for ChatGPT if no partial credit is awarded and 56.5 percent if partial credit is awarded. Still, ChatGPT performs better than the student average for 15.8 percent of assessments when we include partial credit. We provide evidence of how ChatGPT performs on different question types, accounting topics, class levels, open/closed assessments, and test bank questions. We also discuss implications for accounting education and research.

This paper synthesizes existing experimental research in the area of investor perceptions and offers directions for future research. Investor-related experimental research has grown substantially, especially in the last decade, as it has made valuable contributions in establishing causal links, examining underlying process measures, and examining areas with little available data. Within this review, I examine 121 papers and identify three broad categories that affect investor perceptions: information format, investor features, and disclosure credibility. Information format describes how investors are influenced by information salience, information labeling, reporting and accounting complexity, financial statement recognition, explanatory disclosures, and proposed disclosure changes. Investor features describes investors’ use of heuristics, investor preferences, and the effect of investor experience. Disclosure credibility is influenced by external and internal assurance, management credibility, disclosure characteristics, and management incentives. Using this framework, I summarize the existing research and identify areas that would benefit from additional research.