Consumers are widely adopting Artificially Intelligent Voice Assistants (AIVAs). AIVAs now handle many different everyday tasks and are also increasingly assisting consumers with purchasing decisions, making AIVAs a rich topic for marketing researchers. We develop a series of propositions regarding how consumer decision-making processes may change when moved from traditional online purchase environments to AI-powered voicebased dialogs, in the hopes of encouraging further academic thinking and research in this rapidly developing, high impact area of consumer-firm interaction. We also provide suggestions for marketing managers and policymakers on points to pay attention to when they respond to the proposed effects of AIVAs on consumer decisions. Keywords Artificial intelligence. Voice assistants. Consumer decision-making. Consumer dialogs. Digital marketing. Consumer models Artificially Intelligent interactive Voice Assistants (AIVAs), also known as Voice-Activated Personal Assistants or Smart-Home Personal Assistants, have become widely adopted by consumers as aids in a variety of everyday tasks. AIVAs currently handle over one billion tasks per month, with the majority of uses being simple information requests ("Cortana, what is the weather today?") or household commands ("Ok Google, turn on the lights.").

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Participants reported evaluating students' performance based on attributes similar to those suggested by the American Physical Therapy Association's Physical Therapist Clinical Performance Instrument and previous research. However, subjectivity also was involved in their decision about whether students were able to practice at the entry level. Participants also concluded that entry-level students need not be independent in all clinical situations.

The nutrient composition of Spring and Fall lambs were investigated. Seven retail cuts from carcasses of lambs raised under commercial conditions, and representing two age groups (4-4s mo and 8-P mo) were analyzed in both raw and cooked form. Separable lean meat was analyzed for proximate composition, 8 vitamins, 8 inorganic nutrients, cholesterol and 12 fatty acids. Except for moisture, total lipid, riboflavin, niacin, Zn and Fe, there were no practical differences in nutrients between cuts or age groups. Thiamin had the lowest cooking retention with a range of 29.0-63.5%.

The nutrient composition of fresh pork was studied in samples from 71 carcasses. On separable lean, nutrient composition was determined for 7 raw retail cuts from one side of each of 11 carcasses, and nutrient retention was determined on the 7 matching cuts from the other side that had been cooked by common household methods. Loins from 60 additional carcasses were analyzed to determine whether USDA grades 1,2, and 3 and region of production affected nutrient composition. The data indicated that variation in nutrient composition of pork is more dependent on the retail cut within the carcass than either the grade or the region of production of the carcass. Cooking method significantly affected retention of most of the nutrients analyzed.