No abstract

Trust is just as essential to online business as it is to offline transactions but can be more difficult to achieve-especially for newer websites with unknown web vendors. Research on web-based trust development explains that web vendor trust can be created by both cognitive and affective (e.g., emotion-based) influences. But under what circumstances will emotion or cognition be more dominate in trust establishment? Theory-based answers to these questions can help online web vendors design better websites that account for unleveraged factors that will increase trust in the web vendor. To this end, we use the Affect Infusion Model and trust transference to propose the Affect-Trust Infusion Model (ATIM) that explains and predicts how and when cognition, through perceived website performance (PwP), and positive emotion (PEmo) each influence web vendor trust. ATIM explains the underlying causal mechanisms that determine the degree of affect infusion and the subsequent processing strategy that a user adopts when interacting with a new website. Under high-affect infusion, PEmo acts as a mediator between PwP and vendor trust; under low-affect infusion, PwP primarily impacts trust and PEmo is dis-intermediated. We review two distinct, rigorously validated experiments that empirically support ATIM. To further extend the contributions of ATIM, we demonstrate how use of specific contextual features-rooted in theory and that drive one's choice of affect infusion and cognitive processing-can be leveraged into a methodology that we propose to further enhance user-centered design (UCD). We further detail several exciting research opportunities that can leverage ATIM.

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.

Extensive data mining and analytics (DM&A) are increasingly requisite for companies to be competitive in this age of information. This demand, combined with (1) accountants' reputation for understanding and generating quality data, and (2) the increased accessibility of DM&A tools, has created a unique opportunity for accountants to play a larger strategic role in their organization. We argue that accountants should own and drive a larger part of the DM&A that occurs in their organization. To support this vision, we introduce a data mining technique called recursive partitioning. We illustrate how it can be applied to a large customer costing and profit dataset to identify the characteristics that differentiate more and less profitable customers. We discuss how the output of the recursive partitioning algorithm (a binary decision tree) can be used to increase customer profitability and identify future profitable customers. We conclude by suggesting and discussing some of the obstacles and research opportunities that this vision presents to the accounting field.

Recent technological advances make it possible to create automated, virtual interviewers, called embodied conversational agents (ECAs). We study how an ECA compares to a human interviewer in three experiments. In experiment 1, we show that two theoretically motivated factors-making the ECA facially and vocally similar to the interviewee-result in the ECA performing similarly to or better than human interviewers for six antecedents of disclosure quality. In two additional experiments, we show that employees are on average 21 to 32 percent more likely to disclose violating internal controls to an ECA than to a human, even if the human interviewer has significant interviewing experience. These findings contribute to the ECA design literature by showing that similarity-enhancing features of ECAs increase the antecedents of disclosure. The findings also contribute to the accounting literature by demonstrating that ECA technology can increase the scope of interviewing in accounting without reducing interview quality.